1
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Rosenberg E, Andersen TI, Samajdar R, Petukhov A, Hoke JC, Abanin D, Bengtsson A, Drozdov IK, Erickson C, Klimov PV, Mi X, Morvan A, Neeley M, Neill C, Acharya R, Allen R, Anderson K, Ansmann M, Arute F, Arya K, Asfaw A, Atalaya J, Bardin JC, Bilmes A, Bortoli G, Bourassa A, Bovaird J, Brill L, Broughton M, Buckley BB, Buell DA, Burger T, Burkett B, Bushnell N, Campero J, Chang HS, Chen Z, Chiaro B, Chik D, Cogan J, Collins R, Conner P, Courtney W, Crook AL, Curtin B, Debroy DM, Barba ADT, Demura S, Di Paolo A, Dunsworth A, Earle C, Faoro L, Farhi E, Fatemi R, Ferreira VS, Burgos LF, Forati E, Fowler AG, Foxen B, Garcia G, Genois É, Giang W, Gidney C, Gilboa D, Giustina M, Gosula R, Dau AG, Gross JA, Habegger S, Hamilton MC, Hansen M, Harrigan MP, Harrington SD, Heu P, Hill G, Hoffmann MR, Hong S, Huang T, Huff A, Huggins WJ, Ioffe LB, Isakov SV, Iveland J, Jeffrey E, Jiang Z, Jones C, Juhas P, Kafri D, Khattar T, Khezri M, Kieferová M, Kim S, Kitaev A, Klots AR, Korotkov AN, Kostritsa F, Kreikebaum JM, Landhuis D, Laptev P, Lau KM, Laws L, Lee J, Lee KW, Lensky YD, Lester BJ, Lill AT, Liu W, Locharla A, Mandrà S, Martin O, Martin S, McClean JR, McEwen M, Meeks S, Miao KC, Mieszala A, Montazeri S, Movassagh R, Mruczkiewicz W, Nersisyan A, Newman M, Ng JH, Nguyen A, Nguyen M, Niu MY, O'Brien TE, Omonije S, Opremcak A, Potter R, Pryadko LP, Quintana C, Rhodes DM, Rocque C, Rubin NC, Saei N, Sank D, Sankaragomathi K, Satzinger KJ, Schurkus HF, Schuster C, Shearn MJ, Shorter A, Shutty N, Shvarts V, Sivak V, Skruzny J, Smith WC, Somma RD, Sterling G, Strain D, Szalay M, Thor D, Torres A, Vidal G, Villalonga B, Heidweiller CV, White T, Woo BWK, Xing C, Yao ZJ, Yeh P, Yoo J, Young G, Zalcman A, Zhang Y, Zhu N, Zobrist N, Neven H, Babbush R, Bacon D, Boixo S, Hilton J, Lucero E, Megrant A, Kelly J, Chen Y, Smelyanskiy V, Khemani V, Gopalakrishnan S, Prosen T, Roushan P. Dynamics of magnetization at infinite temperature in a Heisenberg spin chain. Science 2024; 384:48-53. [PMID: 38574139 DOI: 10.1126/science.adi7877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 03/01/2024] [Indexed: 04/06/2024]
Abstract
Understanding universal aspects of quantum dynamics is an unresolved problem in statistical mechanics. In particular, the spin dynamics of the one-dimensional Heisenberg model were conjectured as to belong to the Kardar-Parisi-Zhang (KPZ) universality class based on the scaling of the infinite-temperature spin-spin correlation function. In a chain of 46 superconducting qubits, we studied the probability distribution of the magnetization transferred across the chain's center, [Formula: see text]. The first two moments of [Formula: see text] show superdiffusive behavior, a hallmark of KPZ universality. However, the third and fourth moments ruled out the KPZ conjecture and allow for evaluating other theories. Our results highlight the importance of studying higher moments in determining dynamic universality classes and provide insights into universal behavior in quantum systems.
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Affiliation(s)
- E Rosenberg
- Google Research, Mountain View, CA, USA
- Department of Physics, Cornell University, Ithaca, NY, USA
| | | | - R Samajdar
- Department of Physics, Princeton University, Princeton, NJ, USA
- Princeton Center for Theoretical Science, Princeton University, Princeton, NJ, USA
| | | | - J C Hoke
- Department of Physics, Stanford University, Stanford, CA, USA
| | - D Abanin
- Google Research, Mountain View, CA, USA
| | | | - I K Drozdov
- Google Research, Mountain View, CA, USA
- Department of Physics, University of Connecticut, Storrs, CT, USA
| | | | | | - X Mi
- Google Research, Mountain View, CA, USA
| | - A Morvan
- Google Research, Mountain View, CA, USA
| | - M Neeley
- Google Research, Mountain View, CA, USA
| | - C Neill
- Google Research, Mountain View, CA, USA
| | - R Acharya
- Google Research, Mountain View, CA, USA
| | - R Allen
- Google Research, Mountain View, CA, USA
| | | | - M Ansmann
- Google Research, Mountain View, CA, USA
| | - F Arute
- Google Research, Mountain View, CA, USA
| | - K Arya
- Google Research, Mountain View, CA, USA
| | - A Asfaw
- Google Research, Mountain View, CA, USA
| | - J Atalaya
- Google Research, Mountain View, CA, USA
| | - J C Bardin
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA
| | - A Bilmes
- Google Research, Mountain View, CA, USA
| | - G Bortoli
- Google Research, Mountain View, CA, USA
| | | | - J Bovaird
- Google Research, Mountain View, CA, USA
| | - L Brill
- Google Research, Mountain View, CA, USA
| | | | | | - D A Buell
- Google Research, Mountain View, CA, USA
| | - T Burger
- Google Research, Mountain View, CA, USA
| | - B Burkett
- Google Research, Mountain View, CA, USA
| | | | - J Campero
- Google Research, Mountain View, CA, USA
| | - H-S Chang
- Google Research, Mountain View, CA, USA
| | - Z Chen
- Google Research, Mountain View, CA, USA
| | - B Chiaro
- Google Research, Mountain View, CA, USA
| | - D Chik
- Google Research, Mountain View, CA, USA
| | - J Cogan
- Google Research, Mountain View, CA, USA
| | - R Collins
- Google Research, Mountain View, CA, USA
| | - P Conner
- Google Research, Mountain View, CA, USA
| | | | - A L Crook
- Google Research, Mountain View, CA, USA
| | - B Curtin
- Google Research, Mountain View, CA, USA
| | | | | | - S Demura
- Google Research, Mountain View, CA, USA
| | | | | | - C Earle
- Google Research, Mountain View, CA, USA
| | - L Faoro
- Google Research, Mountain View, CA, USA
| | - E Farhi
- Google Research, Mountain View, CA, USA
| | - R Fatemi
- Google Research, Mountain View, CA, USA
| | | | | | - E Forati
- Google Research, Mountain View, CA, USA
| | | | - B Foxen
- Google Research, Mountain View, CA, USA
| | - G Garcia
- Google Research, Mountain View, CA, USA
| | - É Genois
- Google Research, Mountain View, CA, USA
| | - W Giang
- Google Research, Mountain View, CA, USA
| | - C Gidney
- Google Research, Mountain View, CA, USA
| | - D Gilboa
- Google Research, Mountain View, CA, USA
| | | | - R Gosula
- Google Research, Mountain View, CA, USA
| | | | - J A Gross
- Google Research, Mountain View, CA, USA
| | | | - M C Hamilton
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA
| | - M Hansen
- Google Research, Mountain View, CA, USA
| | | | | | - P Heu
- Google Research, Mountain View, CA, USA
| | - G Hill
- Google Research, Mountain View, CA, USA
| | | | - S Hong
- Google Research, Mountain View, CA, USA
| | - T Huang
- Google Research, Mountain View, CA, USA
| | - A Huff
- Google Research, Mountain View, CA, USA
| | | | - L B Ioffe
- Google Research, Mountain View, CA, USA
| | | | - J Iveland
- Google Research, Mountain View, CA, USA
| | - E Jeffrey
- Google Research, Mountain View, CA, USA
| | - Z Jiang
- Google Research, Mountain View, CA, USA
| | - C Jones
- Google Research, Mountain View, CA, USA
| | - P Juhas
- Google Research, Mountain View, CA, USA
| | - D Kafri
- Google Research, Mountain View, CA, USA
| | - T Khattar
- Google Research, Mountain View, CA, USA
| | - M Khezri
- Google Research, Mountain View, CA, USA
| | - M Kieferová
- Google Research, Mountain View, CA, USA
- QSI, Faculty of Engineering & Information Technology, University of Technology Sydney, Ultimo, NSW, Australia
| | - S Kim
- Google Research, Mountain View, CA, USA
| | - A Kitaev
- Google Research, Mountain View, CA, USA
| | - A R Klots
- Google Research, Mountain View, CA, USA
| | - A N Korotkov
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of California, Riverside, CA, USA
| | | | | | | | - P Laptev
- Google Research, Mountain View, CA, USA
| | - K-M Lau
- Google Research, Mountain View, CA, USA
| | - L Laws
- Google Research, Mountain View, CA, USA
| | - J Lee
- Google Research, Mountain View, CA, USA
- Department of Chemistry, Columbia University, New York, NY, USA
| | - K W Lee
- Google Research, Mountain View, CA, USA
| | | | | | - A T Lill
- Google Research, Mountain View, CA, USA
| | - W Liu
- Google Research, Mountain View, CA, USA
| | | | - S Mandrà
- Google Research, Mountain View, CA, USA
| | - O Martin
- Google Research, Mountain View, CA, USA
| | - S Martin
- Google Research, Mountain View, CA, USA
| | | | - M McEwen
- Google Research, Mountain View, CA, USA
| | - S Meeks
- Google Research, Mountain View, CA, USA
| | - K C Miao
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - M Newman
- Google Research, Mountain View, CA, USA
| | - J H Ng
- Google Research, Mountain View, CA, USA
| | - A Nguyen
- Google Research, Mountain View, CA, USA
| | - M Nguyen
- Google Research, Mountain View, CA, USA
| | - M Y Niu
- Google Research, Mountain View, CA, USA
| | | | - S Omonije
- Google Research, Mountain View, CA, USA
| | | | - R Potter
- Google Research, Mountain View, CA, USA
| | - L P Pryadko
- Department of Physics and Astronomy, University of California, Riverside, CA, USA
| | | | | | - C Rocque
- Google Research, Mountain View, CA, USA
| | - N C Rubin
- Google Research, Mountain View, CA, USA
| | - N Saei
- Google Research, Mountain View, CA, USA
| | - D Sank
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - A Shorter
- Google Research, Mountain View, CA, USA
| | - N Shutty
- Google Research, Mountain View, CA, USA
| | - V Shvarts
- Google Research, Mountain View, CA, USA
| | - V Sivak
- Google Research, Mountain View, CA, USA
| | - J Skruzny
- Google Research, Mountain View, CA, USA
| | | | - R D Somma
- Google Research, Mountain View, CA, USA
| | | | - D Strain
- Google Research, Mountain View, CA, USA
| | - M Szalay
- Google Research, Mountain View, CA, USA
| | - D Thor
- Google Research, Mountain View, CA, USA
| | - A Torres
- Google Research, Mountain View, CA, USA
| | - G Vidal
- Google Research, Mountain View, CA, USA
| | | | | | - T White
- Google Research, Mountain View, CA, USA
| | - B W K Woo
- Google Research, Mountain View, CA, USA
| | - C Xing
- Google Research, Mountain View, CA, USA
| | | | - P Yeh
- Google Research, Mountain View, CA, USA
| | - J Yoo
- Google Research, Mountain View, CA, USA
| | - G Young
- Google Research, Mountain View, CA, USA
| | - A Zalcman
- Google Research, Mountain View, CA, USA
| | - Y Zhang
- Google Research, Mountain View, CA, USA
| | - N Zhu
- Google Research, Mountain View, CA, USA
| | - N Zobrist
- Google Research, Mountain View, CA, USA
| | - H Neven
- Google Research, Mountain View, CA, USA
| | - R Babbush
- Google Research, Mountain View, CA, USA
| | - D Bacon
- Google Research, Mountain View, CA, USA
| | - S Boixo
- Google Research, Mountain View, CA, USA
| | - J Hilton
- Google Research, Mountain View, CA, USA
| | - E Lucero
- Google Research, Mountain View, CA, USA
| | - A Megrant
- Google Research, Mountain View, CA, USA
| | - J Kelly
- Google Research, Mountain View, CA, USA
| | - Y Chen
- Google Research, Mountain View, CA, USA
| | | | - V Khemani
- Department of Physics, Stanford University, Stanford, CA, USA
| | | | - T Prosen
- Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia
| | - P Roushan
- Google Research, Mountain View, CA, USA
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2
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Mi X, Michailidis AA, Shabani S, Miao KC, Klimov PV, Lloyd J, Rosenberg E, Acharya R, Aleiner I, Andersen TI, Ansmann M, Arute F, Arya K, Asfaw A, Atalaya J, Bardin JC, Bengtsson A, Bortoli G, Bourassa A, Bovaird J, Brill L, Broughton M, Buckley BB, Buell DA, Burger T, Burkett B, Bushnell N, Chen Z, Chiaro B, Chik D, Chou C, Cogan J, Collins R, Conner P, Courtney W, Crook AL, Curtin B, Dau AG, Debroy DM, Del Toro Barba A, Demura S, Di Paolo A, Drozdov IK, Dunsworth A, Erickson C, Faoro L, Farhi E, Fatemi R, Ferreira VS, Burgos LF, Forati E, Fowler AG, Foxen B, Genois É, Giang W, Gidney C, Gilboa D, Giustina M, Gosula R, Gross JA, Habegger S, Hamilton MC, Hansen M, Harrigan MP, Harrington SD, Heu P, Hoffmann MR, Hong S, Huang T, Huff A, Huggins WJ, Ioffe LB, Isakov SV, Iveland J, Jeffrey E, Jiang Z, Jones C, Juhas P, Kafri D, Kechedzhi K, Khattar T, Khezri M, Kieferová M, Kim S, Kitaev A, Klots AR, Korotkov AN, Kostritsa F, Kreikebaum JM, Landhuis D, Laptev P, Lau KM, Laws L, Lee J, Lee KW, Lensky YD, Lester BJ, Lill AT, Liu W, Locharla A, Malone FD, Martin O, McClean JR, McEwen M, Mieszala A, Montazeri S, Morvan A, Movassagh R, Mruczkiewicz W, Neeley M, Neill C, Nersisyan A, Newman M, Ng JH, Nguyen A, Nguyen M, Niu MY, O'Brien TE, Opremcak A, Petukhov A, Potter R, Pryadko LP, Quintana C, Rocque C, Rubin NC, Saei N, Sank D, Sankaragomathi K, Satzinger KJ, Schurkus HF, Schuster C, Shearn MJ, Shorter A, Shutty N, Shvarts V, Skruzny J, Smith WC, Somma R, Sterling G, Strain D, Szalay M, Torres A, Vidal G, Villalonga B, Heidweiller CV, White T, Woo BWK, Xing C, Yao ZJ, Yeh P, Yoo J, Young G, Zalcman A, Zhang Y, Zhu N, Zobrist N, Neven H, Babbush R, Bacon D, Boixo S, Hilton J, Lucero E, Megrant A, Kelly J, Chen Y, Roushan P, Smelyanskiy V, Abanin DA. Stable quantum-correlated many-body states through engineered dissipation. Science 2024; 383:1332-1337. [PMID: 38513021 DOI: 10.1126/science.adh9932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 02/13/2024] [Indexed: 03/23/2024]
Abstract
Engineered dissipative reservoirs have the potential to steer many-body quantum systems toward correlated steady states useful for quantum simulation of high-temperature superconductivity or quantum magnetism. Using up to 49 superconducting qubits, we prepared low-energy states of the transverse-field Ising model through coupling to dissipative auxiliary qubits. In one dimension, we observed long-range quantum correlations and a ground-state fidelity of 0.86 for 18 qubits at the critical point. In two dimensions, we found mutual information that extends beyond nearest neighbors. Lastly, by coupling the system to auxiliaries emulating reservoirs with different chemical potentials, we explored transport in the quantum Heisenberg model. Our results establish engineered dissipation as a scalable alternative to unitary evolution for preparing entangled many-body states on noisy quantum processors.
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Affiliation(s)
- X Mi
- Google Research, Mountain View, CA, USA
| | - A A Michailidis
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
| | - S Shabani
- Google Research, Mountain View, CA, USA
| | - K C Miao
- Google Research, Mountain View, CA, USA
| | | | - J Lloyd
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
| | | | - R Acharya
- Google Research, Mountain View, CA, USA
| | - I Aleiner
- Google Research, Mountain View, CA, USA
| | | | - M Ansmann
- Google Research, Mountain View, CA, USA
| | - F Arute
- Google Research, Mountain View, CA, USA
| | - K Arya
- Google Research, Mountain View, CA, USA
| | - A Asfaw
- Google Research, Mountain View, CA, USA
| | - J Atalaya
- Google Research, Mountain View, CA, USA
| | - J C Bardin
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA
| | | | - G Bortoli
- Google Research, Mountain View, CA, USA
| | | | - J Bovaird
- Google Research, Mountain View, CA, USA
| | - L Brill
- Google Research, Mountain View, CA, USA
| | | | | | - D A Buell
- Google Research, Mountain View, CA, USA
| | - T Burger
- Google Research, Mountain View, CA, USA
| | - B Burkett
- Google Research, Mountain View, CA, USA
| | | | - Z Chen
- Google Research, Mountain View, CA, USA
| | - B Chiaro
- Google Research, Mountain View, CA, USA
| | - D Chik
- Google Research, Mountain View, CA, USA
| | - C Chou
- Google Research, Mountain View, CA, USA
| | - J Cogan
- Google Research, Mountain View, CA, USA
| | - R Collins
- Google Research, Mountain View, CA, USA
| | - P Conner
- Google Research, Mountain View, CA, USA
| | | | - A L Crook
- Google Research, Mountain View, CA, USA
| | - B Curtin
- Google Research, Mountain View, CA, USA
| | - A G Dau
- Google Research, Mountain View, CA, USA
| | | | | | - S Demura
- Google Research, Mountain View, CA, USA
| | | | | | | | | | - L Faoro
- Google Research, Mountain View, CA, USA
| | - E Farhi
- Google Research, Mountain View, CA, USA
| | - R Fatemi
- Google Research, Mountain View, CA, USA
| | | | | | - E Forati
- Google Research, Mountain View, CA, USA
| | | | - B Foxen
- Google Research, Mountain View, CA, USA
| | - É Genois
- Google Research, Mountain View, CA, USA
| | - W Giang
- Google Research, Mountain View, CA, USA
| | - C Gidney
- Google Research, Mountain View, CA, USA
| | - D Gilboa
- Google Research, Mountain View, CA, USA
| | | | - R Gosula
- Google Research, Mountain View, CA, USA
| | - J A Gross
- Google Research, Mountain View, CA, USA
| | | | - M C Hamilton
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA
| | - M Hansen
- Google Research, Mountain View, CA, USA
| | | | | | - P Heu
- Google Research, Mountain View, CA, USA
| | | | - S Hong
- Google Research, Mountain View, CA, USA
| | - T Huang
- Google Research, Mountain View, CA, USA
| | - A Huff
- Google Research, Mountain View, CA, USA
| | | | - L B Ioffe
- Google Research, Mountain View, CA, USA
| | | | - J Iveland
- Google Research, Mountain View, CA, USA
| | - E Jeffrey
- Google Research, Mountain View, CA, USA
| | - Z Jiang
- Google Research, Mountain View, CA, USA
| | - C Jones
- Google Research, Mountain View, CA, USA
| | - P Juhas
- Google Research, Mountain View, CA, USA
| | - D Kafri
- Google Research, Mountain View, CA, USA
| | | | - T Khattar
- Google Research, Mountain View, CA, USA
| | - M Khezri
- Google Research, Mountain View, CA, USA
| | - M Kieferová
- Google Research, Mountain View, CA, USA
- Centre for Quantum Software and Information (QSI), Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, Australia
| | - S Kim
- Google Research, Mountain View, CA, USA
| | - A Kitaev
- Google Research, Mountain View, CA, USA
| | - A R Klots
- Google Research, Mountain View, CA, USA
| | - A N Korotkov
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of California, Riverside, CA, USA
| | | | | | | | - P Laptev
- Google Research, Mountain View, CA, USA
| | - K-M Lau
- Google Research, Mountain View, CA, USA
| | - L Laws
- Google Research, Mountain View, CA, USA
| | - J Lee
- Google Research, Mountain View, CA, USA
- Department of Chemistry, Columbia University, New York, NY, USA
| | - K W Lee
- Google Research, Mountain View, CA, USA
| | | | | | - A T Lill
- Google Research, Mountain View, CA, USA
| | - W Liu
- Google Research, Mountain View, CA, USA
| | | | | | - O Martin
- Google Research, Mountain View, CA, USA
| | | | - M McEwen
- Google Research, Mountain View, CA, USA
| | | | | | - A Morvan
- Google Research, Mountain View, CA, USA
| | | | | | - M Neeley
- Google Research, Mountain View, CA, USA
| | - C Neill
- Google Research, Mountain View, CA, USA
| | | | - M Newman
- Google Research, Mountain View, CA, USA
| | - J H Ng
- Google Research, Mountain View, CA, USA
| | - A Nguyen
- Google Research, Mountain View, CA, USA
| | - M Nguyen
- Google Research, Mountain View, CA, USA
| | - M Y Niu
- Google Research, Mountain View, CA, USA
| | | | | | | | - R Potter
- Google Research, Mountain View, CA, USA
| | - L P Pryadko
- Google Research, Mountain View, CA, USA
- Department of Physics and Astronomy, University of California, Riverside, CA, USA
| | | | - C Rocque
- Google Research, Mountain View, CA, USA
| | - N C Rubin
- Google Research, Mountain View, CA, USA
| | - N Saei
- Google Research, Mountain View, CA, USA
| | - D Sank
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - A Shorter
- Google Research, Mountain View, CA, USA
| | - N Shutty
- Google Research, Mountain View, CA, USA
| | - V Shvarts
- Google Research, Mountain View, CA, USA
| | - J Skruzny
- Google Research, Mountain View, CA, USA
| | - W C Smith
- Google Research, Mountain View, CA, USA
| | - R Somma
- Google Research, Mountain View, CA, USA
| | | | - D Strain
- Google Research, Mountain View, CA, USA
| | - M Szalay
- Google Research, Mountain View, CA, USA
| | - A Torres
- Google Research, Mountain View, CA, USA
| | - G Vidal
- Google Research, Mountain View, CA, USA
| | | | | | - T White
- Google Research, Mountain View, CA, USA
| | - B W K Woo
- Google Research, Mountain View, CA, USA
| | - C Xing
- Google Research, Mountain View, CA, USA
| | - Z J Yao
- Google Research, Mountain View, CA, USA
| | - P Yeh
- Google Research, Mountain View, CA, USA
| | - J Yoo
- Google Research, Mountain View, CA, USA
| | - G Young
- Google Research, Mountain View, CA, USA
| | - A Zalcman
- Google Research, Mountain View, CA, USA
| | - Y Zhang
- Google Research, Mountain View, CA, USA
| | - N Zhu
- Google Research, Mountain View, CA, USA
| | - N Zobrist
- Google Research, Mountain View, CA, USA
| | - H Neven
- Google Research, Mountain View, CA, USA
| | - R Babbush
- Google Research, Mountain View, CA, USA
| | - D Bacon
- Google Research, Mountain View, CA, USA
| | - S Boixo
- Google Research, Mountain View, CA, USA
| | - J Hilton
- Google Research, Mountain View, CA, USA
| | - E Lucero
- Google Research, Mountain View, CA, USA
| | - A Megrant
- Google Research, Mountain View, CA, USA
| | - J Kelly
- Google Research, Mountain View, CA, USA
| | - Y Chen
- Google Research, Mountain View, CA, USA
| | - P Roushan
- Google Research, Mountain View, CA, USA
| | | | - D A Abanin
- Google Research, Mountain View, CA, USA
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
- Department of Physics, Princeton University, Princeton, NJ, USA
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3
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Ward MC, Atlas JL, Carrizosa DR, Milas ZL, Brickman DS, Frenkel CH, Hong S, Heinzerling JH, Prabhu RS, Moeller BJ. Weekly vs. Bolus Cisplatin Concurrent with Definitive Radiotherapy for Squamous Carcinoma of the Head and Neck: A Systematic Review and Network Meta-Analysis. Int J Radiat Oncol Biol Phys 2023; 117:e632-e633. [PMID: 37785889 DOI: 10.1016/j.ijrobp.2023.06.2031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) The optimal schedule for cisplatin delivered concurrently with definitive radiation for squamous carcinoma of the head and neck remains controversial. Randomized data in the postoperative setting is mixed, and definitive studies are ongoing. Meanwhile, multiple trials have already compared cetuximab to cisplatin in the definitive setting. Across these trials, the cetuximab dosing was identical, but cisplatin dosing was variable and can be categorized as weekly (40 mg/m2 q1 week) or bolus (100 mg/m2 q3 weeks). We indirectly compared these two cisplatin schedules by performing a network meta-analysis of cetuximab trials. MATERIALS/METHODS We performed a PRISMA-concordant systematic review to identify randomized controlled trials comparing cisplatin to cetuximab for patients with non-metastatic squamous carcinoma of the head and neck treated with definitive radiation therapy. Trials of primary surgery, incorporating induction therapy, or mixing other therapeutics were excluded. The analysis was pre-registered with the Open Science Foundation. Individual patient survival data was extracted from the published overall survival curves using a digitizer, and outcomes were validated against published point-estimates and hazard ratios. A random effects Cox regression was used to perform the individual-patient analysis using a one-step approach under a frequentist framework. Random effects were applied to model heterogeneity in the baseline hazard function and treatment effect. Models were adjusted by HPV and smoking status, which were trial-level covariates. Alternative endpoints (DFS, LRF, DM, etc.) were analyzed qualitatively. IRB approval was not required. RESULTS Five randomized trials were identified, including 1,678 patients. Bolus cisplatin was delivered to 572 patients in 2 trials, and weekly to 271 in 3 trials. The risk of bias was low. Relative to cetuximab, both bolus and weekly cisplatin reduced the risk of death (adjusted HR 0.63, 95% CI 0.46-0.87, p = 0.004 & HR 0.56, 95% CI 0.37-0.86, p = 0.008 respectively). No interaction was identified between regimen and HPV or smoking status. Between-study heterogeneity (δ2) was 0.148 and treatment effect heterogeneity (τ2) was small (<0.0002). There was no statistical difference in OS between bolus vs. weekly regimens (weekly vs. bolus HR 0.90, 95% CI 0.53-1.52, p = 0.345). This Cox model therefore suggested that on average, the absolute difference in 5-year OS is <1.5% between the two regimens, which was not statistically significant. Secondary endpoints and toxicity were not obviously different by regimen, qualitatively. CONCLUSION Using cetuximab as a common reference, there was no significant difference in survival between weekly and bolus cisplatin schedules.
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Affiliation(s)
- M C Ward
- Levine Cancer Institute, Atrium Health and Southeast Radiation Oncology Group, Charlotte, NC
| | - J L Atlas
- Levine Cancer Institute, Atrium Health, Charlotte, NC
| | - D R Carrizosa
- Levine Cancer Institute, Atrium Health, Charlotte, NC
| | - Z L Milas
- Levine Cancer Institute, Atrium Health, Charlotte, NC
| | - D S Brickman
- Levine Cancer Institute, Atrium Health, Charlotte, NC
| | - C H Frenkel
- Levine Cancer Institute, Atrium Health, Charlotte, NC
| | - S Hong
- Levine Cancer Institute, Atrium Health, Charlotte, NC
| | - J H Heinzerling
- Levine Cancer Institute, Atrium Health and Southeast Radiation Oncology Group, Charlotte, NC
| | - R S Prabhu
- Levine Cancer Institute, Atrium Health and Southeast Radiation Oncology Group, Charlotte, NC
| | - B J Moeller
- Levine Cancer Institute, Atrium Health and Southeast Radiation Oncology Group, Charlotte, NC
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Hoke JC, Ippoliti M, Rosenberg E, Abanin D, Acharya R, Andersen TI, Ansmann M, Arute F, Arya K, Asfaw A, Atalaya J, Bardin JC, Bengtsson A, Bortoli G, Bourassa A, Bovaird J, Brill L, Broughton M, Buckley BB, Buell DA, Burger T, Burkett B, Bushnell N, Chen Z, Chiaro B, Chik D, Cogan J, Collins R, Conner P, Courtney W, Crook AL, Curtin B, Dau AG, Debroy DM, Del Toro Barba A, Demura S, Di Paolo A, Drozdov IK, Dunsworth A, Eppens D, Erickson C, Farhi E, Fatemi R, Ferreira VS, Burgos LF, Forati E, Fowler AG, Foxen B, Giang W, Gidney C, Gilboa D, Giustina M, Gosula R, Gross JA, Habegger S, Hamilton MC, Hansen M, Harrigan MP, Harrington SD, Heu P, Hoffmann MR, Hong S, Huang T, Huff A, Huggins WJ, Isakov SV, Iveland J, Jeffrey E, Jiang Z, Jones C, Juhas P, Kafri D, Kechedzhi K, Khattar T, Khezri M, Kieferová M, Kim S, Kitaev A, Klimov PV, Klots AR, Korotkov AN, Kostritsa F, Kreikebaum JM, Landhuis D, Laptev P, Lau KM, Laws L, Lee J, Lee KW, Lensky YD, Lester BJ, Lill AT, Liu W, Locharla A, Martin O, McClean JR, McEwen M, Miao KC, Mieszala A, Montazeri S, Morvan A, Movassagh R, Mruczkiewicz W, Neeley M, Neill C, Nersisyan A, Newman M, Ng JH, Nguyen A, Nguyen M, Niu MY, O’Brien TE, Omonije S, Opremcak A, Petukhov A, Potter R, Pryadko LP, Quintana C, Rocque C, Rubin NC, Saei N, Sank D, Sankaragomathi K, Satzinger KJ, Schurkus HF, Schuster C, Shearn MJ, Shorter A, Shutty N, Shvarts V, Skruzny J, Smith WC, Somma R, Sterling G, Strain D, Szalay M, Torres A, Vidal G, Villalonga B, Heidweiller CV, White T, Woo BWK, Xing C, Yao ZJ, Yeh P, Yoo J, Young G, Zalcman A, Zhang Y, Zhu N, Zobrist N, Neven H, Babbush R, Bacon D, Boixo S, Hilton J, Lucero E, Megrant A, Kelly J, Chen Y, Smelyanskiy V, Mi X, Khemani V, Roushan P. Measurement-induced entanglement and teleportation on a noisy quantum processor. Nature 2023; 622:481-486. [PMID: 37853150 PMCID: PMC10584681 DOI: 10.1038/s41586-023-06505-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 08/01/2023] [Indexed: 10/20/2023]
Abstract
Measurement has a special role in quantum theory1: by collapsing the wavefunction, it can enable phenomena such as teleportation2 and thereby alter the 'arrow of time' that constrains unitary evolution. When integrated in many-body dynamics, measurements can lead to emergent patterns of quantum information in space-time3-10 that go beyond the established paradigms for characterizing phases, either in or out of equilibrium11-13. For present-day noisy intermediate-scale quantum (NISQ) processors14, the experimental realization of such physics can be problematic because of hardware limitations and the stochastic nature of quantum measurement. Here we address these experimental challenges and study measurement-induced quantum information phases on up to 70 superconducting qubits. By leveraging the interchangeability of space and time, we use a duality mapping9,15-17 to avoid mid-circuit measurement and access different manifestations of the underlying phases, from entanglement scaling3,4 to measurement-induced teleportation18. We obtain finite-sized signatures of a phase transition with a decoding protocol that correlates the experimental measurement with classical simulation data. The phases display remarkably different sensitivity to noise, and we use this disparity to turn an inherent hardware limitation into a useful diagnostic. Our work demonstrates an approach to realizing measurement-induced physics at scales that are at the limits of current NISQ processors.
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Sud S, Poellmann M, Garg V, King T, Casey DL, Wang AZ, Hong S, Weiner AA. Prospective Characterization of Circulating Tumor Cell Kinetics in Patients with Localized Lung Cancer Treated with Radiotherapy or Chemoradiotherapy with Definitive Intent. Int J Radiat Oncol Biol Phys 2023; 117:e60. [PMID: 37785811 DOI: 10.1016/j.ijrobp.2023.06.778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) To characterize circulating tumor cell (CTC) kinetics in response to definitive therapy in patients with local or locoregional lung cancer and identify CTC kinetic profiles associated with favorable disease response versus progression. MATERIALS/METHODS In this single-institution prospective correlative biomarker study, we enrolled patients receiving definitive intent radiotherapy (RT) or chemoradiotherapy for non-metastatic lung cancer. Blood specimens were collected prior to RT (baseline), during RT and at follow up visits up to 24 months post RT. Subsequent lines of therapy were administered per standard of care. CTCs were captured and enumerated using a previously reported nanotechnology-based assay functionalized with aEpCAM, aHER-2, and aEGFR to facilitate biomimetic cell rolling and dendrimer-mediated multivalent binding. Disease status was assessed per RECIST 1.1 criteria. CTC kinetics and absolute values were analyzed to identify patterns associated with disease control versus progression. RESULTS We enrolled 24 patients with median follow up of 8 months corresponding to 114 CTC measurements. Seven patients (30%) had biopsy proven disease, while 17 (70%) were diagnosed based on clinical and radiographic features alone. Nineteen patients (79%) received stereotactic body radiation therapy. Median baseline CTC count was 12.6 CTCs/ml (range 0-290) and post RT decreased to median 4 CTCs/ml (0-42.7). For 95% of patients, a favorable kinetic profile (defined as stable CTC count, decreased CTC count or <24 CTCs/ml corresponding to the 80th percentile) during radiotherapy or at the time of first follow up corresponded to local control of the irradiated lesion. Five patients (20%) experienced disease progression within the follow up period. In the two patients with local progression of the irradiated lesion, the CTC count rose >10 fold prior to or at the time of radiographic detection of progression. In the three patients with systemic progression, CTC count rose 1.46-5.8-fold at the time of progression. Notably, four of the five patients with disease progression did not have initial biopsy confirmation of disease but did experience a CTC elevation at the time of progression. CONCLUSION Our data suggests CTCs may serve as a biomarker for response to therapy in patients being treated with RT with definitive intent for early stage or locally advanced lung cancer. This finding is of importance given important limitations in obtaining pathologic confirmation of disease in select patients and challenges distinguishing disease progression versus benign post radiotherapy radiographic changes. Further studies are needed to characterize the predictive and prognostic value of circulating biomarker levels and kinetics in lung cancer.
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Affiliation(s)
- S Sud
- Department of Radiation Oncology, University of North Carolina, Chapel Hill, NC
| | - M Poellmann
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin, Madison, WI
| | - V Garg
- University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - T King
- University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - D L Casey
- Department of Radiation Oncology, University of North Carolina, Chapel Hill, NC
| | - A Z Wang
- Department of Radiation Oncology, University of North Carolina, Chapel Hill, NC; UT Southwestern Department of Radiation Oncology, Dallas, TX
| | - S Hong
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin, Madison, WI
| | - A A Weiner
- Department of Radiation Oncology, University of North Carolina, Chapel Hill, NC
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Sud S, Poellmann MJ, Hall J, Tan X, Bu J, Myung JH, Wang AZ, Hong S, Casey DL. Prospective Characterization of Circulating Tumor Cell Kinetics in Patients With Oligometastatic Disease Receiving Definitive Intent Radiation Therapy. JCO Precis Oncol 2023; 7:e2300303. [PMID: 38096474 PMCID: PMC10730071 DOI: 10.1200/po.23.00303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 08/14/2023] [Accepted: 09/27/2023] [Indexed: 12/18/2023] Open
Abstract
PURPOSE There are currently no predictive molecular biomarkers to identify patients with oligometastatic disease (OMD) who will benefit from definitive-intent radiation therapy (RT). We prospectively characterized circulating tumor cell (CTC) kinetics in patients with OMD undergoing definitive-intent RT. METHODS This prospective correlative biomarker study included patients with any solid malignancy ≤5 metastatic sites in ≤3 anatomic organ systems undergoing definitive-intent RT to all disease sites. Circulating tumor cells (CTCs) were captured and enumerated using a biomimetic cell rolling and nanotechnology-based assay functionalized with antibodies against epithelial cell adhesion molecule, against human epidermal growth factor receptor 2, and against epidermal growth factor receptor before and during RT and at follow-up visits up to 2 years post-RT. RESULTS We enrolled 43 patients with a median follow-up of 14.3 months. The pretreatment CTC level (cells captured/mL) was not associated with the number of disease sites (median one metastatic site/patient, range 1-5) or metastasis location (bone, brain, visceral) on Wilcoxon signed-rank test, P > .05. Post-RT, 56% of patients received systemic therapy, and 72% of patients experienced subsequent local or systemic progression. For 90% of patients, a CTC level <15 within 130 days post-RT corresponded to a durable control of irradiated lesions. Patients with a favorable versus an unfavorable clearance profile experienced significantly longer progression-free survival after RT (median 13 v 4 months, log-rank test, P = .0011). On logistic regression, CTC level >15 at a given time point was associated with clinical disease progression within the subsequent 6 months (odds ratio 3.31, P = .007). In 26% of patients with disease progression, a CTC level >15 preceded radiographic or clinical progression. CONCLUSION CTCs may serve as a biomarker for disease control in OMD and may predict disease progression before standard assessments for patients receiving diverse cancer-directed therapies.
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Affiliation(s)
- Shivani Sud
- Department of Radiation Oncology University of North Carolina-Chapel Hill, Chapel Hill, NC
| | - Michael J. Poellmann
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin-Madison, Madison, WI
- Capio Biosciences, Madison, WI
| | - Jacob Hall
- Department of Radiation Oncology University of North Carolina-Chapel Hill, Chapel Hill, NC
| | - Xianming Tan
- Department of Radiation Oncology University of North Carolina-Chapel Hill, Chapel Hill, NC
| | - Jiyoon Bu
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin-Madison, Madison, WI
| | - Ja Hye Myung
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Illinois at Chicago, Chicago, IL
| | - Andrew Z. Wang
- Department of Radiation Oncology University of North Carolina-Chapel Hill, Chapel Hill, NC
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX
| | - Seungpyo Hong
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin-Madison, Madison, WI
- Capio Biosciences, Madison, WI
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Illinois at Chicago, Chicago, IL
- Carbone Cancer Center, Lachman Institute for Pharmaceutical Developmnet, Wisconsin Center for NanoBioSystems, and Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI
| | - Dana L. Casey
- Department of Radiation Oncology University of North Carolina-Chapel Hill, Chapel Hill, NC
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Lee D, Ryoo JE, Hong S, Kim HY, Kim Y. Carprofen alleviates Alzheimer-like phenotypes of 5XFAD transgenic mice by targeting the pathological hallmarks induced by amyloid-β aggregation. Sci Rep 2023; 13:10889. [PMID: 37407605 DOI: 10.1038/s41598-023-36167-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Accepted: 05/30/2023] [Indexed: 07/07/2023] Open
Abstract
Alzheimer's disease (AD) is characterized by misfolding, oligomerization, and accumulation of amyloid-β (Aβ) peptides in the brain. Aβ monomers transform into Aβ oligomers, which are toxic species, inducing tau hyperphosphorylation and the downstream effects on microglia and astrocytes, triggering synaptic and cognitive dysfunctions. The oligomers then deposit into Aβ plaques, primarily composed of β-stranded fibrils, required for definitive AD diagnosis. As amyloid burden plays the pivotal role in AD pathogenesis, many efforts are devoted in preventing amyloidosis as a therapeutic approach to impede the disease progression. Here, we discovered carprofen, a non-steroidal anti-inflammatory drug, accelerates Aβ aggregating into fibrils and increases Aβ plaques when intraperitoneally injected to 5XFAD transgenic mouse model. However, the drug seems to alleviate the key Alzheimer-like phenotypes induced by Aβ aggregation as we found attenuated neuroinflammation, improved post-synaptic density expression, associated with synaptic plasticity, and decreased phosphorylated tau levels. Carprofen also rescued impaired working memory as we discovered improved spontaneous alternation performance through Y-maze test assessed with Aβ(1-42)-infused mouse model. Collectively, while carprofen accelerates the conversion of Aβ monomers into fibrils in vitro, the drug ameliorates the major pathological hallmarks of AD in vivo.
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Affiliation(s)
- Donghee Lee
- Department of Pharmacy, College of Pharmacy, Yonsei University, Incheon, 21983, Republic of Korea
- Yonsei Institute of Pharmaceutical Sciences, College of Pharmacy, Yonsei University, Incheon, 21983, Republic of Korea
| | - Ji Eun Ryoo
- Department of Pharmacy, College of Pharmacy, Yonsei University, Incheon, 21983, Republic of Korea
- Yonsei Institute of Pharmaceutical Sciences, College of Pharmacy, Yonsei University, Incheon, 21983, Republic of Korea
| | - Seungpyo Hong
- Department of Pharmacy, College of Pharmacy, Yonsei University, Incheon, 21983, Republic of Korea
- Yonsei Frontier Lab, Yonsei University, Seoul, 03722, Republic of Korea
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin-Madison, Madison, WI, USA
- Wisconsin Center for NanoBioSystems, University of Wisconsin-Madison, Madison, WI, USA
| | - Hye Yun Kim
- Department of Pharmacy, College of Pharmacy, Yonsei University, Incheon, 21983, Republic of Korea
- Yonsei Institute of Pharmaceutical Sciences, College of Pharmacy, Yonsei University, Incheon, 21983, Republic of Korea
| | - YoungSoo Kim
- Department of Pharmacy, College of Pharmacy, Yonsei University, Incheon, 21983, Republic of Korea.
- Yonsei Institute of Pharmaceutical Sciences, College of Pharmacy, Yonsei University, Incheon, 21983, Republic of Korea.
- Yonsei Frontier Lab, Yonsei University, Seoul, 03722, Republic of Korea.
- Department of Integrative Biotechnology and Translational Medicine, Yonsei University, Incheon, 21983, Republic of Korea.
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Smitherman EA, Chahine RA, Beukelman T, Lewandowski LB, Rahman AKMF, Wenderfer SE, Curtis JR, Hersh AO, Abulaban K, Adams A, Adams M, Agbayani R, Aiello J, Akoghlanian S, Alejandro C, Allenspach E, Alperin R, Alpizar M, Amarilyo G, Ambler W, Anderson E, Ardoin S, Armendariz S, Baker E, Balboni I, Balevic S, Ballenger L, Ballinger S, Balmuri N, Barbar‐Smiley F, Barillas‐Arias L, Basiaga M, Baszis K, Becker M, Bell‐Brunson H, Beltz E, Benham H, Benseler S, Bernal W, Beukelman T, Bigley T, Binstadt B, Black C, Blakley M, Bohnsack J, Boland J, Boneparth A, Bowman S, Bracaglia C, Brooks E, Brothers M, Brown A, Brunner H, Buckley M, Buckley M, Bukulmez H, Bullock D, Cameron B, Canna S, Cannon L, Carper P, Cartwright V, Cassidy E, Cerracchio L, Chalom E, Chang J, Chang‐Hoftman A, Chauhan V, Chira P, Chinn T, Chundru K, Clairman H, Co D, Confair A, Conlon H, Connor R, Cooper A, Cooper J, Cooper S, Correll C, Corvalan R, Costanzo D, Cron R, Curiel‐Duran L, Curington T, Curry M, Dalrymple A, Davis A, Davis C, Davis C, Davis T, De Benedetti F, De Ranieri D, Dean J, Dedeoglu F, DeGuzman M, Delnay N, Dempsey V, DeSantis E, Dickson T, Dingle J, Donaldson B, Dorsey E, Dover S, Dowling J, Drew J, Driest K, Du Q, Duarte K, Durkee D, Duverger E, Dvergsten J, Eberhard A, Eckert M, Ede K, Edelheit B, Edens C, Edens C, Edgerly Y, Elder M, Ervin B, Fadrhonc S, Failing C, Fair D, Falcon M, Favier L, Federici S, Feldman B, Fennell J, Ferguson I, Ferguson P, Ferreira B, Ferrucho R, Fields K, Finkel T, Fitzgerald M, Fleming C, Flynn O, Fogel L, Fox E, Fox M, Franco L, Freeman M, Fritz K, Froese S, Fuhlbrigge R, Fuller J, George N, Gerhold K, Gerstbacher D, Gilbert M, Gillispie‐Taylor M, Giverc E, Godiwala C, Goh I, Goheer H, Goldsmith D, Gotschlich E, Gotte A, Gottlieb B, Gracia C, Graham T, Grevich S, Griffin T, Griswold J, Grom A, Guevara M, Guittar P, Guzman M, Hager M, Hahn T, Halyabar O, Hammelev E, Hance M, Hanson A, Harel L, Haro S, Harris J, Harry O, Hartigan E, Hausmann J, Hay A, Hayward K, Heiart J, Hekl K, Henderson L, Henrickson M, Hersh A, Hickey K, Hill P, Hillyer S, Hiraki L, Hiskey M, Hobday P, Hoffart C, Holland M, Hollander M, Hong S, Horwitz M, Hsu J, Huber A, Huggins J, Hui‐Yuen J, Hung C, Huntington J, Huttenlocher A, Ibarra M, Imundo L, Inman C, Insalaco A, Jackson A, Jackson S, James K, Janow G, Jaquith J, Jared S, Johnson N, Jones J, Jones J, Jones J, Jones K, Jones S, Joshi S, Jung L, Justice C, Justiniano A, Karan N, Kaufman K, Kemp A, Kessler E, Khalsa U, Kienzle B, Kim S, Kimura Y, Kingsbury D, Kitcharoensakkul M, Klausmeier T, Klein K, Klein‐Gitelman M, Kompelien B, Kosikowski A, Kovalick L, Kracker J, Kramer S, Kremer C, Lai J, Lam J, Lang B, Lapidus S, Lapin B, Lasky A, Latham D, Lawson E, Laxer R, Lee P, Lee P, Lee T, Lentini L, Lerman M, Levy D, Li S, Lieberman S, Lim L, Lin C, Ling N, Lingis M, Lo M, Lovell D, Lowman D, Luca N, Lvovich S, Madison C, Madison J, Manzoni SM, Malla B, Maller J, Malloy M, Mannion M, Manos C, Marques L, Martyniuk A, Mason T, Mathus S, McAllister L, McCarthy K, McConnell K, McCormick E, McCurdy D, Stokes PM, McGuire S, McHale I, McMonagle A, McMullen‐Jackson C, Meidan E, Mellins E, Mendoza E, Mercado R, Merritt A, Michalowski L, Miettunen P, Miller M, Milojevic D, Mirizio E, Misajon E, Mitchell M, Modica R, Mohan S, Moore K, Moorthy L, Morgan S, Dewitt EM, Moss C, Moussa T, Mruk V, Murphy A, Muscal E, Nadler R, Nahal B, Nanda K, Nasah N, Nassi L, Nativ S, Natter M, Neely J, Nelson B, Newhall L, Ng L, Nicholas J, Nicolai R, Nigrovic P, Nocton J, Nolan B, Oberle E, Obispo B, O'Brien B, O'Brien T, Okeke O, Oliver M, Olson J, O'Neil K, Onel K, Orandi A, Orlando M, Osei‐Onomah S, Oz R, Pagano E, Paller A, Pan N, Panupattanapong S, Pardeo M, Paredes J, Parsons A, Patel J, Pentakota K, Pepmueller P, Pfeiffer T, Phillippi K, Marafon DP, Phillippi K, Ponder L, Pooni R, Prahalad S, Pratt S, Protopapas S, Puplava B, Quach J, Quinlan‐Waters M, Rabinovich C, Radhakrishna S, Rafko J, Raisian J, Rakestraw A, Ramirez C, Ramsay E, Ramsey S, Randell R, Reed A, Reed A, Reed A, Reid H, Remmel K, Repp A, Reyes A, Richmond A, Riebschleger M, Ringold S, Riordan M, Riskalla M, Ritter M, Rivas‐Chacon R, Robinson A, Rodela E, Rodriquez M, Rojas K, Ronis T, Rosenkranz M, Rosolowski B, Rothermel H, Rothman D, Roth‐Wojcicki E, Rouster – Stevens K, Rubinstein T, Ruth N, Saad N, Sabbagh S, Sacco E, Sadun R, Sandborg C, Sanni A, Santiago L, Sarkissian A, Savani S, Scalzi L, Schanberg L, Scharnhorst S, Schikler K, Schlefman A, Schmeling H, Schmidt K, Schmitt E, Schneider R, Schollaert‐Fitch K, Schulert G, Seay T, Seper C, Shalen J, Sheets R, Shelly A, Shenoi S, Shergill K, Shirley J, Shishov M, Shivers C, Silverman E, Singer N, Sivaraman V, Sletten J, Smith A, Smith C, Smith J, Smith J, Smitherman E, Soep J, Son M, Spence S, Spiegel L, Spitznagle J, Sran R, Srinivasalu H, Stapp H, Steigerwald K, Rakovchik YS, Stern S, Stevens A, Stevens B, Stevenson R, Stewart K, Stingl C, Stokes J, Stoll M, Stringer E, Sule S, Sumner J, Sundel R, Sutter M, Syed R, Syverson G, Szymanski A, Taber S, Tal R, Tambralli A, Taneja A, Tanner T, Tapani S, Tarshish G, Tarvin S, Tate L, Taxter A, Taylor J, Terry M, Tesher M, Thatayatikom A, Thomas B, Tiffany K, Ting T, Tipp A, Toib D, Torok K, Toruner C, Tory H, Toth M, Tse S, Tubwell V, Twilt M, Uriguen S, Valcarcel T, Van Mater H, Vannoy L, Varghese C, Vasquez N, Vazzana K, Vehe R, Veiga K, Velez J, Verbsky J, Vilar G, Volpe N, von Scheven E, Vora S, Wagner J, Wagner‐Weiner L, Wahezi D, Waite H, Walker J, Walters H, Muskardin TW, Waqar L, Waterfield M, Watson M, Watts A, Weiser P, Weiss J, Weiss P, Wershba E, White A, Williams C, Wise A, Woo J, Woolnough L, Wright T, Wu E, Yalcindag A, Yee M, Yen E, Yeung R, Yomogida K, Yu Q, Zapata R, Zartoshti A, Zeft A, Zeft R, Zhang Y, Zhao Y, Zhu A, Zic C. Childhood-Onset Lupus Nephritis in the Childhood Arthritis and Rheumatology Research Alliance Registry: Short-Term Kidney Status and Variation in Care. Arthritis Care Res (Hoboken) 2023; 75:1553-1562. [PMID: 36775844 PMCID: PMC10500561 DOI: 10.1002/acr.25002] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 07/14/2022] [Accepted: 08/16/2022] [Indexed: 11/10/2022]
Abstract
OBJECTIVE The goal was to characterize short-term kidney status and describe variation in early care utilization in a multicenter cohort of patients with childhood-onset systemic lupus erythematosus (cSLE) and nephritis. METHODS We analyzed previously collected prospective data from North American patients with cSLE with kidney biopsy-proven nephritis enrolled in the Childhood Arthritis and Rheumatology Research Alliance (CARRA) Registry from March 2017 through December 2019. We determined the proportion of patients with abnormal kidney status at the most recent registry visit and applied generalized linear mixed models to identify associated factors. We also calculated frequency of medication use, both during induction and ever recorded. RESULTS We identified 222 patients with kidney biopsy-proven nephritis, with 64% class III/IV nephritis on initial biopsy. At the most recent registry visit at median (interquartile range) of 17 (8-29) months from initial kidney biopsy, 58 of 106 patients (55%) with available data had abnormal kidney status. This finding was associated with male sex (odds ratio [OR] 3.88, 95% confidence interval [95% CI] 1.21-12.46) and age at cSLE diagnosis (OR 1.23, 95% CI 1.01-1.49). Patients with class IV nephritis were more likely than class III to receive cyclophosphamide and rituximab during induction. There was substantial variation in mycophenolate, cyclophosphamide, and rituximab ever use patterns across rheumatology centers. CONCLUSION In this cohort with predominately class III/IV nephritis, male sex and older age at cSLE diagnosis were associated with abnormal short-term kidney status. We also observed substantial variation in contemporary medication use for pediatric lupus nephritis between pediatric rheumatology centers. Additional studies are needed to better understand the impact of this variation on long-term kidney outcomes.
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Burlingham WJ, Jankowska-Gan E, Fechner JH, Little CJ, Wang J, Hong S, Molla M, Sullivan JA, Foley DP. Extracellular Vesicle-associated GARP/TGFβ:LAP Mediates "Infectious" Allo-tolerance. Transplant Direct 2023; 9:e1475. [PMID: 37250483 PMCID: PMC10212611 DOI: 10.1097/txd.0000000000001475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 02/13/2023] [Accepted: 02/15/2023] [Indexed: 05/31/2023] Open
Abstract
Here we test the hypothesis that, like CD81-associated "latent" IL35, the transforming growth factor (TGF)β:latency-associated peptide (LAP)/glycoprotein A repetitions predominant (GARP) complex was also tethered to small extracellular vesicles (sEVs), aka exosomes, produced by lymphocytes from allo-tolerized mice. Once these sEVs are taken up by conventional T cells, we also test whether TGFβ could be activated suppressing the local immune response. Methods C57BL/6 mice were tolerized by i.p. injection of CBA/J splenocytes followed by anti-CD40L/CD154 antibody treatment on days 0, 2, and 4. On day 35, spleen and lymph nodes were extracted and isolated lymphocytes were restimulated with sonicates of CBA splenocytes overnight. sEVs were extracted from culture supernatants by ultracentrifugation (100 000g) and assayed for (a) the presence of TGFβ:LAP associated with tetraspanins CD81,CD63, and CD9 by enzyme-linked immunosorbent assay; (b) GARP, critical to membrane association of TGFβ:LAP and to activation from its latent form, as well as various TGFβ receptors; and (c) TGFβ-dependent function in 1° and 2° immunosuppression of tetanus toxoid-immunized B6 splenocytes using trans-vivo delayed-type hypersensitivity assay. Results After tolerization, CBA-restimulated lymphocytes secreted GARP/TGFβ:LAP-coated extracellular vesicles. Like IL35 subunits, but unlike IL10, which was absent from ultracentrifuge pellets, GARP/TGFβ:LAP was mainly associated with CD81+ exosomes. sEV-bound GARP/TGFβ:LAP became active in both 1° and 2° immunosuppression, the latter requiring sEV uptake by "bystander" T cells and reexpression on the cell surface. Conclusions Like other immune-suppressive components of the Treg exosome, which are produced in a latent form, exosomal GARP/TGFβ:LAP produced by allo-specific regulatory T cells undergoes either immediate activation (1° suppression) or internalization by naive T cells, followed by surface reexpression and subsequent activation (2°), to become suppressive. Our results imply a membrane-associated form of TGFβ:LAP that, like exosomal IL35, can target "bystander" lymphocytes. This new finding implicates exosomal TGFβ:LAP along with Treg-derived GARP as part of the infectious tolerance network.
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Affiliation(s)
- William J. Burlingham
- Division of Transplantation, Department of Surgery, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI
| | - Ewa Jankowska-Gan
- Division of Transplantation, Department of Surgery, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI
| | - John H. Fechner
- Division of Transplantation, Department of Surgery, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI
| | - Christopher J. Little
- Division of Transplantation, Department of Surgery, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI
| | - Jianxin Wang
- Wisconsin Center for NanoBioSystems, University of Wisconsin-Madison, Madison, WI
| | - Seungpyo Hong
- Wisconsin Center for NanoBioSystems, University of Wisconsin-Madison, Madison, WI
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin-Madison, Madison, WI
| | - Miraf Molla
- Division of Transplantation, Department of Surgery, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI
| | - Jeremy A. Sullivan
- Department of Anesthesiology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI
| | - David P. Foley
- Division of Transplantation, Department of Surgery, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI
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10
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Andersen TI, Lensky YD, Kechedzhi K, Drozdov IK, Bengtsson A, Hong S, Morvan A, Mi X, Opremcak A, Acharya R, Allen R, Ansmann M, Arute F, Arya K, Asfaw A, Atalaya J, Babbush R, Bacon D, Bardin JC, Bortoli G, Bourassa A, Bovaird J, Brill L, Broughton M, Buckley BB, Buell DA, Burger T, Burkett B, Bushnell N, Chen Z, Chiaro B, Chik D, Chou C, Cogan J, Collins R, Conner P, Courtney W, Crook AL, Curtin B, Debroy DM, Del Toro Barba A, Demura S, Dunsworth A, Eppens D, Erickson C, Faoro L, Farhi E, Fatemi R, Ferreira VS, Burgos LF, Forati E, Fowler AG, Foxen B, Giang W, Gidney C, Gilboa D, Giustina M, Gosula R, Dau AG, Gross JA, Habegger S, Hamilton MC, Hansen M, Harrigan MP, Harrington SD, Heu P, Hilton J, Hoffmann MR, Huang T, Huff A, Huggins WJ, Ioffe LB, Isakov SV, Iveland J, Jeffrey E, Jiang Z, Jones C, Juhas P, Kafri D, Khattar T, Khezri M, Kieferová M, Kim S, Kitaev A, Klimov PV, Klots AR, Korotkov AN, Kostritsa F, Kreikebaum JM, Landhuis D, Laptev P, Lau KM, Laws L, Lee J, Lee KW, Lester BJ, Lill AT, Liu W, Locharla A, Lucero E, Malone FD, Martin O, McClean JR, McCourt T, McEwen M, Miao KC, Mieszala A, Mohseni M, Montazeri S, Mount E, Movassagh R, Mruczkiewicz W, Naaman O, Neeley M, Neill C, Nersisyan A, Newman M, Ng JH, Nguyen A, Nguyen M, Niu MY, O’Brien TE, Omonije S, Petukhov A, Potter R, Pryadko LP, Quintana C, Rocque C, Rubin NC, Saei N, Sank D, Sankaragomathi K, Satzinger KJ, Schurkus HF, Schuster C, Shearn MJ, Shorter A, Shutty N, Shvarts V, Skruzny J, Smith WC, Somma R, Sterling G, Strain D, Szalay M, Torres A, Vidal G, Villalonga B, Heidweiller CV, White T, Woo BWK, Xing C, Yao ZJ, Yeh P, Yoo J, Young G, Zalcman A, Zhang Y, Zhu N, Zobrist N, Neven H, Boixo S, Megrant A, Kelly J, Chen Y, Smelyanskiy V, Kim EA, Aleiner I, Roushan P. Non-Abelian braiding of graph vertices in a superconducting processor. Nature 2023; 618:264-269. [PMID: 37169834 DOI: 10.1038/s41586-023-05954-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 03/14/2023] [Indexed: 06/09/2023]
Abstract
Indistinguishability of particles is a fundamental principle of quantum mechanics1. For all elementary and quasiparticles observed to date-including fermions, bosons and Abelian anyons-this principle guarantees that the braiding of identical particles leaves the system unchanged2,3. However, in two spatial dimensions, an intriguing possibility exists: braiding of non-Abelian anyons causes rotations in a space of topologically degenerate wavefunctions4-8. Hence, it can change the observables of the system without violating the principle of indistinguishability. Despite the well-developed mathematical description of non-Abelian anyons and numerous theoretical proposals9-22, the experimental observation of their exchange statistics has remained elusive for decades. Controllable many-body quantum states generated on quantum processors offer another path for exploring these fundamental phenomena. Whereas efforts on conventional solid-state platforms typically involve Hamiltonian dynamics of quasiparticles, superconducting quantum processors allow for directly manipulating the many-body wavefunction by means of unitary gates. Building on predictions that stabilizer codes can host projective non-Abelian Ising anyons9,10, we implement a generalized stabilizer code and unitary protocol23 to create and braid them. This allows us to experimentally verify the fusion rules of the anyons and braid them to realize their statistics. We then study the prospect of using the anyons for quantum computation and use braiding to create an entangled state of anyons encoding three logical qubits. Our work provides new insights about non-Abelian braiding and, through the future inclusion of error correction to achieve topological protection, could open a path towards fault-tolerant quantum computing.
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11
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Poellmann MJ, Bu J, Kim D, Iida M, Hong H, Wang AZ, Wheeler DL, Kimple RJ, Hong S. Circulating tumor cell abundance in head and neck squamous cell carcinoma decreases with successful chemoradiation and cetuximab treatment. Cancer Lett 2023; 562:216187. [PMID: 37068555 PMCID: PMC10510654 DOI: 10.1016/j.canlet.2023.216187] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 04/07/2023] [Accepted: 04/11/2023] [Indexed: 04/19/2023]
Abstract
Head and neck squamous cell carcinoma (HNSCC) is a common and deadly cancer. Circulating tumor cell (CTC) abundance may a valuable, prognostic biomarker in low- and intermediate-risk patients. However, few technologies have demonstrated success in detecting CTCs in these populations. We prospectively collected longitudinal CTC counts from two cohorts of patients receiving treatments at our institution using a highly sensitive device that purifies CTCs using biomimetic cell rolling and dendrimer-conjugated antibodies. In patients with intermediate risk human papillomavirus (HPV)-positive HNSCC, elevated CTC counts were detected in 13 of 14 subjects at screening with a median of 17 CTC/ml (range 0.2-2986.5). A second cohort of non-metastatic, HPV- HNSCC subjects received cetuximab monotherapy followed by surgical resection. In this cohort, all subjects had elevated baseline CTC counts median of 73 CTC/ml (range 5.4-332.9) with statistically significant declines during treatment. Interestingly, two patients with recurrent disease had elevated CTC counts during and following treatment, which also correlated with growth of size and ki67 expression in the primary tumor. The results suggest that our device may be a valuable tool for evaluating the success of less intensive treatment regimens.
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Affiliation(s)
- Michael J Poellmann
- Pharmaceutical Sciences Division, University of Wisconsin-Madison, Madison, WI, 53705, USA; Capio Biosciences, Madison, WI, 53719, USA; Capio Biosciences Korea, Incheon, South Korea
| | - Jiyoon Bu
- Pharmaceutical Sciences Division, University of Wisconsin-Madison, Madison, WI, 53705, USA; Capio Biosciences, Madison, WI, 53719, USA; Capio Biosciences Korea, Incheon, South Korea
| | - DaWon Kim
- Pharmaceutical Sciences Division, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Mari Iida
- Department of Human Oncology, University of Wisconsin-Madison, Madison, WI, 53792, USA
| | - Heejoo Hong
- Department of Clinical Pharmacology & Therapeutics, Asan Medical Center, University of Ulsan, Seoul, South Korea
| | - Andrew Z Wang
- Capio Biosciences, Madison, WI, 53719, USA; Capio Biosciences Korea, Incheon, South Korea; Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Deric L Wheeler
- Department of Human Oncology, University of Wisconsin-Madison, Madison, WI, 53792, USA
| | - Randall J Kimple
- Department of Human Oncology, University of Wisconsin-Madison, Madison, WI, 53792, USA
| | - Seungpyo Hong
- Pharmaceutical Sciences Division, University of Wisconsin-Madison, Madison, WI, 53705, USA; Capio Biosciences, Madison, WI, 53719, USA; Capio Biosciences Korea, Incheon, South Korea; Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, 53706, USA; Wisconsin Center for NanoBioSystems, University of Wisconsin-Madison, Madison, WI, 53705, USA; Lachman Institute for Pharmaceutical Development, School of Pharmacy, University of Wisconsin-Madison, Madison, WI, 53705, USA; Yonsei Frontier Lab and Department of Pharmacy, Yonsei University, Seoul, South Korea.
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12
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Kostecki KL, Iida M, Wiley AL, Kimani S, Mehall B, Tetreault K, Alexandridis R, Yu M, Hong S, Salgia R, Bruce JY, Birge RB, Harari P, Wheeler DL. Dual Axl/MerTK inhibitor INCB081776 creates a proinflammatory tumor immune microenvironment and enhances anti-PDL1 efficacy in head and neck cancer. Head Neck 2023; 45:1255-1271. [PMID: 36939040 PMCID: PMC10079616 DOI: 10.1002/hed.27340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 02/01/2023] [Accepted: 02/22/2023] [Indexed: 03/21/2023] Open
Abstract
BACKGROUND The tyrosine kinase receptors Axl and MerTK are highly overexpressed in head and neck cancer (HNC) cells, where they are critical drivers of survival, proliferation, metastasis, and therapeutic resistance. METHODS We investigated the role of Axl and MerTK in creating an immunologically "cold" tumor immune microenvironment (TIME) by targeting both receptors simultaneously with a small molecule inhibitor of Axl and MerTK (INCB081776). Effects of INCB081776 and/or anti-PDL1 on mouse oral cancer (MOC) cell growth and on the TIME were evaluated. RESULTS Targeting Axl and MerTK can reduce M2 and induce M1 macrophage polarization. In vivo, INCB081776 treatment alone or with anti-PDL1 appears to slow MOC tumor growth, increase proinflammatory immune infiltration, and decrease anti-inflammatory immune infiltration. CONCLUSIONS This data indicates that simultaneous targeting of Axl and MerTK with INCB081776, either alone or in combination with anti-PDL1, slows tumor growth and creates a proinflammatory TIME in mouse models of HNC.
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Affiliation(s)
- Kourtney L Kostecki
- Department of Human Oncology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Mari Iida
- Department of Human Oncology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Anne L Wiley
- Department of Human Oncology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Stanley Kimani
- Rutgers Biomedical Health and Sciences, Rutgers University, Newark, NJ, USA
| | - Bridget Mehall
- Department of Human Oncology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Kaitlin Tetreault
- Department of Biostatistics and Medical Informatics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Roxana Alexandridis
- Department of Biostatistics and Medical Informatics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Menggang Yu
- Department of Biostatistics and Medical Informatics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Seungpyo Hong
- Pharmaceutical Sciences Division, University of Wisconsin School of Pharmacy, Madison, WI, USA
- Yonsei Frontier Lab and Department of Pharmacy, Yonsei University, Seoul, Korea
- University of Wisconsin Carbone Cancer Center, Madison, WI, USA
| | - Ravi Salgia
- Department of Medical Oncology and Experimental Therapeutics, Comprehensive Cancer Center, City of Hope, Duarte, CA, USA
| | - Justine Y Bruce
- University of Wisconsin Carbone Cancer Center, Madison, WI, USA
- Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Raymond B Birge
- Rutgers Biomedical Health and Sciences, Rutgers University, Newark, NJ, USA
| | - Paul Harari
- Department of Human Oncology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
- University of Wisconsin Carbone Cancer Center, Madison, WI, USA
| | - Deric L Wheeler
- Department of Human Oncology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
- University of Wisconsin Carbone Cancer Center, Madison, WI, USA
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13
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Poellmann MJ, Bu J, Liu S, Wang AZ, Seyedin SN, Chandrasekharan C, Hong H, Kim Y, Caster JM, Hong S. Nanotechnology and machine learning enable circulating tumor cells as a reliable biomarker for radiotherapy responses of gastrointestinal cancer patients. Biosens Bioelectron 2023; 226:115117. [PMID: 36753988 PMCID: PMC10034717 DOI: 10.1016/j.bios.2023.115117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 01/13/2023] [Accepted: 01/31/2023] [Indexed: 02/04/2023]
Abstract
A highly sensitive, circulating tumor cell (CTC)-based liquid biopsy was used to monitor gastrointestinal cancer patients during treatment to determine if CTC abundance was predictive of disease recurrence. The approach used a combination of biomimetic cell rolling on recombinant E-selectin and dendrimer-mediated multivalent immunocapture at the nanoscale to purify CTCs from peripheral blood mononuclear cells. Due to the exceptionally high numbers of CTCs captured, a machine learning algorithm approach was developed to efficiently and reliably quantify abundance of immunocytochemically-labeled cells. A convolutional neural network and logistic regression model achieved 82.9% true-positive identification of CTCs with a false positive rate below 0.1% on a validation set. The approach was then used to quantify CTC abundance in peripheral blood samples from 27 subjects before, during, and following treatments. Samples drawn from the patients either prior to receiving radiotherapy or early in chemotherapy had a median 50 CTC ml-1 whole blood (range 0.6-541.6). We found that the CTC counts drawn 3 months post treatment were predictive of disease progression (p = .045). This approach to quantifying CTC abundance may be a clinically impactful in the timely determination of gastrointestinal cancer progression or response to treatment.
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Affiliation(s)
- Michael J Poellmann
- Pharmaceutical Sciences Division, University of Wisconsin-Madison, Madison, WI, 53705, USA; Capio Biosciences, Inc., Madison, WI, 53719, USA and Capio Biosciences Korea, Incheon, 21983 South Korea
| | - Jiyoon Bu
- Pharmaceutical Sciences Division, University of Wisconsin-Madison, Madison, WI, 53705, USA; Capio Biosciences, Inc., Madison, WI, 53719, USA and Capio Biosciences Korea, Incheon, 21983 South Korea; Department of Biological Engineering, Inha University, Incheon, 22212, South Korea
| | - Stanley Liu
- Capio Biosciences, Inc., Madison, WI, 53719, USA and Capio Biosciences Korea, Incheon, 21983 South Korea
| | - Andrew Z Wang
- Capio Biosciences, Inc., Madison, WI, 53719, USA and Capio Biosciences Korea, Incheon, 21983 South Korea; Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Steven N Seyedin
- Department of Radiation Oncology, University of California Irvine, Irvine, CA, 92697, USA
| | | | - Heejoo Hong
- Department of Clinical Pharmacology & Therapeutics, Asan Medical Center, University of Ulsan, Seoul, 05505, South Korea
| | - YoungSoo Kim
- Department of Pharmacy, College of Pharmacy, Yonsei University, Incheon, 21983, South Korea
| | - Joseph M Caster
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, 52242, USA
| | - Seungpyo Hong
- Pharmaceutical Sciences Division, University of Wisconsin-Madison, Madison, WI, 53705, USA; Capio Biosciences, Inc., Madison, WI, 53719, USA and Capio Biosciences Korea, Incheon, 21983 South Korea; Department of Pharmacy, College of Pharmacy, Yonsei University, Incheon, 21983, South Korea; Lachman Institute for Pharmaceutical Development, University of Wisconsin-Madison, Madison, WI, 53705, USA; Wisconsin Center for NanoBioSystems, University of Wisconsin-Madison, Madison, WI, 53705, USA.
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14
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Akhtar M, Bonus F, Lebrun-Gallagher FR, Johnson NI, Siegele-Brown M, Hong S, Hile SJ, Kulmiya SA, Weidt S, Hensinger WK. A high-fidelity quantum matter-link between ion-trap microchip modules. Nat Commun 2023; 14:531. [PMID: 36754957 PMCID: PMC9908934 DOI: 10.1038/s41467-022-35285-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 11/25/2022] [Indexed: 02/10/2023] Open
Abstract
System scalability is fundamental for large-scale quantum computers (QCs) and is being pursued over a variety of hardware platforms. For QCs based on trapped ions, architectures such as the quantum charge-coupled device (QCCD) are used to scale the number of qubits on a single device. However, the number of ions that can be hosted on a single quantum computing module is limited by the size of the chip being used. Therefore, a modular approach is of critical importance and requires quantum connections between individual modules. Here, we present the demonstration of a quantum matter-link in which ion qubits are transferred between adjacent QC modules. Ion transport between adjacent modules is realised at a rate of 2424 s-1 and with an infidelity associated with ion loss during transport below 7 × 10-8. Furthermore, we show that the link does not measurably impact the phase coherence of the qubit. The quantum matter-link constitutes a practical mechanism for the interconnection of QCCD devices. Our work will facilitate the implementation of modular QCs capable of fault-tolerant utility-scale quantum computation.
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Affiliation(s)
- M. Akhtar
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK ,Universal Quantum Ltd, Brighton, BN1 6SB UK
| | - F. Bonus
- Universal Quantum Ltd, Brighton, BN1 6SB UK ,grid.83440.3b0000000121901201Department of Physics and Astronomy, University College London, London, WC1E 6BT UK
| | - F. R. Lebrun-Gallagher
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK ,Universal Quantum Ltd, Brighton, BN1 6SB UK
| | - N. I. Johnson
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK
| | - M. Siegele-Brown
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK
| | - S. Hong
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK
| | - S. J. Hile
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK
| | - S. A. Kulmiya
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK ,grid.5337.20000 0004 1936 7603Quantum Engineering Centre for Doctoral Training, University of Bristol, Bristol, BS8 1TH UK
| | - S. Weidt
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK ,Universal Quantum Ltd, Brighton, BN1 6SB UK
| | - W. K. Hensinger
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK ,Universal Quantum Ltd, Brighton, BN1 6SB UK
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15
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Bang S, Kwon H, Yoon C, Rhew S, Shin D, Moon H, Cho H, Ha U, Lee J, Hong S. Development and validation of a machine learning-based CT radiomics model for differentiation of benign and malignant solid renal tumors. Eur Urol 2023. [DOI: 10.1016/s0302-2838(23)01313-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
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16
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Choi A, Javius-Jones K, Hong S, Park H. Cell-Based Drug Delivery Systems with Innate Homing Capability as a Novel Nanocarrier Platform. Int J Nanomedicine 2023; 18:509-525. [PMID: 36742991 PMCID: PMC9893846 DOI: 10.2147/ijn.s394389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Accepted: 01/12/2023] [Indexed: 01/29/2023] Open
Abstract
Nanoparticle-based drug delivery systems have been designed to treat various diseases. However, many problems remain, such as inadequate tumor targeting and poor therapeutic outcomes. To overcome these obstacles, cell-based drug delivery systems have been developed. Candidates for cell-mediated drug delivery include blood cells, immune cells, and stem cells with innate tumor tropism and low immunogenicity; they act as a disguise to deliver the therapeutic payload. In drug delivery systems, therapeutic agents are encapsulated intracellularly or attached to the surface of the plasma membrane and transported to the desired site. Here, we review the pros and cons of cell-based therapies and discuss their homing mechanisms in the tumor microenvironment. In addition, different strategies to load therapeutic agents inside or on the surface of circulating cells and the current applications for a wide range of disease treatments are summarized.
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Affiliation(s)
- Anseo Choi
- School of Integrative Engineering, Chung-Ang University, Seoul, Republic of Korea
| | - Kaila Javius-Jones
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin, Madison, WI, USA
| | - Seungpyo Hong
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin, Madison, WI, USA
| | - Hansoo Park
- School of Integrative Engineering, Chung-Ang University, Seoul, Republic of Korea,Correspondence: Hansoo Park; Seungpyo Hong, School of Integrative Engineering, Chung-Ang University, Seoul, 06974, Republic of Korea, Tel +82-2 820 5804, Fax +82-2 813 8159, Email ;
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17
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Jeong WJ, Bu J, Mickel P, Han Y, Rawding PA, Wang J, Kang H, Hong H, Král P, Hong S. Dendrimer-Peptide Conjugates for Effective Blockade of the Interactions between SARS-CoV-2 Spike Protein and Human ACE2 Receptor. Biomacromolecules 2023; 24:141-149. [PMID: 36562668 PMCID: PMC9811402 DOI: 10.1021/acs.biomac.2c01018] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 12/01/2022] [Indexed: 12/24/2022]
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has threatened the stability of global healthcare, which is becoming an endemic issue. Despite the development of various treatment strategies to fight COVID-19, the currently available treatment options have shown varied efficacy. Herein, we have developed an avidity-based SARS-CoV-2 antagonist using dendrimer-peptide conjugates (DPCs) for effective COVID-19 treatment. Two different peptide fragments obtained from angiotensin-converting enzyme 2 (ACE2) were integrated into a single sequence, followed by the conjugation to poly(amidoamine) (PAMAM) dendrimers. We hypothesized that the strong multivalent binding avidity endowed by dendrimers would help peptides effectively block the interaction between SARS-CoV-2 and ACE2, and this antagonist effect would be dependent upon the generation (size) of the dendrimers. To assess this, binding kinetics of the DPCs prepared from generation 4 (G4) and G7 PAMAM dendrimers to spike protein of SARS-CoV-2 were quantitatively measured using surface plasmon resonance. The larger dendrimer-based DPCs exhibited significantly enhanced binding strength by 3 orders of magnitude compared to the free peptides, whereas the smaller one showed a 12.8-fold increase only. An in vitro assay using SARS-CoV-2-mimicking microbeads also showed the improved SARS-CoV-2 blockade efficiency of the G7-peptide conjugates compared to G4. In addition, the interaction between the DPCs and SARS-CoV-2 was analyzed using molecular dynamics (MD) simulation, providing an insight into how the dendrimer-mediated multivalent binding effect can enhance the SARS-CoV-2 blockade. Our findings demonstrate that the DPCs having strong binding to SARS-CoV-2 effectively block the interaction between ACE2 and SARS-CoV-2, providing a potential as a high-affinity drug delivery system to direct anti-COVID payloads to the virus.
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Affiliation(s)
- Woo-jin Jeong
- Pharmaceutical Sciences Division, The University of Wisconsin-Madison, 777 Highland Ave., Madison, WI 53705, USA
- Wisconsin Center for NanoBioSystems, The University of Wisconsin-Madison, 777 Highland Ave., Madison, WI 53705, USA
- Department of Biological Sciences and Bioengineering, Inha University, 100 Inha-ro, Michuholgu, Incheon 22212, KOREA
| | - Jiyoon Bu
- Pharmaceutical Sciences Division, The University of Wisconsin-Madison, 777 Highland Ave., Madison, WI 53705, USA
- Wisconsin Center for NanoBioSystems, The University of Wisconsin-Madison, 777 Highland Ave., Madison, WI 53705, USA
- Department of Biological Sciences and Bioengineering, Inha University, 100 Inha-ro, Michuholgu, Incheon 22212, KOREA
| | - Philip Mickel
- Department of Chemistry, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Yanxiao Han
- Department of Chemistry, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Piper A Rawding
- Pharmaceutical Sciences Division, The University of Wisconsin-Madison, 777 Highland Ave., Madison, WI 53705, USA
- Wisconsin Center for NanoBioSystems, The University of Wisconsin-Madison, 777 Highland Ave., Madison, WI 53705, USA
| | - Jianxin Wang
- Wisconsin Center for NanoBioSystems, The University of Wisconsin-Madison, 777 Highland Ave., Madison, WI 53705, USA
| | - Hanbit Kang
- Department of Biological Sciences and Bioengineering, Inha University, 100 Inha-ro, Michuholgu, Incheon 22212, KOREA
| | - Heejoo Hong
- Department of Clinical Pharmacology & Therapeutics, Asan Medical Center, University of Ulsan, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, KOREA
| | - Petr Král
- Department of Chemistry, University of Illinois at Chicago, Chicago, IL 60607, USA
- Department of Physics, University of Illinois at Chicago, Chicago, IL 60607, USA
- Department of Pharmaceutical Sciences, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Seungpyo Hong
- Pharmaceutical Sciences Division, The University of Wisconsin-Madison, 777 Highland Ave., Madison, WI 53705, USA
- Wisconsin Center for NanoBioSystems, The University of Wisconsin-Madison, 777 Highland Ave., Madison, WI 53705, USA
- Lachman Institute for Pharmaceutical Development, The University of Wisconsin-Madison, 777 Highland Ave., Madison, WI 53705, USA
- Yonsei Frontier Lab and Department of Pharmacy, Yonsei University, Seoul 03722, KOREA
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18
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Seo JW, Jo S, Jung YS, Mijan MA, Cha J, Hong S, Byun S, Lim TG. Rosa gallica and its active compound, cyanidin-3,5-O-diglucoside, improve skin hydration via the GLK signaling pathway. Biofactors 2022; 49:415-427. [PMID: 36573713 DOI: 10.1002/biof.1922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 11/21/2022] [Indexed: 12/28/2022]
Abstract
Rosa gallica has been previously reported to display anti-inflammatory, anti-oxidative, and anti-skin wrinkle activities. However, the effect of Rosa gallica on skin hydration and its active components are largely unknown. Herein, we aimed to investigate the skin hydration effect of rose petal extract (RPE) in humans and elucidate the underlying molecular mechanism. A double-blinded clinical study was performed to investigate the effect of RPE on skin hydration. Stratum corneum moisture analysis demonstrated that RPE treatment significantly improved hydration levels in human skin. Furthermore, HAS2 and hyaluronic acid levels were notably increased by RPE in keratinocytes and 3D human skin equivalent model. By comparing the modulatory effect on HAS2 expression, cyanidin-3,5-O-diglucoside (CDG) was identified as the most potent compound in RPE likely responsible for skin hydration. The kinase activity of GLK, an upstream regulator of MAPK signaling, was increased by CDG in a dose-dependent manner. Importantly, silencing GLK reversed CDG-mediated HAS2 upregulation, further supporting the involvement of GLK in the CDG-mediated effects. Binding of CDG to GLK was confirmed by pull-down assay and computer modeling. These findings suggest that RPE and its active component CDG increases skin hydration by upregulating HAS2 expression through modulating the GLK-MAP2K-MAPK signaling pathway.
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Affiliation(s)
- Ji-Won Seo
- Department of Agricultural Biotechnology and Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul, Republic of Korea
| | - Seongin Jo
- Department of Biotechnology, Yonsei University, Seoul, Republic of Korea
| | | | - Mohammad-Al Mijan
- Department of Food Science and Biotechnology, Sejong University, Seoul, Republic of Korea
| | - Joy Cha
- Division of Bioengineering, Incheon National University, Incheon, Republic of Korea
| | - Seungpyo Hong
- Department of Molecular Biology, Jeonbuk National University, Jeonju, Republic of Korea
| | - Sanguine Byun
- Department of Biotechnology, Yonsei University, Seoul, Republic of Korea
| | - Tae-Gyu Lim
- Department of Food Science and Biotechnology, Sejong University, Seoul, Republic of Korea
- R&D Center, NOVAWells Co., Ltd., Cheongju, South Korea
- Department of Food Science and Biotechnology, and Carbohydrate Bioproduct Research Center, Sejong University, Seoul, Republic of Korea
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19
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Choi H, Pyo KH, Lim S, Cho B, Hong S. PP223 Single-cell RNA sequencing in metastatic lung cancer uncovers the efficacy of PD-1/PD-L1 inhibitors on immune cell population. ESMO Open 2022. [DOI: 10.1016/j.esmoop.2022.100719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
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20
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Knight, Imwattana K, Lim SC, Hong S, Putsathit P, Collins DA, Riley TV. WS1.6: GENOMIC EPIDEMIOLOGY OF RECURRENT CLOSTRIDIOIDES DIFFICILE INFECTION IN WESTERN AUSTRALIA. J Glob Antimicrob Resist 2022. [DOI: 10.1016/s2213-7165(22)00274-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
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21
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Morvan A, Andersen TI, Mi X, Neill C, Petukhov A, Kechedzhi K, Abanin DA, Michailidis A, Acharya R, Arute F, Arya K, Asfaw A, Atalaya J, Bardin JC, Basso J, Bengtsson A, Bortoli G, Bourassa A, Bovaird J, Brill L, Broughton M, Buckley BB, Buell DA, Burger T, Burkett B, Bushnell N, Chen Z, Chiaro B, Collins R, Conner P, Courtney W, Crook AL, Curtin B, Debroy DM, Del Toro Barba A, Demura S, Dunsworth A, Eppens D, Erickson C, Faoro L, Farhi E, Fatemi R, Flores Burgos L, Forati E, Fowler AG, Foxen B, Giang W, Gidney C, Gilboa D, Giustina M, Grajales Dau A, Gross JA, Habegger S, Hamilton MC, Harrigan MP, Harrington SD, Hoffmann M, Hong S, Huang T, Huff A, Huggins WJ, Isakov SV, Iveland J, Jeffrey E, Jiang Z, Jones C, Juhas P, Kafri D, Khattar T, Khezri M, Kieferová M, Kim S, Kitaev AY, Klimov PV, Klots AR, Korotkov AN, Kostritsa F, Kreikebaum JM, Landhuis D, Laptev P, Lau KM, Laws L, Lee J, Lee KW, Lester BJ, Lill AT, Liu W, Locharla A, Malone F, Martin O, McClean JR, McEwen M, Meurer Costa B, Miao KC, Mohseni M, Montazeri S, Mount E, Mruczkiewicz W, Naaman O, Neeley M, Nersisyan A, Newman M, Nguyen A, Nguyen M, Niu MY, O'Brien TE, Olenewa R, Opremcak A, Potter R, Quintana C, Rubin NC, Saei N, Sank D, Sankaragomathi K, Satzinger KJ, Schurkus HF, Schuster C, Shearn MJ, Shorter A, Shvarts V, Skruzny J, Smith WC, Strain D, Sterling G, Su Y, Szalay M, Torres A, Vidal G, Villalonga B, Vollgraff-Heidweiller C, White T, Xing C, Yao Z, Yeh P, Yoo J, Zalcman A, Zhang Y, Zhu N, Neven H, Bacon D, Hilton J, Lucero E, Babbush R, Boixo S, Megrant A, Kelly J, Chen Y, Smelyanskiy V, Aleiner I, Ioffe LB, Roushan P. Formation of robust bound states of interacting microwave photons. Nature 2022; 612:240-245. [PMID: 36477133 PMCID: PMC9729104 DOI: 10.1038/s41586-022-05348-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 09/14/2022] [Indexed: 12/12/2022]
Abstract
Systems of correlated particles appear in many fields of modern science and represent some of the most intractable computational problems in nature. The computational challenge in these systems arises when interactions become comparable to other energy scales, which makes the state of each particle depend on all other particles1. The lack of general solutions for the three-body problem and acceptable theory for strongly correlated electrons shows that our understanding of correlated systems fades when the particle number or the interaction strength increases. One of the hallmarks of interacting systems is the formation of multiparticle bound states2-9. Here we develop a high-fidelity parameterizable fSim gate and implement the periodic quantum circuit of the spin-½ XXZ model in a ring of 24 superconducting qubits. We study the propagation of these excitations and observe their bound nature for up to five photons. We devise a phase-sensitive method for constructing the few-body spectrum of the bound states and extract their pseudo-charge by introducing a synthetic flux. By introducing interactions between the ring and additional qubits, we observe an unexpected resilience of the bound states to integrability breaking. This finding goes against the idea that bound states in non-integrable systems are unstable when their energies overlap with the continuum spectrum. Our work provides experimental evidence for bound states of interacting photons and discovers their stability beyond the integrability limit.
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Affiliation(s)
- A Morvan
- Google Research, Mountain View, CA, USA
| | | | - X Mi
- Google Research, Mountain View, CA, USA
| | - C Neill
- Google Research, Mountain View, CA, USA
| | | | | | - D A Abanin
- Google Research, Mountain View, CA, USA
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
| | - A Michailidis
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
| | - R Acharya
- Google Research, Mountain View, CA, USA
| | - F Arute
- Google Research, Mountain View, CA, USA
| | - K Arya
- Google Research, Mountain View, CA, USA
| | - A Asfaw
- Google Research, Mountain View, CA, USA
| | - J Atalaya
- Google Research, Mountain View, CA, USA
| | - J C Bardin
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA
| | - J Basso
- Google Research, Mountain View, CA, USA
| | | | - G Bortoli
- Google Research, Mountain View, CA, USA
| | | | - J Bovaird
- Google Research, Mountain View, CA, USA
| | - L Brill
- Google Research, Mountain View, CA, USA
| | | | | | - D A Buell
- Google Research, Mountain View, CA, USA
| | - T Burger
- Google Research, Mountain View, CA, USA
| | - B Burkett
- Google Research, Mountain View, CA, USA
| | | | - Z Chen
- Google Research, Mountain View, CA, USA
| | - B Chiaro
- Google Research, Mountain View, CA, USA
| | - R Collins
- Google Research, Mountain View, CA, USA
| | - P Conner
- Google Research, Mountain View, CA, USA
| | | | - A L Crook
- Google Research, Mountain View, CA, USA
| | - B Curtin
- Google Research, Mountain View, CA, USA
| | | | | | - S Demura
- Google Research, Mountain View, CA, USA
| | | | - D Eppens
- Google Research, Mountain View, CA, USA
| | | | - L Faoro
- Google Research, Mountain View, CA, USA
| | - E Farhi
- Google Research, Mountain View, CA, USA
| | - R Fatemi
- Google Research, Mountain View, CA, USA
| | | | - E Forati
- Google Research, Mountain View, CA, USA
| | | | - B Foxen
- Google Research, Mountain View, CA, USA
| | - W Giang
- Google Research, Mountain View, CA, USA
| | - C Gidney
- Google Research, Mountain View, CA, USA
| | - D Gilboa
- Google Research, Mountain View, CA, USA
| | | | | | - J A Gross
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - S Hong
- Google Research, Mountain View, CA, USA
| | - T Huang
- Google Research, Mountain View, CA, USA
| | - A Huff
- Google Research, Mountain View, CA, USA
| | | | | | - J Iveland
- Google Research, Mountain View, CA, USA
| | - E Jeffrey
- Google Research, Mountain View, CA, USA
| | - Z Jiang
- Google Research, Mountain View, CA, USA
| | - C Jones
- Google Research, Mountain View, CA, USA
| | - P Juhas
- Google Research, Mountain View, CA, USA
| | - D Kafri
- Google Research, Mountain View, CA, USA
| | - T Khattar
- Google Research, Mountain View, CA, USA
| | - M Khezri
- Google Research, Mountain View, CA, USA
| | - M Kieferová
- Google Research, Mountain View, CA, USA
- Centre for Quantum Computation and Communication Technology, Centre for Quantum Software and Information, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, New South Wales, Australia
| | - S Kim
- Google Research, Mountain View, CA, USA
| | - A Y Kitaev
- Google Research, Mountain View, CA, USA
- Institute for Quantum Information and Matter, California Institute of Technology, Pasadena, CA, USA
| | | | - A R Klots
- Google Research, Mountain View, CA, USA
| | - A N Korotkov
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of California, Riverside, CA, USA
| | | | | | | | - P Laptev
- Google Research, Mountain View, CA, USA
| | - K-M Lau
- Google Research, Mountain View, CA, USA
| | - L Laws
- Google Research, Mountain View, CA, USA
| | - J Lee
- Google Research, Mountain View, CA, USA
| | - K W Lee
- Google Research, Mountain View, CA, USA
| | | | - A T Lill
- Google Research, Mountain View, CA, USA
| | - W Liu
- Google Research, Mountain View, CA, USA
| | | | - F Malone
- Google Research, Mountain View, CA, USA
| | - O Martin
- Google Research, Mountain View, CA, USA
| | | | - M McEwen
- Google Research, Mountain View, CA, USA
- Department of Physics, University of California, Santa Barbara, CA, USA
| | | | - K C Miao
- Google Research, Mountain View, CA, USA
| | - M Mohseni
- Google Research, Mountain View, CA, USA
| | | | - E Mount
- Google Research, Mountain View, CA, USA
| | | | - O Naaman
- Google Research, Mountain View, CA, USA
| | - M Neeley
- Google Research, Mountain View, CA, USA
| | | | - M Newman
- Google Research, Mountain View, CA, USA
| | - A Nguyen
- Google Research, Mountain View, CA, USA
| | - M Nguyen
- Google Research, Mountain View, CA, USA
| | - M Y Niu
- Google Research, Mountain View, CA, USA
| | | | - R Olenewa
- Google Research, Mountain View, CA, USA
| | | | - R Potter
- Google Research, Mountain View, CA, USA
| | | | - N C Rubin
- Google Research, Mountain View, CA, USA
| | - N Saei
- Google Research, Mountain View, CA, USA
| | - D Sank
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - A Shorter
- Google Research, Mountain View, CA, USA
| | - V Shvarts
- Google Research, Mountain View, CA, USA
| | - J Skruzny
- Google Research, Mountain View, CA, USA
| | - W C Smith
- Google Research, Mountain View, CA, USA
| | - D Strain
- Google Research, Mountain View, CA, USA
| | | | - Y Su
- Google Research, Mountain View, CA, USA
| | - M Szalay
- Google Research, Mountain View, CA, USA
| | - A Torres
- Google Research, Mountain View, CA, USA
| | - G Vidal
- Google Research, Mountain View, CA, USA
| | | | | | - T White
- Google Research, Mountain View, CA, USA
| | - C Xing
- Google Research, Mountain View, CA, USA
| | - Z Yao
- Google Research, Mountain View, CA, USA
| | - P Yeh
- Google Research, Mountain View, CA, USA
| | - J Yoo
- Google Research, Mountain View, CA, USA
| | - A Zalcman
- Google Research, Mountain View, CA, USA
| | - Y Zhang
- Google Research, Mountain View, CA, USA
| | - N Zhu
- Google Research, Mountain View, CA, USA
| | - H Neven
- Google Research, Mountain View, CA, USA
| | - D Bacon
- Google Research, Mountain View, CA, USA
| | - J Hilton
- Google Research, Mountain View, CA, USA
| | - E Lucero
- Google Research, Mountain View, CA, USA
| | - R Babbush
- Google Research, Mountain View, CA, USA
| | - S Boixo
- Google Research, Mountain View, CA, USA
| | - A Megrant
- Google Research, Mountain View, CA, USA
| | - J Kelly
- Google Research, Mountain View, CA, USA
| | - Y Chen
- Google Research, Mountain View, CA, USA
| | | | - I Aleiner
- Google Research, Mountain View, CA, USA.
| | - L B Ioffe
- Google Research, Mountain View, CA, USA.
| | - P Roushan
- Google Research, Mountain View, CA, USA.
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22
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Hahn T, Daymont C, Beukelman T, Groh B, Hays K, Bingham CA, Scalzi L, Abel N, Abulaban K, Adams A, Adams M, Agbayani R, Aiello J, Akoghlanian S, Alejandro C, Allenspach E, Alperin R, Alpizar M, Amarilyo G, Ambler W, Anderson E, Ardoin S, Armendariz S, Baker E, Balboni I, Balevic S, Ballenger L, Ballinger S, Balmuri N, Barbar-Smiley F, Barillas-Arias L, Basiaga M, Baszis K, Becker M, Bell-Brunson H, Beltz E, Benham H, Benseler S, Bernal W, Beukelman T, Bigley T, Binstadt B, Black C, Blakley M, Bohnsack J, Boland J, Boneparth A, Bowman S, Bracaglia C, Brooks E, Brothers M, Brown A, Brunner H, Buckley M, Buckley M, Bukulmez H, Bullock D, Cameron B, Canna S, Cannon L, Carper P, Cartwright V, Cassidy E, Cerracchio L, Chalom E, Chang J, Chang-Hoftman A, Chauhan V, Chira P, Chinn T, Chundru K, Clairman H, Co D, Confair A, Conlon H, Connor R, Cooper A, Cooper J, Cooper S, Correll C, Corvalan R, Costanzo D, Cron R, Curiel-Duran L, Curington T, Curry M, Dalrymple A, Davis A, Davis C, Davis C, Davis T, De Benedetti F, De Ranieri D, Dean J, Dedeoglu F, DeGuzman M, Delnay N, Dempsey V, DeSantis E, Dickson T, Dingle J, Donaldson B, Dorsey E, Dover S, Dowling J, Drew J, Driest K, Du Q, Duarte K, Durkee D, Duverger E, Dvergsten J, Eberhard A, Eckert M, Ede K, Edelheit B, Edens C, Edens C, Edgerly Y, Elder M, Ervin B, Fadrhonc S, Failing C, Fair D, Falcon M, Favier L, Federici S, Feldman B, Fennell J, Ferguson I, Ferguson P, Ferreira B, Ferrucho R, Fields K, Finkel T, Fitzgerald M, Fleming C, Flynn O, Fogel L, Fox E, Fox M, Franco L, Freeman M, Fritz K, Froese S, Fuhlbrigge R, Fuller J, George N, Gerhold K, Gerstbacher D, Gilbert M, Gillispie-Taylor M, Giverc E, Godiwala C, Goh I, Goheer H, Goldsmith D, Gotschlich E, Gotte A, Gottlieb B, Gracia C, Graham T, Grevich S, Griffin T, Griswold J, Grom A, Guevara M, Guittar P, Guzman M, Hager M, Hahn T, Halyabar O, Hammelev E, Hance M, Hanson A, Harel L, Haro S, Harris J, Harry O, Hartigan E, Hausmann J, Hay A, Hayward K, Heiart J, Hekl K, Henderson L, Henrickson M, Hersh A, Hickey K, Hill P, Hillyer S, Hiraki L, Hiskey M, Hobday P, Hoffart C, Holland M, Hollander M, Hong S, Horwitz M, Hsu J, Huber A, Huggins J, Hui-Yuen J, Hung C, Huntington J, Huttenlocher A, Ibarra M, Imundo L, Inman C, Insalaco A, Jackson A, Jackson S, James K, Janow G, Jaquith J, Jared S, Johnson N, Jones J, Jones J, Jones J, Jones K, Jones S, Joshi S, Jung L, Justice C, Justiniano A, Karan N, Kaufman K, Kemp A, Kessler E, Khalsa U, Kienzle B, Kim S, Kimura Y, Kingsbury D, Kitcharoensakkul M, Klausmeier T, Klein K, Klein-Gitelman M, Kompelien B, Kosikowski A, Kovalick L, Kracker J, Kramer S, Kremer C, Lai J, Lam J, Lang B, Lapidus S, Lapin B, Lasky A, Latham D, Lawson E, Laxer R, Lee P, Lee P, Lee T, Lentini L, Lerman M, Levy D, Li S, Lieberman S, Lim L, Lin C, Ling N, Lingis M, Lo M, Lovell D, Lowman D, Luca N, Lvovich S, Madison C, Madison J, Manzoni SM, Malla B, Maller J, Malloy M, Mannion M, Manos C, Marques L, Martyniuk A, Mason T, Mathus S, McAllister L, McCarthy K, McConnell K, McCormick E, McCurdy D, Stokes PMC, McGuire S, McHale I, McMonagle A, McMullen-Jackson C, Meidan E, Mellins E, Mendoza E, Mercado R, Merritt A, Michalowski L, Miettunen P, Miller M, Milojevic D, Mirizio E, Misajon E, Mitchell M, Modica R, Mohan S, Moore K, Moorthy L, Morgan S, Dewitt EM, Moss C, Moussa T, Mruk V, Murphy A, Muscal E, Nadler R, Nahal B, Nanda K, Nasah N, Nassi L, Nativ S, Natter M, Neely J, Nelson B, Newhall L, Ng L, Nicholas J, Nicolai R, Nigrovic P, Nocton J, Nolan B, Oberle E, Obispo B, O’Brien B, O’Brien T, Okeke O, Oliver M, Olson J, O’Neil K, Onel K, Orandi A, Orlando M, Osei-Onomah S, Oz R, Pagano E, Paller A, Pan N, Panupattanapong S, Pardeo M, Paredes J, Parsons A, Patel J, Pentakota K, Pepmueller P, Pfeiffer T, Phillippi K, Marafon DP, Phillippi K, Ponder L, Pooni R, Prahalad S, Pratt S, Protopapas S, Puplava B, Quach J, Quinlan-Waters M, Rabinovich C, Radhakrishna S, Rafko J, Raisian J, Rakestraw A, Ramirez C, Ramsay E, Ramsey S, Randell R, Reed A, Reed A, Reed A, Reid H, Remmel K, Repp A, Reyes A, Richmond A, Riebschleger M, Ringold S, Riordan M, Riskalla M, Ritter M, Rivas-Chacon R, Robinson A, Rodela E, Rodriquez M, Rojas K, Ronis T, Rosenkranz M, Rosolowski B, Rothermel H, Rothman D, Roth-Wojcicki E, Rouster-Stevens K, Rubinstein T, Ruth N, Saad N, Sabbagh S, Sacco E, Sadun R, Sandborg C, Sanni A, Santiago L, Sarkissian A, Savani S, Scalzi L, Schanberg L, Scharnhorst S, Schikler K, Schlefman A, Schmeling H, Schmidt K, Schmitt E, Schneider R, Schollaert-Fitch K, Schulert G, Seay T, Seper C, Shalen J, Sheets R, Shelly A, Shenoi S, Shergill K, Shirley J, Shishov M, Shivers C, Silverman E, Singer N, Sivaraman V, Sletten J, Smith A, Smith C, Smith J, Smith J, Smitherman E, Soep J, Son M, Spence S, Spiegel L, Spitznagle J, Sran R, Srinivasalu H, Stapp H, Steigerwald K, Rakovchik YS, Stern S, Stevens A, Stevens B, Stevenson R, Stewart K, Stingl C, Stokes J, Stoll M, Stringer E, Sule S, Sumner J, Sundel R, Sutter M, Syed R, Syverson G, Szymanski A, Taber S, Tal R, Tambralli A, Taneja A, Tanner T, Tapani S, Tarshish G, Tarvin S, Tate L, Taxter A, Taylor J, Terry M, Tesher M, Thatayatikom A, Thomas B, Tiffany K, Ting T, Tipp A, Toib D, Torok K, Toruner C, Tory H, Toth M, Tse S, Tubwell V, Twilt M, Uriguen S, Valcarcel T, Van Mater H, Vannoy L, Varghese C, Vasquez N, Vazzana K, Vehe R, Veiga K, Velez J, Verbsky J, Vilar G, Volpe N, von Scheven E, Vora S, Wagner J, Wagner-Weiner L, Wahezi D, Waite H, Walker J, Walters H, Muskardin TW, Waqar L, Waterfield M, Watson M, Watts A, Weiser P, Weiss J, Weiss P, Wershba E, White A, Williams C, Wise A, Woo J, Woolnough L, Wright T, Wu E, Yalcindag A, Yee M, Yen E, Yeung R, Yomogida K, Yu Q, Zapata R, Zartoshti A, Zeft A, Zeft R, Zhang Y, Zhao Y, Zhu A, Zic C. Intraarticular steroids as DMARD-sparing agents for juvenile idiopathic arthritis flares: Analysis of the Childhood Arthritis and Rheumatology Research Alliance Registry. Pediatr Rheumatol Online J 2022; 20:107. [PMID: 36434731 PMCID: PMC9701017 DOI: 10.1186/s12969-022-00770-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 11/08/2022] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Children with juvenile idiopathic arthritis (JIA) who achieve a drug free remission often experience a flare of their disease requiring either intraarticular steroids (IAS) or systemic treatment with disease modifying anti-rheumatic drugs (DMARDs). IAS offer an opportunity to recapture disease control and avoid exposure to side effects from systemic immunosuppression. We examined a cohort of patients treated with IAS after drug free remission and report the probability of restarting systemic treatment within 12 months. METHODS We analyzed a cohort of patients from the Childhood Arthritis and Rheumatology Research Alliance (CARRA) Registry who received IAS for a flare after a period of drug free remission. Historical factors and clinical characteristics and of the patients including data obtained at the time of treatment were analyzed. RESULTS We identified 46 patients who met the inclusion criteria. Of those with follow up data available 49% had restarted systemic treatment 6 months after IAS injection and 70% had restarted systemic treatment at 12 months. The proportion of patients with prior use of a biologic DMARD was the only factor that differed between patients who restarted systemic treatment those who did not, both at 6 months (79% vs 35%, p < 0.01) and 12 months (81% vs 33%, p < 0.05). CONCLUSION While IAS are an option for all patients who flare after drug free remission, it may not prevent the need to restart systemic treatment. Prior use of a biologic DMARD may predict lack of success for IAS. Those who previously received methotrexate only, on the other hand, are excellent candidates for IAS.
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Affiliation(s)
- Timothy Hahn
- Department of Pediatrics, Penn State Children's Hospital, 500 University Dr, Hershey, 90 Hope Drive, P.O. Box 855, Hershey, PA, 17033-0855, USA.
| | - Carrie Daymont
- grid.240473.60000 0004 0543 9901Department of Pediatrics, Penn State Children’s Hospital, 500 University Dr, Hershey, 90 Hope Drive, P.O. Box 855, Hershey, PA 17033-0855 USA
| | - Timothy Beukelman
- grid.265892.20000000106344187Department of Pediatrics, University of Alabama at Birmingham, CPPN G10, 1600 7th Ave South, Birmingham, AL 35233 USA
| | - Brandt Groh
- grid.240473.60000 0004 0543 9901Department of Pediatrics, Penn State Children’s Hospital, 500 University Dr, Hershey, 90 Hope Drive, P.O. Box 855, Hershey, PA 17033-0855 USA
| | | | - Catherine April Bingham
- grid.240473.60000 0004 0543 9901Department of Pediatrics, Penn State Children’s Hospital, 500 University Dr, Hershey, 90 Hope Drive, P.O. Box 855, Hershey, PA 17033-0855 USA
| | - Lisabeth Scalzi
- grid.240473.60000 0004 0543 9901Department of Pediatrics, Penn State Children’s Hospital, 500 University Dr, Hershey, 90 Hope Drive, P.O. Box 855, Hershey, PA 17033-0855 USA
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23
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He LN, Fu S, Ma H, Chen C, Zhang X, Li H, Du W, Chen T, Jiang Y, Wang Y, Wang Y, Zhou Y, Lin Z, Yang Y, Huang Y, Zhao H, Fang W, Zhang H, Zhang L, Hong S. Early on-treatment tumor growth rate (EOT-TGR) determines treatment outcomes of advanced non-small-cell lung cancer patients treated with programmed cell death protein 1 axis inhibitor. ESMO Open 2022; 7:100630. [PMID: 36442353 PMCID: PMC9808481 DOI: 10.1016/j.esmoop.2022.100630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Revised: 10/02/2022] [Accepted: 10/09/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND Tumor growth rate (TGR), denoted as percentage change in tumor size per month, is a well-established indicator of tumor growth kinetics. The predictive value of early on-treatment TGR (EOT-TGR) for immunotherapy remains unclear. We sought to establish and validate the association of EOT-TGR with treatment outcomes in patients with advanced non-small-cell lung cancer (aNSCLC) undergoing anti-PD-1/PD-L1 (programmed cell death protein 1/programmed death-ligand 1) therapy. PATIENTS AND METHODS This bicenter retrospective cohort study included a training cohort, a contemporaneously treated internal validation cohort, and an external validation cohort. Computed tomography images were retrieved to calculate EOT-TGR, denoted as tumor burden change per month during a period between baseline and the first imaging evaluation after immunotherapy. Kaplan-Meier methodology and Cox regression analysis were conducted for survival analyses. RESULTS In the pooled cohort (n = 172), 125 patients (72.7%) were males; median age at diagnosis was 58 (range 28-79) years. Based on the training cohort, we determined the optimal cut-off value for EOT-TGR as 10.4%/month. Higher EOT-TGR was significantly associated with inferior overall survival [OS; hazard ratio (HR) 2.93, 95% confidence interval (CI) 1.47-5.83; P = 0.002], worse progression-free survival (PFS; HR 2.44, 95% CI 1.46-4.08; P = 0.001), and lower objective response rate (3.3% versus 20.9%; P = 0.040) and durable clinical benefit rate (6.7% versus 41.9%; P = 0.001). Results were reproducible in the two validation cohorts for OS and PFS. Among 43 patients who had a best response of progressive disease in the training cohort, those with high EOT-TGR had worse OS (HR 2.64; P = 0.041) and were more likely to progress due to target lesions at the first tumor evaluation (85.2% versus 0.0%; P <0.001). CONCLUSIONS Higher EOT-TGR was associated with inferior OS and immunotherapeutic response in patients with aNSCLC undergoing anti-PD-1/PD-L1 therapy. This easy-to-calculate radiologic biomarker may help evaluate the abilities of immunotherapy to prolong survival and assist in tailoring patients' management. TRIAL REGISTRATION ClinicalTrials.govNCT04722406; https://clinicaltrials.gov/ct2/show/NCT04722406.
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Affiliation(s)
- L.-N. He
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - S. Fu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation of Sun Yat-Sen University; Department of Cellular & Molecular Diagnostics Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - H. Ma
- Department of Oncology, the Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China,Guangdong Provincial Key Laboratory of Clinical Research on Traditional Chinese Medicine Syndrome, Guangzhou, China
| | - C. Chen
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Departments of Radiation Oncology, Guangzhou, China
| | - X. Zhang
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - H. Li
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - W. Du
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - T. Chen
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Nuclear Medicine, Guangzhou, China
| | - Y. Jiang
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Nuclear Medicine, Guangzhou, China
| | - Y. Wang
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Y. Wang
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Endoscopy, Guangzhou, China
| | - Y. Zhou
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,VIP Region, Guangzhou, China
| | - Z. Lin
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Clinical Research, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Y. Yang
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Y. Huang
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - H. Zhao
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Clinical Research, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - W. Fang
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - H. Zhang
- Department of Oncology, the Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China,Guangdong Provincial Key Laboratory of Clinical Research on Traditional Chinese Medicine Syndrome, Guangzhou, China,Prof. Haibo Zhang, Department of Oncology, the Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, 111 Dade Road, Guangzhou, Guangdong 510120, People’s Republic of China. Tel: +86-20-81887233-34830
| | - L. Zhang
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China,Prof. Li Zhang, MD, Department of Medical Oncology, Sun Yat-sen University Cancer Center, 651 Dongfeng East Road, Guangzhou, Guangdong 510060, People’s Republic of China. Tel: +86-20-87343458
| | - S. Hong
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China,Correspondence to: Prof. Shaodong Hong, Department of Medical Oncology, Sun Yat-sen University Cancer Center, 651 Dongfeng East Road, Guangzhou, Guangdong 510060, People’s Republic of China. Tel: +86-20-87342480
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24
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Mi X, Sonner M, Niu MY, Lee KW, Foxen B, Acharya R, Aleiner I, Andersen TI, Arute F, Arya K, Asfaw A, Atalaya J, Bardin JC, Basso J, Bengtsson A, Bortoli G, Bourassa A, Brill L, Broughton M, Buckley BB, Buell DA, Burkett B, Bushnell N, Chen Z, Chiaro B, Collins R, Conner P, Courtney W, Crook AL, Debroy DM, Demura S, Dunsworth A, Eppens D, Erickson C, Faoro L, Farhi E, Fatemi R, Flores L, Forati E, Fowler AG, Giang W, Gidney C, Gilboa D, Giustina M, Dau AG, Gross JA, Habegger S, Harrigan MP, Hoffmann M, Hong S, Huang T, Huff A, Huggins WJ, Ioffe LB, Isakov SV, Iveland J, Jeffrey E, Jiang Z, Jones C, Kafri D, Kechedzhi K, Khattar T, Kim S, Kitaev AY, Klimov PV, Klots AR, Korotkov AN, Kostritsa F, Kreikebaum JM, Landhuis D, Laptev P, Lau KM, Lee J, Laws L, Liu W, Locharla A, Martin O, McClean JR, McEwen M, Meurer Costa B, Miao KC, Mohseni M, Montazeri S, Morvan A, Mount E, Mruczkiewicz W, Naaman O, Neeley M, Neill C, Newman M, O’Brien TE, Opremcak A, Petukhov A, Potter R, Quintana C, Rubin NC, Saei N, Sank D, Sankaragomathi K, Satzinger KJ, Schuster C, Shearn MJ, Shvarts V, Strain D, Su Y, Szalay M, Vidal G, Villalonga B, Vollgraff-Heidweiller C, White T, Yao Z, Yeh P, Yoo J, Zalcman A, Zhang Y, Zhu N, Neven H, Bacon D, Hilton J, Lucero E, Babbush R, Boixo S, Megrant A, Chen Y, Kelly J, Smelyanskiy V, Abanin DA, Roushan P. Noise-resilient edge modes on a chain of superconducting qubits. Science 2022; 378:785-790. [DOI: 10.1126/science.abq5769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Inherent symmetry of a quantum system may protect its otherwise fragile states. Leveraging such protection requires testing its robustness against uncontrolled environmental interactions. Using 47 superconducting qubits, we implement the one-dimensional kicked Ising model, which exhibits nonlocal Majorana edge modes (MEMs) with
ℤ
2
parity symmetry. We find that any multiqubit Pauli operator overlapping with the MEMs exhibits a uniform late-time decay rate comparable to single-qubit relaxation rates, irrespective of its size or composition. This characteristic allows us to accurately reconstruct the exponentially localized spatial profiles of the MEMs. Furthermore, the MEMs are found to be resilient against certain symmetry-breaking noise owing to a prethermalization mechanism. Our work elucidates the complex interplay between noise and symmetry-protected edge modes in a solid-state environment.
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Affiliation(s)
- X. Mi
- Google Research, Mountain View, CA, USA
| | - M. Sonner
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
| | - M. Y. Niu
- Google Research, Mountain View, CA, USA
| | - K. W. Lee
- Google Research, Mountain View, CA, USA
| | - B. Foxen
- Google Research, Mountain View, CA, USA
| | | | | | | | - F. Arute
- Google Research, Mountain View, CA, USA
| | - K. Arya
- Google Research, Mountain View, CA, USA
| | - A. Asfaw
- Google Research, Mountain View, CA, USA
| | | | - J. C. Bardin
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA
| | - J. Basso
- Google Research, Mountain View, CA, USA
| | | | | | | | - L. Brill
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - Z. Chen
- Google Research, Mountain View, CA, USA
| | - B. Chiaro
- Google Research, Mountain View, CA, USA
| | | | - P. Conner
- Google Research, Mountain View, CA, USA
| | | | | | | | - S. Demura
- Google Research, Mountain View, CA, USA
| | | | - D. Eppens
- Google Research, Mountain View, CA, USA
| | | | - L. Faoro
- Google Research, Mountain View, CA, USA
| | - E. Farhi
- Google Research, Mountain View, CA, USA
| | - R. Fatemi
- Google Research, Mountain View, CA, USA
| | - L. Flores
- Google Research, Mountain View, CA, USA
| | - E. Forati
- Google Research, Mountain View, CA, USA
| | | | - W. Giang
- Google Research, Mountain View, CA, USA
| | - C. Gidney
- Google Research, Mountain View, CA, USA
| | - D. Gilboa
- Google Research, Mountain View, CA, USA
| | | | - A. G. Dau
- Google Research, Mountain View, CA, USA
| | | | | | | | | | - S. Hong
- Google Research, Mountain View, CA, USA
| | - T. Huang
- Google Research, Mountain View, CA, USA
| | - A. Huff
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - Z. Jiang
- Google Research, Mountain View, CA, USA
| | - C. Jones
- Google Research, Mountain View, CA, USA
| | - D. Kafri
- Google Research, Mountain View, CA, USA
| | | | | | - S. Kim
- Google Research, Mountain View, CA, USA
| | - A. Y. Kitaev
- Google Research, Mountain View, CA, USA
- Institute for Quantum Information and Matter, California Institute of Technology, Pasadena, CA, USA
| | | | | | - A. N. Korotkov
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of California, Riverside, CA, USA
| | | | | | | | - P. Laptev
- Google Research, Mountain View, CA, USA
| | - K.-M. Lau
- Google Research, Mountain View, CA, USA
| | - J. Lee
- Google Research, Mountain View, CA, USA
| | - L. Laws
- Google Research, Mountain View, CA, USA
| | - W. Liu
- Google Research, Mountain View, CA, USA
| | | | - O. Martin
- Google Research, Mountain View, CA, USA
| | | | - M. McEwen
- Google Research, Mountain View, CA, USA
- Department of Physics, University of California, Santa Barbara, CA, USA
| | | | | | | | | | - A. Morvan
- Google Research, Mountain View, CA, USA
| | - E. Mount
- Google Research, Mountain View, CA, USA
| | | | - O. Naaman
- Google Research, Mountain View, CA, USA
| | - M. Neeley
- Google Research, Mountain View, CA, USA
| | - C. Neill
- Google Research, Mountain View, CA, USA
| | - M. Newman
- Google Research, Mountain View, CA, USA
| | | | | | | | - R. Potter
- Google Research, Mountain View, CA, USA
| | | | | | - N. Saei
- Google Research, Mountain View, CA, USA
| | - D. Sank
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - D. Strain
- Google Research, Mountain View, CA, USA
| | - Y. Su
- Google Research, Mountain View, CA, USA
| | - M. Szalay
- Google Research, Mountain View, CA, USA
| | - G. Vidal
- Google Research, Mountain View, CA, USA
| | | | | | - T. White
- Google Research, Mountain View, CA, USA
| | - Z. Yao
- Google Research, Mountain View, CA, USA
| | - P. Yeh
- Google Research, Mountain View, CA, USA
| | - J. Yoo
- Google Research, Mountain View, CA, USA
| | | | - Y. Zhang
- Google Research, Mountain View, CA, USA
| | - N. Zhu
- Google Research, Mountain View, CA, USA
| | - H. Neven
- Google Research, Mountain View, CA, USA
| | - D. Bacon
- Google Research, Mountain View, CA, USA
| | - J. Hilton
- Google Research, Mountain View, CA, USA
| | - E. Lucero
- Google Research, Mountain View, CA, USA
| | | | - S. Boixo
- Google Research, Mountain View, CA, USA
| | | | - Y. Chen
- Google Research, Mountain View, CA, USA
| | - J. Kelly
- Google Research, Mountain View, CA, USA
| | | | - D. A. Abanin
- Google Research, Mountain View, CA, USA
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
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25
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Poellmann MJ, Javius-Jones K, Hopkins C, Lee JW, Hong S. Dendritic-Linear Copolymer and Dendron Lipid Nanoparticles for Drug and Gene Delivery. Bioconjug Chem 2022; 33:2008-2017. [PMID: 35512322 DOI: 10.1021/acs.bioconjchem.2c00128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Polymers constitute a diverse class of macromolecules that have demonstrated their unique advantages to be utilized for drug or gene delivery applications. In particular, polymers with a highly ordered, hyperbranched structure─"dendrons"─offer significant benefits to the design of such nanomedicines. The incorporation of dendrons into block copolymer micelles can endow various unique properties that are not typically observed from linear polymer counterparts. Specifically, the dendritic structure induces the conical shape of unimers that form micelles, thereby improving the thermodynamic stability and achieving a low critical micelle concentration (CMC). Furthermore, through a high density of highly ordered functional groups, dendrons can enhance gene complexation, drug loading, and stimuli-responsive behavior. In addition, outward-branching dendrons can support a high density of nonfouling polymers, such as poly(ethylene glycol), for serum stability and variable densities of multifunctional groups for multivalent cellular targeting and interactions. In this paper, we review the design considerations for dendron-lipid nanoparticles and dendron micelles formed from amphiphilic block copolymers intended for gene transfection and cancer drug delivery applications. These technologies are early in preclinical development and, as with other nanomedicines, face many obstacles on the way to clinical adoption. Nevertheless, the utility of dendron micelles for drug delivery remains relatively underexplored, and we believe there are significant and dramatic advancements to be made in tumor targeting with these platforms.
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Affiliation(s)
- Michael J Poellmann
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin, Madison, Wisconsin 53705, United States
| | - Kaila Javius-Jones
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin, Madison, Wisconsin 53705, United States
| | - Caroline Hopkins
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin, Madison, Wisconsin 53705, United States
| | - Jin Woong Lee
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin, Madison, Wisconsin 53705, United States
| | - Seungpyo Hong
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin, Madison, Wisconsin 53705, United States.,Wisconsin Center for NanoBioSystems, University of Wisconsin, Madison, Wisconsin 53705, United States.,Yonsei Frontier Lab and Department of Pharmacy, Yonsei University, Seoul 03722, Republic of Korea
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26
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Hong S, Lee J, Heo J, Suh K, Kim S, Kim Y, Kim J, Lee JS. 413P Association of concomitant medications on survival outcomes in cancer patients treated with immune checkpoint inhibitors: Analysis of health insurance review and assessment database. Ann Oncol 2022. [DOI: 10.1016/j.annonc.2022.10.444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022] Open
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27
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Hall J, Sud S, Casey D, Poellmann M, Bu J, Wang A, Hong S, Shen C. Prospective Characterization of Circulating Tumor Cell Kinetics in Patients with Locoregional Head and Neck Cancer Receiving Definitive Therapy. Int J Radiat Oncol Biol Phys 2022. [DOI: 10.1016/j.ijrobp.2022.07.1318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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28
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Song S, Kim J, Nam J, Ko Y, Kim J, Jung S, Kang S, Park J, Seo H, Kim H, Jeong B, Kim T, Choi S, Nam J, Ku J, Joo K, Jang W, Yoon Y, Yun S, Hong S, Oh J. Stage matched head-to-head comparison between urachal carcinoma and urothelial bladder cancer: TNM-stage based analysis from a national multicenter database. EUR UROL SUPPL 2022. [DOI: 10.1016/s2666-1683(22)02591-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
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29
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Jung JH, Hong S, Jeon EJ, Kim MK, Seo DH, Woo EJ, Holden JF, Park CS. Acceptor dependent catalytic properties of GH57 4-α-glucanotransferase from Pyrococcus sp. ST04. Front Microbiol 2022; 13:1016675. [PMID: 36274706 PMCID: PMC9582752 DOI: 10.3389/fmicb.2022.1016675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 09/20/2022] [Indexed: 11/13/2022] Open
Abstract
The 4-α-glucanotransferase (4-α-GTase or amylomaltase) is an essential enzyme in maltodextrin metabolism. Generally, most bacterial 4-α-GTase is classified into glycoside hydrolase (GH) family 77. However, hyperthermophiles have unique 4-α-GTases belonging to GH family 57. These enzymes are the main amylolytic protein in hyperthermophiles, but their mode of action in maltooligosaccharide utilization is poorly understood. In the present study, we investigated the catalytic properties of 4-α-GTase from the hyperthermophile Pyrococcus sp. ST04 (PSGT) in the presence of maltooligosaccharides of various lengths. Unlike 4-α-GTases in GH family 77, GH family 57 PSGT produced maltotriose in the early stage of reaction and preferred maltose and maltotriose over glucose as the acceptor. The kinetic analysis showed that maltotriose had the lowest KM value, which increased amylose degradation activity by 18.3-fold. Structural models of PSGT based on molecular dynamic simulation revealed two aromatic amino acids interacting with the substrate at the +2 and +3 binding sites, and the mutational study demonstrated they play a critical role in maltotriose binding. These results clarify the mode of action in carbohydrate utilization and explain acceptor binding mechanism of GH57 family 4-α-GTases in hyperthermophilic archaea.
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Affiliation(s)
- Jong-Hyun Jung
- Radiation Research Division, Korea Atomic Energy Research Institute, Jeongeup, South Korea
| | - Seungpyo Hong
- Department of Molecular Biology, Jeonbuk National University, Jeonju, South Korea
| | - Eun Jung Jeon
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
| | - Min-Kyu Kim
- Radiation Research Division, Korea Atomic Energy Research Institute, Jeongeup, South Korea
| | - Dong-Ho Seo
- Department of Food Science and Technology, Jeonbuk National University, Jeonju, South Korea
| | - Eui-Jeon Woo
- Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, South Korea
| | - James F. Holden
- Department of Microbiology, University of Messachusetts, Amherst, MA, United States
| | - Cheon-Seok Park
- Department of Food Science and Biotechnology and Institute of Life Science and Resources, Kyung Hee University, Yongin, South Korea
- *Correspondence: Cheon-Seok Park,
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30
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Hopkins C, Javius-Jones K, Wang Y, Hong H, Hu Q, Hong S. Combinations of chemo-, immuno-, and gene therapies using nanocarriers as a multifunctional drug platform. Expert Opin Drug Deliv 2022; 19:1337-1349. [PMID: 35949105 DOI: 10.1080/17425247.2022.2112569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
INTRODUCTION Cancer immunotherapies have created a new generation of therapeutics to employ the immune system to attack cancer cells. However, these therapies are typically based on biologics that are nonspecific and often exhibit poor tumor penetration and dose-limiting toxicities. Nanocarriers allow the opportunity to overcome these barriers as they have the capabilities to direct immunomodulating drugs to tumor sites via passive and active targeting, decreasing potential adverse effects from nonspecific targeting. In addition, nanocarriers can be multifunctionalized to deliver multiple cancer therapeutics in a single drug platform, offering synergistic potential from co-delivery approaches. AREAS COVERED This review focuses on the delivery of cancer therapeutics using emerging nanocarriers to achieve synergistic results via co-delivery of immune-modulating components (i.e. chemotherapeutics, monoclonal antibodies, and genes). EXPERT OPINION Nanocarrier-mediated delivery of combinatorial immunotherapy creates the opportunity to fine-tune drug release while achieving superior tumor targeting and tumor cell death, compared to free drug counterparts. As these nanoplatforms are constantly improved upon, combinatorial immunotherapy will afford the greatest benefit to treat an array of tumor types while inhibiting cancer evasion pathways.
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Affiliation(s)
- Caroline Hopkins
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin, Madison, Wisconsin, USA
| | - Kaila Javius-Jones
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin, Madison, Wisconsin, USA
| | - Yixin Wang
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin, Madison, Wisconsin, USA.,Wisconsin Center for NanoBioSystems, School of Pharmacy, University of Wisconsin, Madison, Wisconsin, USA
| | - Heejoo Hong
- Department of Clinical Pharmacology & Therapeutics, Asan Medical Center, University of Ulsan, Seoul, Republic of Korea
| | - Quanyin Hu
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin, Madison, Wisconsin, USA.,Wisconsin Center for NanoBioSystems, School of Pharmacy, University of Wisconsin, Madison, Wisconsin, USA
| | - Seungpyo Hong
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin, Madison, Wisconsin, USA.,Wisconsin Center for NanoBioSystems, School of Pharmacy, University of Wisconsin, Madison, Wisconsin, USA.,Yonsei Frontier Lab and Department of Pharmacy, Yonsei University, Seoul, Republic of Korea
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Yu M, Yun M, Lee S, Rajasekaran N, Park K, Kim N, Hong S, Oh S, Lee Y, Lee E, Kim C, Lim S, Choi J, Cho B. 1174P The MET inhibitor ABN401 in combination with the third-generation EGFR-TKI is effective MET-amplified and EGFR-mutant NSCLC with acquired resistance to third-generation EGFR-TKI in preclinical models. Ann Oncol 2022. [DOI: 10.1016/j.annonc.2022.07.1297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
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Gupta K, Brown KA, Hsieh ML, Hoover BM, Wang J, Khoury MK, Pilli VSS, Beyer RSH, Voruganti NR, Chaudhary S, Roberts DS, Murphy RM, Hong S, Ge Y, Liu B. Necroptosis is associated with Rab27-independent expulsion of extracellular vesicles containing RIPK3 and MLKL. J Extracell Vesicles 2022; 11:e12261. [PMID: 36063142 PMCID: PMC9443950 DOI: 10.1002/jev2.12261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 07/23/2022] [Accepted: 08/13/2022] [Indexed: 11/30/2022] Open
Abstract
Extracellular vesicle (EV) secretion is an important mechanism used by cells to release biomolecules. A common necroptosis effector—mixed lineage kinase domain like (MLKL)—was recently found to participate in the biogenesis of small and large EVs independent of its function in necroptosis. The objective of the current study is to gain mechanistic insights into EV biogenesis during necroptosis. Assessing EV number by nanoparticle tracking analysis revealed an increased number of EVs released during necroptosis. To evaluate the nature of such vesicles, we performed a newly adapted, highly sensitive mass spectrometry‐based proteomics on EVs released by healthy or necroptotic cells. Compared to EVs released by healthy cells, EVs released during necroptosis contained a markedly higher number of unique proteins. Receptor interacting protein kinase‐3 (RIPK3) and MLKL were among the proteins enriched in EVs released during necroptosis. Further, mouse embryonic fibroblasts (MEFs) derived from mice deficient of Rab27a and Rab27b showed diminished basal EV release but responded to necroptosis with enhanced EV biogenesis as the wildtype MEFs. In contrast, necroptosis‐associated EVs were sensitive to Ca2+ depletion or lysosomal disruption. Neither treatment affected the RIPK3‐mediated MLKL phosphorylation. An unbiased screen using RIPK3 immunoprecipitation‐mass spectrometry on necroptotic EVs led to the identification of Rab11b in RIPK3 immune‐complexes. Our data suggests that necroptosis switches EV biogenesis from a Rab27a/b dependent mechanism to a lysosomal mediated mechanism.
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Affiliation(s)
- Kartik Gupta
- Division of Vascular Surgery, Department of Surgery, University of Wisconsin-Madison, Madison, Wisconsin, 53705, USA
| | - Kyle A Brown
- Division of Vascular Surgery, Department of Surgery, University of Wisconsin-Madison, Madison, Wisconsin, 53705, USA.,Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, 53705, USA
| | - Marvin L Hsieh
- Division of Vascular Surgery, Department of Surgery, University of Wisconsin-Madison, Madison, Wisconsin, 53705, USA
| | - Brandon M Hoover
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin, 53705, USA
| | - Jianxin Wang
- Wisconsin Center for NanoBioSystems, School of Pharmacy, University of Wisconsin-Madison, Madison, Wisconsin, 53705, USA
| | - Mitri K Khoury
- Division of Vascular Surgery, Department of Surgery, University of Wisconsin-Madison, Madison, Wisconsin, 53705, USA
| | - Vijaya Satish Sekhar Pilli
- Division of Vascular Surgery, Department of Surgery, University of Wisconsin-Madison, Madison, Wisconsin, 53705, USA
| | - Reagan S H Beyer
- Division of Vascular Surgery, Department of Surgery, University of Wisconsin-Madison, Madison, Wisconsin, 53705, USA
| | - Nihal R Voruganti
- Division of Vascular Surgery, Department of Surgery, University of Wisconsin-Madison, Madison, Wisconsin, 53705, USA
| | - Sahil Chaudhary
- Division of Vascular Surgery, Department of Surgery, University of Wisconsin-Madison, Madison, Wisconsin, 53705, USA
| | - David S Roberts
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, 53705, USA
| | - Regina M Murphy
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin, 53705, USA
| | - Seungpyo Hong
- Wisconsin Center for NanoBioSystems, School of Pharmacy, University of Wisconsin-Madison, Madison, Wisconsin, 53705, USA.,Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin-Madison, Madison, Wisconsin, 53705, USA
| | - Ying Ge
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, 53705, USA.,Department of Cell and Regenerative Biology, University of Wisconsin-Madison, Madison, Wisconsin, 53705, USA.,Human Proteomics Program, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, 53705, USA
| | - Bo Liu
- Division of Vascular Surgery, Department of Surgery, University of Wisconsin-Madison, Madison, Wisconsin, 53705, USA.,Department of Cell and Regenerative Biology, University of Wisconsin-Madison, Madison, Wisconsin, 53705, USA
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Ramesh P, Jaishankar D, Cosgrove C, Kosche C, Li A, Hong S, Shivde R, Munir S, Zhang H, Choi J, Le Poole I. 318 Skin rash composition after checkpoint inhibitor therapy varies by therapeutic regimen. J Invest Dermatol 2022. [DOI: 10.1016/j.jid.2022.05.326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Kostecki KL, Iida M, Wiley AL, Hong S, Salgia R, Harari PM, Wheeler DL. Abstract 3535: Simultaneous inhibition of Axl and MerTK enhances anti-PDL1 efficacy and creates a pro-inflammatory tumor immune microenvironment in head and neck cancer. Cancer Res 2022. [DOI: 10.1158/1538-7445.am2022-3535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
The tyrosine kinase receptors Axl and MerTK, known for their role on macrophages in regulating clearance of apoptotic cells, are highly overexpressed in head and neck cancer (HNC). Previous studies in our laboratory have shown that Axl is a critical driver of survival, proliferation, metastasis, and therapeutic resistance in HNC, and that MerTK is functionally redundant to Axl. In this study, we investigated the cooperative role of Axl and MerTK in creating an immunologically cold tumor immune microenvironment (TIME) by targeting both receptors simultaneously with a small molecule inhibitor of Axl and MerTK (INCB081776). Because Axl and MerTK are expressed on both macrophages and HNC cancer cells, we examined the effect of INCB081776 treatment on each cell type. In macrophages, Axl and MerTK signaling leads to M2-type polarization, an anti-inflammatory state that leads to the resolution of inflammation and, in cancer settings, promotes tumor growth. Our experiments suggest that treatment with INCB081776 can reduce M2 polarization and increase M1-type polarization, a pro-inflammatory state that promotes inflammation and tumor cell killing. Next, to determine the efficacy of INCB081776 on HNC cancer cells, mouse oral cancer (MOC) tumors were implanted in syngeneic mice and treated with INCB081776 alone or in combination with a monoclonal antibody against PDL1 (anti-PDL1), thereby mimicking current standard-of-care immune checkpoint inhibitor treatment. The results showed a marked effect of INCB081776 single-agent treatment on MOC tumor growth and an increase in several pro-inflammatory cell types (M1 macrophages, CD8+ T cells, total infiltrating leukocytes), suggesting that INCB081776 treatment can create an immunologically hot TIME. Further, in-depth analysis of tumor infiltrating leukocytes following INCB081776 treatment in both immunologically hot (MOC1) and cold (MOC2) HNC tumors suggested that INCB081776 has a greater effect in cold tumors. In cold tumors, levels of pro-inflammatory cells (CD8+ T cells, M1 macrophages, etc.) increased, and levels of anti-inflammatory cells (M2 macrophages, regulatory T cells, etc.) decreased, both to a greater extent than in hot tumors. Finally, the combination of INCB081776 and anti-PDL1 was superior to either treatment alone in slowing tumor growth. Together, these studies indicate that INCB081776 cooperates with anti-PDL1 in a syngeneic mouse model of HNC to slow tumor growth and create a pro-inflammatory environment, especially in immunologically cold tumors, thereby highlighting the potential clinical benefit of this therapeutic combination.
Citation Format: Kourtney L. Kostecki, Mari Iida, Anne L. Wiley, Seungpyo Hong, Ravi Salgia, Paul M. Harari, Deric L. Wheeler. Simultaneous inhibition of Axl and MerTK enhances anti-PDL1 efficacy and creates a pro-inflammatory tumor immune microenvironment in head and neck cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3535.
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Affiliation(s)
| | - Mari Iida
- 1University of Wisconsin School of Medicine and Public Health, Madison, WI
| | - Anne L. Wiley
- 1University of Wisconsin School of Medicine and Public Health, Madison, WI
| | - Seungpyo Hong
- 2University of Wisconsin School of Pharmacy, Madison, WI
| | - Ravi Salgia
- 3City of Hope Comprehensive Cancer Center, Madison, WI
| | - Paul M. Harari
- 1University of Wisconsin School of Medicine and Public Health, Madison, WI
| | - Deric L. Wheeler
- 1University of Wisconsin School of Medicine and Public Health, Madison, WI
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Kang E, Kim YG, Oh JS, Hong S, Lee CK, Yoo B, Ahn SM. POS1247 THE EFFECT OF IMMUNOSUPPRESSIVE AGENTS ON ANTIBODY FORMATION AFTER COVID-19 VACCINATION IN RHEUMATOID ARTHRITIS PATIENTS. Ann Rheum Dis 2022. [DOI: 10.1136/annrheumdis-2022-eular.3412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
BackgroundThere is still controversy about the efficacy of COVID-19 vaccination and its extent in lowering immunogenicity of Rheumatoid Arthritis (RA) patients. The guideline in whether immunosuppressive agents need to be discontinued before the vaccination is continuously updated because it is considered to lower immunogenicity. Furthermore, there is great discussion on the effectiveness of the COVID-19 booster vaccine and interest in antibody generation in different types of vaccine, as in South Korea there are many patients who were prescribed the mRNA booster vaccine after two doses of ChAdOx1-S nCoV-19 vaccine.ObjectivesThus, we investigated the differences of antibody production between patients who received only two doses of ChAdOx1-S nCoV-19 and those who received the mRNA booster vaccine. Also, antibody production under different types of immunosuppressive agents was analyzed.MethodsFrom October 14, 2021 to January 21, 2022 at a tertiary referral center, two patient groups diagnosed with RA were studied prospectively; one group that completed 1st and 2nd doses of ChAdOx1-S nCoV-19 vaccine, second group that completed mRNA booster vaccine as well as two doses of ChAdOx1-S nCoV-19 vaccine. SARS-CoV-2 antibody testing on the semiquantitative anti-SARS-CoV-2 S enzyme immunoassay was done, and differences in antibody titers were analyzed in patients who received different immunosuppressive agents such as csDMARD, TNF inhibitor, JAK inhibitor, Tocilizumab, Abatacept and Corticosteroid. Statistical analysis with a multivariate logistic regression model was performed.ResultsIn a total of 261 patients, 153 patients had completed two doses of ChAdOx1-S nCoV-19, 108 patients had completed third mRNA booster vaccine. Anti-SARS-CoV-2 RBD antibody positive rate (titer>0.8U/mL) was 97%(149/153) and 99%(107/108) respectively, and only 5 patients showed negative result. In the aspect of high antibody titer(>250U/mL), which is the upper limit of the RBD antibody immunoassay, the result showed rate of 31% (47/153) in the non-booster group and 94%(102/108) in the booster group respectively.Among the different immunosuppressive agents and other clinical aspects, multivariate analysis revealed that corticosteroid use (OR 0.91; 95% CI: 0.86-0.98), older age(OR 4.33; 95% CI: 1.34-13.91), and male gender(OR 0.35; 95% CI 0.16-0.75) were significantly associated with low rate of high antibody titer.Furthermore, out of 14 patients who underwent antibody test twice before and after the mRNA booster vaccine, other than four patients who already showed high titer of >250U/mL before the mRNA booster vaccine, 10 patients showed an increase in titer after the booster vaccine and 7 patients were acquired high titer of >250U/mL.Figure 1.Anti-SARS-CoV RBD antibody titer of two groupsTable 1.Analysis of immunosuppressive agents and other clinical aspects for high antibody titer(>250U/mL) after two doses of ChAdOx1-S nCoV-19Univariate analysisMultivariate analysisParameterOR95% CIp valueOR95% CIp valueClinical features Age0.9170.860-0.9780.0080.9170.857-0.9810.012 Sex3.6741.206-11.1910.0224.3301.348-13.9120.014 DAS 281.1440.670-1.9500.622 Duration0.9300.830-1.0430.214Medications csDMARD1.2730.639-2.5331.273 TNF inhibitor2.2110.795-6.1450.128 JAK inhibitor0.6650.275-1.6070.365 Abatacept0.3680.038-3.6020.391 Tocilizumab1.2640.438-3.6480.665 Corticosteroid0.4720.235-0.9490.0350.3490.163-0.7480.007Medication dose Methotrexate0.9930.919-1.0720.855 Corticosteroid0.8490.719-1.0030.054ConclusionAnti-SARS-CoV-2 RBD antibody positive rate was 97% or more regardless of the mRNA booster vaccination. However, patients who received the mRNA booster vaccine after two doses of ChAdOx1-S nCoV-19 vaccine showed high antibody titer (>250U/mL) three times more than those who did not receive the booster shot.Our findings also showed that corticosteroid use, old age, and male gender is significantly associated with low rate of acquiring high antibody titer.Disclosure of InterestsNone declared
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Ahn SM, Oh JS, Kim YG, Lee CK, Yoo B, Hong S. AB0476 PREDICTIVE FACTORS FOR THE DEVELOPMENT OF SYSTEMIC LUPUS ERYTHEMATOSUS IN PATIENTS WITH IMMUNE THROMBOCYTOPENIA. Ann Rheum Dis 2022. [DOI: 10.1136/annrheumdis-2022-eular.1436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
BackgroundPatients with immune thrombocytopenia (ITP) have a risk of developing systemic lupus erythematosus (SLE). We sought to examine the clinical characteristics of patients with primary ITP who later developed SLE, and identified the risk factors for the development of SLE.ObjectivesWe retrospectively examined patients who were diagnosed with primary ITP at a tertiary hospital between August 2001 and November 2019. We compared the clinical characteristics according to the development of SLE. Logistic regression analysis was performed to identify the factors associated with the development of SLE.MethodsOf 130 patients with primary ITP, 10 (7.7%) were later diagnosed with SLE during follow-up (median, 30 months [IQR, 15.5–105]). The presence of skin bleeding, organ bleeding, lymphopenia, anemia, and positive antinuclear antibody (ANA) titer (> 1:160) were more common among patients who later developed SLE than did those who did not develop SLE. Multivariate analysis showed that young age (< 40 years; odds ratio [OR], 8.359 [95% confidence interval (CI), 1.230–56.793]; p = 0.033), organ bleeding (OR, 18.349 [95% CI, 2.771–121.517]; p = 0.003), and ANA positivity (>1:160; OR, 7.692 [95% CI, 1.482–39.910]; p = 0.015) were significantly associated with the development of SLE.ResultsYoung age (< 40 years), organ bleeding, and ANA positivity (> 1:160) were risk factors for the development of SLE in patients with primary ITP.ConclusionThese results suggest that continued follow-up for the detection of SLE development is needed for patients with ITP, particularly those with young age, ANA positivity, or organ bleeding.References[1]Zhu, Fang-Xiao, et al. “Risk of systemic lupus erythematosus in patients with idiopathic thrombocytopenic purpura: a population-based cohort study.” Annals of the rheumatic diseases 79.6 (2020): 793-799.Table 1.Factors associated with the development of SLE in patients with primary ITPUnivariateMultivariateOR95% CIP valueOR95% CIP valueYoung agea5.4441.332–22.2500.0188.3591.230–56.7930.033Female4.3330.530–35.4220.17BMI0.8730.717–1.0700.20Skin bleeding8.4191.034–68.5330.046Mucosa bleeding1.2500.247–6.3300.79Organ bleeding14.8643.633–60.815< 0.00118.3492.771–121.5170.003Platelet counts0.9110.828–1.0020.06ANA positivityb16.5003.984–68.341< 0.0017.6921.482–39.9100.015Neutropeniac2.1110.229–19.4990.51Lymphopeniad4.8461.189–19.7590.028Anemiae10.1182.044–50.0910.005SLE: systemic lupus erythematosus, ITP: immune thrombocytopenia, BMI: body mass index, ANA: antinuclear antibody, OR: odds ratio, CI: confidence interval.aYoung age = age < 40 yearsbANA positivity ≥ 1:160cNeutropenia = Absolute neutrophil count < 1500 μLdLymphopenia = Absolute lymphocyte count < 1500 μLeAnemia = Hemoglobin < 12 g/dLDisclosure of InterestsNone declared
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Kim YE, Choi SJ, Lim DH, Ahn SM, Oh JS, Kim YG, Lee CK, Yoo B, Hong S. AB0456 DISEASE FLARE OF SYSTEMIC LUPUS ERYTHEMATOSUS IN PATIENTS WITH END-STAGE RENAL DISEASE ON DIALYSIS. Ann Rheum Dis 2022. [DOI: 10.1136/annrheumdis-2022-eular.696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
BackgroundThe systemic lupus erythematosus (SLE) disease activity in patients with lupus nephritis (LN) generally declines after the initiation of renal replacement therapy (RRT); this is known as the “burn out” phenomenon that possibly occurs due to the suppression of cellular and humoral immunity in the end-stage renal disease (ESRD) state and elimination of disease pathogenic factor by dialysis [1-4]. However, several studies showed that SLE flares could occur even during RRT [5-8]. Nevertheless, the details of disease flares of SLE in patients under dialysis have not been studied yet.ObjectivesThis study aimed to investigate the clinical features, risk factors, and treatment details of SLE patients experiencing disease flare under RRT.MethodsThe medical records of SLE patients who received dialysis at two tertiary referral hospitals in Seoul and Ulsan, South Korea were reviewed. All patients in this study were either clinically or histologically diagnosed with LNResultsOf a total of 121 patients with SLE on dialysis, 96 (79.3%) were on hemodialysis (HD) and 25 (20.6%) were on peritoneal dialysis (PD). During a median follow-up of 45 months (IQR, 23–120) after the initiation of dialysis, 32 (26.4%) patients experienced SLE flare (HD, n = 25; PD, n = 7). The most common features of SLE flare were hematologic (40.6%) and constitutional manifestations (40.6%). Treatments for disease flares were based on corticosteroids, and 11 (34.3%) patients required additional immunosuppressants including cyclophosphamide and mycophenolate mofetil. There was no case of severe adverse events related to medication. non-renal SLE Disease Activity Index (SLEDAI) score before dialysis initiation (HR 1.235; 95% CI, 1.122–1.359; P = 0.001) was a significant risk factor for disease flare during dialysis.Table 1.Multivariable analysis of factors associated with SLE flare under dialysisHazard ratio95% CIP-valueNon-renal SLEDAI at the initiation of dialysis1.2351.122–1.3590.001Hematologic manifestation prior to dialysis1.2560.690–2.8260.150Cumulative amount of steroid during 1 year prior to the initiation of dialysis1.0400.995–1.0870.086Dialysis modality: hemodialysis0.7660.262–2.2430.630ConclusionMore than one-quarter of SLE patients experienced disease flare during dialysis, which most commonly had hematologic manifestations. Continued follow-up and appropriate treatments including immunosuppressants should be considered for patients with SLE under dialysis.References[1]Coplon NS, Diskin CJ, Petersen J, Swenson RS. The Long-Term Clinical Course of Systemic Lupus Erythematosus in End-Stage Renal Disease. New England Journal of Medicine 1983;308:186-90.[2]Lee P-T, Fang H-C, Chen C-L, Chiou Y-H, Chou K-J, Chung H-M. Poor prognosis of end-stage renal disease in systemic lupus erythematosus: a cohort of Chinese patients. Lupus 2003;12:827-32.[3]Pahl MV, Gollapudi S, Sepassi L, Gollapudi P, Elahimehr R, Vaziri ND. Effect of end-stage renal disease on B-lymphocyte subpopulations, IL-7, BAFF and BAFF receptor expression. Nephrology Dialysis Transplantation 2010;25:205-12.[4]Ribeiro FM, Fabris CL, Bendet I, Lugon JR. Survival of lupus patients on dialysis: a Brazilian cohort. Rheumatology 2013;52:494-500.[5]Okano K, Yumura W, Nitta K et al. Analysis of Lupus Activity in End-Stage Renal Disease Treated by Hemodialysis. Internal Medicine 2001;40:598-602.[6]Barrera-Vargas A, Quintanar-Martínez M, Merayo-Chalico J, Alcocer-Varela J, Gómez-Martín D. Risk factors for systemic lupus erythematosus flares in patients with end-stage renal disease: a case–control study. Rheumatology 2015:kev349.[7]Cucchiari D, Graziani G, Ponticelli C. The dialysis scenario in patients with systemic lupus erythematosus. Nephrology Dialysis Transplantation 2014;29:1507-13.[8]Kang S-H, Chung B-H, Choi S-R et al. Comparison of Clinical Outcomes by Different Renal Replacement Therapy in Patients with End-Stage Renal Disease Secondary to Lupus Nephritis. The Korean Journal of Internal Medicine 2011;26:60.Disclosure of InterestsNone declared
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Sud S, Poellmann M, Hall J, Tan X, Bu J, Park SJ, Hong S, Wang AZ, Casey D. Prospective characterization of circulating tumor cell kinetics in patients treated with radiation therapy per definitive intent oligometastatic paradigm. J Clin Oncol 2022. [DOI: 10.1200/jco.2022.40.16_suppl.3053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
3053 Background: Definitive intent oligometastatic paradigm describes a state with limited metastatic sites amenable to comprehensive radiation therapy (RT). We characterized circulating tumor cell (CTC) kinetics in response to definitive RT among patients with oligometastatic cancer and identify a CTC kinetic profile associated with progression free survival (PFS). Methods: In this single-institution prospective correlative biomarker study, we enrolled patients with any solid malignancy, ≤ 5 metastatic sites in ≤3 anatomic organ systems undergoing definitive intent RT to all disease sites. Blood specimens were collected prior to RT (baseline), during RT and at follow up visits up to 24 months post RT. Additional lines of therapy were administered per standard of care. CTCs were captured and enumerated using a previously reported nanotechnology-based assay functionalized with aEpCAM, aHER-2, and aEGFR to facilitate biomimetic cell rolling and dendrimer-mediated multivalent binding. Disease status was assessed per RECIST 1.1 criteria. On exploratory analysis disease status was correlated with CTCs as a continuous and ordinal variable (cut-point upper bound of the 3rd quartile). A favorable CTC clearance profile was defined as a decrease in CTC count between pre-treatment and end of treatment - an unfavorable CTC clearance profile was defined as the opposite. Results: We enrolled 43 patients with median follow up of 14.3 months corresponding to 255 CTC measurements. Median baseline CTC count was 28 CTCs/ml (range 0.17-1085). Thirty four patients (79%) received stereotactic body radiation therapy. On Wilcoxon signed-rank test there was no association between pre-treatment CTC count and number of disease sites (median 1 metastatic site/patient, range 1-5) nor metastases site (bone, brain, visceral), p > 0.05. Thirty one patients (72%) experienced local or systemic progression at subsequent time points. For 90% of patients, a CTC count <15/ml < 100 days post-RT corresponded to durable local control of irradiated lesions. Patients with a favorable versus unfavorable clearance profile had significantly longer PFS (median 13 vs 4 months, log rank test, p = 0.0011). During the post-RT period 24 patients (56%) went on to receive systemic therapy (cytotoxic chemotherapy, hormone therapy, immunotherapy, kinase inhibitors). On logistic regression, CTC > 15/ml at a given time point was associated with clinical disease progression within the subsequent 6 months (odds ratio 3.31, p = 0.007). An increase in CTCs to > 15/ml preceded radiographic or biochemical progression in 8 of 31 (26%) of patients experiencing disease progression. Conclusions: Our data suggests CTCs may serve as a biomarker for disease control in oligometastatic disease and may predict disease progression prior to standard assessments for patients receiving diverse therapies. Clinical trial information: NCT03161821.
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Affiliation(s)
- Shivani Sud
- University of North Carolina at Chapel Hill, Chapel Hill, NC
| | | | - Jacob Hall
- University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Xianming Tan
- University of North Carolina, Chapel Hill, Lineberger Comprehensive Cancer Center, Chapel Hill, NC
| | - Jiyoon Bu
- University of Wisconsin, Madison, WI
| | | | | | | | - Dana Casey
- The University of North Carolina at Chapel Hill, Chapel Hill, NC
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Nam SH, Ahn SM, Oh JS, Hong S, Lee CK, Yoo B, Kim YG. AB1273 MACROPHAGE ACTIVATION SYNDROME IN RHEUMATIC DISEASE: CLINICAL CHARACTERISTICS AND PROGNOSIS OF 20 PATIENTS. Ann Rheum Dis 2022. [DOI: 10.1136/annrheumdis-2022-eular.461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
BackgroundMacrophage activation syndrome (MAS) is a hyperinflammatory condition that is known to be secondary hemophagocytic lymphohistiocytosis (HLH) in patients with rheumatic disease.ObjectivesThe aim of study was to evaluate the clinical manifestations and outcomes in patients with MAS with rheumatic disease.MethodsWe performed a retrospective study of 20 adult patients who were diagnosed with MAS from 2012 to 2020. MAS was classified according to the HLH-2004 criteria. Patients’ information, including clinical features, laboratory findings, and treatment regimens, was collected, and the overall survival rate was estimated by the Kaplan–Meier method.ResultsTwenty patients (18 women, 35.6 ± 18.3 years) who met the HLH-2004 criteria also fulfilled the 2016 EULAR/ACR/PRINTO classification criteria for MAS, and HScore was higher than 169 (median, 238.5). Fourteen patients with systemic lupus erythematosus and 6 patients with adult-onset Still’s disease were included. All patients were treated initially with corticosteroids, and 16 patients required additional immunosuppressants. The overall survival at 3 and 6 months was 75.2% and 64.3%. In survivors, renal impairment was less common (23.1% versus 42.9%, p = 0.007), the levels of AST (202.0 versus 72.0 IU/L, p = 0.006) and LDH (1144.0 versus 343.0IU/L, p = 0.001), and platelet count (90.0 versus 46.0 × 109/L, p = 0.016) were higher in compared to non-survivors. Nine patients had opportunistic infections, five of whom died during admission.ConclusionThe mortality of patients with MAS remains high. Renal impairment, levels of AST and LDH, and platelet count might be associated with prognosis.Table 1.Treatments and management characteristics of patients with MASNo.Age/sexDiseaseDisease duration (months)1st Treatment (corticosteroids)2nd Treatment3rd TreatmentCombined infectionAlive/dead119/FSLE11 mg/kgIVIG + PPTCZ, RTXBacteremiaDead220/MSLE01 mg/kg---Alive320/FAOSD11 mg/kgVP16--Alive422/FSLE1100 mgIVIG + PP-PneumoniaDead522/FAOSD0500 mgIVIG--Alive623/FSLE1821 mg/kg---Alive723/FSLE411 mg/kg---Alive830/FSLE1461 mg/kgIVIGCsA-Alive932/FSLE1271 mg/kgIVIG + PPCsA, TCZPneumoniaAlive1035/FAOSD01 mg/kgCsA-Viral infectionAlive1137/FSLE651 mg/kgCsA, VP16-BacteremiaAlive1238/FSLE01 mg/kgIVIG + PPRTX-Dead1340/FAOSD00.5 mg/kgCsA--Alive1443/FSLE601 mg/kgIVIG + PPTCZ, RTX, CsA,PCP,DeadVP16, IFXViral infection1549/FSLE01 mg/kgCYC-BacteremiaAlive1651/FAOSD01 mg/kg---Alive1757/FSLE01 mg/kgIVIG + PPCsA, VP16Fungal infectionDead1861/FSLE21 mg/kgIVIG + PPTCZ-Dead1968/FSLE21 mg/kgIVIG + PPCsAFungal infectionAlive2070/MAOSD01 mg/kgIVIG + PPCsA, VP16Fungal infectionDeadSLE: Systemic lupus erythematosus, IVIG: Intravenous immunoglobulin, PP: Plasmapheresis, TCZ: Tocilizumab, RTX: Rituximab, AOSD: Adult-onset still’s disease, VP16: Etoposide, PCP: Pneumocystis pneumonia, CsA: Cyclosporin, IFX: Infliximab, MCTD: Mixed connective tissue disease.Disclosure of InterestsNone declared
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Kang E, Hong S, Kim YG, Lee CK, Oh JS, Yoo B, Ahn SM. POS0762 LONG-TERM RENAL OUTCOMES OF PATIENTS WITH NON-PROLIFERATIVE LUPUS NEPHRITIS. Ann Rheum Dis 2022. [DOI: 10.1136/annrheumdis-2022-eular.3393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
BackgroundAlthough proliferative (class III or IV) lupus nephritis (LN) is the most common finding in the classification of LN, pure membranous (class V) or mesangial (class I or II) LN can occur as a form of LN. Even though non-proliferative LN (class I, II, or V) is a less severe form with good outcomes, data on long-term renal prognosis are limited.ObjectivesThis study investigated the long-term outcomes and prognostic factors in non-proliferative LN.MethodsWe retrospectively reviewed the medical records of patients with systemic lupus erythematosus who were diagnosed with LN class I, II, V or II+IV by kidney biopsy between 1997 and 2021 at a tertiary referral center. Clinical and laboratory data were compared between patients with and without poor renal outcomes. Poor renal outcome was defined as an estimated glomerular filtration rate (eGFR) of < 60 mL/min/1.73 m2 or death due to renal cause. Univariate and multivariate analyses were performed with the Cox proportional hazard model to identify the factors associated with poor renal outcomes.ResultsWe included 71 patients with non-proliferative LN (4: class I; 17: class II; 48: class V, 17; 2: class II+V). Median follow-up duration was 103 months (interquartile range 27–185) and the overall rate of poor renal outcomes at last follow-up was 29% (21/71), including end-stage renal disease (n=2) and renal death (n=1).Univariate analysis indicated that older age (HR 1.05; 95% CI: 1.00–1.09), low eGFR (HR 0.97; 95% CI: 0.95–0.99) and failure to reach complete remission at 6 months (HR 0.332; 95% CI: 0.12–0.92) were significantly associated with poor renal outcomes. Multivariate analysis revealed that low eGFR at 6 months (HR 0.97; 95% CI: 0.95–0.99) was significantly associated with poor renal outcomes.Figure 1.Renal outcomes at last follow upeGFR, estimated glomerular filtration rate (ml/min/1.73m2)Table 1.Univariate and multivariate Cox proportional hazard regression analyses of the factor associated with poor renal outcomesParameterUnivariate analysisMultivariate analysisHR95% CIp valueHR95% CIp valueClinical features Age1.0461.003-1.0910.0361.0020.960-1.0470.921 Sex1.6540.375-7.2980.506 SLEDAI1.0360.965-1.1120.327 Extra renal SLEDAI1.0380.971-1.110.272Renal profiles eGFR at LN diagnosis0.9930.976-1.0110.456 Proteinuria at LN diagnosis1.0001.000-1.0000.444 > 1g/24 hours0.6690.243-1.8410.437 > 3g/24 hours0.6240.229-1.6990.356 eGFR at 6M0.9670.948-0.9860.0010.9680.948-0.9880.002 eGFR at 12M0.9640.947-0.9810.000 Complete remission at 6M0.3320.119-0.9240.0350.5530.179-1.7070.303 Complete remission at 12M0.6670.232-1.9140.451 Transformation1.2460.423-0.7010.692Laboratory data Anti-dsDNA1.0010.999-1.0030.196 C31.0201.000-1.0410.051 C41.0270.969-1.0890.367 Albumin1.1800.661-2.1090.576ClassificationaClass I0.8020.102-6.3030.834Class II1.2980.412-4.0880.656Class V0.8870.308-2.5570.824Class II+V0.0480.000-16850.837Medicationsb ACEi/ARB1.6520.603-4.5280.329 Hydroxychloroquine1.3260.414-4.2420.635 Corticosteroid1.1860.154-9.1080.870 CNI2.4390.464-12.8240.292 MMF3.7880.959-14.9650.057 AZA0.5890.133-2.6110.486a LN classifications were based on the International Society of Pathology/Renal Pathology Society (ISN/RPS) classification.b Medications maintained at least one year since Lupus Nephritis diagnosis.HR, hazard ratio; 95% CI, 95% confidence interval; SLEDAI, systemic lupus erythematosus disease activity index; eGFR, estimated glomerular filtration rate; LN, lupus nephritis; anti-dsDNA, anti-double strand DNA; C3/C4; complement 3/4; ACEi, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; CNI, carcineurin inhibitor; MMF, mycophenolate mofetil; AZA, azathioprine.ConclusionPoor renal outcomes occurred in approximately 30% of patients with non-proliferative LN (class I, II or V) after long-term follow-up.Our findings suggest that more active management may be needed for non-proliferative LN, particularly in patients with low eGFR at 6 months.Disclosure of InterestsNone declared
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Bu J, Jeong WJ, Jafari R, Kubiatowicz LJ, Nair A, Poellmann MJ, Hong RS, Liu EW, Owen RH, Rawding PA, Hopkins CM, Kim D, George DJ, Armstrong AJ, Král P, Wang AZ, Bruce J, Zhang T, Kimple RJ, Hong S. Bimodal liquid biopsy for cancer immunotherapy based on peptide engineering and nanoscale analysis. Biosens Bioelectron 2022; 213:114445. [PMID: 35679646 DOI: 10.1016/j.bios.2022.114445] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Revised: 05/13/2022] [Accepted: 05/30/2022] [Indexed: 11/02/2022]
Abstract
Despite its high potential, PD-L1 expressed by tumors has not been successfully utilized as a biomarker for estimating treatment responses to immunotherapy. Circulating tumor cells (CTCs) and tumor-derived exosomes that express PD-L1 can potentially be used as biomarkers; however, currently available assays lack clinically significant sensitivity and specificity. Here, a novel peptide-based capture surface is developed to effectively isolate PD-L1-expressing CTCs and exosomes from human blood. For the effective targeting of PD-L1, this study integrates peptide engineering strategies to enhance the binding strength and specificity of a β-hairpin peptide derived from PD-1 (pPD-1). Specifically, this study examines the effect of poly(ethylene glycol) spacers, the secondary peptide structure, and modification of peptide sequences (e.g., removal of biologically redundant amino acid residues) on capture efficiency. The optimized pPD-1 configuration captures PD-L1-expressing tumor cells and tumor-derived exosomes with 1.5-fold (p = 0.016) and 1.2-fold (p = 0.037) higher efficiencies, respectively, than their whole antibody counterpart (aPD-L1). This enhanced efficiency is translated into more clinically significant detection of CTCs (1.9-fold increase; p = 0.035) and exosomes (1.5-fold increase; p = 0.047) from patients' baseline samples, demonstrating stronger correlation with patients' treatment responses. Additionally, we confirmed that the clinical accuracy of our system can be further improved by co-analyzing the two biomarkers (bimodal CTC/exosome analysis). These data demonstrate that pPD-1-based capture is a promising approach for capturing PD-L1-expressing CTCs and exosomes, which can be used as a reliable biomarker for cancer immunotherapy.
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Affiliation(s)
- Jiyoon Bu
- Pharmaceutical Sciences Division and Wisconsin Center for NanoBioSystems (WisCNano), School of Pharmacy, University of Wisconsin - Madison, 777 Highland Ave, Madison, WI, 53705, USA; Department of Biological Sciences and Bioengineering, Inha University, 100 Inha-ro, Michuhol-gu, Incheon, 22212, Republic of Korea
| | - Woo-Jin Jeong
- Pharmaceutical Sciences Division and Wisconsin Center for NanoBioSystems (WisCNano), School of Pharmacy, University of Wisconsin - Madison, 777 Highland Ave, Madison, WI, 53705, USA; Department of Biological Sciences and Bioengineering, Inha University, 100 Inha-ro, Michuhol-gu, Incheon, 22212, Republic of Korea
| | - Roya Jafari
- Department of Chemistry, University of Illinois at Chicago, 845 W Taylor St, Chicago, IL, 60607, USA
| | - Luke J Kubiatowicz
- Pharmaceutical Sciences Division and Wisconsin Center for NanoBioSystems (WisCNano), School of Pharmacy, University of Wisconsin - Madison, 777 Highland Ave, Madison, WI, 53705, USA
| | - Ashita Nair
- Pharmaceutical Sciences Division and Wisconsin Center for NanoBioSystems (WisCNano), School of Pharmacy, University of Wisconsin - Madison, 777 Highland Ave, Madison, WI, 53705, USA
| | - Michael J Poellmann
- Pharmaceutical Sciences Division and Wisconsin Center for NanoBioSystems (WisCNano), School of Pharmacy, University of Wisconsin - Madison, 777 Highland Ave, Madison, WI, 53705, USA
| | - Rachel S Hong
- Pharmaceutical Sciences Division and Wisconsin Center for NanoBioSystems (WisCNano), School of Pharmacy, University of Wisconsin - Madison, 777 Highland Ave, Madison, WI, 53705, USA
| | - Elizabeth W Liu
- Pharmaceutical Sciences Division and Wisconsin Center for NanoBioSystems (WisCNano), School of Pharmacy, University of Wisconsin - Madison, 777 Highland Ave, Madison, WI, 53705, USA
| | - Randall H Owen
- Pharmaceutical Sciences Division and Wisconsin Center for NanoBioSystems (WisCNano), School of Pharmacy, University of Wisconsin - Madison, 777 Highland Ave, Madison, WI, 53705, USA
| | - Piper A Rawding
- Pharmaceutical Sciences Division and Wisconsin Center for NanoBioSystems (WisCNano), School of Pharmacy, University of Wisconsin - Madison, 777 Highland Ave, Madison, WI, 53705, USA
| | - Caroline M Hopkins
- Pharmaceutical Sciences Division and Wisconsin Center for NanoBioSystems (WisCNano), School of Pharmacy, University of Wisconsin - Madison, 777 Highland Ave, Madison, WI, 53705, USA
| | - DaWon Kim
- Pharmaceutical Sciences Division and Wisconsin Center for NanoBioSystems (WisCNano), School of Pharmacy, University of Wisconsin - Madison, 777 Highland Ave, Madison, WI, 53705, USA
| | - Daniel J George
- Department of Medicine, Division of Medical Oncology, Duke Cancer Institute, Duke University, Durham, 10 Bryan Searle Drive, Durham, NC, 27710, USA; Duke Cancer Institute Center for Prostate and Urologic Cancers, Duke University, 20 Duke Medicine Cir, Durham, NC, 27710, USA
| | - Andrew J Armstrong
- Department of Medicine, Division of Medical Oncology, Duke Cancer Institute, Duke University, Durham, 10 Bryan Searle Drive, Durham, NC, 27710, USA; Duke Cancer Institute Center for Prostate and Urologic Cancers, Duke University, 20 Duke Medicine Cir, Durham, NC, 27710, USA
| | - Petr Král
- Department of Chemistry, University of Illinois at Chicago, 845 W Taylor St, Chicago, IL, 60607, USA; Department of Physics, Department of Pharmaceutical Sciences, University of Illinois at Chicago, 845 W Taylar St, Chicage, IL, 60607, USA
| | - Andrew Z Wang
- Department of Radiation Oncology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA; Department of Radiation Oncology and Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Justine Bruce
- Department of Human Oncology, University of Wisconsin-Madison, Madison, 600 Highland Ave, WI, 53792, USA; UW Carbone Cancer Center, University of Wisconsin-Madison, Madison, 600 Highland Ave, WI, 53792, USA
| | - Tian Zhang
- Department of Medicine, Division of Medical Oncology, Duke Cancer Institute, Duke University, Durham, 10 Bryan Searle Drive, Durham, NC, 27710, USA; Duke Cancer Institute Center for Prostate and Urologic Cancers, Duke University, 20 Duke Medicine Cir, Durham, NC, 27710, USA; Department of Internal Medicine and Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Randall J Kimple
- Department of Human Oncology, University of Wisconsin-Madison, Madison, 600 Highland Ave, WI, 53792, USA; UW Carbone Cancer Center, University of Wisconsin-Madison, Madison, 600 Highland Ave, WI, 53792, USA
| | - Seungpyo Hong
- Pharmaceutical Sciences Division and Wisconsin Center for NanoBioSystems (WisCNano), School of Pharmacy, University of Wisconsin - Madison, 777 Highland Ave, Madison, WI, 53705, USA; UW Carbone Cancer Center, University of Wisconsin-Madison, Madison, 600 Highland Ave, WI, 53792, USA; Department of Biomedical Engineering, The University of Wisconsin-Madison, 1550 Engineering Dr., Madison, WI, 53705, USA; Yonsei Frontier Lab, Department of Pharmacy, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.
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Lema DA, Jankowska‐Gan E, Nair A, Kanaan SB, Little CJ, Foley DP, Raza Naqvi A, Wang J, Hong S, Nelson JL, Al‐Adra D, Burlingham WJ, Sullivan JA. Cross-decoration of dendritic cells by non-inherited maternal antigen-containing extracellular vesicles: Potential mechanism for PD-L1-based tolerance in cord blood and organ transplantation. Am J Transplant 2022; 22:1329-1338. [PMID: 35143105 PMCID: PMC9235410 DOI: 10.1111/ajt.16970] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 12/10/2021] [Accepted: 01/14/2022] [Indexed: 01/25/2023]
Abstract
Exposure to non-inherited maternal antigens (NIMA) during the fetal period induces lifelong split tolerance to grafts expressing these allo-antigens. In adult mice, the production of extracellular vesicles (EVs) from maternal microchimeric cells causes cross-decoration (XD) of offspring dendritic cells (DC) with NIMA and upregulation of PD-L1, contributing to NIMA tolerance. To see how this may apply to humans, we tested NIMA acquisition by fetal DCS in human cord blood. The average percentage of NIMA-XD among total DCs was 2.6% for myeloid and 4.5% for Plasmacytoid DC. These cells showed higher PD-L1 expression than their non-XD counterparts (mDC: p = .0016; pDC: p = .024). We detected CD9+ EVs bearing NIMA and PD-L1 in cord blood. To determine if this immune regulatory mechanism persists beyond the pregnancy, we analyzed NIMA-expressing kidney and liver transplant recipients. We found donor antigen XD DCs in peripheral blood and graft-infiltrating DCs. As in cord blood, the pattern of donor antigen expression was punctate, and PD-L1 expression was upregulated, likely due to both protein and miRNA acquired from EV. Our findings support a mechanism for split tolerance to NIMAs that develops during pregnancy and is recapitulated in adult transplant recipients.
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Affiliation(s)
- Diego A. Lema
- Department of SurgeryDivision of TransplantationUniversity of WisconsinMadisonWisconsinUSA
| | - Ewa Jankowska‐Gan
- Department of SurgeryDivision of TransplantationUniversity of WisconsinMadisonWisconsinUSA
| | - Ashita Nair
- Pharmaceutical Sciences DivisionSchool of PharmacyUniversity of WisconsinMadisonWisconsinUSA
| | - Sami B. Kanaan
- Clinical Research DivisionFred Hutchinson Cancer Research CenterSeattleWashingtonUSA
| | - Christopher J. Little
- Department of SurgeryDivision of TransplantationUniversity of WisconsinMadisonWisconsinUSA
| | - David P. Foley
- Department of SurgeryDivision of TransplantationUniversity of WisconsinMadisonWisconsinUSA
| | - Afsar Raza Naqvi
- Department of PeriodontologyUniversity of Illinois at ChicagoChicagoIllinoisUSA
| | - Jianxin Wang
- Pharmaceutical Sciences DivisionSchool of PharmacyUniversity of WisconsinMadisonWisconsinUSA
| | - Seungpyo Hong
- Pharmaceutical Sciences DivisionSchool of PharmacyUniversity of WisconsinMadisonWisconsinUSA
| | - J. Lee Nelson
- Clinical Research DivisionFred Hutchinson Cancer Research CenterSeattleWashingtonUSA
- Department of MedicineRheumatology DivisionUniversity of WashingtonSeattleWashingtonUSA
| | - David Al‐Adra
- Department of SurgeryDivision of TransplantationUniversity of WisconsinMadisonWisconsinUSA
| | - William J. Burlingham
- Department of SurgeryDivision of TransplantationUniversity of WisconsinMadisonWisconsinUSA
| | - Jeremy A. Sullivan
- Department of SurgeryDivision of TransplantationUniversity of WisconsinMadisonWisconsinUSA
- Department of AnesthesiologyUniversity of WisconsinMadisonWisconsinUSA
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Poellmann MJ, Rawding P, Kim D, Bu J, Kim Y, Hong S. Branched, dendritic, and hyperbranched polymers in liquid biopsy device design. Wiley Interdiscip Rev Nanomed Nanobiotechnol 2022; 14:e1770. [PMID: 34984833 PMCID: PMC9480505 DOI: 10.1002/wnan.1770] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 12/02/2021] [Accepted: 12/09/2021] [Indexed: 12/15/2022]
Abstract
The development of minimally invasive tests for cancer diagnosis and prognosis will aid in the research of new treatments and improve survival rates. Liquid biopsies seek to derive actionable information from tumor material found in routine blood samples. The relative scarcity of tumor material in this complex mixture makes isolating and detecting cancerous material such as proteins, circulating tumor DNA, exosomes, and whole circulating tumor cells a challenge for device engineers. This review describes the chemistry and applications of branched and hyperbranched to improve the performance of liquid biopsy devices. These polymers can improve the performance of a liquid biopsy through several mechanisms. For example, polymers designed to increase the affinity of capture enhance device sensitivity. On the other hand, polymers designed to increase binding avidity or repel nonspecific adsorption enhance device specificity. Branched and hyperbranched polymers can also be used to amplify the signal from small amounts of detected material. The further development of hyperbranched polymers in liquid biopsy applications will enhance device capabilities and help these critical technologies reach the oncology clinic where they are sorely needed. This article is categorized under: Diagnostic Tools > Biosensing Diagnostic Tools > Diagnostic Nanodevices.
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Affiliation(s)
- Michael J Poellmann
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin, Madison, Wisconsin, USA.,Capio Biosciences, Madison, Wisconsin, USA
| | - Piper Rawding
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin, Madison, Wisconsin, USA
| | - DaWon Kim
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin, Madison, Wisconsin, USA
| | - Jiyoon Bu
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin, Madison, Wisconsin, USA
| | - YoungSoo Kim
- Department of Pharmacy, Yonsei University, Incheon, South Korea
| | - Seungpyo Hong
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin, Madison, Wisconsin, USA.,Capio Biosciences, Madison, Wisconsin, USA.,Department of Pharmacy, Yonsei University, Incheon, South Korea.,Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, Wisconsin, USA.,Carbone Cancer Center, University of Wisconsin-Madison, Madison, Wisconsin, USA.,Wisconsin Center for NanoBioSystems, University of Wisconsin-Madison, Madison, Wisconsin, USA
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Jeong H, Shin H, Hong S, Kim Y. Physiological Roles of Monomeric Amyloid-β and Implications for Alzheimer's Disease Therapeutics. Exp Neurobiol 2022; 31:65-88. [PMID: 35673997 PMCID: PMC9194638 DOI: 10.5607/en22004] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 03/26/2022] [Accepted: 03/30/2022] [Indexed: 12/23/2022] Open
Abstract
Alzheimer's disease (AD) progressively inflicts impairment of synaptic functions with notable deposition of amyloid-β (Aβ) as senile plaques within the extracellular space of the brain. Accordingly, therapeutic directions for AD have focused on clearing Aβ plaques or preventing amyloidogenesis based on the amyloid cascade hypothesis. However, the emerging evidence suggests that Aβ serves biological roles, which include suppressing microbial infections, regulating synaptic plasticity, promoting recovery after brain injury, sealing leaks in the blood-brain barrier, and possibly inhibiting the proliferation of cancer cells. More importantly, these functions were found in in vitro and in vivo investigations in a hormetic manner, that is to be neuroprotective at low concentrations and pathological at high concentrations. We herein summarize the physiological roles of monomeric Aβ and current Aβ-directed therapies in clinical trials. Based on the evidence, we propose that novel therapeutics targeting Aβ should selectively target Aβ in neurotoxic forms such as oligomers while retaining monomeric Aβ in order to preserve the physiological functions of Aβ monomers.
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Affiliation(s)
- Hyomin Jeong
- Division of Integrated Science and Engineering, Underwood International College, Yonsei University, Incheon 21983, Korea
- Department of Pharmacy, College of Pharmacy, Yonsei University, Incheon 21983, Korea
- Yonsei Institute of Pharmaceutical Sciences, College of Pharmacy, Yonsei University, Incheon 21983, Korea
| | - Heewon Shin
- Department of Pharmacy, College of Pharmacy, Yonsei University, Incheon 21983, Korea
- Yonsei Institute of Pharmaceutical Sciences, College of Pharmacy, Yonsei University, Incheon 21983, Korea
| | - Seungpyo Hong
- Department of Pharmacy, College of Pharmacy, Yonsei University, Incheon 21983, Korea
- Yonsei Frontier Lab, Yonsei University, Seoul 03722, Korea
- Division of Pharmaceutical Sciences, School of Pharmacy, University of Wisconsin-Madison, Madison, WI 53705, USA
- Wisconsin Center for NanoBioSystems, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - YoungSoo Kim
- Division of Integrated Science and Engineering, Underwood International College, Yonsei University, Incheon 21983, Korea
- Department of Pharmacy, College of Pharmacy, Yonsei University, Incheon 21983, Korea
- Yonsei Institute of Pharmaceutical Sciences, College of Pharmacy, Yonsei University, Incheon 21983, Korea
- Yonsei Frontier Lab, Yonsei University, Seoul 03722, Korea
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Iida M, McDaniel NK, Kostecki KL, Welke NB, Kranjac CA, Liu P, Longhurst C, Bruce JY, Hong S, Salgia R, Wheeler DL. AXL regulates neuregulin1 expression leading to cetuximab resistance in head and neck cancer. BMC Cancer 2022; 22:447. [PMID: 35461210 PMCID: PMC9035247 DOI: 10.1186/s12885-022-09511-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 04/07/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND The receptor tyrosine kinase (RTK) epidermal growth factor receptor (EGFR) is overexpressed and an important therapeutic target in Head and Neck cancer (HNC). Cetuximab is currently the only EGFR-targeting agent approved by the FDA for treatment of HNC; however, intrinsic and acquired resistance to cetuximab is a major problem in the clinic. Our lab previously reported that AXL leads to cetuximab resistance via activation of HER3. In this study, we investigate the connection between AXL, HER3, and neuregulin1 (NRG1) gene expression with a focus on understanding how their interdependent signaling promotes resistance to cetuximab in HNC. METHODS Plasmid or siRNA transfections and cell-based assays were conducted to test cetuximab sensitivity. Quantitative PCR and immunoblot analysis were used to analyze gene and protein expression levels. Seven HNC patient-derived xenografts (PDXs) were evaluated for protein expression levels. RESULTS We found that HER3 expression was necessary but not sufficient for cetuximab resistance without AXL expression. Our results demonstrated that addition of the HER3 ligand NRG1 to cetuximab-sensitive HNC cells leads to cetuximab resistance. Further, AXL-overexpressing cells regulate NRG1 at the level of transcription, thereby promoting cetuximab resistance. Immunoblot analysis revealed that NRG1 expression was relatively high in cetuximab-resistant HNC PDXs compared to cetuximab-sensitive HNC PDXs. Finally, genetic inhibition of NRG1 resensitized AXL-overexpressing cells to cetuximab. CONCLUSIONS The results of this study indicate that AXL may signal through HER3 via NRG1 to promote cetuximab resistance and that targeting of NRG1 could have significant clinical implications for HNC therapeutic approaches.
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Affiliation(s)
- Mari Iida
- grid.28803.310000 0001 0701 8607Department of Human Oncology, School of Medicine and Public Health, University of Wisconsin, 1111 highland Ave, WIMR 3159, Madison, WI 53705 USA
| | - Nellie K. McDaniel
- grid.28803.310000 0001 0701 8607Department of Human Oncology, School of Medicine and Public Health, University of Wisconsin, 1111 highland Ave, WIMR 3159, Madison, WI 53705 USA
| | - Kourtney L. Kostecki
- grid.28803.310000 0001 0701 8607Department of Human Oncology, School of Medicine and Public Health, University of Wisconsin, 1111 highland Ave, WIMR 3159, Madison, WI 53705 USA
| | - Noah B. Welke
- grid.28803.310000 0001 0701 8607Department of Human Oncology, School of Medicine and Public Health, University of Wisconsin, 1111 highland Ave, WIMR 3159, Madison, WI 53705 USA
| | - Carlene A. Kranjac
- grid.28803.310000 0001 0701 8607Department of Human Oncology, School of Medicine and Public Health, University of Wisconsin, 1111 highland Ave, WIMR 3159, Madison, WI 53705 USA
| | - Peng Liu
- grid.14003.360000 0001 2167 3675School of Medicine and Public Health, University of Wisconsin Carbone Cancer Center, University of Wisconsin, Madison, WI USA ,grid.28803.310000 0001 0701 8607Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin, Madison, WI USA
| | - Colin Longhurst
- grid.28803.310000 0001 0701 8607Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin, Madison, WI USA
| | - Justine Y. Bruce
- grid.14003.360000 0001 2167 3675School of Medicine and Public Health, University of Wisconsin Carbone Cancer Center, University of Wisconsin, Madison, WI USA ,grid.28803.310000 0001 0701 8607Department of Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, WI USA
| | - Seungpyo Hong
- grid.14003.360000 0001 2167 3675Pharmaceutical Sciences Division, University of Wisconsin School of Pharmacy, Madison, WI USA ,grid.28803.310000 0001 0701 8607Wisconsin Center for NanoBioSystems, University of Wisconsin, Madison, WI USA ,grid.15444.300000 0004 0470 5454Yonsei Frontier Lab, Department of Pharmacy, Yonsei University, Seoul, Korea
| | - Ravi Salgia
- grid.410425.60000 0004 0421 8357Department of Medical Oncology and Experimental Therapeutics, Comprehensive Cancer Center, City of Hope, Duarte, CA USA
| | - Deric L. Wheeler
- grid.28803.310000 0001 0701 8607Department of Human Oncology, School of Medicine and Public Health, University of Wisconsin, 1111 highland Ave, WIMR 3159, Madison, WI 53705 USA ,grid.14003.360000 0001 2167 3675School of Medicine and Public Health, University of Wisconsin Carbone Cancer Center, University of Wisconsin, Madison, WI USA ,grid.28803.310000 0001 0701 8607Wisconsin Center for NanoBioSystems, University of Wisconsin, Madison, WI USA
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Lee T, Rawding PA, Bu J, Hyun S, Rou W, Jeon H, Kim S, Lee B, Kubiatowicz LJ, Kim D, Hong S, Eun H. Machine-Learning-Based Clinical Biomarker Using Cell-Free DNA for Hepatocellular Carcinoma (HCC). Cancers (Basel) 2022; 14:cancers14092061. [PMID: 35565192 PMCID: PMC9103537 DOI: 10.3390/cancers14092061] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Revised: 04/04/2022] [Accepted: 04/12/2022] [Indexed: 02/08/2023] Open
Abstract
Simple Summary Circulating cell-free DNA (cfDNA) has attracted a great deal of scientific interest as a predictive biomarker for the diagnosis and prognosis of hepatocellular carcinoma (HCC). HCC result in high mortality due to the absence of blood biomarkers for early diagnosis and prognosis. We established cfDHCC as a new scoring system by applying a machine learning algorithm that integrates the expression profiles of cfDNA. Based on this, it was possible to accurately predict the clinico-pathological characteristics of patients with HCC as well as improve their survival. Abstract (1) Background: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death worldwide. Although various serum enzymes have been utilized for the diagnosis and prognosis of HCC, the currently available biomarkers lack the sensitivity needed to detect HCC at early stages and accurately predict treatment responses. (2) Methods: We utilized our highly sensitive cell-free DNA (cfDNA) detection system, in combination with a machine learning algorithm, to provide a platform for improved diagnosis and prognosis of HCC. (3) Results: cfDNA, specifically alpha-fetoprotein (AFP) expression in captured cfDNA, demonstrated the highest accuracy for diagnosing malignancies among the serum/plasma biomarkers used in this study, including AFP, aspartate aminotransferase, alanine aminotransferase, albumin, alkaline phosphatase, and bilirubin. The diagnostic/prognostic capability of cfDNA was further improved by establishing a cfDNA score (cfDHCC), which integrated the total plasma cfDNA levels and cfAFP-DNA expression into a single score using machine learning algorithms. (4) Conclusion: The cfDHCC score demonstrated significantly improved accuracy in determining the pathological features of HCC and predicting patients’ survival outcomes compared to the other biomarkers. The results presented herein reveal that our cfDNA capture/analysis platform is a promising approach to effectively utilize cfDNA as a biomarker for the diagnosis and prognosis of HCC.
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Affiliation(s)
- Taehee Lee
- Department of Biomedical Laboratory Science, Daegu Health College, Daegu 41453, Korea;
- Department of Senior Healthcare, Graduate School, Eulji University, Uijeongbu-si 11759, Korea;
| | - Piper A. Rawding
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin—Madison, Madison, WI 53705, USA; (P.A.R.); (J.B.); (L.J.K.); (D.K.)
- Wisconsin Center for NanoBioSystems (WisCNano), University of Wisconsin—Madison, Madison, WI 53705, USA
| | - Jiyoon Bu
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin—Madison, Madison, WI 53705, USA; (P.A.R.); (J.B.); (L.J.K.); (D.K.)
- Wisconsin Center for NanoBioSystems (WisCNano), University of Wisconsin—Madison, Madison, WI 53705, USA
- Department of Biological Sciences and Bioengineering, Inha University, Incheon 22212, Korea
- Industry-Academia Interactive R&E Center for Bioprocess Innovation, Inha University, Incheon 22212, Korea
| | - Sunghee Hyun
- Department of Senior Healthcare, Graduate School, Eulji University, Uijeongbu-si 11759, Korea;
| | - Woosun Rou
- Department of Internal Medicine, Chungnam National University Sejong Hospital (CNUSH), Sejong 30099, Korea; (W.R.); (H.J.)
| | - Hongjae Jeon
- Department of Internal Medicine, Chungnam National University Sejong Hospital (CNUSH), Sejong 30099, Korea; (W.R.); (H.J.)
| | - Seokhyun Kim
- Department of Internal Medicine, Chungnam National University Hospital, Daejeon 35015, Korea; (S.K.); (B.L.)
| | - Byungseok Lee
- Department of Internal Medicine, Chungnam National University Hospital, Daejeon 35015, Korea; (S.K.); (B.L.)
| | - Luke J. Kubiatowicz
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin—Madison, Madison, WI 53705, USA; (P.A.R.); (J.B.); (L.J.K.); (D.K.)
| | - Dawon Kim
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin—Madison, Madison, WI 53705, USA; (P.A.R.); (J.B.); (L.J.K.); (D.K.)
- Wisconsin Center for NanoBioSystems (WisCNano), University of Wisconsin—Madison, Madison, WI 53705, USA
| | - Seungpyo Hong
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin—Madison, Madison, WI 53705, USA; (P.A.R.); (J.B.); (L.J.K.); (D.K.)
- Wisconsin Center for NanoBioSystems (WisCNano), University of Wisconsin—Madison, Madison, WI 53705, USA
- Yonsei Frontier Lab, Department of Pharmacy, Yonsei University, Seoul 03722, Korea
- Correspondence: (S.H.); (H.E.); Tel.: +82-42-280-7418 (H.E.)
| | - Hyuksoo Eun
- Yonsei Frontier Lab, Department of Pharmacy, Yonsei University, Seoul 03722, Korea
- Department of Internal Medicine, College of Medicine, Chungnam National University, Daejeon 35015, Korea
- Correspondence: (S.H.); (H.E.); Tel.: +82-42-280-7418 (H.E.)
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Li Z, Ding Y, Liu J, Wang J, Mo F, Wang Y, Chen-Mayfield TJ, Sondel PM, Hong S, Hu Q. Depletion of tumor associated macrophages enhances local and systemic platelet-mediated anti-PD-1 delivery for post-surgery tumor recurrence treatment. Nat Commun 2022; 13:1845. [PMID: 35387972 PMCID: PMC8987059 DOI: 10.1038/s41467-022-29388-0] [Citation(s) in RCA: 65] [Impact Index Per Article: 32.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 03/15/2022] [Indexed: 02/06/2023] Open
Abstract
Immunosuppressive cells residing in the tumor microenvironment, especially tumor associated macrophages (TAMs), hinder the infiltration and activation of T cells, limiting the anti-cancer outcomes of immune checkpoint blockade. Here, we report a biocompatible alginate-based hydrogel loaded with Pexidartinib (PLX)-encapsulated nanoparticles that gradually release PLX at the tumor site to block colony-stimulating factor 1 receptors (CSF1R) for depleting TAMs. The controlled TAM depletion creates a favorable milieu for facilitating local and systemic delivery of anti-programmed cell death protein 1 (aPD-1) antibody-conjugated platelets to inhibit post-surgery tumor recurrence. The tumor immunosuppressive microenvironment is also reprogrammed by TAM elimination, further promoting the infiltration of T cells into tumor tissues. Moreover, the inflammatory environment after surgery could trigger the activation of platelets to facilitate the release of aPD-1 accompanied with platelet-derived microparticles binding to PD-1 receptors for re-activating T cells. All these results collectively indicate that the immunotherapeutic efficacy against tumor recurrence of both local and systemic administration of aPD-1 antibody-conjugated platelets could be strengthened by local depletion of TAMs through the hydrogel reservoir. Increased density of tumor associated macrophages has been correlated with tumor recurrence following surgery. Here the authors design an alginate-based hydrogel encapsulating anti-PD-1-conjugated platelets and nanoparticles loaded with the macrophage-depleting CSF-1R inhibitor pexidartinib, showing inhibition of post-surgery tumor recurrence in preclinical models.
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Affiliation(s)
- Zhaoting Li
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin-Madison, Madison, WI, 53705, USA.,Carbone Cancer Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, 53705, USA.,Wisconsin Center for NanoBioSystems, School of Pharmacy, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Yingyue Ding
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin-Madison, Madison, WI, 53705, USA.,Carbone Cancer Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, 53705, USA.,Wisconsin Center for NanoBioSystems, School of Pharmacy, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Jun Liu
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin-Madison, Madison, WI, 53705, USA.,Carbone Cancer Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, 53705, USA.,Wisconsin Center for NanoBioSystems, School of Pharmacy, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Jianxin Wang
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin-Madison, Madison, WI, 53705, USA.,Wisconsin Center for NanoBioSystems, School of Pharmacy, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Fanyi Mo
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin-Madison, Madison, WI, 53705, USA.,Carbone Cancer Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, 53705, USA.,Wisconsin Center for NanoBioSystems, School of Pharmacy, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Yixin Wang
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin-Madison, Madison, WI, 53705, USA.,Carbone Cancer Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, 53705, USA.,Wisconsin Center for NanoBioSystems, School of Pharmacy, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Ting-Jing Chen-Mayfield
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin-Madison, Madison, WI, 53705, USA.,Carbone Cancer Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, 53705, USA.,Wisconsin Center for NanoBioSystems, School of Pharmacy, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Paul M Sondel
- Carbone Cancer Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, 53705, USA.,Department of Human Oncology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.,Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Seungpyo Hong
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin-Madison, Madison, WI, 53705, USA.,Carbone Cancer Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, 53705, USA.,Wisconsin Center for NanoBioSystems, School of Pharmacy, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Quanyin Hu
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin-Madison, Madison, WI, 53705, USA. .,Carbone Cancer Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, 53705, USA. .,Wisconsin Center for NanoBioSystems, School of Pharmacy, University of Wisconsin-Madison, Madison, WI, 53705, USA.
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Chidambaram S, Hong S, Simpson M, Osazuwa-Peters N, Ward G, Massa S. Temporal Trends in Oropharyngeal Cancer Incidence, Survival, and Cancer-Directed Surgery Among Elderly Americans. Int J Radiat Oncol Biol Phys 2022. [DOI: 10.1016/j.ijrobp.2021.12.067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Soulsby WD, Balmuri N, Cooley V, Gerber LM, Lawson E, Goodman S, Onel K, Mehta B, Abel N, Abulaban K, Adams A, Adams M, Agbayani R, Aiello J, Akoghlanian S, Alejandro C, Allenspach E, Alperin R, Alpizar M, Amarilyo G, Ambler W, Anderson E, Ardoin S, Armendariz S, Baker E, Balboni I, Balevic S, Ballenger L, Ballinger S, Balmuri N, Barbar-Smiley F, Barillas-Arias L, Basiaga M, Baszis K, Becker M, Bell-Brunson H, Beltz E, Benham H, Benseler S, Bernal W, Beukelman T, Bigley T, Binstadt B, Black C, Blakley M, Bohnsack J, Boland J, Boneparth A, Bowman S, Bracaglia C, Brooks E, Brothers M, Brown A, Brunner H, Buckley M, Buckley M, Bukulmez H, Bullock D, Cameron B, Canna S, Cannon L, Carper P, Cartwright V, Cassidy E, Cerracchio L, Chalom E, Chang J, Chang-Hoftman A, Chauhan V, Chira P, Chinn T, Chundru K, Clairman H, Co D, Confair A, Conlon H, Connor R, Cooper A, Cooper J, Cooper S, Correll C, Corvalan R, Costanzo D, Cron R, Curiel-Duran L, Curington T, Curry M, Dalrymple A, Davis A, Davis C, Davis C, Davis T, De Benedetti F, De Ranieri D, Dean J, Dedeoglu F, DeGuzman M, Delnay N, Dempsey V, DeSantis E, Dickson T, Dingle J, Donaldson B, Dorsey E, Dover S, Dowling J, Drew J, Driest K, Du Q, Duarte K, Durkee D, Duverger E, Dvergsten J, Eberhard A, Eckert M, Ede K, Edelheit B, Edens C, Edens C, Edgerly Y, Elder M, Ervin B, Fadrhonc S, Failing C, Fair D, Falcon M, Favier L, Federici S, Feldman B, Fennell J, Ferguson I, Ferguson P, Ferreira B, Ferrucho R, Fields K, Finkel T, Fitzgerald M, Fleming C, Flynn O, Fogel L, Fox E, Fox M, Franco L, Freeman M, Fritz K, Froese S, Fuhlbrigge R, Fuller J, George N, Gerhold K, Gerstbacher D, Gilbert M, Gillispie-Taylor M, Giverc E, Godiwala C, Goh I, Goheer H, Goldsmith D, Gotschlich E, Gotte A, Gottlieb B, Gracia C, Graham T, Grevich S, Griffin T, Griswold J, Grom A, Guevara M, Guittar P, Guzman M, Hager M, Hahn T, Halyabar O, Hammelev E, Hance M, Hanson A, Harel L, Haro S, Harris J, Harry O, Hartigan E, Hausmann J, Hay A, Hayward K, Heiart J, Hekl K, Henderson L, Henrickson M, Hersh A, Hickey K, Hill P, Hillyer S, Hiraki L, Hiskey M, Hobday P, Hoffart C, Holland M, Hollander M, Hong S, Horwitz M, Hsu J, Huber A, Huggins J, Hui-Yuen J, Hung C, Huntington J, Huttenlocher A, Ibarra M, Imundo L, Inman C, Insalaco A, Jackson A, Jackson S, James K, Janow G, Jaquith J, Jared S, Johnson N, Jones J, Jones J, Jones J, Jones K, Jones S, Joshi S, Jung L, Justice C, Justiniano A, Karan N, Kaufman K, Kemp A, Kessler E, Khalsa U, Kienzle B, Kim S, Kimura Y, Kingsbury D, Kitcharoensakkul M, Klausmeier T, Klein K, Klein-Gitelman M, Kompelien B, Kosikowski A, Kovalick L, Kracker J, Kramer S, Kremer C, Lai J, Lam J, Lang B, Lapidus S, Lapin B, Lasky A, Latham D, Lawson E, Laxer R, Lee P, Lee P, Lee T, Lentini L, Lerman M, Levy D, Li S, Lieberman S, Lim L, Lin C, Ling N, Lingis M, Lo M, Lovell D, Lowman D, Luca N, Lvovich S, Madison C, Madison J, Manzoni SM, Malla B, Maller J, Malloy M, Mannion M, Manos C, Marques L, Martyniuk A, Mason T, Mathus S, McAllister L, McCarthy K, McConnell K, McCormick E, McCurdy D, Stokes PMC, McGuire S, McHale I, McMonagle A, McMullen-Jackson C, Meidan E, Mellins E, Mendoza E, Mercado R, Merritt A, Michalowski L, Miettunen P, Miller M, Milojevic D, Mirizio E, Misajon E, Mitchell M, Modica R, Mohan S, Moore K, Moorthy L, Morgan S, Dewitt EM, Moss C, Moussa T, Mruk V, Murphy A, Muscal E, Nadler R, Nahal B, Nanda K, Nasah N, Nassi L, Nativ S, Natter M, Neely J, Nelson B, Newhall L, Ng L, Nicholas J, Nicolai R, Nigrovic P, Nocton J, Nolan B, Oberle E, Obispo B, O’Brien B, O’Brien T, Okeke O, Oliver M, Olson J, O’Neil K, Onel K, Orandi A, Orlando M, Osei-Onomah S, Oz R, Pagano E, Paller A, Pan N, Panupattanapong S, Pardeo M, Paredes J, Parsons A, Patel J, Pentakota K, Pepmueller P, Pfeiffer T, Phillippi K, Marafon DP, Phillippi K, Ponder L, Pooni R, Prahalad S, Pratt S, Protopapas S, Puplava B, Quach J, Quinlan-Waters M, Rabinovich C, Radhakrishna S, Rafko J, Raisian J, Rakestraw A, Ramirez C, Ramsay E, Ramsey S, Randell R, Reed A, Reed A, Reed A, Reid H, Remmel K, Repp A, Reyes A, Richmond A, Riebschleger M, Ringold S, Riordan M, Riskalla M, Ritter M, Rivas-Chacon R, Robinson A, Rodela E, Rodriquez M, Rojas K, Ronis T, Rosenkranz M, Rosolowski B, Rothermel H, Rothman D, Roth-Wojcicki E, Rouster-Stevens K, Rubinstein T, Ruth N, Saad N, Sabbagh S, Sacco E, Sadun R, Sandborg C, Sanni A, Santiago L, Sarkissian A, Savani S, Scalzi L, Schanberg L, Scharnhorst S, Schikler K, Schlefman A, Schmeling H, Schmidt K, Schmitt E, Schneider R, Schollaert-Fitch K, Schulert G, Seay T, Seper C, Shalen J, Sheets R, Shelly A, Shenoi S, Shergill K, Shirley J, Shishov M, Shivers C, Silverman E, Singer N, Sivaraman V, Sletten J, Smith A, Smith C, Smith J, Smith J, Smitherman E, Soep J, Son M, Spence S, Spiegel L, Spitznagle J, Sran R, Srinivasalu H, Stapp H, Steigerwald K, Rakovchik YS, Stern S, Stevens A, Stevens B, Stevenson R, Stewart K, Stingl C, Stokes J, Stoll M, Stringer E, Sule S, Sumner J, Sundel R, Sutter M, Syed R, Syverson G, Szymanski A, Taber S, Tal R, Tambralli A, Taneja A, Tanner T, Tapani S, Tarshish G, Tarvin S, Tate L, Taxter A, Taylor J, Terry M, Tesher M, Thatayatikom A, Thomas B, Tiffany K, Ting T, Tipp A, Toib D, Torok K, Toruner C, Tory H, Toth M, Tse S, Tubwell V, Twilt M, Uriguen S, Valcarcel T, Van Mater H, Vannoy L, Varghese C, Vasquez N, Vazzana K, Vehe R, Veiga K, Velez J, Verbsky J, Vilar G, Volpe N, von Scheven E, Vora S, Wagner J, Wagner-Weiner L, Wahezi D, Waite H, Walker J, Walters H, Muskardin TW, Waqar L, Waterfield M, Watson M, Watts A, Weiser P, Weiss J, Weiss P, Wershba E, White A, Williams C, Wise A, Woo J, Woolnough L, Wright T, Wu E, Yalcindag A, Yee M, Yen E, Yeung R, Yomogida K, Yu Q, Zapata R, Zartoshti A, Zeft A, Zeft R, Zhang Y, Zhao Y, Zhu A, Zic C. Social determinants of health influence disease activity and functional disability in Polyarticular Juvenile Idiopathic Arthritis. Pediatr Rheumatol Online J 2022; 20:18. [PMID: 35255941 PMCID: PMC8903717 DOI: 10.1186/s12969-022-00676-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 02/07/2022] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Social determinants of health (SDH) greatly influence outcomes during the first year of treatment in rheumatoid arthritis, a disease similar to polyarticular juvenile idiopathic arthritis (pJIA). We investigated the correlation of community poverty level and other SDH with the persistence of moderate to severe disease activity and functional disability over the first year of treatment in pJIA patients enrolled in the Childhood Arthritis and Rheumatology Research Alliance Registry. METHODS In this cohort study, unadjusted and adjusted generalized linear mixed effects models analyzed the effect of community poverty and other SDH on disease activity, using the clinical Juvenile Arthritis Disease Activity Score-10, and disability, using the Child Health Assessment Questionnaire, measured at baseline, 6, and 12 months. RESULTS One thousand six hundred eighty-four patients were identified. High community poverty (≥20% living below the federal poverty level) was associated with increased odds of functional disability (OR 1.82, 95% CI 1.28-2.60) but was not statistically significant after adjustment (aOR 1.23, 95% CI 0.81-1.86) and was not associated with increased disease activity. Non-white race/ethnicity was associated with higher disease activity (aOR 2.48, 95% CI: 1.41-4.36). Lower self-reported household income was associated with higher disease activity and persistent functional disability. Public insurance (aOR 1.56, 95% CI 1.06-2.29) and low family education (aOR 1.89, 95% CI 1.14-3.12) was associated with persistent functional disability. CONCLUSION High community poverty level was associated with persistent functional disability in unadjusted analysis but not with persistent moderate to high disease activity. Race/ethnicity and other SDH were associated with persistent disease activity and functional disability.
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Affiliation(s)
- William Daniel Soulsby
- University of California, San Francisco, 550 16th Street, 4th Floor, Box #0632, San Francisco, CA, 94158, USA.
| | - Nayimisha Balmuri
- grid.239915.50000 0001 2285 8823Hospital for Special Surgery, New York, NY USA ,grid.5386.8000000041936877XWeill Cornell Medicine, New York, NY USA
| | - Victoria Cooley
- grid.5386.8000000041936877XWeill Cornell Medicine, New York, NY USA
| | - Linda M. Gerber
- grid.5386.8000000041936877XWeill Cornell Medicine, New York, NY USA
| | - Erica Lawson
- grid.266102.10000 0001 2297 6811University of California, San Francisco, 550 16th Street, 4th Floor, Box #0632, San Francisco, CA 94158 USA
| | - Susan Goodman
- grid.239915.50000 0001 2285 8823Hospital for Special Surgery, New York, NY USA ,grid.5386.8000000041936877XWeill Cornell Medicine, New York, NY USA
| | - Karen Onel
- grid.239915.50000 0001 2285 8823Hospital for Special Surgery, New York, NY USA ,grid.5386.8000000041936877XWeill Cornell Medicine, New York, NY USA
| | - Bella Mehta
- grid.239915.50000 0001 2285 8823Hospital for Special Surgery, New York, NY USA ,grid.5386.8000000041936877XWeill Cornell Medicine, New York, NY USA
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Rawding PA, Bu J, Wang J, Kim D, Drelich AJ, Kim Y, Hong S. Dendrimers for cancer immunotherapy: Avidity-based drug delivery vehicles for effective anti-tumor immune response. Wiley Interdiscip Rev Nanomed Nanobiotechnol 2022; 14:e1752. [PMID: 34414690 PMCID: PMC9485970 DOI: 10.1002/wnan.1752] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 07/25/2021] [Accepted: 07/29/2021] [Indexed: 12/19/2022]
Abstract
Cancer immunotherapy, or the utilization of a patient's own immune system to treat cancer, has shifted the paradigm of cancer treatment. Despite meaningful responses being observed in multiple studies, currently available immunotherapy platforms have only proven effective to a small subset of patients. To address this, nanoparticles have been utilized as a novel carrier for immunotherapeutic drugs, achieving robust anti-tumor effects with increased adaptive and durable responses. Specifically, dendrimer nanoparticles have attracted a great deal of scientific interest due to their versatility in various therapeutic applications, resulting from their unique physicochemical properties and chemically well-defined architecture. This review offers a comprehensive overview of dendrimer-based immunotherapy technologies, including their formulations, biological functionalities, and therapeutic applications. Common formulations include: (1) modulators of cytokine secretion of immune cells (adjuvants); (2) facilitators of the recognition of tumorous antigens (vaccines); (3) stimulators of immune effectors to selectively attack cells expressing specific antigens (antibodies); and (4) inhibitors of immune-suppressive responses (immune checkpoint inhibitors). On-going works and prospects of dendrimer-based immunotherapies are also discussed. Overall, this review provides a critical overview on rapidly growing dendrimer-based immunotherapy technologies and serves as a guideline for researchers and clinicians who are interested in this field. This article is categorized under: Nanotechnology Approaches to Biology > Nanoscale Systems in Biology Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease Therapeutic Approaches and Drug Discovery > Emerging Technologies.
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Affiliation(s)
- Piper A Rawding
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin-Madison, Madison, WI 53705, USA,Wisconsin Center for NanoBioSystems, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Jiyoon Bu
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin-Madison, Madison, WI 53705, USA,Wisconsin Center for NanoBioSystems, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Jianxin Wang
- Wisconsin Center for NanoBioSystems, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - DaWon Kim
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin-Madison, Madison, WI 53705, USA,Wisconsin Center for NanoBioSystems, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Adam J Drelich
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin-Madison, Madison, WI 53705, USA,Wisconsin Center for NanoBioSystems, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Youngsoo Kim
- Wisconsin Center for NanoBioSystems, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Seungpyo Hong
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin-Madison, Madison, WI 53705, USA,Wisconsin Center for NanoBioSystems, University of Wisconsin-Madison, Madison, WI 53705, USA,Yonsei Frontier Lab and Department of Pharmacy, Yonsei University, Seoul 03722, Republic of Korea
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