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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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2
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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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3
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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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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: 4] [Impact Index Per Article: 4.0] [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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6
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Brummer AB, Xella A, Woodall R, Adhikarla V, Cho H, Gutova M, Brown CE, Rockne RC. Data driven model discovery and interpretation for CAR T-cell killing using sparse identification and latent variables. Front Immunol 2023; 14:1115536. [PMID: 37256133 PMCID: PMC10226275 DOI: 10.3389/fimmu.2023.1115536] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Accepted: 03/27/2023] [Indexed: 06/01/2023] Open
Abstract
In the development of cell-based cancer therapies, quantitative mathematical models of cellular interactions are instrumental in understanding treatment efficacy. Efforts to validate and interpret mathematical models of cancer cell growth and death hinge first on proposing a precise mathematical model, then analyzing experimental data in the context of the chosen model. In this work, we present the first application of the sparse identification of non-linear dynamics (SINDy) algorithm to a real biological system in order discover cell-cell interaction dynamics in in vitro experimental data, using chimeric antigen receptor (CAR) T-cells and patient-derived glioblastoma cells. By combining the techniques of latent variable analysis and SINDy, we infer key aspects of the interaction dynamics of CAR T-cell populations and cancer. Importantly, we show how the model terms can be interpreted biologically in relation to different CAR T-cell functional responses, single or double CAR T-cell-cancer cell binding models, and density-dependent growth dynamics in either of the CAR T-cell or cancer cell populations. We show how this data-driven model-discovery based approach provides unique insight into CAR T-cell dynamics when compared to an established model-first approach. These results demonstrate the potential for SINDy to improve the implementation and efficacy of CAR T-cell therapy in the clinic through an improved understanding of CAR T-cell dynamics.
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Affiliation(s)
- Alexander B. Brummer
- Division of Mathematical Oncology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, United States
- Department of Physics and Astronomy, College of Charleston, Charleston, SC, United States
| | - Agata Xella
- Department of Hemtaology and Hematopoietic Cell Translation and Immuno-Oncology, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, United States
| | - Ryan Woodall
- Division of Mathematical Oncology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, United States
| | - Vikram Adhikarla
- Division of Mathematical Oncology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, United States
| | - Heyrim Cho
- Department of Mathematics, University of California, Riverside, Riverside, CA, United States
| | - Margarita Gutova
- Department of Stem Cell Biology and Regenerative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, United States
| | - Christine E. Brown
- Department of Hemtaology and Hematopoietic Cell Translation and Immuno-Oncology, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, United States
| | - Russell C. Rockne
- Division of Mathematical Oncology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, United States
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Zhao X, Wang Z, Ji X, Bu S, Fang P, Wang Y, Wang M, Yang Y, Zhang W, Leung AY, Shi P. Discrete single-cell microRNA analysis for phenotyping the heterogeneity of acute myeloid leukemia. Biomaterials 2022; 291:121869. [DOI: 10.1016/j.biomaterials.2022.121869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 10/14/2022] [Accepted: 10/19/2022] [Indexed: 11/28/2022]
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