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Frankhouser DE, Rockne RC, Uechi L, Zhao D, Branciamore S, O'Meally D, Irizarry J, Ghoda L, Ali H, Trent JM, Forman S, Fu YH, Kuo YH, Zhang B, Marcucci G. State-transition modeling of blood transcriptome predicts disease evolution and treatment response in chronic myeloid leukemia. Leukemia 2024; 38:769-780. [PMID: 38307941 PMCID: PMC10997512 DOI: 10.1038/s41375-024-02142-9] [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: 10/20/2023] [Revised: 12/22/2023] [Accepted: 01/05/2024] [Indexed: 02/04/2024]
Abstract
Chronic myeloid leukemia (CML) is initiated and maintained by BCR::ABL which is clinically targeted using tyrosine kinase inhibitors (TKIs). TKIs can induce long-term remission but are also not curative. Thus, CML is an ideal system to test our hypothesis that transcriptome-based state-transition models accurately predict cancer evolution and treatment response. We collected time-sequential blood samples from tetracycline-off (Tet-Off) BCR::ABL-inducible transgenic mice and wild-type controls. From the transcriptome, we constructed a CML state-space and a three-well leukemogenic potential landscape. The potential's stable critical points defined observable disease states. Early states were characterized by anti-CML genes opposing leukemia; late states were characterized by pro-CML genes. Genes with expression patterns shaped similarly to the potential landscape were identified as drivers of disease transition. Re-introduction of tetracycline to silence the BCR::ABL gene returned diseased mice transcriptomes to a near healthy state, without reaching it, suggesting parts of the transition are irreversible. TKI only reverted the transcriptome to an intermediate disease state, without approaching a state of health; disease relapse occurred soon after treatment. Using only the earliest time-point as initial conditions, our state-transition models accurately predicted both disease progression and treatment response, supporting this as a potentially valuable approach to time clinical intervention, before phenotypic changes become detectable.
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Affiliation(s)
- David E Frankhouser
- Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, CAL, 91010, USA.
| | - Russell C Rockne
- Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, CAL, 91010, USA.
| | - Lisa Uechi
- Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, CAL, 91010, USA
| | - Dandan Zhao
- Department of Hematologic Malignancies Translational Science, Beckman Research Institute and Division of Leukemia, City of Hope National Medical Center, Duarte, CAL, 91010, USA
| | - Sergio Branciamore
- Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, CAL, 91010, USA
| | - Denis O'Meally
- Department of Diabetes and & Cancer Discovery Science, Beckman Research Institute, City of Hope National Medical Center, Duarte, CAL, 91010, USA
| | - Jihyun Irizarry
- Department of Hematologic Malignancies Translational Science, Beckman Research Institute and Division of Leukemia, City of Hope National Medical Center, Duarte, CAL, 91010, USA
| | - Lucy Ghoda
- Department of Hematologic Malignancies Translational Science, Beckman Research Institute and Division of Leukemia, City of Hope National Medical Center, Duarte, CAL, 91010, USA
| | - Haris Ali
- Department of Hematologic Malignancies Translational Science, Beckman Research Institute and Division of Leukemia, City of Hope National Medical Center, Duarte, CAL, 91010, USA
| | | | - Stephen Forman
- Department of Hematologic Malignancies Translational Science, Beckman Research Institute and Division of Leukemia, City of Hope National Medical Center, Duarte, CAL, 91010, USA
| | - Yu-Hsuan Fu
- Department of Hematologic Malignancies Translational Science, Beckman Research Institute and Division of Leukemia, City of Hope National Medical Center, Duarte, CAL, 91010, USA
| | - Ya-Huei Kuo
- Department of Hematologic Malignancies Translational Science, Beckman Research Institute and Division of Leukemia, City of Hope National Medical Center, Duarte, CAL, 91010, USA
| | - Bin Zhang
- Department of Hematologic Malignancies Translational Science, Beckman Research Institute and Division of Leukemia, City of Hope National Medical Center, Duarte, CAL, 91010, USA.
| | - Guido Marcucci
- Department of Hematologic Malignancies Translational Science, Beckman Research Institute and Division of Leukemia, City of Hope National Medical Center, Duarte, CAL, 91010, USA.
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Uechi L, Vasudevan S, Vilenski D, Branciamore S, Frankhouser D, O'Meally D, Meshinchi S, Marcucci G, Kuo YH, Rockne R, Kravchenko-Balasha N. Transcriptome free energy can serve as a dynamic patient-specific biomarker in acute myeloid leukemia. NPJ Syst Biol Appl 2024; 10:32. [PMID: 38527998 DOI: 10.1038/s41540-024-00352-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 02/26/2024] [Indexed: 03/27/2024] Open
Abstract
Acute myeloid leukemia (AML) is prevalent in both adult and pediatric patients. Despite advances in patient categorization, the heterogeneity of AML remains a challenge. Recent studies have explored the use of gene expression data to enhance AML diagnosis and prognosis, however, alternative approaches rooted in physics and chemistry may provide another level of insight into AML transformation. Utilizing publicly available databases, we analyze 884 human and mouse blood and bone marrow samples. We employ a personalized medicine strategy, combining state-transition theory and surprisal analysis, to assess the RNA transcriptome of individual patients. The transcriptome is transformed into physical parameters that represent each sample's steady state and the free energy change (FEC) from that steady state, which is the state with the lowest free energy.We found the transcriptome steady state was invariant across normal and AML samples. FEC, representing active molecular processes, varied significantly between samples and was used to create patient-specific barcodes to characterize the biology of the disease. We discovered that AML samples that were in a transition state had the highest FEC. This disease state may be characterized as the most unstable and hence the most therapeutically targetable since a change in free energy is a thermodynamic requirement for disease progression. We also found that distinct sets of ongoing processes may be at the root of otherwise similar clinical phenotypes, implying that our integrated analysis of transcriptome profiles may facilitate a personalized medicine approach to cure AML and restore a steady state in each patient.
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Affiliation(s)
- Lisa Uechi
- Division of Mathematical Oncology and Computational Systems Biology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, 91010, USA
| | - Swetha Vasudevan
- The Institute of Biomedical and Oral Research, Faculty of Dental Medicine, The Hebrew University of Jerusalem, P.O.B. 12272, Ein Kerem, Jerusalem, 91120, Israel
| | - Daniela Vilenski
- The Institute of Biomedical and Oral Research, Faculty of Dental Medicine, The Hebrew University of Jerusalem, P.O.B. 12272, Ein Kerem, Jerusalem, 91120, Israel
| | - Sergio Branciamore
- Division of Mathematical Oncology and Computational Systems Biology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, 91010, USA
| | - David Frankhouser
- Division of Mathematical Oncology and Computational Systems Biology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, 91010, USA
| | - Denis O'Meally
- Department of Diabetes and Cancer Discovery Science, Arthur Riggs Diabetes and Metabolism Research Institute, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, 91010, USA
| | - Soheil Meshinchi
- Clinical Research Division, Fred Hutchinson Cancer Center, 1100 Fairview Ave N, D5-112, Seattle, WA, 98109, USA
| | - Guido Marcucci
- Department of Hematological Malignancies Translational Science, Gehr Family Center for Leukemia Research, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, USA
| | - Ya-Huei Kuo
- Department of Hematological Malignancies Translational Science, Gehr Family Center for Leukemia Research, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, USA
| | - Russell Rockne
- Division of Mathematical Oncology and Computational Systems Biology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, 91010, USA.
| | - Nataly Kravchenko-Balasha
- The Institute of Biomedical and Oral Research, Faculty of Dental Medicine, The Hebrew University of Jerusalem, P.O.B. 12272, Ein Kerem, Jerusalem, 91120, Israel.
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Frankhouser DE, Rockne RC, Uechi L, Zhao D, Branciamore S, O’Meally D, Irizarry J, Ghoda L, Ali H, Trent JM, Forman S, Fu YH, Kuo YH, Zhang B, Marcucci G. State-transition Modeling of Blood Transcriptome Predicts Disease Evolution and Treatment Response in Chronic Myeloid Leukemia. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.11.561908. [PMID: 37873185 PMCID: PMC10592732 DOI: 10.1101/2023.10.11.561908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Chronic myeloid leukemia (CML) is initiated and maintained by BCR::ABL which is clinically targeted using tyrosine kinase inhibitors (TKIs). TKIs can induce long-term remission but are also not curative. Thus, CML is an ideal system to test our hypothesis that transcriptome-based state-transition models accurately predict cancer evolution and treatment response. We collected time-sequential blood samples from tetracycline-off (Tet-Off) BCR::ABL-inducible transgenic mice and wild-type controls. From the transcriptome, we constructed a CML state-space and a three-well leukemogenic potential landscape. The potential's stable critical points defined observable disease states. Early states were characterized by anti-CML genes opposing leukemia; late states were characterized by pro-CML genes. Genes with expression patterns shaped similarly to the potential landscape were identified as drivers of disease transition. Re-introduction of tetracycline to silence the BCR::ABL gene returned diseased mice transcriptomes to a near healthy state, without reaching it, suggesting parts of the transition are irreversible. TKI only reverted the transcriptome to an intermediate disease state, without approaching a state of health; disease relapse occurred soon after treatment. Using only the earliest time-point as initial conditions, our state-transition models accurately predicted both disease progression and treatment response, supporting this as a potentially valuable approach to time clinical intervention even before phenotypic changes become detectable.
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Affiliation(s)
- David E. Frankhouser
- Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, California, 91010, USA
| | - Russell C. Rockne
- Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, California, 91010, USA
| | - Lisa Uechi
- Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, California, 91010, USA
| | - Dandan Zhao
- Department of Hematologic Malignancies Translational Science, Beckman Research Institute and Division of Leukemia, City of Hope National Medical Center, Duarte, California, 91010, USA
| | - Sergio Branciamore
- Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, California, 91010, USA
| | - Denis O’Meally
- Department of Diabetes and & Cancer Discovery Science, Beckman Research Institute, City of Hope National Medical Center, Duarte, California, 91010, USA
| | - Jihyun Irizarry
- Department of Hematologic Malignancies Translational Science, Beckman Research Institute and Division of Leukemia, City of Hope National Medical Center, Duarte, California, 91010, USA
| | - Lucy Ghoda
- Department of Hematologic Malignancies Translational Science, Beckman Research Institute and Division of Leukemia, City of Hope National Medical Center, Duarte, California, 91010, USA
| | - Haris Ali
- Department of Hematologic Malignancies Translational Science, Beckman Research Institute and Division of Leukemia, City of Hope National Medical Center, Duarte, California, 91010, USA
| | | | - Stephen Forman
- Department of Hematologic Malignancies Translational Science, Beckman Research Institute and Division of Leukemia, City of Hope National Medical Center, Duarte, California, 91010, USA
| | - Yu-Hsuan Fu
- Department of Hematologic Malignancies Translational Science, Beckman Research Institute and Division of Leukemia, City of Hope National Medical Center, Duarte, California, 91010, USA
| | - Ya-Huei Kuo
- Department of Hematologic Malignancies Translational Science, Beckman Research Institute and Division of Leukemia, City of Hope National Medical Center, Duarte, California, 91010, USA
| | - Bin Zhang
- Department of Hematologic Malignancies Translational Science, Beckman Research Institute and Division of Leukemia, City of Hope National Medical Center, Duarte, California, 91010, USA
| | - Guido Marcucci
- Department of Hematologic Malignancies Translational Science, Beckman Research Institute and Division of Leukemia, City of Hope National Medical Center, Duarte, California, 91010, USA
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Vu L, Garcia‐Mansfield K, Pompeiano A, An J, David‐Dirgo V, Sharma R, Venugopal V, Halait H, Marcucci G, Kuo Y, Uechi L, Rockne RC, Pirrotte P, Bowser R. Proteomics and mathematical modeling of longitudinal CSF differentiates fast versus slow ALS progression. Ann Clin Transl Neurol 2023; 10:2025-2042. [PMID: 37646115 PMCID: PMC10647001 DOI: 10.1002/acn3.51890] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 08/12/2023] [Indexed: 09/01/2023] Open
Abstract
OBJECTIVE Amyotrophic lateral sclerosis (ALS) is a heterogeneous disease with a complex etiology that lacks biomarkers predicting disease progression. The objective of this study was to use longitudinal cerebrospinal fluid (CSF) samples to identify biomarkers that distinguish fast progression (FP) from slow progression (SP) and assess their temporal response. METHODS We utilized mass spectrometry (MS)-based proteomics to identify candidate biomarkers using longitudinal CSF from a discovery cohort of SP and FP ALS patients. Immunoassays were used to quantify and validate levels of the top biomarkers. A state-transition mathematical model was created using the longitudinal MS data that also predicted FP versus SP. RESULTS We identified a total of 1148 proteins in the CSF of all ALS patients. Pathway analysis determined enrichment of pathways related to complement and coagulation cascades in FPs and synaptogenesis and glucose metabolism in SPs. Longitudinal analysis revealed a panel of 59 candidate markers that could segregate FP and SP ALS. Based on multivariate analysis, we identified three biomarkers (F12, RBP4, and SERPINA4) as top candidates that segregate ALS based on rate of disease progression. These proteins were validated in the discovery and a separate validation cohort. Our state-transition model determined that the overall variance of the proteome over time was predictive of the disease progression rate. INTERPRETATION We identified pathways and protein biomarkers that distinguish rate of ALS disease progression. A mathematical model of the CSF proteome determined that the change in entropy of the proteome over time was predictive of FP versus SP.
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Affiliation(s)
- Lucas Vu
- Department of Translational NeuroscienceBarrow Neurological InstitutePhoenixArizona85013USA
| | - Krystine Garcia‐Mansfield
- Cancer & Cell Biology DivisionTranslational Genomics Research InstitutePhoenixArizona85004USA
- Integrated Mass Spectrometry, City of Hope Comprehensive Cancer CenterDuarteCalifornia19050USA
| | - Antonio Pompeiano
- International Clinical Research CenterSt. Anne's University HospitalBrnoCzech Republic
| | - Jiyan An
- Department of Translational NeuroscienceBarrow Neurological InstitutePhoenixArizona85013USA
| | - Victoria David‐Dirgo
- Integrated Mass Spectrometry, City of Hope Comprehensive Cancer CenterDuarteCalifornia19050USA
| | - Ritin Sharma
- Cancer & Cell Biology DivisionTranslational Genomics Research InstitutePhoenixArizona85004USA
- Integrated Mass Spectrometry, City of Hope Comprehensive Cancer CenterDuarteCalifornia19050USA
| | - Vinisha Venugopal
- Department of Translational NeuroscienceBarrow Neurological InstitutePhoenixArizona85013USA
| | - Harkeerat Halait
- Department of Translational NeuroscienceBarrow Neurological InstitutePhoenixArizona85013USA
| | - Guido Marcucci
- Department of Hematologic Malignances Translational Science, Gehr Family Center for Leukemia ResearchBeckman Research Institute, City of Hope Medical CenterDuarteCalifornia91010USA
| | - Ya‐Huei Kuo
- Department of Hematologic Malignances Translational Science, Gehr Family Center for Leukemia ResearchBeckman Research Institute, City of Hope Medical CenterDuarteCalifornia91010USA
| | - Lisa Uechi
- Department of Computational and Quantitative MedicineBeckman Research Institute, City of Hope Medical CenterDuarteCalifornia91010USA
| | - Russell C. Rockne
- Department of Computational and Quantitative MedicineBeckman Research Institute, City of Hope Medical CenterDuarteCalifornia91010USA
| | - Patrick Pirrotte
- Cancer & Cell Biology DivisionTranslational Genomics Research InstitutePhoenixArizona85004USA
- Integrated Mass Spectrometry, City of Hope Comprehensive Cancer CenterDuarteCalifornia19050USA
| | - Robert Bowser
- Department of Translational NeuroscienceBarrow Neurological InstitutePhoenixArizona85013USA
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Zhang B, Zhao D, Chen F, Frankhouser D, Wang H, Pathak KV, Dong L, Torres A, Garcia-Mansfield K, Zhang Y, Hoang DH, Chen MH, Tao S, Cho H, Liang Y, Perrotti D, Branciamore S, Rockne R, Wu X, Ghoda L, Li L, Jin J, Chen J, Yu J, Caligiuri MA, Kuo YH, Boldin M, Su R, Swiderski P, Kortylewski M, Pirrotte P, Nguyen LXT, Marcucci G. Acquired miR-142 deficit in leukemic stem cells suffices to drive chronic myeloid leukemia into blast crisis. Nat Commun 2023; 14:5325. [PMID: 37658085 PMCID: PMC10474062 DOI: 10.1038/s41467-023-41167-z] [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: 11/07/2022] [Accepted: 08/23/2023] [Indexed: 09/03/2023] Open
Abstract
The mechanisms underlying the transformation of chronic myeloid leukemia (CML) from chronic phase (CP) to blast crisis (BC) are not fully elucidated. Here, we show lower levels of miR-142 in CD34+CD38- blasts from BC CML patients than in those from CP CML patients, suggesting that miR-142 deficit is implicated in BC evolution. Thus, we create miR-142 knockout CML (i.e., miR-142-/-BCR-ABL) mice, which develop BC and die sooner than miR-142 wt CML (i.e., miR-142+/+BCR-ABL) mice, which instead remain in CP CML. Leukemic stem cells (LSCs) from miR-142-/-BCR-ABL mice recapitulate the BC phenotype in congenic recipients, supporting LSC transformation by miR-142 deficit. State-transition and mutual information analyses of "bulk" and single cell RNA-seq data, metabolomic profiling and functional metabolic assays identify enhanced fatty acid β-oxidation, oxidative phosphorylation and mitochondrial fusion in LSCs as key steps in miR-142-driven BC evolution. A synthetic CpG-miR-142 mimic oligodeoxynucleotide rescues the BC phenotype in miR-142-/-BCR-ABL mice and patient-derived xenografts.
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Affiliation(s)
- Bin Zhang
- Department of Hematological Malignancies Translational Science, Gehr Family Center for Leukemia Research, City of Hope Medical Center and Beckman Research Institute, Duarte, CA, USA.
| | - Dandan Zhao
- Department of Hematological Malignancies Translational Science, Gehr Family Center for Leukemia Research, City of Hope Medical Center and Beckman Research Institute, Duarte, CA, USA
| | - Fang Chen
- Department of Hematological Malignancies Translational Science, Gehr Family Center for Leukemia Research, City of Hope Medical Center and Beckman Research Institute, Duarte, CA, USA
| | - David Frankhouser
- Department of Computational and Quantitative Medicine, City of Hope Medical Center and Beckman Research Institute, Duarte, CA, USA
| | - Huafeng Wang
- Department of Hematological Malignancies Translational Science, Gehr Family Center for Leukemia Research, City of Hope Medical Center and Beckman Research Institute, Duarte, CA, USA
- Department of Hematology, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, PR China
| | - Khyatiben V Pathak
- Cancer & Cell Biology Division, Translational Genomics Research Institute, Phoenix, AZ, USA
- Integrated Mass Spectrometry Shared Resource, City of Hope Comprehensive Cancer Center, Duarte, CA, USA
| | - Lei Dong
- Department of Systems Biology, Beckman Research Institute of City of Hope, Monrovia, CA, USA
| | - Anakaren Torres
- Cancer & Cell Biology Division, Translational Genomics Research Institute, Phoenix, AZ, USA
- Integrated Mass Spectrometry Shared Resource, City of Hope Comprehensive Cancer Center, Duarte, CA, USA
| | - Krystine Garcia-Mansfield
- Cancer & Cell Biology Division, Translational Genomics Research Institute, Phoenix, AZ, USA
- Integrated Mass Spectrometry Shared Resource, City of Hope Comprehensive Cancer Center, Duarte, CA, USA
| | - Yi Zhang
- Department of Hematological Malignancies Translational Science, Gehr Family Center for Leukemia Research, City of Hope Medical Center and Beckman Research Institute, Duarte, CA, USA
- Department of Hematology, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, PR China
| | - Dinh Hoa Hoang
- Department of Hematological Malignancies Translational Science, Gehr Family Center for Leukemia Research, City of Hope Medical Center and Beckman Research Institute, Duarte, CA, USA
| | - Min-Hsuan Chen
- City of Hope National Medical Center, Integrative Genomics Core, Department of Computational and Quantitative Medicine, Beckman Research Institute, Duarte, CA, USA
| | - Shu Tao
- City of Hope National Medical Center, Integrative Genomics Core, Department of Computational and Quantitative Medicine, Beckman Research Institute, Duarte, CA, USA
| | - Hyejin Cho
- City of Hope National Medical Center, Integrative Genomics Core, Department of Computational and Quantitative Medicine, Beckman Research Institute, Duarte, CA, USA
| | - Yong Liang
- DNA/RNA Peptide Shared Resources, Beckman Research Institute, Duarte, CA, USA
| | - Danilo Perrotti
- Department of Medicine and Greenebaum Comprehensive Cancer Center, University of Maryland School of Medicine Baltimore, Baltimore, MD, USA
- Department of Immunology and Inflammation, Centre of Hematology, Imperial College of London, London, UK
| | - Sergio Branciamore
- Department of Computational and Quantitative Medicine, City of Hope Medical Center and Beckman Research Institute, Duarte, CA, USA
| | - Russell Rockne
- Department of Computational and Quantitative Medicine, City of Hope Medical Center and Beckman Research Institute, Duarte, CA, USA
| | - Xiwei Wu
- City of Hope National Medical Center, Integrative Genomics Core, Department of Computational and Quantitative Medicine, Beckman Research Institute, Duarte, CA, USA
| | - Lucy Ghoda
- Department of Hematological Malignancies Translational Science, Gehr Family Center for Leukemia Research, City of Hope Medical Center and Beckman Research Institute, Duarte, CA, USA
| | - Ling Li
- Department of Hematological Malignancies Translational Science, Gehr Family Center for Leukemia Research, City of Hope Medical Center and Beckman Research Institute, Duarte, CA, USA
| | - Jie Jin
- Department of Hematology, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, PR China
| | - Jianjun Chen
- Department of Systems Biology, Beckman Research Institute of City of Hope, Monrovia, CA, USA
| | - Jianhua Yu
- Department of Hematology & Hematopoietic Cell Transplantation, City of Hope National Medical Center, Duarte, CA, USA
| | - Michael A Caligiuri
- Department of Hematology & Hematopoietic Cell Transplantation, City of Hope National Medical Center, Duarte, CA, USA
| | - Ya-Huei Kuo
- Department of Hematological Malignancies Translational Science, Gehr Family Center for Leukemia Research, City of Hope Medical Center and Beckman Research Institute, Duarte, CA, USA
| | - Mark Boldin
- Department of Systems Biology, Beckman Research Institute of City of Hope, Monrovia, CA, USA
| | - Rui Su
- Department of Systems Biology, Beckman Research Institute of City of Hope, Monrovia, CA, USA
| | - Piotr Swiderski
- DNA/RNA Peptide Shared Resources, Beckman Research Institute, Duarte, CA, USA
| | - Marcin Kortylewski
- Department of Immuno-Oncology, Beckman Research Institute, Duarte, CA, USA
| | - Patrick Pirrotte
- Cancer & Cell Biology Division, Translational Genomics Research Institute, Phoenix, AZ, USA
- Integrated Mass Spectrometry Shared Resource, City of Hope Comprehensive Cancer Center, Duarte, CA, USA
| | - Le Xuan Truong Nguyen
- Department of Hematological Malignancies Translational Science, Gehr Family Center for Leukemia Research, City of Hope Medical Center and Beckman Research Institute, Duarte, CA, USA.
- Cancer & Cell Biology Division, Translational Genomics Research Institute, Phoenix, AZ, USA.
| | - Guido Marcucci
- Department of Hematological Malignancies Translational Science, Gehr Family Center for Leukemia Research, City of Hope Medical Center and Beckman Research Institute, Duarte, CA, USA.
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Sharon S, Daher-Ghanem N, Zaid D, Gough MJ, Kravchenko-Balasha N. The immunogenic radiation and new players in immunotherapy and targeted therapy for head and neck cancer. FRONTIERS IN ORAL HEALTH 2023; 4:1180869. [PMID: 37496754 PMCID: PMC10366623 DOI: 10.3389/froh.2023.1180869] [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: 03/06/2023] [Accepted: 06/27/2023] [Indexed: 07/28/2023] Open
Abstract
Although treatment modalities for head and neck cancer have evolved considerably over the past decades, survival rates have plateaued. The treatment options remained limited to definitive surgery, surgery followed by fractionated radiotherapy with optional chemotherapy, and a definitive combination of fractionated radiotherapy and chemotherapy. Lately, immunotherapy has been introduced as the fourth modality of treatment, mainly administered as a single checkpoint inhibitor for recurrent or metastatic disease. While other regimens and combinations of immunotherapy and targeted therapy are being tested in clinical trials, adapting the appropriate regimens to patients and predicting their outcomes have yet to reach the clinical setting. Radiotherapy is mainly regarded as a means to target cancer cells while minimizing the unwanted peripheral effect. Radiotherapy regimens and fractionation are designed to serve this purpose, while the systemic effect of radiation on the immune response is rarely considered a factor while designing treatment. To bridge this gap, this review will highlight the effect of radiotherapy on the tumor microenvironment locally, and the immune response systemically. We will review the methodology to identify potential targets for therapy in the tumor microenvironment and the scientific basis for combining targeted therapy and radiotherapy. We will describe a current experience in preclinical models to test these combinations and propose how challenges in this realm may be faced. We will review new players in targeted therapy and their utilization to drive immunogenic response against head and neck cancer. We will outline the factors contributing to head and neck cancer heterogeneity and their effect on the response to radiotherapy. We will review in-silico methods to decipher intertumoral and intratumoral heterogeneity and how these algorithms can predict treatment outcomes. We propose that (a) the sequence of surgery, radiotherapy, chemotherapy, and targeted therapy should be designed not only to annul cancer directly, but to prime the immune response. (b) Fractionation of radiotherapy and the extent of the irradiated field should facilitate systemic immunity to develop. (c) New players in targeted therapy should be evaluated in translational studies toward clinical trials. (d) Head and neck cancer treatment should be personalized according to patients and tumor-specific factors.
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Affiliation(s)
- Shay Sharon
- Department of Oral and Maxillofacial Surgery, Hadassah Medical Center, Faculty of Dental Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
- The Institute of Biomedical and Oral Research, The Hebrew University of Jerusalem, Jerusalem, Israel
- Department of Oral and Maxillofacial Surgery, Boston University and Boston Medical Center, Boston, MA, United States
| | - Narmeen Daher-Ghanem
- The Institute of Biomedical and Oral Research, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Deema Zaid
- The Institute of Biomedical and Oral Research, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Michael J. Gough
- Earle A. Chiles Research Institute, Robert W. Franz Cancer Center, Providence Portland Medical Center, Portland, OR, United States
| | - Nataly Kravchenko-Balasha
- The Institute of Biomedical and Oral Research, The Hebrew University of Jerusalem, Jerusalem, Israel
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7
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Feinberg AP, Levchenko A. Epigenetics as a mediator of plasticity in cancer. Science 2023; 379:eaaw3835. [PMID: 36758093 PMCID: PMC10249049 DOI: 10.1126/science.aaw3835] [Citation(s) in RCA: 41] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Accepted: 12/22/2022] [Indexed: 02/11/2023]
Abstract
The concept of an epigenetic landscape describing potential cellular fates arising from pluripotent cells, first advanced by Conrad Waddington, has evolved in light of experiments showing nondeterministic outcomes of regulatory processes and mathematical methods for quantifying stochasticity. In this Review, we discuss modern approaches to epigenetic and gene regulation landscapes and the associated ideas of entropy and attractor states, illustrating how their definitions are both more precise and relevant to understanding cancer etiology and the plasticity of cancerous states. We address the interplay between different types of regulatory landscapes and how their changes underlie cancer progression. We also consider the roles of cellular aging and intrinsic and extrinsic stimuli in modulating cellular states and how landscape alterations can be quantitatively mapped onto phenotypic outcomes and thereby used in therapy development.
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Affiliation(s)
- Andrew P Feinberg
- Center for Epigenetics, Johns Hopkins University Schools of Medicine, Biomedical Engineering, and Public Health, Baltimore, MD 21205, USA
| | - Andre Levchenko
- Yale Systems Biology Institute and Department of Biomedical Engineering, Yale University, West Haven, CT 06516, USA
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8
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Uthamacumaran A. Dissecting cell fate dynamics in pediatric glioblastoma through the lens of complex systems and cellular cybernetics. BIOLOGICAL CYBERNETICS 2022; 116:407-445. [PMID: 35678918 DOI: 10.1007/s00422-022-00935-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 05/04/2022] [Indexed: 06/15/2023]
Abstract
Cancers are complex dynamic ecosystems. Reductionist approaches to science are inadequate in characterizing their self-organized patterns and collective emergent behaviors. Since current approaches to single-cell analysis in cancer systems rely primarily on single time-point multiomics, many of the temporal features and causal adaptive behaviors in cancer dynamics are vastly ignored. As such, tools and concepts from the interdisciplinary paradigm of complex systems theory are introduced herein to decode the cellular cybernetics of cancer differentiation dynamics and behavioral patterns. An intuition for the attractors and complex networks underlying cancer processes such as cell fate decision-making, multiscale pattern formation systems, and epigenetic state-transitions is developed. The applications of complex systems physics in paving targeted therapies and causal pattern discovery in precision oncology are discussed. Pediatric high-grade gliomas are discussed as a model-system to demonstrate that cancers are complex adaptive systems, in which the emergence and selection of heterogeneous cellular states and phenotypic plasticity are driven by complex multiscale network dynamics. In specific, pediatric glioblastoma (GBM) is used as a proof-of-concept model to illustrate the applications of the complex systems framework in understanding GBM cell fate decisions and decoding their adaptive cellular dynamics. The scope of these tools in forecasting cancer cell fate dynamics in the emerging field of computational oncology and patient-centered systems medicine is highlighted.
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9
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Uthamacumaran A, Zenil H. A Review of Mathematical and Computational Methods in Cancer Dynamics. Front Oncol 2022; 12:850731. [PMID: 35957879 PMCID: PMC9359441 DOI: 10.3389/fonc.2022.850731] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Accepted: 05/25/2022] [Indexed: 12/16/2022] Open
Abstract
Cancers are complex adaptive diseases regulated by the nonlinear feedback systems between genetic instabilities, environmental signals, cellular protein flows, and gene regulatory networks. Understanding the cybernetics of cancer requires the integration of information dynamics across multidimensional spatiotemporal scales, including genetic, transcriptional, metabolic, proteomic, epigenetic, and multi-cellular networks. However, the time-series analysis of these complex networks remains vastly absent in cancer research. With longitudinal screening and time-series analysis of cellular dynamics, universally observed causal patterns pertaining to dynamical systems, may self-organize in the signaling or gene expression state-space of cancer triggering processes. A class of these patterns, strange attractors, may be mathematical biomarkers of cancer progression. The emergence of intracellular chaos and chaotic cell population dynamics remains a new paradigm in systems medicine. As such, chaotic and complex dynamics are discussed as mathematical hallmarks of cancer cell fate dynamics herein. Given the assumption that time-resolved single-cell datasets are made available, a survey of interdisciplinary tools and algorithms from complexity theory, are hereby reviewed to investigate critical phenomena and chaotic dynamics in cancer ecosystems. To conclude, the perspective cultivates an intuition for computational systems oncology in terms of nonlinear dynamics, information theory, inverse problems, and complexity. We highlight the limitations we see in the area of statistical machine learning but the opportunity at combining it with the symbolic computational power offered by the mathematical tools explored.
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Affiliation(s)
| | - Hector Zenil
- Machine Learning Group, Department of Chemical Engineering and Biotechnology, The University of Cambridge, Cambridge, United Kingdom
- The Alan Turing Institute, British Library, London, United Kingdom
- Oxford Immune Algorithmics, Reading, United Kingdom
- Algorithmic Dynamics Lab, Karolinska Institute, Stockholm, Sweden
- Algorithmic Nature Group, LABORES, Paris, France
- *Correspondence: Hector Zenil,
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10
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Comparing Bayesian-Based Reconstruction Strategies in Topology-Based Pathway Enrichment Analysis. Biomolecules 2022; 12:biom12070906. [PMID: 35883462 PMCID: PMC9313337 DOI: 10.3390/biom12070906] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 06/21/2022] [Accepted: 06/24/2022] [Indexed: 02/01/2023] Open
Abstract
The development of high-throughput omics technologies has enabled the quantification of vast amounts of genes and gene products in the whole genome. Pathway enrichment analysis (PEA) provides an intuitive solution for extracting biological insights from massive amounts of data. Topology-based pathway analysis (TPA) represents the latest generation of PEA methods, which exploit pathway topology in addition to lists of differentially expressed genes and their expression profiles. A subset of these TPA methods, such as BPA, BNrich, and PROPS, reconstruct pathway structures by training Bayesian networks (BNs) from canonical biological pathways, providing superior representations that explain causal relationships between genes. However, these methods have never been compared for their differences in the PEA and their different topology reconstruction strategies. In this study, we aim to compare the BN reconstruction strategies of the BPA, BNrich, PROPS, Clipper, and Ensemble methods and their PEA and performance on tumor and non-tumor classification based on gene expression data. Our results indicate that they performed equally well in distinguishing tumor and non-tumor samples (AUC > 0.95) yet with a varying ranking of pathways, which can be attributed to the different BN structures resulting from the different cyclic structure removal strategies. This can be clearly seen from the reconstructed JAK-STAT networks by different strategies. In a nutshell, BNrich, which relies on expert intervention to remove loops and cyclic structures, produces BNs that best fit the biological facts. The plausibility of the Clipper strategy can also be partially explained by intuitive biological rules and theorems. Our results may offer an informed reference for the proper method for a given data analysis task.
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11
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Cohen AA, Leung DL, Legault V, Gravel D, Blanchet FG, Côté AM, Fülöp T, Lee J, Dufour F, Liu M, Nakazato Y. Synchrony of biomarker variability indicates a critical transition: Application to mortality prediction in hemodialysis. iScience 2022; 25:104385. [PMID: 35620427 PMCID: PMC9127602 DOI: 10.1016/j.isci.2022.104385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 03/22/2022] [Accepted: 05/05/2022] [Indexed: 12/03/2022] Open
Abstract
Critical transition theory suggests that complex systems should experience increased temporal variability just before abrupt state changes. We tested this hypothesis in 763 patients on long-term hemodialysis, using 11 biomarkers collected every two weeks and all-cause mortality as a proxy for critical transitions. We find that variability-measured by coefficients of variation (CVs)-increases before death for all 11 clinical biomarkers, and is strikingly synchronized across all biomarkers: the first axis of a principal component analysis on all CVs explains 49% of the variance. This axis then generates powerful predictions of mortality (HR95 = 9.7, p < 0.0001, where HR95 is a scale-invariant metric of hazard ratio; AUC up to 0.82) and starts to increase markedly ∼3 months prior to death. Our results provide an early warning sign of physiological collapse and, more broadly, a quantification of joint system dynamics that opens questions of how system modularity may break down before critical transitions.
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Affiliation(s)
- Alan A. Cohen
- PRIMUS Research Group, Department of Family Medicine, University of Sherbrooke, Sherbrooke, Quebec J1H 5N4, Canada
- Research Center on Aging, Sherbrooke, Quebec J1H 4C4, Canada
- Research Center of Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, Quebec J1H 5N4, Canada
| | - Diana L. Leung
- PRIMUS Research Group, Department of Family Medicine, University of Sherbrooke, Sherbrooke, Quebec J1H 5N4, Canada
| | - Véronique Legault
- PRIMUS Research Group, Department of Family Medicine, University of Sherbrooke, Sherbrooke, Quebec J1H 5N4, Canada
| | - Dominique Gravel
- Département de Biologie, Université de Sherbrooke, Sherbrooke, Quebec J1K 2R1, Canada
| | - F. Guillaume Blanchet
- Research Center on Aging, Sherbrooke, Quebec J1H 4C4, Canada
- Département de Biologie, Université de Sherbrooke, Sherbrooke, Quebec J1K 2R1, Canada
- Département de mathématique, Université de Sherbrooke, Sherbrooke, Québec J1K 2R1, Canada
- Département des Sciences de la Santé Communautaires, Université de Sherbrooke, Sherbrooke, Québec J1H 5N4, Canada
| | - Anne-Marie Côté
- Department of Medicine, Nephrology Division, University of Sherbrooke, Sherbrooke, Quebec J1H 5N4, Canada
| | - Tamàs Fülöp
- Research Center on Aging, Sherbrooke, Quebec J1H 4C4, Canada
- Department of Medicine, Geriatric Division, University of Sherbrooke, Sherbrooke, Quebec J1H 5N4, Canada
| | - Juhong Lee
- InfoCentre, Centre intégré universitaire de santé et de services sociaux de l’Estrie – Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, Quebec J1H 5N4, Canada
| | - Frédérik Dufour
- PRIMUS Research Group, Department of Family Medicine, University of Sherbrooke, Sherbrooke, Quebec J1H 5N4, Canada
- Département de Biologie, Université de Sherbrooke, Sherbrooke, Quebec J1K 2R1, Canada
| | - Mingxin Liu
- PRIMUS Research Group, Department of Family Medicine, University of Sherbrooke, Sherbrooke, Quebec J1H 5N4, Canada
| | - Yuichi Nakazato
- Division of Nephrology, Hakuyukai Medical Corporation, Yuai Nisshin Clinic, 2-1914-6 Nisshin-cho, Kita-ku, Saitama-City, Saitama 331-0823, Japan
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12
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Cho H, Kuo YH, Rockne RC. Comparison of cell state models derived from single-cell RNA sequencing data: graph versus multi-dimensional space. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:8505-8536. [PMID: 35801475 PMCID: PMC9308174 DOI: 10.3934/mbe.2022395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Single-cell sequencing technologies have revolutionized molecular and cellular biology and stimulated the development of computational tools to analyze the data generated from these technology platforms. However, despite the recent explosion of computational analysis tools, relatively few mathematical models have been developed to utilize these data. Here we compare and contrast two cell state geometries for building mathematical models of cell state-transitions with single-cell RNA-sequencing data with hematopoeisis as a model system; (i) by using partial differential equations on a graph representing intermediate cell states between known cell types, and (ii) by using the equations on a multi-dimensional continuous cell state-space. As an application of our approach, we demonstrate how the calibrated models may be used to mathematically perturb normal hematopoeisis to simulate, predict, and study the emergence of novel cell states during the pathogenesis of acute myeloid leukemia. We particularly focus on comparing the strength and weakness of the graph model and multi-dimensional model.
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Affiliation(s)
- Heyrim Cho
- Department of Mathematics, University of California Riverside, Riverside, CA, USA
- Interdisciplinary Center for Quantitative Modeling in Biology, University of California Riverside, Riverside, CA, USA
| | - Ya-Huei Kuo
- Department of Hematologic Malignancies Translational Science, City of Hope, Duarte, CA, USA
| | - Russell C. Rockne
- Department of Computational and Quantitative Medicine, Division of Mathematical Oncology, City of Hope, Duarte, CA, USA
- Interdisciplinary Center for Quantitative Modeling in Biology, University of California Riverside, Riverside, CA, USA
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13
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Frankhouser DE, O’Meally D, Branciamore S, Uechi L, Zhang L, Chen YC, Li M, Qin H, Wu X, Carlesso N, Marcucci G, Rockne RC, Kuo YH. Dynamic patterns of microRNA expression during acute myeloid leukemia state-transition. SCIENCE ADVANCES 2022; 8:eabj1664. [PMID: 35452289 PMCID: PMC9032952 DOI: 10.1126/sciadv.abj1664] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 03/08/2022] [Indexed: 06/02/2023]
Abstract
MicroRNAs (miRNAs) have been shown to hold prognostic value in acute myeloid leukemia (AML); however, the temporal dynamics of miRNA expression in AML are poorly understood. Using serial samples from a mouse model of AML to generate time-series miRNA sequencing data, we are the first to show that the miRNA transcriptome undergoes state-transition during AML initiation and progression. We modeled AML state-transition as a particle undergoing Brownian motion in a quasi-potential and validated the AML state-space and state-transition model to accurately predict time to AML in an independent cohort of mice. The critical points of the model provided a framework to align samples from mice that developed AML at different rates. Our mathematical approach allowed discovery of dynamic processes involved during AML development and, if translated to humans, has the potential to predict an individual's disease trajectory.
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Affiliation(s)
- David E. Frankhouser
- Department of Population Sciences, City of Hope National Medical Center, Duarte, CA 91010, USA
- Division of Mathematical Oncology, Department of Computational and Quantitative Medicine, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Denis O’Meally
- Center for Gene Therapy, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Sergio Branciamore
- Department of Diabetes Complications and Metabolism, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Lisa Uechi
- Division of Mathematical Oncology, Department of Computational and Quantitative Medicine, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Lianjun Zhang
- Department of Hematological Malignancies Translational Science, Hematology and Hematopoietic Cell Transplantation, City of Hope National Medical Center, Duarte, CA 91010, USA
- The Gehr Family Center for Leukemia Research, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Ying-Chieh Chen
- Department of Hematological Malignancies Translational Science, Hematology and Hematopoietic Cell Transplantation, City of Hope National Medical Center, Duarte, CA 91010, USA
- The Gehr Family Center for Leukemia Research, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Man Li
- Department of Hematological Malignancies Translational Science, Hematology and Hematopoietic Cell Transplantation, City of Hope National Medical Center, Duarte, CA 91010, USA
- The Gehr Family Center for Leukemia Research, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Hanjun Qin
- Department of Computational and Quantitative Medicine, Integrative Genomics Core, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Xiwei Wu
- Department of Computational and Quantitative Medicine, Integrative Genomics Core, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Nadia Carlesso
- The Gehr Family Center for Leukemia Research, City of Hope National Medical Center, Duarte, CA 91010, USA
- Department of Stem Cell and Regenerative Medicine, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Guido Marcucci
- Department of Hematological Malignancies Translational Science, Hematology and Hematopoietic Cell Transplantation, City of Hope National Medical Center, Duarte, CA 91010, USA
- The Gehr Family Center for Leukemia Research, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Russell C. Rockne
- Division of Mathematical Oncology, Department of Computational and Quantitative Medicine, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Ya-Huei Kuo
- Department of Hematological Malignancies Translational Science, Hematology and Hematopoietic Cell Transplantation, City of Hope National Medical Center, Duarte, CA 91010, USA
- The Gehr Family Center for Leukemia Research, City of Hope National Medical Center, Duarte, CA 91010, USA
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14
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Bose I. Tipping the Balance: A Criticality Perspective. ENTROPY 2022; 24:e24030405. [PMID: 35327916 PMCID: PMC8947304 DOI: 10.3390/e24030405] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 03/10/2022] [Accepted: 03/12/2022] [Indexed: 01/02/2023]
Abstract
Cell populations are often characterised by phenotypic heterogeneity in the form of two distinct subpopulations. We consider a model of tumour cells consisting of two subpopulations: non-cancer promoting (NCP) and cancer-promoting (CP). Under steady state conditions, the model has similarities with a well-known model of population genetics which exhibits a purely noise-induced transition from unimodality to bimodality at a critical value of the noise intensity σ2. The noise is associated with the parameter λ representing the system-environment coupling. In the case of the tumour model, λ has a natural interpretation in terms of the tissue microenvironment which has considerable influence on the phenotypic composition of the tumour. Oncogenic transformations give rise to considerable fluctuations in the parameter. We compute the λ−σ2 phase diagram in a stochastic setting, drawing analogies between bifurcations and phase transitions. In the region of bimodality, a transition from a state of balance to a state of dominance, in terms of the competing subpopulations, occurs at λ = 0. Away from this point, the NCP (CP) subpopulation becomes dominant as λ changes towards positive (negative) values. The variance of the steady state probability density function as well as two entropic measures provide characteristic signatures at the transition point.
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Affiliation(s)
- Indrani Bose
- Department of Physics, Bose Institute, 93/1, A. P. C. Road, Kolkata 700009, India
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15
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Foo J, Basanta D, Rockne RC, Strelez C, Shah C, Ghaffarian K, Mumenthaler SM, Mitchell K, Lathia JD, Frankhouser D, Branciamore S, Kuo YH, Marcucci G, Vander Velde R, Marusyk A, Hang S, Hari K, Jolly MK, Hatzikirou H, Poels K, Spilker M, Shtylla B, Robertson-Tessi M, Anderson ARA. Roadmap on plasticity and epigenetics in cancer. Phys Biol 2022; 19. [PMID: 35078159 PMCID: PMC9190291 DOI: 10.1088/1478-3975/ac4ee2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 01/25/2022] [Indexed: 11/22/2022]
Abstract
The role of plasticity and epigenetics in shaping cancer evolution and response to therapy has taken center stage with recent technological advances including single cell sequencing. This roadmap article is focused on state-of-the-art mathematical and experimental approaches to interrogate plasticity in cancer, and addresses the following themes and questions: is there a formal overarching framework that encompasses both non-genetic plasticity and mutation-driven somatic evolution? How do we measure and model the role of the microenvironment in influencing/controlling non-genetic plasticity? How can we experimentally study non-genetic plasticity? Which mathematical techniques are required or best suited? What are the clinical and practical applications and implications of these concepts?
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Affiliation(s)
- Jasmine Foo
- University of Minnesota System, School of Mathematics, Minneapolis, Minnesota, 55455-2020, UNITED STATES
| | - David Basanta
- Integrated Mathematical Oncology, H Lee Moffitt Cancer Center and Research Center Inc, H Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Drive, MRC-3 West/IMO, Tampa, Florida 33612USA, Tampa, Florida, 33612-9416, UNITED STATES
| | - Russell C Rockne
- Computational and Quantitative Medicine; Division of Mathematical Oncology, Beckman Research Institute, 1500 E Duarte Rd, Rose Vogel Building (74), Duarte, California, 91010, UNITED STATES
| | - Carly Strelez
- Lawrence J. Ellison Institute , Transformative Medicine, Los Angeles, CA 90064, UNITED STATES
| | - Curran Shah
- Lawrence J. Ellison Institute , Transformative Medicine, Los Angeles, CA 90064, UNITED STATES
| | - Kimya Ghaffarian
- Lawrence J. Ellison Institute , Transformative Medicine, Los Angeles, CA 90064, UNITED STATES
| | - Shannon M Mumenthaler
- Lawrence J. Ellison Institute , Transformative Medicine, Los Angeles, CA 90064, UNITED STATES
| | - Kelly Mitchell
- Department of Cardiovascular & Metabolic Sciences, Cleveland Clinic, Lerner Research Institute, Cleveland, Ohio, 44195-5243, UNITED STATES
| | - Justin D Lathia
- Department of Cardiovascular & Metabolic Sciences, Cleveland Clinic, Lerner Research Institute, Cleveland, Ohio, 44195-5243, UNITED STATES
| | - David Frankhouser
- Computational and Quantitative Medicine; Division of Mathematical Oncology, Beckman Research Institute, 1500 E Duarte Rd, Rose Vogel Building (74), Duarte, California, 91010, UNITED STATES
| | - Sergio Branciamore
- Computational and Quantitative Medicine; Division of Mathematical Oncology, Beckman Research Institute, 1500 E Duarte Rd, Rose Vogel Building (74), Duarte, California, 91010, UNITED STATES
| | - Ya-Huei Kuo
- Hematologic Malignancies Translational Science, City of Hope National Medical Center, Beckman Research Institute, 1500 E Duarte Rd, Rose Vogel Building (74), Duarte, California, 91010, UNITED STATES
| | - Guido Marcucci
- Hematologic Malignancies Translational Science, City of Hope National Medical Center, Beckman Research Institute, 1500 E Duarte Rd, Rose Vogel Building (74), Duarte, California, 91010, UNITED STATES
| | - Robert Vander Velde
- Department of Cancer Physiology, H Lee Moffitt Cancer Center and Research Center Inc, H Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Drive, MRC-3 West/IMO, Tampa, Florida 33612USA, Tampa, Florida, 33612-9416, UNITED STATES
| | - Andriy Marusyk
- Cancer Physiology, Moffitt Cancer Center, 12902 USF Magnolia Drive, Tampa, Florida, 33612, UNITED STATES
| | - Sui Hang
- Institute for Systems Biology, Systems Biology, WA , WA 98109, UNITED STATES
| | - Kishore Hari
- Indian Institute of Science, 560012 Bangalore, Bangalore, 560012, INDIA
| | - Mohit Kumar Jolly
- Centre for BioSystems Science and Engineering,, Indian Institute of Science, 560012 Bangalore, Bangalore, 560012, INDIA
| | - Haralampos Hatzikirou
- Khalifa University, P.O. Box: 127788, Abu Dhabi, Abu Dhabi, NA, UNITED ARAB EMIRATES
| | - Kamrine Poels
- Early Clinical Development, Pfizer Global Research and Development, Early Clinical Development, Groton, Connecticut, 06340, UNITED STATES
| | - Mary Spilker
- Medicine Design, Pfizer Global Research and Development, Medicine Design, Groton, Connecticut, 06340, UNITED STATES
| | - Blerta Shtylla
- Early Clinical Development, Pfizer Global Research and Development, Early Clinical Development, Groton, Connecticut, 06340, UNITED STATES
| | - Mark Robertson-Tessi
- Integrated Mathematical Oncology Department, Moffitt Cancer Center, 12902 USF Magnolia Drive, Tampa, Florida, 33612, UNITED STATES
| | - Alexander R A Anderson
- Integrated Mathematical Oncology, Moffitt Cancer Center, Co-Director of Integrated Mathematical Oncology, 12902 Magnolia Drive, SRB 4 Rm 24000H, Tampa, Florida 33612, Tampa, 33612, UNITED STATES
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16
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Uthamacumaran A. A review of dynamical systems approaches for the detection of chaotic attractors in cancer networks. PATTERNS (NEW YORK, N.Y.) 2021; 2:100226. [PMID: 33982021 PMCID: PMC8085613 DOI: 10.1016/j.patter.2021.100226] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Cancers are complex dynamical systems. They remain the leading cause of disease-related pediatric mortality in North America. To overcome this burden, we must decipher the state-space attractor dynamics of gene expression patterns and protein oscillations orchestrated by cancer stemness networks. The review provides an overview of dynamical systems theory to steer cancer research in pattern science. While most of our current tools in network medicine rely on statistical correlation methods, causality inference remains primitively developed. As such, a survey of attractor reconstruction methods and machine algorithms for the detection of causal structures applicable in experimentally derived time series cancer datasets is presented. A toolbox of complex systems approaches are discussed for reconstructing the signaling state space of cancer networks, interpreting causal relationships in their time series gene expression patterns, and assisting clinical decision making in computational oncology. As a proof of concept, the applicability of some algorithms are demonstrated on pediatric brain cancer datasets and the requirement of their time series analysis is highlighted.
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17
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Oh VKS, Li RW. Temporal Dynamic Methods for Bulk RNA-Seq Time Series Data. Genes (Basel) 2021; 12:352. [PMID: 33673721 PMCID: PMC7997275 DOI: 10.3390/genes12030352] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 02/19/2021] [Accepted: 02/22/2021] [Indexed: 02/06/2023] Open
Abstract
Dynamic studies in time course experimental designs and clinical approaches have been widely used by the biomedical community. These applications are particularly relevant in stimuli-response models under environmental conditions, characterization of gradient biological processes in developmental biology, identification of therapeutic effects in clinical trials, disease progressive models, cell-cycle, and circadian periodicity. Despite their feasibility and popularity, sophisticated dynamic methods that are well validated in large-scale comparative studies, in terms of statistical and computational rigor, are less benchmarked, comparing to their static counterparts. To date, a number of novel methods in bulk RNA-Seq data have been developed for the various time-dependent stimuli, circadian rhythms, cell-lineage in differentiation, and disease progression. Here, we comprehensively review a key set of representative dynamic strategies and discuss current issues associated with the detection of dynamically changing genes. We also provide recommendations for future directions for studying non-periodical, periodical time course data, and meta-dynamic datasets.
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Affiliation(s)
- Vera-Khlara S. Oh
- Animal Genomics and Improvement Laboratory, United States Department of Agriculture, Agricultural Research Service, Beltsville, MD 20705, USA;
- Department of Computer Science and Statistics, College of Natural Sciences, Jeju National University, Jeju City 63243, Korea
| | - Robert W. Li
- Animal Genomics and Improvement Laboratory, United States Department of Agriculture, Agricultural Research Service, Beltsville, MD 20705, USA;
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18
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Lai CH, Chen AT, Burns AB, Sriram K, Luo Y, Tang X, Branciamore S, O'Meally D, Chang SL, Huang PH, Shyy JYJ, Chien S, Rockne RC, Chen ZB. RAMP2-AS1 Regulates Endothelial Homeostasis and Aging. Front Cell Dev Biol 2021; 9:635307. [PMID: 33644072 PMCID: PMC7907448 DOI: 10.3389/fcell.2021.635307] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 01/22/2021] [Indexed: 01/23/2023] Open
Abstract
The homeostasis of vascular endothelium is crucial for cardiovascular health and endothelial cell (EC) aging and dysfunction could negatively impact vascular function. Leveraging transcriptome profiles from ECs subjected to various stimuli, including time-series data obtained from ECs under physiological pulsatile flow vs. pathophysiological oscillatory flow, we performed principal component analysis (PCA) to identify key genes contributing to divergent transcriptional states of ECs. Through bioinformatics analysis, we identified that a long non-coding RNA (lncRNA) RAMP2-AS1 encoded on the antisense of RAMP2, a determinant of endothelial homeostasis and vascular integrity, is a novel regulator essential for EC homeostasis and function. Knockdown of RAMP2-AS1 suppressed RAMP2 expression and caused EC functional changes promoting aging, including impaired angiogenesis and increased senescence. Our study demonstrates an integrative approach to quantifying EC aging based on transcriptome changes, which also identified a number of novel regulators, including protein-coding genes and many lncRNAs involved EC functional modulation, exemplified by RAMP2-AS1.
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Affiliation(s)
- Chih-Hung Lai
- Department of Diabetes Complications and Metabolism, City of Hope, Duarte, CA, United States.,Cardiovascular Center, Taichung Veterans General Hospital, Taichung, Taiwan.,Institute of Clinical Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Aleysha T Chen
- Department of Diabetes Complications and Metabolism, City of Hope, Duarte, CA, United States
| | - Andrew B Burns
- Department of Diabetes Complications and Metabolism, City of Hope, Duarte, CA, United States
| | - Kiran Sriram
- Department of Diabetes Complications and Metabolism, City of Hope, Duarte, CA, United States.,Irell and Manella Graduate School of Biological Sciences, City of Hope, Duarte, CA, United States
| | - Yingjun Luo
- Department of Diabetes Complications and Metabolism, City of Hope, Duarte, CA, United States
| | - Xiaofang Tang
- Department of Diabetes Complications and Metabolism, City of Hope, Duarte, CA, United States
| | - Sergio Branciamore
- Department of Diabetes Complications and Metabolism, City of Hope, Duarte, CA, United States
| | - Denis O'Meally
- Center for Gene Therapy, City of Hope, Duarte, CA, United States
| | - Szu-Ling Chang
- Department of Diabetes Complications and Metabolism, City of Hope, Duarte, CA, United States.,Cardiovascular Center, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Po-Hsun Huang
- Institute of Clinical Medicine, National Yang-Ming University, Taipei, Taiwan
| | - John Y-J Shyy
- Department of Medicine, University of California, San Diego, La Jolla, CA, United States
| | - Shu Chien
- Department of Medicine, University of California, San Diego, La Jolla, CA, United States.,Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States
| | - Russell C Rockne
- Irell and Manella Graduate School of Biological Sciences, City of Hope, Duarte, CA, United States.,Division of Mathematical Oncology, Department of Computational and Quantitative Medicine, City of Hope, Duarte, CA, United States
| | - Zhen Bouman Chen
- Department of Diabetes Complications and Metabolism, City of Hope, Duarte, CA, United States.,Irell and Manella Graduate School of Biological Sciences, City of Hope, Duarte, CA, United States
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19
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Kuijjer ML. Predicting Cancer Evolution Using Cell State Dynamics. Cancer Res 2020; 80:3072-3073. [PMID: 32753487 DOI: 10.1158/0008-5472.can-20-1878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Accepted: 06/03/2020] [Indexed: 11/16/2022]
Abstract
One of the biggest challenges in cancer is predicting its initiation and course of progression. In this issue of Cancer Research, Rockne and colleagues use state transition theory to predict how peripheral mononuclear blood cells in mice transition from a healthy state to acute myeloid leukemia. They found that critical transcriptomic perturbations could predict initiation and progression of the disease. This is an important step toward accurately predicting cancer evolution, which may eventually facilitate early diagnosis of cancer and disease recurrence, and which could potentially inform on timing of therapeutic interventions.See related article by Rockne et al., 3157.
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Affiliation(s)
- Marieke L Kuijjer
- Centre for Molecular Medicine Norway, University of Oslo, Oslo, Norway.
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