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Chu LX, Wang WJ, Gu XP, Wu P, Gao C, Zhang Q, Wu J, Jiang DW, Huang JQ, Ying XW, Shen JM, Jiang Y, Luo LH, Xu JP, Ying YB, Chen HM, Fang A, Feng ZY, An SH, Li XK, Wang ZG. Spatiotemporal multi-omics: exploring molecular landscapes in aging and regenerative medicine. Mil Med Res 2024; 11:31. [PMID: 38797843 PMCID: PMC11129507 DOI: 10.1186/s40779-024-00537-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 05/07/2024] [Indexed: 05/29/2024] Open
Abstract
Aging and regeneration represent complex biological phenomena that have long captivated the scientific community. To fully comprehend these processes, it is essential to investigate molecular dynamics through a lens that encompasses both spatial and temporal dimensions. Conventional omics methodologies, such as genomics and transcriptomics, have been instrumental in identifying critical molecular facets of aging and regeneration. However, these methods are somewhat limited, constrained by their spatial resolution and their lack of capacity to dynamically represent tissue alterations. The advent of emerging spatiotemporal multi-omics approaches, encompassing transcriptomics, proteomics, metabolomics, and epigenomics, furnishes comprehensive insights into these intricate molecular dynamics. These sophisticated techniques facilitate accurate delineation of molecular patterns across an array of cells, tissues, and organs, thereby offering an in-depth understanding of the fundamental mechanisms at play. This review meticulously examines the significance of spatiotemporal multi-omics in the realms of aging and regeneration research. It underscores how these methodologies augment our comprehension of molecular dynamics, cellular interactions, and signaling pathways. Initially, the review delineates the foundational principles underpinning these methods, followed by an evaluation of their recent applications within the field. The review ultimately concludes by addressing the prevailing challenges and projecting future advancements in the field. Indubitably, spatiotemporal multi-omics are instrumental in deciphering the complexities inherent in aging and regeneration, thus charting a course toward potential therapeutic innovations.
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Affiliation(s)
- Liu-Xi Chu
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Wen-Jia Wang
- State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Xin-Pei Gu
- School of Pharmaceutical Sciences, Guangdong Provincial Key Laboratory of New Drug Screening, Southern Medical University, Guangzhou, 510515, China
- Department of Human Anatomy, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271000, Shandong, China
| | - Ping Wu
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Chen Gao
- State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Quan Zhang
- Integrative Muscle Biology Laboratory, Division of Regenerative and Rehabilitative Sciences, University of Tennessee Health Science Center, Memphis, TN, 38163, United States
| | - Jia Wu
- Key Laboratory for Laboratory Medicine, Ministry of Education, Zhejiang Provincial Key Laboratory of Medical Genetics, School of Laboratory Medicine and Life Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Da-Wei Jiang
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Jun-Qing Huang
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Hospital of Zhejiang University, Lishui, 323000, Zhejiang, China
| | - Xin-Wang Ying
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Jia-Men Shen
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Yi Jiang
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Li-Hua Luo
- School and Hospital of Stomatology, Wenzhou Medical University, Wenzhou, 324025, Zhejiang, China
| | - Jun-Peng Xu
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Yi-Bo Ying
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Hao-Man Chen
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Ao Fang
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Zun-Yong Feng
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- Departments of Diagnostic Radiology, Surgery, Chemical and Biomolecular Engineering, and Biomedical Engineering, Yong Loo Lin School of Medicine and College of Design and Engineering, National University of Singapore, Singapore, 119074, Singapore.
- Clinical Imaging Research Centre, Centre for Translational Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117599, Singapore.
- Nanomedicine Translational Research Program, NUS Center for Nanomedicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117597, Singapore.
- Institute of Molecular and Cell Biology, Agency for Science, Technology, and Research (A*STAR), Singapore, 138673, Singapore.
| | - Shu-Hong An
- Department of Human Anatomy, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271000, Shandong, China.
| | - Xiao-Kun Li
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
| | - Zhou-Guang Wang
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China.
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Hospital of Zhejiang University, Lishui, 323000, Zhejiang, China.
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2
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Benesch MGK, Cherkassky L, Nurkin SJ. Multi-Omic Analysis: A Possible Platform Toward Personalized and Adaptable Cancer Treatment. Ann Surg Oncol 2024:10.1245/s10434-024-15449-9. [PMID: 38777897 DOI: 10.1245/s10434-024-15449-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Accepted: 04/28/2024] [Indexed: 05/25/2024]
Affiliation(s)
- Matthew G K Benesch
- Department of Surgical Oncology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
| | - Leonid Cherkassky
- Department of Surgical Oncology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
| | - Steven J Nurkin
- Department of Surgical Oncology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.
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3
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Mori Y, Okimoto Y, Sakai H, Kanda Y, Ohata H, Shiokawa D, Suzuki M, Yoshida H, Ueda H, Sekizuka T, Tamura R, Yamawaki K, Ishiguro T, Mateos RN, Shiraishi Y, Yatabe Y, Hamada A, Yoshihara K, Enomoto T, Okamoto K. Targeting PDGF signaling of cancer-associated fibroblasts blocks feedback activation of HIF-1α and tumor progression of clear cell ovarian cancer. Cell Rep Med 2024; 5:101532. [PMID: 38670097 DOI: 10.1016/j.xcrm.2024.101532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 01/04/2024] [Accepted: 04/04/2024] [Indexed: 04/28/2024]
Abstract
Ovarian clear cell carcinoma (OCCC) is a gynecological cancer with a dismal prognosis; however, the mechanism underlying OCCC chemoresistance is not well understood. To explore the intracellular networks associated with the chemoresistance, we analyze surgical specimens by performing integrative analyses that combine single-cell analyses and spatial transcriptomics. We find that a chemoresistant OCCC subpopulation with elevated HIF activity localizes mainly in areas populated by cancer-associated fibroblasts (CAFs) with a myofibroblastic phenotype, which is corroborated by quantitative immunostaining. CAF-enhanced chemoresistance and HIF-1α induction are recapitulated in co-culture assays, which show that cancer-derived platelet-derived growth factor (PDGF) contributes to the chemoresistance and HIF-1α induction via PDGF receptor signaling in CAFs. Ripretinib is identified as an effective receptor tyrosine kinase inhibitor against CAF survival. In the co-culture system and xenograft tumors, ripretinib prevents CAF survival and suppresses OCCC proliferation in the presence of carboplatin, indicating that combination of conventional chemotherapy and CAF-targeted agents is effective against OCCC.
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MESH Headings
- Female
- Humans
- Cancer-Associated Fibroblasts/metabolism
- Cancer-Associated Fibroblasts/pathology
- Cancer-Associated Fibroblasts/drug effects
- Hypoxia-Inducible Factor 1, alpha Subunit/metabolism
- Hypoxia-Inducible Factor 1, alpha Subunit/genetics
- Ovarian Neoplasms/pathology
- Ovarian Neoplasms/metabolism
- Ovarian Neoplasms/drug therapy
- Ovarian Neoplasms/genetics
- Platelet-Derived Growth Factor/metabolism
- Signal Transduction/drug effects
- Animals
- Mice
- Cell Line, Tumor
- Drug Resistance, Neoplasm/drug effects
- Drug Resistance, Neoplasm/genetics
- Disease Progression
- Coculture Techniques
- Cell Proliferation/drug effects
- Mice, Nude
- Adenocarcinoma, Clear Cell/metabolism
- Adenocarcinoma, Clear Cell/pathology
- Adenocarcinoma, Clear Cell/drug therapy
- Adenocarcinoma, Clear Cell/genetics
- Feedback, Physiological/drug effects
- Xenograft Model Antitumor Assays
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Affiliation(s)
- Yutaro Mori
- Advanced Comprehensive Research Organization, Teikyo University, Tokyo 173-0003, Japan; Department of Obstetrics and Gynecology, Niigata University Graduate School of Medical and Dental Sciences, Niigata 951-8520, Japan
| | - Yoshie Okimoto
- Advanced Comprehensive Research Organization, Teikyo University, Tokyo 173-0003, Japan
| | - Hiroaki Sakai
- Advanced Comprehensive Research Organization, Teikyo University, Tokyo 173-0003, Japan
| | - Yusuke Kanda
- Advanced Comprehensive Research Organization, Teikyo University, Tokyo 173-0003, Japan
| | - Hirokazu Ohata
- Advanced Comprehensive Research Organization, Teikyo University, Tokyo 173-0003, Japan
| | - Daisuke Shiokawa
- Ehime University Hospital Translational Research Center, Shitsukawa, Toon, Ehime 791-0295, Japan
| | - Mikiko Suzuki
- Division of Molecular Pharmacology, National Cancer Center Research Institute, Tokyo 104-0045, Japan
| | - Hiroshi Yoshida
- Department of Diagnostic Pathology, National Cancer Center Hospital, Tokyo 104-0045, Japan
| | - Haruka Ueda
- Department of Obstetrics and Gynecology, Niigata University Graduate School of Medical and Dental Sciences, Niigata 951-8520, Japan
| | - Tomoyuki Sekizuka
- Department of Obstetrics and Gynecology, Niigata University Graduate School of Medical and Dental Sciences, Niigata 951-8520, Japan
| | - Ryo Tamura
- Department of Obstetrics and Gynecology, Niigata University Graduate School of Medical and Dental Sciences, Niigata 951-8520, Japan
| | - Kaoru Yamawaki
- Department of Obstetrics and Gynecology, Niigata University Graduate School of Medical and Dental Sciences, Niigata 951-8520, Japan
| | - Tatsuya Ishiguro
- Department of Obstetrics and Gynecology, Niigata University Graduate School of Medical and Dental Sciences, Niigata 951-8520, Japan
| | - Raul Nicolas Mateos
- Division of Genome Analysis Platform Development, National Cancer Center Research Institute, Tokyo 104-0045, Japan
| | - Yuichi Shiraishi
- Division of Genome Analysis Platform Development, National Cancer Center Research Institute, Tokyo 104-0045, Japan
| | - Yasushi Yatabe
- Department of Diagnostic Pathology, National Cancer Center Hospital, Tokyo 104-0045, Japan
| | - Akinobu Hamada
- Division of Molecular Pharmacology, National Cancer Center Research Institute, Tokyo 104-0045, Japan
| | - Kosuke Yoshihara
- Department of Obstetrics and Gynecology, Niigata University Graduate School of Medical and Dental Sciences, Niigata 951-8520, Japan
| | - Takayuki Enomoto
- Department of Obstetrics and Gynecology, Niigata University Graduate School of Medical and Dental Sciences, Niigata 951-8520, Japan
| | - Koji Okamoto
- Advanced Comprehensive Research Organization, Teikyo University, Tokyo 173-0003, Japan.
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Valous NA, Popp F, Zörnig I, Jäger D, Charoentong P. Graph machine learning for integrated multi-omics analysis. Br J Cancer 2024:10.1038/s41416-024-02706-7. [PMID: 38729996 DOI: 10.1038/s41416-024-02706-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 04/25/2024] [Accepted: 04/26/2024] [Indexed: 05/12/2024] Open
Abstract
Multi-omics experiments at bulk or single-cell resolution facilitate the discovery of hypothesis-generating biomarkers for predicting response to therapy, as well as aid in uncovering mechanistic insights into cellular and microenvironmental processes. Many methods for data integration have been developed for the identification of key elements that explain or predict disease risk or other biological outcomes. The heterogeneous graph representation of multi-omics data provides an advantage for discerning patterns suitable for predictive/exploratory analysis, thus permitting the modeling of complex relationships. Graph-based approaches-including graph neural networks-potentially offer a reliable methodological toolset that can provide a tangible alternative to scientists and clinicians that seek ideas and implementation strategies in the integrated analysis of their omics sets for biomedical research. Graph-based workflows continue to push the limits of the technological envelope, and this perspective provides a focused literature review of research articles in which graph machine learning is utilized for integrated multi-omics data analyses, with several examples that demonstrate the effectiveness of graph-based approaches.
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Affiliation(s)
- Nektarios A Valous
- Applied Tumor Immunity Clinical Cooperation Unit, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 460, 69120, Heidelberg, Germany.
- Center for Quantitative Analysis of Molecular and Cellular Biosystems (Bioquant), Heidelberg University, Im Neuenheimer Feld 267, 69120, Heidelberg, Germany.
| | - Ferdinand Popp
- Applied Tumor Immunity Clinical Cooperation Unit, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 460, 69120, Heidelberg, Germany
- Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
| | - Inka Zörnig
- Center for Quantitative Analysis of Molecular and Cellular Biosystems (Bioquant), Heidelberg University, Im Neuenheimer Feld 267, 69120, Heidelberg, Germany
- Department of Medical Oncology, National Center for Tumor Diseases (NCT), Heidelberg University Hospital (UKHD), Im Neuenheimer Feld 460, 69120, Heidelberg, Germany
| | - Dirk Jäger
- Applied Tumor Immunity Clinical Cooperation Unit, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 460, 69120, Heidelberg, Germany
- Center for Quantitative Analysis of Molecular and Cellular Biosystems (Bioquant), Heidelberg University, Im Neuenheimer Feld 267, 69120, Heidelberg, Germany
- Department of Medical Oncology, National Center for Tumor Diseases (NCT), Heidelberg University Hospital (UKHD), Im Neuenheimer Feld 460, 69120, Heidelberg, Germany
| | - Pornpimol Charoentong
- Center for Quantitative Analysis of Molecular and Cellular Biosystems (Bioquant), Heidelberg University, Im Neuenheimer Feld 267, 69120, Heidelberg, Germany
- Department of Medical Oncology, National Center for Tumor Diseases (NCT), Heidelberg University Hospital (UKHD), Im Neuenheimer Feld 460, 69120, Heidelberg, Germany
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5
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Chen G, Shi Y, Xiao W, Kreil DP. Editorial: Comprehensive profiling cancer immunity with multimodal approaches for clinical management. Front Immunol 2024; 15:1421576. [PMID: 38745672 PMCID: PMC11091410 DOI: 10.3389/fimmu.2024.1421576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Accepted: 04/23/2024] [Indexed: 05/16/2024] Open
Affiliation(s)
- Geng Chen
- School of Life Sciences, East China Normal University, Shanghai, China
| | - Yi Shi
- Bio-X Institutes, Shanghai Jiao Tong University, Shanghai, China
| | - Wenming Xiao
- The Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States
| | - David P. Kreil
- Department of Biotechnology, Boku University Vienna, Vienna, Austria
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6
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Okojie J, O’Neal N, Burr M, Worley P, Packer I, Anderson D, Davis J, Kearns B, Fatema K, Dixon K, Barrott JJ. DNA Quantity and Quality Comparisons between Cryopreserved and FFPE Tumors from Matched Pan-Cancer Samples. Curr Oncol 2024; 31:2441-2452. [PMID: 38785464 PMCID: PMC11119490 DOI: 10.3390/curroncol31050183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 04/25/2024] [Accepted: 04/27/2024] [Indexed: 05/25/2024] Open
Abstract
Personalized cancer care requires molecular characterization of neoplasms. While the research community accepts frozen tissues as the gold standard analyte for molecular assays, the source of tissue for testing in clinical cancer care comes almost universally from formalin-fixed, paraffin-embedded tissue (FFPE). As newer technologies emerge for DNA characterization that requires higher molecular weight DNA, it was necessary to compare the quality of DNA in terms of DNA length between FFPE and cryopreserved samples. We hypothesized that cryopreserved samples would yield higher quantity and superior quality DNA compared to FFPE samples. We analyzed DNA metrics by performing a head-to-head comparison between FFPE and cryopreserved samples from 38 human tumors representing various cancer types. DNA quantity and purity were measured by UV spectrophotometry, and DNA from cryopreserved tissue demonstrated a 4.2-fold increase in DNA yield per mg of tissue (p-value < 0.001). DNA quality was measured on a fragment microelectrophoresis analyzer, and again, DNA from cryopreserved tissue demonstrated a 223% increase in the DNA quality number and a 9-fold increase in DNA fragments > 40,000 bp (p-value < 0.0001). DNA from the cryopreserved tissues was superior to the DNA from FFPE samples in terms of DNA yield and quality.
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Affiliation(s)
- Jeffrey Okojie
- Department of Cell Biology & Physiology, Brigham Young University, Provo, UT 84602, USA; (J.O.); (M.B.); (P.W.); (I.P.); (D.A.); (J.D.); (B.K.)
- Department of Biomedical and Pharmaceutical Sciences, Idaho State University, Pocatello, ID 83209, USA; (N.O.); (K.F.)
| | - Nikole O’Neal
- Department of Biomedical and Pharmaceutical Sciences, Idaho State University, Pocatello, ID 83209, USA; (N.O.); (K.F.)
| | - Mackenzie Burr
- Department of Cell Biology & Physiology, Brigham Young University, Provo, UT 84602, USA; (J.O.); (M.B.); (P.W.); (I.P.); (D.A.); (J.D.); (B.K.)
| | - Peyton Worley
- Department of Cell Biology & Physiology, Brigham Young University, Provo, UT 84602, USA; (J.O.); (M.B.); (P.W.); (I.P.); (D.A.); (J.D.); (B.K.)
| | - Isaac Packer
- Department of Cell Biology & Physiology, Brigham Young University, Provo, UT 84602, USA; (J.O.); (M.B.); (P.W.); (I.P.); (D.A.); (J.D.); (B.K.)
| | - DeLaney Anderson
- Department of Cell Biology & Physiology, Brigham Young University, Provo, UT 84602, USA; (J.O.); (M.B.); (P.W.); (I.P.); (D.A.); (J.D.); (B.K.)
| | - Jack Davis
- Department of Cell Biology & Physiology, Brigham Young University, Provo, UT 84602, USA; (J.O.); (M.B.); (P.W.); (I.P.); (D.A.); (J.D.); (B.K.)
| | - Bridger Kearns
- Department of Cell Biology & Physiology, Brigham Young University, Provo, UT 84602, USA; (J.O.); (M.B.); (P.W.); (I.P.); (D.A.); (J.D.); (B.K.)
| | - Kaniz Fatema
- Department of Biomedical and Pharmaceutical Sciences, Idaho State University, Pocatello, ID 83209, USA; (N.O.); (K.F.)
| | - Ken Dixon
- Specicare, 690 Medical Park Ln, Gainesville, GA 30501, USA
| | - Jared J. Barrott
- Department of Cell Biology & Physiology, Brigham Young University, Provo, UT 84602, USA; (J.O.); (M.B.); (P.W.); (I.P.); (D.A.); (J.D.); (B.K.)
- Department of Biomedical and Pharmaceutical Sciences, Idaho State University, Pocatello, ID 83209, USA; (N.O.); (K.F.)
- Specicare, 690 Medical Park Ln, Gainesville, GA 30501, USA
- Simmons Center for Cancer Research, Brigham Young University, Provo, UT 84602, USA
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7
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Jiang M, Li Q, Xu B. Spotlight on ideal target antigens and resistance in antibody-drug conjugates: Strategies for competitive advancement. Drug Resist Updat 2024; 75:101086. [PMID: 38677200 DOI: 10.1016/j.drup.2024.101086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 04/09/2024] [Accepted: 04/18/2024] [Indexed: 04/29/2024]
Abstract
Antibody-drug conjugates (ADCs) represent a novel and promising approach in targeted therapy, uniting the specificity of antibodies that recognize specific antigens with payloads, all connected by the stable linker. These conjugates combine the best targeted and cytotoxic therapies, offering the killing effect of precisely targeting specific antigens and the potent cell-killing power of small molecule drugs. The targeted approach minimizes the off-target toxicities associated with the payloads and broadens the therapeutic window, enhancing the efficacy and safety profile of cancer treatments. Within precision oncology, ADCs have garnered significant attention as a cutting-edge research area and have been approved to treat a range of malignant tumors. Correspondingly, the issue of resistance to ADCs has gradually come to the fore. Any dysfunction in the steps leading to the ADCs' action within tumor cells can lead to the development of resistance. A deeper understanding of resistance mechanisms may be crucial for developing novel ADCs and exploring combination therapy strategies, which could further enhance the clinical efficacy of ADCs in cancer treatment. This review outlines the brief historical development and mechanism of ADCs and discusses the impact of their key components on the activity of ADCs. Furthermore, it provides a detailed account of the application of ADCs with various target antigens in cancer therapy, the categorization of potential resistance mechanisms, and the current state of combination therapies. Looking forward, breakthroughs in overcoming technical barriers, selecting differentiated target antigens, and enhancing resistance management and combination therapy strategies will broaden the therapeutic indications for ADCs. These progresses are anticipated to advance cancer treatment and yield benefits for patients.
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Affiliation(s)
- Mingxia Jiang
- Department of Medical Oncology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Qiao Li
- Department of Medical Oncology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| | - Binghe Xu
- Department of Medical Oncology, State Key Laboratory of Mocelular Oncology, National Cancer Center, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
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Acharya D, Mukhopadhyay A. A comprehensive review of machine learning techniques for multi-omics data integration: challenges and applications in precision oncology. Brief Funct Genomics 2024:elae013. [PMID: 38600757 DOI: 10.1093/bfgp/elae013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 03/12/2024] [Accepted: 03/22/2024] [Indexed: 04/12/2024] Open
Abstract
Multi-omics data play a crucial role in precision medicine, mainly to understand the diverse biological interaction between different omics. Machine learning approaches have been extensively employed in this context over the years. This review aims to comprehensively summarize and categorize these advancements, focusing on the integration of multi-omics data, which includes genomics, transcriptomics, proteomics and metabolomics, alongside clinical data. We discuss various machine learning techniques and computational methodologies used for integrating distinct omics datasets and provide valuable insights into their application. The review emphasizes both the challenges and opportunities present in multi-omics data integration, precision medicine and patient stratification, offering practical recommendations for method selection in various scenarios. Recent advances in deep learning and network-based approaches are also explored, highlighting their potential to harmonize diverse biological information layers. Additionally, we present a roadmap for the integration of multi-omics data in precision oncology, outlining the advantages, challenges and implementation difficulties. Hence this review offers a thorough overview of current literature, providing researchers with insights into machine learning techniques for patient stratification, particularly in precision oncology. Contact: anirban@klyuniv.ac.in.
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Affiliation(s)
- Debabrata Acharya
- Department of Computer Science & Engineering, University of Kalyani, Kalyani-741235, West Bengal, India
| | - Anirban Mukhopadhyay
- Department of Computer Science & Engineering, University of Kalyani, Kalyani-741235, West Bengal, India
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Ahn S, Lee HS. Applicability of Spatial Technology in Cancer Research. Cancer Res Treat 2024; 56:343-356. [PMID: 38291743 PMCID: PMC11016655 DOI: 10.4143/crt.2023.1302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Accepted: 01/29/2024] [Indexed: 02/01/2024] Open
Abstract
This review explores spatial mapping technologies in cancer research, highlighting their crucial role in understanding the complexities of the tumor microenvironment (TME). The TME, which is an intricate ecosystem of diverse cell types, has a significant impact on tumor dynamics and treatment outcomes. This review closely examines cutting-edge spatial mapping technologies, categorizing them into capture-, imaging-, and antibody-based approaches. Each technology was scrutinized for its advantages and disadvantages, factoring in aspects such as spatial profiling area, multiplexing capabilities, and resolution. Additionally, we draw attention to the nuanced choices researchers face, with capture-based methods lending themselves to hypothesis generation, and imaging/antibody-based methods that fit neatly into hypothesis testing. Looking ahead, we anticipate a scenario in which multi-omics data are seamlessly integrated, artificial intelligence enhances data analysis, and spatiotemporal profiling opens up new dimensions.
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Affiliation(s)
- Sangjeong Ahn
- Department of Pathology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
- Artificial Intelligence Center, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
- Department of Medical Informatics, Korea University College of Medicine, Seoul, Korea
| | - Hye Seung Lee
- Department of Pathology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
- Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
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10
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Zhang Z, Lu T, Zhang Z, Liu Z, Qian R, Qi R, Zhou F, Li M. Unraveling the immune landscape and therapeutic biomarker PMEPA1 for oxaliplatin resistance in colorectal cancer: A comprehensive approach. Biochem Pharmacol 2024; 222:116117. [PMID: 38461903 DOI: 10.1016/j.bcp.2024.116117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 02/20/2024] [Accepted: 03/06/2024] [Indexed: 03/12/2024]
Abstract
Oxaliplatin (OXA) is a platinum-based chemotherapeutic agent with promising applications in the treatment of various malignancies, particularly colorectal cancer (CRC). However, the management of OXA resistance remains an ongoing obstacle in CRC therapy. This study aims to comprehensively investigate the immune landscape, targeted therapeutic biomarkers, and mechanisms that influence OXA resistance in CRC. Our results demonstrated that our OXA- resistant CRC prognostic model not only provides risk assessment for patients but also reflects the immune landscape of patients. Additionally, we identified prostate transmembrane protein, androgen-induced1 (PMEPA1) as a promising molecular targeted therapeutic biomarker for patients with OXA-resistant CRC. The mechanism of PMEPA1 may involve cell adhesion, pathways in cancer, and the TGF-β signaling pathway. Furthermore, analysis of CRC clinical samples indicated that patients resistant to OXA exhibited elevated serum levels of TGF-β1, increased expression of PMEPA1 in tumors, a lower proportion of CD8+ T cell positivity, and a higher proportion of M0 macrophage positivity, in comparison to OXA-sensitive individuals. Cellular experiments indicated that selective silencing of PMEPA1, alone or in combination with OXA, inhibited proliferation and metastasis in OXA-resistant CRC cells, HCT116R. Animal experiments further confirmed that PMEPA1 silencing suppressed subcutaneous graft tumor growth and liver metastasis in mice bearing HCT116R and synergistically enhanced the efficacy of OXA. These data highlight the potential of leveraging the therapeutic biomarker PMEPA1, CD8+ T cells, and M0 macrophages as innovative targets for effectively addressing the challenges associated with OXA resistance. Our findings hold promising implications for further clinical advancements in this field.
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Affiliation(s)
- Zhengguang Zhang
- School of Medicine, Nanjing University of Chinese Medicine, Jiangsu, Nanjing, China.
| | - Tianming Lu
- School of Medicine, Nanjing University of Chinese Medicine, Jiangsu, Nanjing, China
| | - Zhe Zhang
- Department of Oncology, Nanjing Hospital of Chinese Medicine Affiliated to Nanjing University of Chinese Medicine, Jiangsu, Nanjing, China
| | - Zixian Liu
- School of Medicine, Nanjing University of Chinese Medicine, Jiangsu, Nanjing, China
| | - Ruoning Qian
- School of Medicine, Nanjing University of Chinese Medicine, Jiangsu, Nanjing, China
| | - Ruogu Qi
- School of Medicine, Nanjing University of Chinese Medicine, Jiangsu, Nanjing, China.
| | - Fuqiong Zhou
- Central Laboratory, Nanjing Hospital of Chinese Medicine Affiliated to Nanjing University of Chinese Medicine, Jiangsu, Nanjing, China.
| | - Min Li
- Department of Oncology, Nanjing Hospital of Chinese Medicine Affiliated to Nanjing University of Chinese Medicine, Jiangsu, Nanjing, China.
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11
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Cai ZM, Li ZZ, Zhong NN, Cao LM, Xiao Y, Li JQ, Huo FY, Liu B, Xu C, Zhao Y, Rao L, Bu LL. Revolutionizing lymph node metastasis imaging: the role of drug delivery systems and future perspectives. J Nanobiotechnology 2024; 22:135. [PMID: 38553735 PMCID: PMC10979629 DOI: 10.1186/s12951-024-02408-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 03/18/2024] [Indexed: 04/02/2024] Open
Abstract
The deployment of imaging examinations has evolved into a robust approach for the diagnosis of lymph node metastasis (LNM). The advancement of technology, coupled with the introduction of innovative imaging drugs, has led to the incorporation of an increasingly diverse array of imaging techniques into clinical practice. Nonetheless, conventional methods of administering imaging agents persist in presenting certain drawbacks and side effects. The employment of controlled drug delivery systems (DDSs) as a conduit for transporting imaging agents offers a promising solution to ameliorate these limitations intrinsic to metastatic lymph node (LN) imaging, thereby augmenting diagnostic precision. Within the scope of this review, we elucidate the historical context of LN imaging and encapsulate the frequently employed DDSs in conjunction with a variety of imaging techniques, specifically for metastatic LN imaging. Moreover, we engage in a discourse on the conceptualization and practical application of fusing diagnosis and treatment by employing DDSs. Finally, we venture into prospective applications of DDSs in the realm of LNM imaging and share our perspective on the potential trajectory of DDS development.
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Affiliation(s)
- Ze-Min Cai
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, 430072, China
| | - Zi-Zhan Li
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, 430072, China
| | - Nian-Nian Zhong
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, 430072, China
| | - Lei-Ming Cao
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, 430072, China
| | - Yao Xiao
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, 430072, China
| | - Jia-Qi Li
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, 430072, China
| | - Fang-Yi Huo
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, 430072, China
| | - Bing Liu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, 430072, China
- Department of Oral & Maxillofacial Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan, 430079, Hubei, China
| | - Chun Xu
- School of Dentistry, The University of Queensland, Brisbane, QLD, 4066, Australia
| | - Yi Zhao
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, 430072, China
- Department of Prosthodontics, School and Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Lang Rao
- Institute of Biomedical Health Technology and Engineering, Shenzhen Bay Laboratory, Shenzhen, 518132, China.
| | - Lin-Lin Bu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, 430072, China.
- Department of Oral & Maxillofacial Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan, 430079, Hubei, China.
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12
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Verkerk K, Voest EE. Generating and using real-world data: A worthwhile uphill battle. Cell 2024; 187:1636-1650. [PMID: 38552611 DOI: 10.1016/j.cell.2024.02.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 01/04/2024] [Accepted: 02/09/2024] [Indexed: 04/02/2024]
Abstract
The precision oncology paradigm challenges the feasibility and data generalizability of traditional clinical trials. Consequently, an unmet need exists for practical approaches to test many subgroups, evaluate real-world drug value, and gather comprehensive, accessible datasets to validate novel biomarkers. Real-world data (RWD) are increasingly recognized to have the potential to fill this gap in research methodology. Established applications of RWD include informing disease epidemiology, pharmacovigilance, and healthcare quality assessment. Currently, concerns regarding RWD quality and comprehensiveness, privacy, and biases hamper their broader application. Nonetheless, RWD may play a pivotal role in supplementing clinical trials, enabling conditional reimbursement and accelerated drug access, and innovating trial conduct. Moreover, purpose-built RWD repositories may support the extension or refinement of drug indications and facilitate the discovery and validation of new biomarkers. This perspective explores the potential of leveraging RWD to advance oncology, highlights its benefits and challenges, and suggests a path forward in this evolving field.
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Affiliation(s)
- K Verkerk
- Department of Molecular Oncology & Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands; Oncode Institute, Utrecht, the Netherlands
| | - E E Voest
- Department of Molecular Oncology & Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands; Oncode Institute, Utrecht, the Netherlands; Netherlands Cancer Institute, Plesmanlaan 121, Amsterdam 1066 CX, the Netherlands.
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13
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Yang Z, Zhang Y, Zhuo L, Sun K, Meng F, Zhou M, Sun J. Prediction of prognosis and treatment response in ovarian cancer patients from histopathology images using graph deep learning: a multicenter retrospective study. Eur J Cancer 2024; 199:113532. [PMID: 38241820 DOI: 10.1016/j.ejca.2024.113532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 12/21/2023] [Accepted: 01/08/2024] [Indexed: 01/21/2024]
Abstract
BACKGROUND Ovarian cancer (OV) is a prevalent and deadly disease with high mortality rates. The development of accurate prognostic tools and personalized therapeutic strategies is crucial for improving patient outcomes. METHODS A graph-based deep learning model, the Ovarian Cancer Digital Pathology Index (OCDPI), was introduced to predict prognosis and response to adjuvant therapy using hematoxylin and eosin (H&E)-stained whole-slide images (WSIs). The OCDPI was developed using formalin-fixed, paraffin-embedded (FFPE) WSIs from the TCGA-OV cohort, and was externally validated in two independent cohorts from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) and Harbin Medical University Cancer Hospital (HMUCH). RESULTS The OCDPI showed prognostic ability for overall survival prediction in the PLCO (HR, 1.916; 95% CI, 1.380-2.660; log-rank test, P < 0.001) and HMUCH (HR, 2.796; 95% CI, 1.404-5.568; log-rank test, P = 0.0022) cohorts. Patients with low OCDPI experienced better survival benefits and lower recurrence rates following adjuvant therapy compared to those with high OCDPI. Multivariable analyses, adjusting for clinicopathological factors, consistently identified OCDPI as an independent prognostic factor across all cohorts (all P < 0.05). Furthermore, OCDPI performed well in patients with low-grade tumors or fresh-frozen slides, and could differentiate between HRD-deficient or HRD-intact patients with and without sensitivity to adjuvant therapy. CONCLUSION The results from this multicenter cohort study indicate that the OCDPI may serve as a valuable and labor-saving tool to improve prognostic and predictive clinical decision-making in patients with OV.
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Affiliation(s)
- Zijian Yang
- School of Biomedical Engineering, Wenzhou Medical University, Wenzhou 325027, PR China
| | - Yibo Zhang
- School of Biomedical Engineering, Wenzhou Medical University, Wenzhou 325027, PR China
| | - Lili Zhuo
- School of Biomedical Engineering, Wenzhou Medical University, Wenzhou 325027, PR China
| | - Kaidi Sun
- Department of Gynecology, Harbin Medical University Cancer Hospital, Harbin 150081, PR China
| | - Fanling Meng
- Department of Gynecology, Harbin Medical University Cancer Hospital, Harbin 150081, PR China.
| | - Meng Zhou
- School of Biomedical Engineering, Wenzhou Medical University, Wenzhou 325027, PR China.
| | - Jie Sun
- School of Biomedical Engineering, Wenzhou Medical University, Wenzhou 325027, PR China.
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14
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López J, Hogan M, Sutton B, Church S, Angulo J, Nunes-Xavier C. Distinct spatial landscapes in clear-cell renal cell carcinoma as revealed by whole transcriptome analysis. IMMUNO-ONCOLOGY TECHNOLOGY 2024; 21:100690. [PMID: 38292905 PMCID: PMC10825646 DOI: 10.1016/j.iotech.2023.100690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Background Clear-cell renal cell carcinoma (ccRCC) is the most common and aggressive form of renal cancer and a paradigm of inter- and intratumor heterogeneity. We carried out an exploratory digital spatial profiling of the tumor interior and periphery of two ccRCC tumor specimens and mapped spatially the molecular and cellular composition of their tumor microenvironment and ecosystem. Materials and methods Digital spatial profiling of the whole transcriptome of 19 regions of interest (ROIs) was carried out from two selected highly immunogenic stage pT3a/grade 3 (G3) and stage pT3a/grade 4 (G4) ccRCC. A total of 9-10 ROIs were selected from distinct areas from each tumor, including tumor interior and tumor periphery, and differences in gene expression were analyzed by RNA sequencing, pathway enrichment analysis, and cell deconvolution. Results The distinct areas from the two locally advanced tumors displayed unique gene expression spatial patterns defining distinct biological pathways. Dimensional reduction analysis showed that the G3 ccRCC, compared to the G4 ccRCC, correlated with more variability between regions from the tumor interior and tumor periphery. Cell deconvolution analysis illustrated higher abundance of immune cells, including macrophages, myeloid dendritic cells, and CD4 T cells, and lower abundance of regulatory T cells in the tumor periphery compared to the tumor interior. Conclusions Transcriptome spatial profiling revealed high inter- and intratumor heterogeneity in the analyzed tumors and provided information with potential clinical utility. This included the finding of less intratumor heterogeneity and more tumor-infiltrated T cells in the ccRCC tumor specimen with a higher grade.
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Affiliation(s)
- J.I. López
- Biobizkaia Health Research Institute, Barakaldo, Spain
| | | | - B. Sutton
- NanoString Technologies, Seattle, USA
| | | | - J.C. Angulo
- Service of Urology, University Hospital of Getafe, Getafe, Madrid
- Clinical Department, Faculty of Biomedical Sciences, European University of Madrid, Madrid, Spain
| | - C.E. Nunes-Xavier
- Biobizkaia Health Research Institute, Barakaldo, Spain
- Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, Oslo, Norway
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15
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Pacini C, Duncan E, Gonçalves E, Gilbert J, Bhosle S, Horswell S, Karakoc E, Lightfoot H, Curry E, Muyas F, Bouaboula M, Pedamallu CS, Cortes-Ciriano I, Behan FM, Zalmas LP, Barthorpe A, Francies H, Rowley S, Pollard J, Beltrao P, Parts L, Iorio F, Garnett MJ. A comprehensive clinically informed map of dependencies in cancer cells and framework for target prioritization. Cancer Cell 2024; 42:301-316.e9. [PMID: 38215750 DOI: 10.1016/j.ccell.2023.12.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 10/20/2023] [Accepted: 12/15/2023] [Indexed: 01/14/2024]
Abstract
Genetic screens in cancer cell lines inform gene function and drug discovery. More comprehensive screen datasets with multi-omics data are needed to enhance opportunities to functionally map genetic vulnerabilities. Here, we construct a second-generation map of cancer dependencies by annotating 930 cancer cell lines with multi-omic data and analyze relationships between molecular markers and cancer dependencies derived from CRISPR-Cas9 screens. We identify dependency-associated gene expression markers beyond driver genes, and observe many gene addiction relationships driven by gain of function rather than synthetic lethal effects. By combining clinically informed dependency-marker associations with protein-protein interaction networks, we identify 370 anti-cancer priority targets for 27 cancer types, many of which have network-based evidence of a functional link with a marker in a cancer type. Mapping these targets to sequenced tumor cohorts identifies tractable targets in different cancer types. This target prioritization map enhances understanding of gene dependencies and identifies candidate anti-cancer targets for drug development.
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Affiliation(s)
- Clare Pacini
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK; Open Targets, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Emma Duncan
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK; Open Targets, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Emanuel Gonçalves
- Instituto Superior Técnico (IST), Universidade de Lisboa, 1049-001 Lisboa, Portugal; INESC-ID, 1000-029 Lisboa, Portugal
| | - James Gilbert
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Shriram Bhosle
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Stuart Horswell
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK; Open Targets, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Emre Karakoc
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK; Open Targets, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Howard Lightfoot
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Ed Curry
- Genome Biology, Genomic Sciences, GSK, Stevenage, UK
| | - Francesc Muyas
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge CB10 1SD, UK
| | | | | | - Isidro Cortes-Ciriano
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge CB10 1SD, UK
| | - Fiona M Behan
- Genome Biology, Genomic Sciences, GSK, Stevenage, UK
| | - Lykourgos-Panagiotis Zalmas
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK; Open Targets, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Andrew Barthorpe
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Hayley Francies
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK; Genome Biology, Genomic Sciences, GSK, Stevenage, UK
| | - Steve Rowley
- Sanofi Research and Development, Cambridge, MA, USA
| | - Jack Pollard
- Sanofi Research and Development, Cambridge, MA, USA
| | - Pedro Beltrao
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK; European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge CB10 1SD, UK
| | - Leopold Parts
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK; Open Targets, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Francesco Iorio
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK; Open Targets, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK; Human Technopole, V.le Rita Levi-Montalcini, 1, 20157 Milano, Italy.
| | - Mathew J Garnett
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK; Open Targets, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK.
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16
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Sacdalan DB, Ul Haq S, Lok BH. Plasma Cell-Free Tumor Methylome as a Biomarker in Solid Tumors: Biology and Applications. Curr Oncol 2024; 31:482-500. [PMID: 38248118 PMCID: PMC10814449 DOI: 10.3390/curroncol31010033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 12/30/2023] [Accepted: 01/10/2024] [Indexed: 01/23/2024] Open
Abstract
DNA methylation is a fundamental mechanism of epigenetic control in cells and its dysregulation is strongly implicated in cancer development. Cancers possess an extensively hypomethylated genome with focal regions of hypermethylation at CPG islands. Due to the highly conserved nature of cancer-specific methylation, its detection in cell-free DNA in plasma using liquid biopsies constitutes an area of interest in biomarker research. The advent of next-generation sequencing and newer computational technologies have allowed for the development of diagnostic and prognostic biomarkers that utilize methylation profiling to diagnose disease and stratify risk. Methylome-based predictive biomarkers can determine the response to anti-cancer therapy. An additional emerging application of these biomarkers is in minimal residual disease monitoring. Several key challenges need to be addressed before cfDNA-based methylation biomarkers become fully integrated into practice. The first relates to the biology and stability of cfDNA. The second concerns the clinical validity and generalizability of methylation-based assays, many of which are cancer type-specific. The third involves their practicability, which is a stumbling block for translating technologies from bench to clinic. Future work on developing pan-cancer assays with their respective validities confirmed using well-designed, prospective clinical trials is crucial in pushing for the greater use of these tools in oncology.
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Affiliation(s)
- Danielle Benedict Sacdalan
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, 1 King’s College Circle, Medical Sciences Building, Room 2374, Toronto, ON M5S 1A8, Canada
- Radiation Medicine Program, Princess Margaret Cancer Centre, 610 University Ave, Toronto, ON M5G 2C4, Canada
| | - Sami Ul Haq
- Radiation Medicine Program, Princess Margaret Cancer Centre, 610 University Ave, Toronto, ON M5G 2C4, Canada
- Schulich School of Medicine & Dentistry, Western University, 1151 Richmond St, London, ON N6A 5C1, Canada
| | - Benjamin H. Lok
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, 1 King’s College Circle, Medical Sciences Building, Room 2374, Toronto, ON M5S 1A8, Canada
- Radiation Medicine Program, Princess Margaret Cancer Centre, 610 University Ave, Toronto, ON M5G 2C4, Canada
- Department of Medical Biophysics, Temerty Faculty of Medicine, University of Toronto, 101 College Street, Room 15-701, Toronto, ON M5G 1L7, Canada
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Suehnholz SP, Nissan MH, Zhang H, Kundra R, Nandakumar S, Lu C, Carrero S, Dhaneshwar A, Fernandez N, Xu BW, Arcila ME, Zehir A, Syed A, Brannon AR, Rudolph JE, Paraiso E, Sabbatini PJ, Levine RL, Dogan A, Gao J, Ladanyi M, Drilon A, Berger MF, Solit DB, Schultz N, Chakravarty D. Quantifying the Expanding Landscape of Clinical Actionability for Patients with Cancer. Cancer Discov 2024; 14:49-65. [PMID: 37849038 PMCID: PMC10784742 DOI: 10.1158/2159-8290.cd-23-0467] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 08/18/2023] [Accepted: 10/02/2023] [Indexed: 10/19/2023]
Abstract
There is a continuing debate about the proportion of cancer patients that benefit from precision oncology, attributable in part to conflicting views as to which molecular alterations are clinically actionable. To quantify the expansion of clinical actionability since 2017, we annotated 47,271 solid tumors sequenced with the MSK-IMPACT clinical assay using two temporally distinct versions of the OncoKB knowledge base deployed 5 years apart. Between 2017 and 2022, we observed an increase from 8.9% to 31.6% in the fraction of tumors harboring a standard care (level 1 or 2) predictive biomarker of therapy response and an almost halving of tumors carrying nonactionable drivers (44.2% to 22.8%). In tumors with limited or no clinical actionability, TP53 (43.2%), KRAS (19.2%), and CDKN2A (12.2%) were the most frequently altered genes. SIGNIFICANCE Although clear progress has been made in expanding the availability of precision oncology-based treatment paradigms, our results suggest a continued unmet need for innovative therapeutic strategies, particularly for cancers with currently undruggable oncogenic drivers. See related commentary by Horak and Fröhling, p. 18. This article is featured in Selected Articles from This Issue, p. 5.
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Affiliation(s)
- Sarah P. Suehnholz
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Moriah H. Nissan
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Hongxin Zhang
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Ritika Kundra
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Subhiksha Nandakumar
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Calvin Lu
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Stephanie Carrero
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Amanda Dhaneshwar
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Nicole Fernandez
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Benjamin W. Xu
- Department of Computer Science, Yale University, New Haven, Connecticut
| | - Maria E. Arcila
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Ahmet Zehir
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Aijazuddin Syed
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - A. Rose Brannon
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Julia E. Rudolph
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Eder Paraiso
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Paul J. Sabbatini
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Ross L. Levine
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Ahmet Dogan
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Jianjiong Gao
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Marc Ladanyi
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Alexander Drilon
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Michael F. Berger
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York
| | - David B. Solit
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Nikolaus Schultz
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Debyani Chakravarty
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
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18
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Monzavi-Karbassi B, Kelly T, Post SR. The Tumor Microenvironment and Immune Response in Breast Cancer. Int J Mol Sci 2024; 25:914. [PMID: 38255987 PMCID: PMC10815817 DOI: 10.3390/ijms25020914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 12/27/2023] [Indexed: 01/24/2024] Open
Abstract
The complex interactions between cancer cells and their surrounding microenvironment are fundamental in determining tumor progression, response to therapy, and, ultimately, patient prognosis [...].
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Affiliation(s)
| | | | - Steven R. Post
- Department of Pathology, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA; (B.M.-K.); (T.K.)
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19
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Abdoul-Latif FM, Ainane A, Houmed Aboubaker I, Mohamed J, Ainane T. An Overview of Cancer in Djibouti: Current Status, Therapeutic Approaches, and Promising Endeavors in Local Essential Oil Treatment. Pharmaceuticals (Basel) 2023; 16:1617. [PMID: 38004482 PMCID: PMC10674319 DOI: 10.3390/ph16111617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 10/24/2023] [Accepted: 10/31/2023] [Indexed: 11/26/2023] Open
Abstract
Djibouti, a developing economy, grapples with significant socioeconomic obstacles and the prevalence of infectious pathologies, including certain forms of neoplasms. These challenges are exacerbated by limited access to affordable medical technologies for diagnosis, coupled with a lack of preventive interventions, particularly in disadvantaged areas. The attention devoted to local phytotherapeutic treatments underscores the uniqueness of Djibouti's flora, resulting from its distinctive geographical position. International focus specifically centers on harnessing this potential as a valuable resource, emphasizing the phytoconstituents used to counter pathologies, notably carcinomas. This comprehensive overview covers a broad spectrum, commencing with an examination of the current state of knowledge, namely an in-depth investigation of oncological risk factors. Essential elements of control are subsequently studied, highlighting the fundamental prerequisites for effective management. The significance of dietary habits in cancer prevention and support is explored in depth, while traditional methods are examined, highlighting the cultural significance of indigenous essential oil therapies and encouraging further research based on the promising results.
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Affiliation(s)
- Fatouma Mohamed Abdoul-Latif
- Medicinal Research Institute, Center for Studies and Research of Djibouti, IRM-CERD, Route de l’Aéroport, Haramous, Djibouti P.O. Box 486, Djibouti;
| | - Ayoub Ainane
- Superior School of Technology of Khenifra (EST-Khenifra), University of Sultan Moulay Slimane, P.O. Box 170, Khenifra 54000, Morocco; (A.A.); (T.A.)
| | | | - Jalludin Mohamed
- Medicinal Research Institute, Center for Studies and Research of Djibouti, IRM-CERD, Route de l’Aéroport, Haramous, Djibouti P.O. Box 486, Djibouti;
| | - Tarik Ainane
- Superior School of Technology of Khenifra (EST-Khenifra), University of Sultan Moulay Slimane, P.O. Box 170, Khenifra 54000, Morocco; (A.A.); (T.A.)
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20
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Fan M, Jin C, Li D, Deng Y, Yao L, Chen Y, Ma YL, Wang T. Multi-level advances in databases related to systems pharmacology in traditional Chinese medicine: a 60-year review. Front Pharmacol 2023; 14:1289901. [PMID: 38035021 PMCID: PMC10682728 DOI: 10.3389/fphar.2023.1289901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 11/03/2023] [Indexed: 12/02/2023] Open
Abstract
The therapeutic effects of traditional Chinese medicine (TCM) involve intricate interactions among multiple components and targets. Currently, computational approaches play a pivotal role in simulating various pharmacological processes of TCM. The application of network analysis in TCM research has provided an effective means to explain the pharmacological mechanisms underlying the actions of herbs or formulas through the lens of biological network analysis. Along with the advances of network analysis, computational science has coalesced around the core chain of TCM research: formula-herb-component-target-phenotype-ZHENG, facilitating the accumulation and organization of the extensive TCM-related data and the establishment of relevant databases. Nonetheless, recent years have witnessed a tendency toward homogeneity in the development and application of these databases. Advancements in computational technologies, including deep learning and foundation model, have propelled the exploration and modeling of intricate systems into a new phase, potentially heralding a new era. This review aims to delves into the progress made in databases related to six key entities: formula, herb, component, target, phenotype, and ZHENG. Systematically discussions on the commonalities and disparities among various database types were presented. In addition, the review raised the issue of research bottleneck in TCM computational pharmacology and envisions the forthcoming directions of computational research within the realm of TCM.
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Affiliation(s)
- Mengyue Fan
- Innovation Research Institute of Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Ching Jin
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL, United States
| | - Daping Li
- Innovation Research Institute of Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Yingshan Deng
- College of Acupuncture and Massage, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Lin Yao
- Innovation Research Institute of Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Yongjun Chen
- Innovation Research Institute of Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Yu-Ling Ma
- Oxford Chinese Medicine Research Centre, University of Oxford, Oxford, United Kingdom
| | - Taiyi Wang
- Innovation Research Institute of Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
- Oxford Chinese Medicine Research Centre, University of Oxford, Oxford, United Kingdom
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21
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Schweizer L, Krishnan R, Shimizu A, Metousis A, Kenny H, Mendoza R, Nordmann TM, Rauch S, Kelliher L, Heide J, Rosenberger FA, Bilecz A, Borrego SN, Strauss MT, Thielert M, Rodriguez E, Müller-Reif JB, Chen M, Yamada SD, Mund A, Lastra RR, Mann M, Lengyel E. Spatial proteo-transcriptomic profiling reveals the molecular landscape of borderline ovarian tumors and their invasive progression. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.13.23298409. [PMID: 38014221 PMCID: PMC10680885 DOI: 10.1101/2023.11.13.23298409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Serous borderline tumors (SBT) are epithelial neoplastic lesions of the ovaries that commonly have a good prognosis. In 10-15% of cases, however, SBT will recur as low-grade serous cancer (LGSC), which is deeply invasive and responds poorly to current standard chemotherapy1,2,3. While genetic alterations suggest a common origin, the transition from SBT to LGSC remains poorly understood4. Here, we integrate spatial proteomics5 with spatial transcriptomics to elucidate the evolution from SBT to LGSC and its corresponding metastasis at the molecular level in both the stroma and the tumor. We show that the transition of SBT to LGSC occurs in the epithelial compartment through an intermediary stage with micropapillary features (SBT-MP), which involves a gradual increase in MAPK signaling. A distinct subset of proteins and transcripts was associated with the transition to invasive tumor growth, including the neuronal splicing factor NOVA2, which was limited to expression in LGSC and its corresponding metastasis. An integrative pathway analysis exposed aberrant molecular signaling of tumor cells supported by alterations in angiogenesis and inflammation in the tumor microenvironment. Integration of spatial transcriptomics and proteomics followed by knockdown of the most altered genes or pharmaceutical inhibition of the most relevant targets confirmed their functional significance in regulating key features of invasiveness. Combining cell-type resolved spatial proteomics and transcriptomics allowed us to elucidate the sequence of tumorigenesis from SBT to LGSC. The approach presented here is a blueprint to systematically elucidate mechanisms of tumorigenesis and find novel treatment strategies.
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Affiliation(s)
- Lisa Schweizer
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
- Department of Obstetrics and Gynecology/Section of Gynecologic Oncology, University of Chicago, Chicago, IL, USA
| | - Rahul Krishnan
- Department of Obstetrics and Gynecology/Section of Gynecologic Oncology, University of Chicago, Chicago, IL, USA
| | - Aasa Shimizu
- Department of Obstetrics and Gynecology/Section of Gynecologic Oncology, University of Chicago, Chicago, IL, USA
| | - Andreas Metousis
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Hilary Kenny
- Department of Obstetrics and Gynecology/Section of Gynecologic Oncology, University of Chicago, Chicago, IL, USA
| | - Rachelle Mendoza
- Department of Pathology, The University of Chicago, Chicago, IL, USA
| | - Thierry M. Nordmann
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Sarah Rauch
- Department of Obstetrics and Gynecology/Section of Gynecologic Oncology, University of Chicago, Chicago, IL, USA
| | - Lucy Kelliher
- Department of Obstetrics and Gynecology/Section of Gynecologic Oncology, University of Chicago, Chicago, IL, USA
| | - Janna Heide
- Department of Obstetrics and Gynecology/Section of Gynecologic Oncology, University of Chicago, Chicago, IL, USA
| | - Florian A. Rosenberger
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Agnes Bilecz
- Department of Obstetrics and Gynecology/Section of Gynecologic Oncology, University of Chicago, Chicago, IL, USA
| | - Sanaa Nakad Borrego
- Department of Obstetrics and Gynecology/Section of Gynecologic Oncology, University of Chicago, Chicago, IL, USA
| | - Maximillian T. Strauss
- Proteomics Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Marvin Thielert
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Edwin Rodriguez
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Johannes B. Müller-Reif
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Mengjie Chen
- Medicine/Section of Genetic Medicine, The University of Chicago, Chicago, IL, USA
| | - S. Diane Yamada
- Department of Obstetrics and Gynecology/Section of Gynecologic Oncology, University of Chicago, Chicago, IL, USA
| | - Andreas Mund
- Proteomics Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ricardo R. Lastra
- Department of Pathology, The University of Chicago, Chicago, IL, USA
| | - Matthias Mann
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
- Proteomics Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ernst Lengyel
- Department of Obstetrics and Gynecology/Section of Gynecologic Oncology, University of Chicago, Chicago, IL, USA
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22
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Velleuer E, Domínguez-Hüttinger E, Rodríguez A, Harris LA, Carlberg C. Concepts of multi-level dynamical modelling: understanding mechanisms of squamous cell carcinoma development in Fanconi anemia. Front Genet 2023; 14:1254966. [PMID: 38028610 PMCID: PMC10652399 DOI: 10.3389/fgene.2023.1254966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 10/18/2023] [Indexed: 12/01/2023] Open
Abstract
Fanconi anemia (FA) is a rare disease (incidence of 1:300,000) primarily based on the inheritance of pathogenic variants in genes of the FA/BRCA (breast cancer) pathway. These variants ultimately reduce the functionality of different proteins involved in the repair of DNA interstrand crosslinks and DNA double-strand breaks. At birth, individuals with FA might present with typical malformations, particularly radial axis and renal malformations, as well as other physical abnormalities like skin pigmentation anomalies. During the first decade of life, FA mostly causes bone marrow failure due to reduced capacity and loss of the hematopoietic stem and progenitor cells. This often makes hematopoietic stem cell transplantation necessary, but this therapy increases the already intrinsic risk of developing squamous cell carcinoma (SCC) in early adult age. Due to the underlying genetic defect in FA, classical chemo-radiation-based treatment protocols cannot be applied. Therefore, detecting and treating the multi-step tumorigenesis process of SCC in an early stage, or even its progenitors, is the best option for prolonging the life of adult FA individuals. However, the small number of FA individuals makes classical evidence-based medicine approaches based on results from randomized clinical trials impossible. As an alternative, we introduce here the concept of multi-level dynamical modelling using large, longitudinally collected genome, proteome- and transcriptome-wide data sets from a small number of FA individuals. This mechanistic modelling approach is based on the "hallmarks of cancer in FA", which we derive from our unique database of the clinical history of over 750 FA individuals. Multi-omic data from healthy and diseased tissue samples of FA individuals are to be used for training constituent models of a multi-level tumorigenesis model, which will then be used to make experimentally testable predictions. In this way, mechanistic models facilitate not only a descriptive but also a functional understanding of SCC in FA. This approach will provide the basis for detecting signatures of SCCs at early stages and their precursors so they can be efficiently treated or even prevented, leading to a better prognosis and quality of life for the FA individual.
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Affiliation(s)
- Eunike Velleuer
- Department of Cytopathology, Heinrich Heine University, Düsseldorf, Germany
- Center for Child and Adolescent Health, Helios Klinikum, Krefeld, Germany
| | - Elisa Domínguez-Hüttinger
- Departamento Düsseldorf Biología Molecular y Biotecnología, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, Ciudad México, Mexico
| | - Alfredo Rodríguez
- Departamento de Medicina Genómica y Toxicología Ambiental, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, Ciudad México, Mexico
- Instituto Nacional de Pediatría, Ciudad México, Mexico
| | - Leonard A. Harris
- Department of Biomedical Engineering, University of Arkansas, Fayetteville, AR, United States
- Interdisciplinary Graduate Program in Cell and Molecular Biology, University of Arkansas, Fayetteville, AR, United States
- Cancer Biology Program, Winthrop P Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Carsten Carlberg
- Institute of Animal Reproduction and Food Research, Polish Academy of Sciences, Olsztyn, Poland
- School of Medicine, Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland
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23
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Margaritelis NV. Personalized redox biology: Designs and concepts. Free Radic Biol Med 2023; 208:112-125. [PMID: 37541453 DOI: 10.1016/j.freeradbiomed.2023.08.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 07/19/2023] [Accepted: 08/01/2023] [Indexed: 08/06/2023]
Abstract
Personalized interventions are regarded as a next-generation approach in almost all fields of biomedicine, such as clinical medicine, exercise, nutrition and pharmacology. At the same time, an increasing body of evidence indicates that redox processes regulate, at least in part, multiple aspects of human physiology and pathology. As a result, the idea of applying personalized redox treatments to improve their efficacy has gained popularity among researchers in recent years. The aim of the present primer-style review was to highlight some crucial yet underappreciated methodological, statistical, and interpretative concepts within the redox biology literature, while also providing a physiology-oriented perspective on personalized redox biology. The topics addressed are: (i) the critical issue of investigating the potential existence of inter-individual variability; (ii) the importance of distinguishing a genuine and consistent response of a subject from a chance finding; (iii) the challenge of accurately quantifying the effect of a redox treatment when dealing with 'extreme' groups due to mathematical coupling and regression to the mean; and (iv) research designs and analyses that have been implemented in other fields, and can be reframed and exploited in a redox biology context.
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Affiliation(s)
- Nikos V Margaritelis
- Department of Physical Education and Sports Science at Serres, Aristotle University of Thessaloniki, Agios Ioannis, 62122, Serres, Greece.
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24
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Iqbal MA, Siddiqui S, Smith K, Singh P, Kumar B, Chouaib S, Chandrasekaran S. Metabolic stratification of human breast tumors reveal subtypes of clinical and therapeutic relevance. iScience 2023; 26:108059. [PMID: 37854701 PMCID: PMC10579441 DOI: 10.1016/j.isci.2023.108059] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 07/17/2023] [Accepted: 09/22/2023] [Indexed: 10/20/2023] Open
Abstract
Extensive metabolic heterogeneity in breast cancers has limited the deployment of metabolic therapies. To enable patient stratification, we studied the metabolic landscape in breast cancers (∼3000 patients combined) and identified three subtypes with increasing degrees of metabolic deregulation. Subtype M1 was found to be dependent on bile-acid biosynthesis, whereas M2 showed reliance on methionine pathway, and M3 engaged fatty-acid, nucleotide, and glucose metabolism. The extent of metabolic alterations correlated strongly with tumor aggressiveness and patient outcome. This pattern was reproducible in independent datasets and using in vivo tumor metabolite data. Using machine-learning, we identified robust and generalizable signatures of metabolic subtypes in tumors and cell lines. Experimental inhibition of metabolic pathways in cell lines representing metabolic subtypes revealed subtype-specific sensitivity, therapeutically relevant drugs, and promising combination therapies. Taken together, metabolic stratification of breast cancers can thus aid in predicting patient outcome and designing precision therapies.
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Affiliation(s)
- Mohammad A. Iqbal
- Thumbay Research Institute for Precision Medicine, Gulf Medical University, Ajman, United Arab Emirates
- College of Medicine, Gulf Medical University, Ajman, United Arab Emirates
| | | | - Kirk Smith
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Prithvi Singh
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia (A Central University), New Delhi, India
| | - Bhupender Kumar
- Department of Microbiology, Swami Shraddhanand College, University of Delhi, New Delhi, Delhi, India
| | - Salem Chouaib
- Thumbay Research Institute for Precision Medicine, Gulf Medical University, Ajman, United Arab Emirates
- College of Medicine, Gulf Medical University, Ajman, United Arab Emirates
- INSERM UMR 1186, Gustave Roussy, EPHE, Faculty of Medicine, University of Paris-Saclay, Villejuif, France
| | - Sriram Chandrasekaran
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
- Program in Chemical Biology, University of Michigan, Ann Arbor, MI, USA
- Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI, USA
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25
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Chan C, Peng J, Rajesh V, Scott EY, Sklavounos AA, Faiz M, Wheeler AR. Digital Microfluidics for Microproteomic Analysis of Minute Mammalian Tissue Samples Enabled by a Photocleavable Surfactant. J Proteome Res 2023; 22:3242-3253. [PMID: 37651704 DOI: 10.1021/acs.jproteome.3c00281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
Proteome profiles of precious tissue samples have great clinical potential for accelerating disease biomarker discovery and promoting novel strategies for early diagnosis and treatment. However, tiny clinical tissue samples are often difficult to handle and analyze with conventional proteomic methods. Automated digital microfluidic (DMF) workflows facilitate the manipulation of size-limited tissue samples. Here, we report the assessment of a DMF microproteomics workflow enabled by a photocleavable surfactant for proteomic analysis of minute tissue samples. The surfactant 4-hexylphenylazosulfonate (Azo) was found to facilitate fast droplet movement on DMF and enhance the proteomics analysis. Comparisons of Azo and n-Dodecyl β-d-maltoside (DDM) using small samples of HeLa digest standards and MCF-7 cell digests revealed distinct differences at the peptide level despite similar results at the protein level. The DMF microproteomics workflow was applied for the sample preparation of ∼3 μg biopsies from murine brain tissue. A total of 1969 proteins were identified in three samples, including established neural biomarkers and proteins related to synaptic signaling. Going forward, we propose that the Azo-enabled DMF workflow has the potential to advance the practical clinical application of DMF for the analysis of size-limited tissue samples.
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Affiliation(s)
- Calvin Chan
- Department of Chemistry, University of Toronto, Toronto M5S 3H6, Ontario, Canada
| | - Jiaxi Peng
- Department of Chemistry, University of Toronto, Toronto M5S 3H6, Ontario, Canada
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto M5S 3E1, Ontario, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto M5S 3G9, Ontario, Canada
| | - Vigneshwar Rajesh
- Department of Chemistry, University of Toronto, Toronto M5S 3H6, Ontario, Canada
| | - Erica Y Scott
- Department of Chemistry, University of Toronto, Toronto M5S 3H6, Ontario, Canada
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto M5S 3E1, Ontario, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto M5S 3G9, Ontario, Canada
- Department of Surgery, University of Toronto, Toronto M5S 1A8, Ontario, Canada
| | - Alexandros A Sklavounos
- Department of Chemistry, University of Toronto, Toronto M5S 3H6, Ontario, Canada
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto M5S 3E1, Ontario, Canada
| | - Maryam Faiz
- Department of Surgery, University of Toronto, Toronto M5S 1A8, Ontario, Canada
| | - Aaron R Wheeler
- Department of Chemistry, University of Toronto, Toronto M5S 3H6, Ontario, Canada
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto M5S 3E1, Ontario, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto M5S 3G9, Ontario, Canada
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26
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Berzero G, Pieri V, Mortini P, Filippi M, Finocchiaro G. The coming of age of liquid biopsy in neuro-oncology. Brain 2023; 146:4015-4024. [PMID: 37289981 PMCID: PMC10545511 DOI: 10.1093/brain/awad195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 04/05/2023] [Accepted: 05/16/2023] [Indexed: 06/10/2023] Open
Abstract
The clinical role of liquid biopsy in oncology is growing significantly. In gliomas and other brain tumours, targeted sequencing of cell-free DNA (cfDNA) from CSF may help differential diagnosis when surgery is not recommended and be more representative of tumour heterogeneity than surgical specimens, unveiling targetable genetic alterations. Given the invasive nature of lumbar puncture to obtain CSF, the quantitative analysis of cfDNA in plasma is a lively option for patient follow-up. Confounding factors may be represented by cfDNA variations due to concomitant pathologies (inflammatory diseases, seizures) or clonal haematopoiesis. Pilot studies suggest that methylome analysis of cfDNA from plasma and temporary opening of the blood-brain barrier by ultrasound have the potential to overcome some of these limitations. Together with this, an increased understanding of mechanisms modulating the shedding of cfDNA by the tumour may help to decrypt the meaning of cfDNA kinetics in blood or CSF.
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Affiliation(s)
- Giulia Berzero
- Neurology Unit, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
- Vita-Salute San Raffaele University, 20132 Milan, Italy
| | - Valentina Pieri
- Neurology Unit, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Pietro Mortini
- Vita-Salute San Raffaele University, 20132 Milan, Italy
- Department of Neurosurgery and Gamma Knife Radiosurgery, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Massimo Filippi
- Neurology Unit, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
- Vita-Salute San Raffaele University, 20132 Milan, Italy
- Neurorehabilitation Unit; Neurophysiology Unit; Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
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27
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Stenzinger A, Moltzen EK, Winkler E, Molnar-Gabor F, Malek N, Costescu A, Jensen BN, Nowak F, Pinto C, Ottersen OP, Schirmacher P, Nordborg J, Seufferlein T, Fröhling S, Edsjö A, Garcia-Foncillas J, Normanno N, Lundgren B, Friedman M, Bolanos N, Tatton-Brown K, Hill S, Rosenquist R. Implementation of precision medicine in healthcare-A European perspective. J Intern Med 2023; 294:437-454. [PMID: 37455247 DOI: 10.1111/joim.13698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
The technical development of high-throughput sequencing technologies and the parallel development of targeted therapies in the last decade have enabled a transition from traditional medicine to personalized treatment and care. In this way, by using comprehensive genomic testing, more effective treatments with fewer side effects are provided to each patient-that is, precision or personalized medicine (PM). In several European countries-such as in England, France, Denmark, and Spain-the governments have adopted national strategies and taken "top-down" decisions to invest in national infrastructure for PM. In other countries-such as Sweden, Germany, and Italy with regionally organized healthcare systems-the profession has instead taken "bottom-up" initiatives to build competence networks and infrastructure to enable equal access to PM. In this review, we summarize key learnings at the European level on the implementation process to establish sustainable governance and organization for PM at the regional, national, and EU/international levels. We also discuss critical ethical and legal aspects of implementing PM, and the importance of access to real-world data and performing clinical trials for evidence generation, as well as the need for improved reimbursement models, increased cross-disciplinary education and patient involvement. In summary, PM represents a paradigm shift, and modernization of healthcare and all relevant stakeholders-that is, healthcare, academia, policymakers, industry, and patients-must be involved in this system transformation to create a sustainable, non-siloed ecosystem for precision healthcare that benefits our patients and society at large.
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Affiliation(s)
- Albrecht Stenzinger
- Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
- Centers for Personalized Medicine (ZPM), Germany
| | - Ejner K Moltzen
- Innovation Fund Denmark, International Consortium for Personalised Medicine (IC PerMed), Aarhus, Denmark
| | - Eva Winkler
- Section of Translational Medical Ethics, National Center for Tumour Diseases, University Hospital Heidelberg, Heidelberg, Germany
| | | | - Nisar Malek
- Centers for Personalized Medicine (ZPM), Germany
- Department for Internal Medicine, University Hospital Tübingen, Tübingen, Germany
| | | | | | | | - Carmine Pinto
- Medical Oncology, Comprehensive Cancer Centre, AUSL-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | | | - Peter Schirmacher
- Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
- Centers for Personalized Medicine (ZPM), Germany
| | - Jenni Nordborg
- Lif - The Research-Based Pharmaceutical Industry, Stockholm, Sweden
| | - Thomas Seufferlein
- Department of Internal Medicine I, Ulm University Hospital, Ulm, Germany
| | - Stefan Fröhling
- Division of Translational Medical Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
- German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Anders Edsjö
- Department of Clinical Genetics, Pathology and Molecular Diagnostics, Office for Medical Services, Region Skåne, Lund, Sweden
- Division of Pathology, Department of Clinical Sciences, Lund University, Lund, Sweden
- Genomic Medicine Sweden (GMS), Sweden
| | - Jesus Garcia-Foncillas
- Department of Oncology and Cancer Institute, Fundacion Jimenez Diaz University Hospital, Autonomous University, Madrid, Spain
| | - Nicola Normanno
- Cell Biology and Biotherapy Unit, Istituto Nazionale Tumori - IRCCS - Fondazione G. Pascale, Napoli, Italy
| | | | - Mikaela Friedman
- Genomic Medicine Sweden (GMS), Sweden
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | | | - Katrina Tatton-Brown
- National Genomics Education, NHS England, London, UK
- St George's University Hospitals NHS Foundation Trust, London, UK
- St George's University of London, London, UK
| | - Sue Hill
- Office of Chief Scientific Officer and the Genomics Unit, NHS England, London, UK
| | - Richard Rosenquist
- Genomic Medicine Sweden (GMS), Sweden
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
- Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
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28
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Zhou S, Zheng J, Zhai W, Chen Y. Spatio-temporal heterogeneity in cancer evolution and tumor microenvironment of renal cell carcinoma with tumor thrombus. Cancer Lett 2023; 572:216350. [PMID: 37574183 DOI: 10.1016/j.canlet.2023.216350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 08/01/2023] [Accepted: 08/10/2023] [Indexed: 08/15/2023]
Abstract
Metastasis is the most fatal aspect of cancer, often preceded by a tumor thrombus (TT) which forms within the vascular system. Renal cell carcinoma (RCC), the predominant form of kidney cancer, witnesses a venous system invasion in 4-10% of cases, resulting in venous tumor thrombus (RCC-TT). This variant represents a formidable clinical challenge due to its escalated surgical complexity, heightened risk of progression and metastasis, and an adverse prognosis. However, recent trials addressing RCC-TT face significant barriers stemming from the profound inter- and intra-tumoral heterogeneity, patient-specific treatment variations, and distinct therapeutic resistance patterns between the primary tumor (PT) and the TT. This review delves into the unique evolutionary pathway of RCC-TT, the relationship between the staging and grading of RCC-TT invasion patterns, and the spatial molecular profiling of RCC-TT. Additionally, we assess the temporal heterogeneity among TT, PT, and distant metastases, as well as the functional phenotypes of TME components. An outlook for future research on RCC-TT is also provided.
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Affiliation(s)
- Sian Zhou
- Department of Urology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China.
| | - Junhua Zheng
- Department of Urology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China.
| | - Wei Zhai
- Department of Urology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China; Department of Urology, State Key Laboratory of Oncogenes and Related Genes, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China.
| | - Yonghui Chen
- Department of Urology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China.
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29
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Yaghoubi Naei V, Bordhan P, Mirakhorli F, Khorrami M, Shrestha J, Nazari H, Kulasinghe A, Ebrahimi Warkiani M. Advances in novel strategies for isolation, characterization, and analysis of CTCs and ctDNA. Ther Adv Med Oncol 2023; 15:17588359231192401. [PMID: 37692363 PMCID: PMC10486235 DOI: 10.1177/17588359231192401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 07/19/2023] [Indexed: 09/12/2023] Open
Abstract
Over the past decade, the detection and analysis of liquid biopsy biomarkers such as circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) have advanced significantly. They have received recognition for their clinical usefulness in detecting cancer at an early stage, monitoring disease, and evaluating treatment response. The emergence of liquid biopsy has been a helpful development, as it offers a minimally invasive, rapid, real-time monitoring, and possible alternative to traditional tissue biopsies. In resource-limited settings, the ideal platform for liquid biopsy should not only extract more CTCs or ctDNA from a minimal sample volume but also accurately represent the molecular heterogeneity of the patient's disease. This review covers novel strategies and advancements in CTC and ctDNA-based liquid biopsy platforms, including microfluidic applications and comprehensive analysis of molecular complexity. We discuss these systems' operational principles and performance efficiencies, as well as future opportunities and challenges for their implementation in clinical settings. In addition, we emphasize the importance of integrated platforms that incorporate machine learning and artificial intelligence in accurate liquid biopsy detection systems, which can greatly improve cancer management and enable precision diagnostics.
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Affiliation(s)
- Vahid Yaghoubi Naei
- School of Biomedical Engineering, University of Technology Sydney, Sydney, Australia
- Faculty of Medicine, Frazer Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Pritam Bordhan
- School of Biomedical Engineering, University of Technology Sydney, Sydney, Australia
- Faculty of Science, Institute for Biomedical Materials & Devices, University of Technology Sydney, Australia
| | - Fatemeh Mirakhorli
- School of Biomedical Engineering, University of Technology Sydney, Sydney, Australia
| | - Motahare Khorrami
- Immunology Research Center, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Jesus Shrestha
- School of Biomedical Engineering, University of Technology Sydney, Sydney, Australia
| | - Hojjatollah Nazari
- School of Biomedical Engineering, University of Technology Sydney, Sydney, Australia
| | - Arutha Kulasinghe
- Faculty of Medicine, Frazer Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Majid Ebrahimi Warkiani
- School of Biomedical Engineering, University of Technology Sydney, 1, Broadway, Ultimo New South Wales 2007, Australia
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30
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Konda P, Garinet S, Van Allen EM, Viswanathan SR. Genome-guided discovery of cancer therapeutic targets. Cell Rep 2023; 42:112978. [PMID: 37572322 DOI: 10.1016/j.celrep.2023.112978] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 07/22/2023] [Accepted: 07/28/2023] [Indexed: 08/14/2023] Open
Abstract
The success of precision oncology-which aims to match the right therapies to the right patients based on molecular status-is predicated on a robust pipeline of molecular targets against which therapies can be developed. Recent advances in genomics and functional genetics have enabled the unbiased discovery of novel molecular targets at scale. We summarize the promise and challenges in integrating genomic and functional genetic landscapes of cancer to establish the next generation of cancer targets.
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Affiliation(s)
- Prathyusha Konda
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Simon Garinet
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Eliezer M Van Allen
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Srinivas R Viswanathan
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA.
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31
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Shi Y, Zhang Q, Mei J, Liu J. Editorial: Multi-omics analysis in tumor microenvironment and tumor heterogeneity. Front Genet 2023; 14:1271295. [PMID: 37680200 PMCID: PMC10482244 DOI: 10.3389/fgene.2023.1271295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 08/17/2023] [Indexed: 09/09/2023] Open
Affiliation(s)
- Yuxin Shi
- Department of Oncology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi People’s Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi, Jiangsu, China
| | - Qinglin Zhang
- Department of Gastroenterology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi People’s Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi, Jiangsu, China
| | - Jie Mei
- Department of Oncology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi People’s Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi, Jiangsu, China
| | - Jinhui Liu
- Department of Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
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32
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Zhang W, Qi L, Liu Z, He S, Wang C, Wu Y, Han L, Liu Z, Fu Z, Tu C, Li Z. Integrated multiomic analysis and high-throughput screening reveal potential gene targets and synergetic drug combinations for osteosarcoma therapy. MedComm (Beijing) 2023; 4:e317. [PMID: 37457661 PMCID: PMC10338795 DOI: 10.1002/mco2.317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 05/14/2023] [Accepted: 05/22/2023] [Indexed: 07/18/2023] Open
Abstract
Although great advances have been made over the past decades, therapeutics for osteosarcoma are quite limited. We performed long-read RNA sequencing and tandem mass tag (TMT)-based quantitative proteome on osteosarcoma and the adjacent normal tissues, next-generation sequencing (NGS) on paired osteosarcoma samples before and after neoadjuvant chemotherapy (NACT), and high-throughput drug combination screen on osteosarcoma cell lines. Single-cell RNA sequencing data were analyzed to reveal the heterogeneity of potential therapeutic target genes. Additionally, we clarified the synergistic mechanisms of doxorubicin (DOX) and HDACs inhibitors for osteosarcoma treatment. Consequently, we identified 2535 osteosarcoma-specific genes and several alternative splicing (AS) events with osteosarcoma specificity and/or patient heterogeneity. Hundreds of potential therapeutic targets were identified among them, which showed the core regulatory roles in osteosarcoma. We also identified 215 inhibitory drugs and 236 synergistic drug combinations for osteosarcoma treatment. More interestingly, the multiomic analysis pointed out the pivotal role of HDAC1 and TOP2A in osteosarcoma. HDAC inhibitors synergized with DOX to suppress osteosarcoma both in vitro and in vivo. Mechanistically, HDAC inhibitors synergized with DOX by downregulating SP1 to transcriptionally modulate TOP2A expression. This study provided a comprehensive view of molecular features, therapeutic targets, and synergistic drug combinations for osteosarcoma.
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Affiliation(s)
- Wenchao Zhang
- Department of OrthopedicsThe Second Xiangya HospitalCentral South UniversityChangshaChina
- Hunan Key Laboratory of Tumor Models and Individualized MedicineThe Second Xiangya HospitalChangshaChina
| | - Lin Qi
- Department of OrthopedicsThe Second Xiangya HospitalCentral South UniversityChangshaChina
- Hunan Key Laboratory of Tumor Models and Individualized MedicineThe Second Xiangya HospitalChangshaChina
| | - Zhongyue Liu
- Department of OrthopedicsThe Second Xiangya HospitalCentral South UniversityChangshaChina
- Hunan Key Laboratory of Tumor Models and Individualized MedicineThe Second Xiangya HospitalChangshaChina
| | - Shasha He
- Department of OncologyThe Second Xiangya HospitalCentral South UniversityChangshaChina
| | | | - Ying Wu
- MegaRobo Technologies Co., LtdSuzhouChina
| | | | | | - Zheng Fu
- MegaRobo Technologies Co., LtdSuzhouChina
| | - Chao Tu
- Department of OrthopedicsThe Second Xiangya HospitalCentral South UniversityChangshaChina
- Hunan Key Laboratory of Tumor Models and Individualized MedicineThe Second Xiangya HospitalChangshaChina
| | - Zhihong Li
- Department of OrthopedicsThe Second Xiangya HospitalCentral South UniversityChangshaChina
- Hunan Key Laboratory of Tumor Models and Individualized MedicineThe Second Xiangya HospitalChangshaChina
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33
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Wang SS, Lewis MJ, Pitzalis C. DNA Methylation Signatures of Response to Conventional Synthetic and Biologic Disease-Modifying Antirheumatic Drugs (DMARDs) in Rheumatoid Arthritis. Biomedicines 2023; 11:1987. [PMID: 37509625 PMCID: PMC10377185 DOI: 10.3390/biomedicines11071987] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 07/03/2023] [Accepted: 07/11/2023] [Indexed: 07/30/2023] Open
Abstract
Rheumatoid arthritis (RA) is a complex condition that displays heterogeneity in disease severity and response to standard treatments between patients. Failure rates for conventional, target synthetic, and biologic disease-modifying rheumatic drugs (DMARDs) are significant. Although there are models for predicting patient response, they have limited accuracy, require replication/validation, or for samples to be obtained through a synovial biopsy. Thus, currently, there are no prediction methods approved for routine clinical use. Previous research has shown that genetics and environmental factors alone cannot explain the differences in response between patients. Recent studies have demonstrated that deoxyribonucleic acid (DNA) methylation plays an important role in the pathogenesis and disease progression of RA. Importantly, specific DNA methylation profiles associated with response to conventional, target synthetic, and biologic DMARDs have been found in the blood of RA patients and could potentially function as predictive biomarkers. This review will summarize and evaluate the evidence for DNA methylation signatures in treatment response mainly in blood but also learn from the progress made in the diseased tissue in cancer in comparison to RA and autoimmune diseases. We will discuss the benefits and challenges of using DNA methylation signatures as predictive markers and the potential for future progress in this area.
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Affiliation(s)
- Susan Siyu Wang
- Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Queen Mary University of London and Barts Health NIHR BRC & NHS Trust, London EC1M 6BQ, UK
| | - Myles J Lewis
- Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Queen Mary University of London and Barts Health NIHR BRC & NHS Trust, London EC1M 6BQ, UK
| | - Costantino Pitzalis
- Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Queen Mary University of London and Barts Health NIHR BRC & NHS Trust, London EC1M 6BQ, UK
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34
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Wang D, Liu B, Zhang Z. Accelerating the understanding of cancer biology through the lens of genomics. Cell 2023; 186:1755-1771. [PMID: 37059071 DOI: 10.1016/j.cell.2023.02.015] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 01/11/2023] [Accepted: 02/08/2023] [Indexed: 04/16/2023]
Abstract
A core mission of cancer genomics is to comprehensively chart molecular underpinnings of cancer-driving events and to provide personalized therapeutic strategies. Primarily focused on cancer cells, cancer genomics studies have successfully uncovered many drivers for major cancer types. Since the emergence of cancer immune evasion as a critical cancer hallmark, the paradigm has been elevated to the holistic tumor ecosystem, with distinct cellular components and their functional states elucidated. We highlight the milestones of cancer genomics, depict the evolving path of the field, and discuss future directions in completing the understanding of the tumor ecosystem and in advancing therapeutic strategies.
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Affiliation(s)
- Dongfang Wang
- Biomedical Pioneering Innovative Center and School of Life Sciences, Peking University, Beijing 100871, China
| | - Baolin Liu
- Biomedical Pioneering Innovative Center and School of Life Sciences, Peking University, Beijing 100871, China
| | - Zemin Zhang
- Biomedical Pioneering Innovative Center and School of Life Sciences, Peking University, Beijing 100871, China; Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; Changping Laboratory, Beijing, China.
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35
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Zhao J, Zhao B, Song X, Lyu C, Chen W, Xiong Y, Wei DQ. Subtype-DCC: decoupled contrastive clustering method for cancer subtype identification based on multi-omics data. Brief Bioinform 2023; 24:7005165. [PMID: 36702755 DOI: 10.1093/bib/bbad025] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 12/21/2022] [Accepted: 01/08/2023] [Indexed: 01/28/2023] Open
Abstract
Due to the high heterogeneity and complexity of cancers, patients with different cancer subtypes often have distinct groups of genomic and clinical characteristics. Therefore, the discovery and identification of cancer subtypes are crucial to cancer diagnosis, prognosis and treatment. Recent technological advances have accelerated the increasing availability of multi-omics data for cancer subtyping. To take advantage of the complementary information from multi-omics data, it is necessary to develop computational models that can represent and integrate different layers of data into a single framework. Here, we propose a decoupled contrastive clustering method (Subtype-DCC) based on multi-omics data integration for clustering to identify cancer subtypes. The idea of contrastive learning is introduced into deep clustering based on deep neural networks to learn clustering-friendly representations. Experimental results demonstrate the superior performance of the proposed Subtype-DCC model in identifying cancer subtypes over the currently available state-of-the-art clustering methods. The strength of Subtype-DCC is also supported by the survival and clinical analysis.
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Affiliation(s)
- Jing Zhao
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Bowen Zhao
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Xiaotong Song
- School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Chujun Lyu
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Weizhi Chen
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yi Xiong
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
- Peng Cheng Laboratory, Vanke Cloud City Phase I Building 8, Xili Street, Nanshan District, Shenzhen, Guangdong, 518055, China
- Zhongjing Research and Industrialization Institute of Chinese Medicine, Zhongguancun Scientific Park, Meixi, Nayang, Henan, 473006, China
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36
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Gonzales LISA, Qiao JW, Buffier AW, Rogers LJ, Suchowerska N, McKenzie DR, Kwan AH. An omics approach to delineating the molecular mechanisms that underlie the biological effects of physical plasma. BIOPHYSICS REVIEWS 2023; 4:011312. [PMID: 38510160 PMCID: PMC10903421 DOI: 10.1063/5.0089831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 02/24/2023] [Indexed: 03/22/2024]
Abstract
The use of physical plasma to treat cancer is an emerging field, and interest in its applications in oncology is increasing rapidly. Physical plasma can be used directly by aiming the plasma jet onto cells or tissue, or indirectly, where a plasma-treated solution is applied. A key scientific question is the mechanism by which physical plasma achieves selective killing of cancer over normal cells. Many studies have focused on specific pathways and mechanisms, such as apoptosis and oxidative stress, and the role of redox biology. However, over the past two decades, there has been a rise in omics, the systematic analysis of entire collections of molecules in a biological entity, enabling the discovery of the so-called "unknown unknowns." For example, transcriptomics, epigenomics, proteomics, and metabolomics have helped to uncover molecular mechanisms behind the action of physical plasma, revealing critical pathways beyond those traditionally associated with cancer treatments. This review showcases a selection of omics and then summarizes the insights gained from these studies toward understanding the biological pathways and molecular mechanisms implicated in physical plasma treatment. Omics studies have revealed how reactive species generated by plasma treatment preferentially affect several critical cellular pathways in cancer cells, resulting in epigenetic, transcriptional, and post-translational changes that promote cell death. Finally, this review considers the outlook for omics in uncovering both synergies and antagonisms with other common cancer therapies, as well as in overcoming challenges in the clinical translation of physical plasma.
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Affiliation(s)
- Lou I. S. A. Gonzales
- School of Life and Environmental Sciences, The University of Sydney, NSW 2006, Australia
| | - Jessica W. Qiao
- School of Life and Environmental Sciences, The University of Sydney, NSW 2006, Australia
| | - Aston W. Buffier
- School of Life and Environmental Sciences, The University of Sydney, NSW 2006, Australia
| | | | | | | | - Ann H. Kwan
- Author to whom correspondence should be addressed:
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37
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Casolino R, Johns AL, Courtot M, Lawlor RT, De Lorenzo F, Horgan D, Mateo J, Normanno N, Rubin M, Stein L, Subbiah V, Westphalen BC, Lawler M, Park K, Perdomo S, Yoshino T, Wu J, Biankin AV. Accelerating cancer omics and precision oncology in health care and research: a Lancet Oncology Commission. Lancet Oncol 2023; 24:123-125. [PMID: 36725142 DOI: 10.1016/s1470-2045(23)00007-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 01/05/2023] [Indexed: 02/01/2023]
Affiliation(s)
- Raffaella Casolino
- Wolfson Wohl Cancer Research Centre, Institute of Cancer Sciences, University of Glasgow, Glasgow G61 1BD, UK.
| | - Amber L Johns
- Garvan Institute of Medical Research and The Kinghorn Cancer Centre, Cancer Division, Sydney, NSW, Australia
| | | | - Rita T Lawlor
- ARC-NET Research Centre, University and Hospital Trust of Verona, Verona, Italy
| | | | - Denis Horgan
- European Alliance for Personalised Medicine, Brussels, Belgium
| | - Joaquin Mateo
- ESMO Translational Research and Precision Medicine Working Group, Lugano, Switzerland; Medical Oncology, Vall d'Hebron Institute of Oncology, Hospital Universitari Vall d'Hebron, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Nicola Normanno
- Cell Biology and Biotherapy Unit, Istituto Nazionale Tumori "Fondazione G. Pascale"-IRCCS, Naples, Italy
| | - Mark Rubin
- Department for BioMedical Research, University of Bern, Bern, Switzerland; Bern Center for Precision Medicine, Inselspital, University Hospital of Bern, Bern, Switzerland
| | - Lincoln Stein
- Ontario Institute for Cancer Research, Toronto, ON, Canada
| | - Vivek Subbiah
- ESMO Translational Research and Precision Medicine Working Group, Lugano, Switzerland; Department of Investigational Cancer Therapeutics, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Benedikt C Westphalen
- ESMO Translational Research and Precision Medicine Working Group, Lugano, Switzerland; Department of Medicine III and Comprehensive Cancer Center, University Hospital, LMU Munich, Munich, Germany
| | - Mark Lawler
- Patrick G Johnston Centre for Cancer Research, Faculty of Medicine, Health and Life Sciences, Queen's University Belfast, Belfast, UK
| | - Keunchil Park
- Department of Thoracic/Head and Neck Medical Oncology, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Sandra Perdomo
- Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France
| | - Takayuki Yoshino
- Department of Gastroenterology and Gastrointestinal Oncology and Division for the Promotion of Drug and Diagnostic Development, National Cancer Center Hospital East, Kashiwa, Japan
| | - Jianmin Wu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Center for Cancer Bioinformatics, Peking University Cancer Hospital & Institute, Beijing, China
| | - Andrew V Biankin
- Wolfson Wohl Cancer Research Centre, Institute of Cancer Sciences, University of Glasgow, Glasgow G61 1BD, UK; West of Scotland Pancreatic Unit, Glasgow Royal Infirmary, Glasgow, UK; South Western Sydney Clinical School, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
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38
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Debley J, Christakis DA. Call for Papers Reporting Pediatric Translational Science Research. JAMA Pediatr 2023; 177:7-8. [PMID: 36469343 DOI: 10.1001/jamapediatrics.2022.4823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Jason Debley
- Center for Immunity and Immunotherapies (CIIT), Seattle Children's Research Institute, Division of Pulmonary and Sleep Medicine, Department of Pediatrics, Seattle Children's Hospital, University of Washington School of Medicine, Seattle
- Section Editor, Translational Science, JAMA Pediatrics
| | - Dimitri A Christakis
- Seattle Children's Research Institute, Department of Pediatrics, University of Washington Center for Child Health, Behavior and Development, Seattle
- Editor, JAMA Pediatrics
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39
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Clark C, Rabl M, Dayon L, Popp J. The promise of multi-omics approaches to discover biological alterations with clinical relevance in Alzheimer's disease. Front Aging Neurosci 2022; 14:1065904. [PMID: 36570537 PMCID: PMC9768448 DOI: 10.3389/fnagi.2022.1065904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 11/21/2022] [Indexed: 12/12/2022] Open
Abstract
Beyond the core features of Alzheimer's disease (AD) pathology, i.e. amyloid pathology, tau-related neurodegeneration and microglia response, multiple other molecular alterations and pathway dysregulations have been observed in AD. Their inter-individual variations, complex interactions and relevance for clinical manifestation and disease progression remain poorly understood, however. Heterogeneity at both pathophysiological and clinical levels complicates diagnosis, prognosis, treatment and drug design and testing. High-throughput "omics" comprise unbiased and untargeted data-driven methods which allow the exploration of a wide spectrum of disease-related changes at different endophenotype levels without focussing a priori on specific molecular pathways or molecules. Crucially, new methodological and statistical advances now allow for the integrative analysis of data resulting from multiple and different omics methods. These multi-omics approaches offer the unique advantage of providing a more comprehensive characterisation of the AD endophenotype and to capture molecular signatures and interactions spanning various biological levels. These new insights can then help decipher disease mechanisms more deeply. In this review, we describe the different multi-omics tools and approaches currently available and how they have been applied in AD research so far. We discuss how multi-omics can be used to explore molecular alterations related to core features of the AD pathologies and how they interact with comorbid pathological alterations. We further discuss whether the identified pathophysiological changes are relevant for the clinical manifestation of AD, in terms of both cognitive impairment and neuropsychiatric symptoms, and for clinical disease progression over time. Finally, we address the opportunities for multi-omics approaches to help discover novel biomarkers for diagnosis and monitoring of relevant pathophysiological processes, along with personalised intervention strategies in AD.
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Affiliation(s)
- Christopher Clark
- Department of Psychiatry, Psychotherapy and Psychosomatics, University of Zürich, Zürich, Switzerland,Geriatric Psychiatry, University Hospital of Psychiatry Zürich, Zürich, Switzerland,*Correspondence: Christopher Clark,
| | - Miriam Rabl
- Geriatric Psychiatry, University Hospital of Psychiatry Zürich, Zürich, Switzerland,University of Lausanne, Lausanne, Switzerland
| | - Loïc Dayon
- Nestlé Institute of Food Safety and Analytical Sciences, Nestlé Research, Lausanne, Switzerland,Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Julius Popp
- Department of Psychiatry, Psychotherapy and Psychosomatics, University of Zürich, Zürich, Switzerland,Geriatric Psychiatry, University Hospital of Psychiatry Zürich, Zürich, Switzerland,Old Age Psychiatry, Department of Psychiatry, Lausanne University Hospital, Lausanne, Switzerland
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Shen S. Editorial: Integrative Approaches to Analyze Cancer Based on Multi‐Omics. Front Genet 2022; 13:1057408. [DOI: 10.3389/fgene.2022.1057408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 10/07/2022] [Indexed: 11/13/2022] Open
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