1
|
Salvioli M, Vandelaer L, Baena E, Schneider K, Cavill R, Staňková K. The effect of tumor composition on the success of adaptive therapy: The case of metastatic Castrate-Resistant Prostate Cancer. PLoS One 2024; 19:e0308173. [PMID: 39325718 PMCID: PMC11426540 DOI: 10.1371/journal.pone.0308173] [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: 03/22/2023] [Accepted: 07/18/2024] [Indexed: 09/28/2024] Open
Abstract
Prostate-specific antigen (PSA) is the most commonly used serum marker for prostate cancer. It plays a role in cancer detection, treatment monitoring, and more recently, in guiding adaptive therapy protocols, where treatment is alternated based on PSA levels. However, the relationship between PSA levels and tumor volume remains poorly understood. Empirical evidence suggests that different cancer cell types produce varying amounts of PSA. Despite this, current mathematical cancer models often assume either that all cell types contribute equally to PSA levels or that only certain subpopulations produce PSA at fixed rates. In this study, we compare Zhang et al.'s classical adaptive therapy protocol with the standard of care, which involves continuous maximum tolerable dose treatment, under different assumptions regarding PSA production. Specifically, we explore the possibility that testosterone-dependent, testosterone-producing, and testosterone-independent cells contribute to PSA production to varying degrees. We use the time to competitive release as a proxy for the time to disease progression. Our findings indicate that adaptive therapy consistently results in a longer time to competitive release compared to the standard of care, regardless of the assumptions about PSA production. However, when testosterone-independent cells are the sole PSA producers, Zhang et al.'s adaptive therapy protocol becomes inapplicable, as PSA levels never fall to half of their initial value, preventing therapy discontinuation. Additionally, we observe that the number and duration of treatment cycles in adaptive therapy are highly sensitive to assumptions about how much each cell type contributes to PSA production. Overall, our results emphasize the need for a deeper understanding of patient-specific PSA dynamics, which could enhance the effectiveness of adaptive therapy in prostate cancer treatment.
Collapse
Affiliation(s)
- Monica Salvioli
- Evolutionary Game Theory Lab, Faculty of Technology, Policy and Management, Delft University of Technology, Delft, The Netherlands
- Department of Advanced Computing Sciences, Maastricht University, Maastricht, The Netherlands
| | - Len Vandelaer
- Department of Advanced Computing Sciences, Maastricht University, Maastricht, The Netherlands
| | - Esther Baena
- Cancer Research UK Manchester Institute, The University of Manchester, Manchester, United Kingdom
| | - Katharina Schneider
- Department of Advanced Computing Sciences, Maastricht University, Maastricht, The Netherlands
| | - Rachel Cavill
- Department of Advanced Computing Sciences, Maastricht University, Maastricht, The Netherlands
| | - Kateřina Staňková
- Evolutionary Game Theory Lab, Faculty of Technology, Policy and Management, Delft University of Technology, Delft, The Netherlands
| |
Collapse
|
2
|
Reed DR, Tulpule A, Metts J, Trucco M, Robertson-Tessi M, O'Donohue TJ, Iglesias-Cardenas F, Isakoff MS, Mauguen A, Shukla N, Dela Cruz FS, Tap W, Kentsis A, Morris CD, Hameed M, Honeyman JN, Behr GG, Sulis ML, Ortiz MV, Slotkin E. Pediatric Leukemia Roadmaps Are a Guide for Positive Metastatic Bone Sarcoma Trials. J Clin Oncol 2024; 42:2955-2960. [PMID: 38843482 DOI: 10.1200/jco.23.02717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 03/02/2024] [Accepted: 04/11/2024] [Indexed: 08/30/2024] Open
Abstract
ALL cures require many MRD therapies. This strategy should drive experiments and trials in metastatic bone sarcomas.
Collapse
Affiliation(s)
- Damon R Reed
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Asmin Tulpule
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Jonathan Metts
- Johns Hopkins All Children's Hospital, St Petersburg, FL
| | | | | | - Tara J O'Donohue
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | - Audrey Mauguen
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Neerav Shukla
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Filemon S Dela Cruz
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - William Tap
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Alex Kentsis
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Carol D Morris
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Meera Hameed
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Joshua N Honeyman
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Gerald G Behr
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Maria Luisa Sulis
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Michael V Ortiz
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Emily Slotkin
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY
| |
Collapse
|
3
|
Han GYQ, Alexander M, Gattozzi J, Day M, Kirsch E, Tafreshi N, Chalar R, Rahni S, Gossner G, Burke W, Damaghi M. Ecological and evolutionary dynamics to design and improve ovarian cancer treatment. Clin Transl Med 2024; 14:e70012. [PMID: 39210542 PMCID: PMC11362027 DOI: 10.1002/ctm2.70012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2024] [Revised: 08/16/2024] [Accepted: 08/18/2024] [Indexed: 09/04/2024] Open
Abstract
Ovarian cancer ecosystems are exceedingly complex, consisting of a high heterogeneity of cancer cells. Development of drugs such as poly ADP-ribose polymerase (PARP) inhibitors, targeted therapies and immunotherapies offer more options for sequential or combined treatments. Nevertheless, mortality in metastatic ovarian cancer patients remains high because cancer cells consistently develop resistance to single and combination therapies, urging a need for treatment designs that target the evolvability of cancer cells. The evolutionary dynamics that lead to resistance emerge from the complex tumour microenvironment, the heterogeneous populations, and the individual cancer cell's plasticity. We propose that successful management of ovarian cancer requires consideration of the ecological and evolutionary dynamics of the disease. Here, we review current options and challenges in ovarian cancer treatment and discuss principles of tumour evolution. We conclude by proposing evolutionarily designed strategies for ovarian cancer, with the goal of integrating such principles with longitudinal, quantitative data to improve the treatment design and management of drug resistance. KEY POINTS/HIGHLIGHTS: Tumours are ecosystems in which cancer and non-cancer cells interact and evolve in complex and dynamic ways. Conventional therapies for ovarian cancer inevitably lead to the development of resistance because they fail to consider tumours' heterogeneity and cellular plasticity. Eco-evolutionarily designed therapies should consider cancer cell plasticity and patient-specific characteristics to improve clinical outcome and prevent relapse.
Collapse
Affiliation(s)
- Grace Y. Q. Han
- Renaissance School of Medicine at Stony Brook UniversityStony BrookNew YorkUSA
| | - Monica Alexander
- Department of Molecular and Cellular BiologyStony Brook UniversityStony BrookNew YorkUSA
| | - Julia Gattozzi
- Department of Molecular and Cellular PharmacologyStony Brook UniversityStony BrookNew YorkUSA
| | - Marilyn Day
- Department of Obstetrics and GynecologyRenaissance School of Medicine at Stony Brook UniversityStony BrookNew YorkUSA
| | - Elayna Kirsch
- Department of Obstetrics and GynecologyRenaissance School of Medicine at Stony Brook UniversityStony BrookNew YorkUSA
| | | | - Raafat Chalar
- Stony Brook Cancer CenterRenaissance School of Medicine at Stony Brook UniversityStony BrookNew YorkUSA
| | | | - Gabrielle Gossner
- Department of Obstetrics and GynecologyStony Brook University HospitalStony BrookNew YorkUSA
| | - William Burke
- Department of Obstetrics and GynecologyStony Brook University HospitalStony BrookNew YorkUSA
| | - Mehdi Damaghi
- Stony Brook Cancer CenterRenaissance School of Medicine at Stony Brook UniversityStony BrookNew YorkUSA
- Department of PathologyRenaissance School of Medicine at Stony Brook UniversityStony BrookNew YorkUSA
- Department of Radiation OncologyRenaissance School of Medicine at Stony Brook UniversityStony BrookNew YorkUSA
| |
Collapse
|
4
|
Patil S, Ahmed A, Viossat Y, Noble R. Preventing evolutionary rescue in cancer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.22.568336. [PMID: 38045391 PMCID: PMC10690287 DOI: 10.1101/2023.11.22.568336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
First-line cancer treatment frequently fails due to initially rare therapeutic resistance. An important clinical question is then how to schedule subsequent treatments to maximize the probability of tumour eradication. Here, we provide a theoretical solution to this problem by using mathematical analysis and extensive stochastic simulations within the framework of evolutionary rescue theory to determine how best to exploit the vulnerability of small tumours to stochastic extinction. Whereas standard clinical practice is to wait for evidence of relapse, we confirm a recent hypothesis that the optimal time to switch to a second treatment is when the tumour is close to its minimum size before relapse, when it is likely undetectable. This optimum can lie slightly before or slightly after the nadir, depending on tumour parameters. Given that this exact time point may be difficult to determine in practice, we study windows of high extinction probability that lie around the optimal switching point, showing that switching after the relapse has begun is typically better than switching too early. We further reveal how treatment dose and tumour demographic and evolutionary parameters influence the predicted clinical outcome, and we determine how best to schedule drugs of unequal efficacy. Our work establishes a foundation for further experimental and clinical investigation of this evolutionarily-informed "extinction therapy" strategy.
Collapse
Affiliation(s)
- Srishti Patil
- Indian Institute of Science Education and Research, Pune, India
- Department of Mathematics, City, University of London, London, UK
| | - Armaan Ahmed
- Department of Applied Math & Statistics, Johns Hopkins University, Baltimore, USA
- Department of Biophysics, Johns Hopkins University, Baltimore, USA
| | - Yannick Viossat
- Ceremade, CNRS, Université Paris-Dauphine, Université PSL, Paris,France
| | - Robert Noble
- Department of Mathematics, City, University of London, London, UK
| |
Collapse
|
5
|
Alajmi M, Roy S. An evolutionary differential game for regulating the role of monoclonal antibodies in treating signalling pathways in oesophageal cancer. ROYAL SOCIETY OPEN SCIENCE 2024; 11:240347. [PMID: 39086820 PMCID: PMC11289643 DOI: 10.1098/rsos.240347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 05/21/2024] [Accepted: 06/11/2024] [Indexed: 08/02/2024]
Abstract
This work presents a new framework for a competitive evolutionary game between monoclonal antibodies and signalling pathways in oesophageal cancer. The framework is based on a novel dynamical model that takes into account the dynamic progression of signalling pathways, resistance mechanisms and monoclonal antibody therapies. This game involves a scenario in which signalling pathways and monoclonal antibodies are the players competing against each other, where monoclonal antibodies use Brentuximab and Pembrolizumab dosages as strategies to counter the evolutionary resistance strategy implemented by the signalling pathways. Their interactions are described by the dynamical model, which serves as the game's playground. The analysis and computation of two game-theoretic strategies, Stackelberg and Nash equilibria, are conducted within this framework to ascertain the most favourable outcome for the patient. By comparing Stackelberg equilibria with Nash equilibria, numerical experiments show that the Stackelberg equilibria are superior for treating signalling pathways and are critical for the success of monoclonal antibodies in improving oesophageal cancer patient outcomes.
Collapse
Affiliation(s)
- Mesfer Alajmi
- Department of Mathematics, The University of Texas at Arlington, Arlington, TX76019-0407, USA
| | - Souvik Roy
- Department of Mathematics, The University of Texas at Arlington, Arlington, TX76019-0407, USA
| |
Collapse
|
6
|
Metts JL, Aye JM, Crane JN, Oberoi S, Balis FM, Bhatia S, Bona K, Carleton B, Dasgupta R, Dela Cruz FS, Greenzang KA, Kaufman JL, Linardic CM, Parsons SK, Robertson-Tessi M, Rudzinski ER, Soragni A, Stewart E, Weigel BJ, Wolden SL, Weiss AR, Venkatramani R, Heske CM. Roadmap for the next generation of Children's Oncology Group rhabdomyosarcoma trials. Cancer 2024. [PMID: 38941509 DOI: 10.1002/cncr.35457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/30/2024]
Abstract
Clinical trials conducted by the Intergroup Rhabdomyosarcoma (RMS) Study Group and the Children's Oncology Group have been pivotal to establishing current standards for diagnosis and therapy for RMS. Recent advancements in understanding the biology and clinical behavior of RMS have led to more nuanced approaches to diagnosis, risk stratification, and treatment. The complexities introduced by these advancements, coupled with the rarity of RMS, pose challenges to conducting large-scale phase 3 clinical trials to evaluate new treatment strategies for RMS. Given these challenges, systematic planning of future clinical trials in RMS is paramount to address pertinent questions regarding the therapeutic efficacy of drugs, biomarkers of response, treatment-related toxicity, and patient quality of life. Herein, the authors outline the proposed strategic approach of the Children's Oncology Group Soft Tissue Sarcoma Committee to the next generation of RMS clinical trials, focusing on five themes: improved novel agent identification and preclinical to clinical translation, more efficient trial development and implementation, expanded opportunities for knowledge generation during trials, therapeutic toxicity reduction and quality of life, and patient engagement.
Collapse
Affiliation(s)
- Jonathan L Metts
- Sarcoma Department, Moffitt Cancer Center, Tampa, Florida, USA
- Cancer and Blood Disorders Institute, Johns Hopkins All Children's Hospital, St Petersburg, Florida, USA
| | - Jamie M Aye
- Division of Pediatric Hematology-Oncology, Department of Pediatrics, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Jacquelyn N Crane
- Division of Oncology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Pediatrics, Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sapna Oberoi
- Department of Pediatric Hematology/Oncology, Cancer Care Manitoba, Winnipeg, Manitoba, Canada
- Department of Pediatrics and Child Health, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Frank M Balis
- Division of Oncology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Smita Bhatia
- Institute for Cancer Outcomes and Survivorship, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Kira Bona
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Division of Population Sciences, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Bruce Carleton
- Division of Translational Therapeutics, Department of Pediatrics, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Roshni Dasgupta
- Division of Pediatric General and Thoracic Surgery, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, USA
| | - Filemon S Dela Cruz
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Katie A Greenzang
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Division of Population Sciences, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Jonathan L Kaufman
- Department of Hematology and Medical Oncology, Emory University, Atlanta, Georgia, USA
- Patient Advocacy Committee, Children's Oncology Group, Monrovia, California, USA
| | - Corinne M Linardic
- Department of Pediatrics, Duke University School of Medicine, Durham, North Carolina, USA
- Department of Pharmacology and Cancer Biology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Susan K Parsons
- Institute for Clinical Research and Health Policy Studies and Division of Hematology/Oncology, Tufts Medical Center, Boston, Massachusetts, USA
| | - Mark Robertson-Tessi
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, USA
| | - Erin R Rudzinski
- Department of Laboratory Medicine and Pathology, Seattle Children's Hospital and University of Washington Medical Center, Seattle, Washington, USA
| | - Alice Soragni
- Department of Orthopedic Surgery, University of California Los Angeles, Los Angeles, CA, USA
- Jonsson Comprehensive Cancer Center, University of California Los Angeles, Los Angeles, California, USA
| | - Elizabeth Stewart
- Department of Oncology, St Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Brenda J Weigel
- Division of Pediatric Hematology Oncology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Suzanne L Wolden
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Aaron R Weiss
- Department of Pediatrics, Maine Medical Center, Portland, Maine, USA
| | | | - Christine M Heske
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, USA
| |
Collapse
|
7
|
Del Pino Herrera A, Ferrall-Fairbanks MC. A war on many fronts: cross disciplinary approaches for novel cancer treatment strategies. Front Genet 2024; 15:1383676. [PMID: 38873108 PMCID: PMC11169904 DOI: 10.3389/fgene.2024.1383676] [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: 02/07/2024] [Accepted: 04/26/2024] [Indexed: 06/15/2024] Open
Abstract
Cancer is a disease characterized by uncontrolled cellular growth where cancer cells take advantage of surrounding cellular populations to obtain resources and promote invasion. Carcinomas are the most common type of cancer accounting for almost 90% of cancer cases. One of the major subtypes of carcinomas are adenocarcinomas, which originate from glandular cells that line certain internal organs. Cancers such as breast, prostate, lung, pancreas, colon, esophageal, kidney are often adenocarcinomas. Current treatment strategies include surgery, chemotherapy, radiation, targeted therapy, and more recently immunotherapy. However, patients with adenocarcinomas often develop resistance or recur after the first line of treatment. Understanding how networks of tumor cells interact with each other and the tumor microenvironment is crucial to avoid recurrence, resistance, and high-dose therapy toxicities. In this review, we explore how mathematical modeling tools from different disciplines can aid in the development of effective and personalized cancer treatment strategies. Here, we describe how concepts from the disciplines of ecology and evolution, economics, and control engineering have been applied to mathematically model cancer dynamics and enhance treatment strategies.
Collapse
Affiliation(s)
- Adriana Del Pino Herrera
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Meghan C. Ferrall-Fairbanks
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
- University of Florida Health Cancer Center, University of Florida, Gainesville, FL, United States
| |
Collapse
|
8
|
Rask GC, Taslim C, Bayanjargal A, Cannon MV, Selich-Anderson J, Crow JC, Duncan A, Theisen ER. Seclidemstat blocks the transcriptional function of multiple FET-fusion oncoproteins. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.19.594897. [PMID: 38826330 PMCID: PMC11142045 DOI: 10.1101/2024.05.19.594897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Genes encoding the RNA-binding proteins FUS, EWSR1, and TAF15 (FET proteins) are involved in chromosomal translocations in rare sarcomas. FET-rearranged sarcomas are often aggressive malignancies affecting patients of all ages. New therapies are needed. These translocations fuse the 5' portion of the FET gene with a 3' partner gene encoding a transcription factor (TF). The resulting fusion proteins are oncogenic TFs with a FET protein low complexity domain (LCD) and a DNA binding domain. FET fusion proteins have proven stubbornly difficult to target directly and promising strategies target critical co-regulators. One candidate is lysine specific demethylase 1 (LSD1). LSD1 is recruited by multiple FET fusions, including EWSR1::FLI1. LSD1 promotes EWSR1::FLI1 activity and treatment with the noncompetitive inhibitor SP-2509 blocks EWSR1::FLI1 transcriptional function. A similar molecule, seclidemstat (SP-2577), is currently in clinical trials for FET-rearranged sarcomas (NCT03600649). However, whether seclidemstat has pharmacological activity against FET fusions has not been demonstrated. Here, we evaluate the in vitro potency of seclidemstat against multiple FET-rearranged sarcoma cell lines, including Ewing sarcoma, desmoplastic small round cell tumor, clear cell sarcoma, and myxoid liposarcoma. We also define the transcriptomic effects of seclidemstat treatment and evaluated the activity of seclidemstat against FET fusion transcriptional regulation. Seclidemstat showed potent activity in cell viability assays across FET-rearranged sarcomas and disrupted the transcriptional function of all tested fusions. Though epigenetic and targeted inhibitors are unlikely to be effective as a single agents in the clinic, these data suggest seclidemstat remains a promising new treatment strategy for patients with FET-rearranged sarcomas.
Collapse
Affiliation(s)
- Galen C. Rask
- Center for Childhood Cancer Research, The Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH, 43215, USA
| | - Cenny Taslim
- Center for Childhood Cancer Research, The Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH, 43215, USA
| | - Ariunaa Bayanjargal
- Center for Childhood Cancer Research, The Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH, 43215, USA
- Medical Scientist Training Program, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA
| | - Matthew V. Cannon
- Center for Childhood Cancer Research, The Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH, 43215, USA
| | - Julia Selich-Anderson
- Center for Childhood Cancer Research, The Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH, 43215, USA
| | - Jesse C. Crow
- Center for Childhood Cancer Research, The Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH, 43215, USA
| | | | - Emily R. Theisen
- Center for Childhood Cancer Research, The Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH, 43215, USA
- Department of Pediatrics, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA
| |
Collapse
|
9
|
Welch DL, Fridley BL, Cen L, Teer JK, Yoder SJ, Pettersson F, Xu L, Cheng CH, Zhang Y, Alexandrow MG, Xiang S, Robertson-Tessi M, Brown JS, Metts J, Brohl AS, Reed DR. Modeling phenotypic heterogeneity towards evolutionarily inspired osteosarcoma therapy. Sci Rep 2023; 13:20125. [PMID: 37978271 PMCID: PMC10656496 DOI: 10.1038/s41598-023-47412-1] [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: 08/10/2023] [Accepted: 11/10/2023] [Indexed: 11/19/2023] Open
Abstract
Osteosarcoma is the most common bone sarcoma in children and young adults. While universally delivered, chemotherapy only benefits roughly half of patients with localized disease. Increasingly, intratumoral heterogeneity is recognized as a source of therapeutic resistance. In this study, we develop and evaluate an in vitro model of osteosarcoma heterogeneity based on phenotype and genotype. Cancer cell populations vary in their environment-specific growth rates and in their sensitivity to chemotherapy. We present the genotypic and phenotypic characterization of an osteosarcoma cell line panel with a focus on co-cultures of the most phenotypically divergent cell lines, 143B and SAOS2. Modest environmental (pH, glutamine) or chemical perturbations dramatically shift the success and composition of cell lines. We demonstrate that in nutrient rich culture conditions 143B outcompetes SAOS2. But, under nutrient deprivation or conventional chemotherapy, SAOS2 growth can be favored in spheroids. Importantly, when the simplest heterogeneity state is evaluated, a two-cell line coculture, perturbations that affect the faster growing cell line have only a modest effect on final spheroid size. Thus the only evaluated therapies to eliminate the spheroids were by switching therapies from a first strike to a second strike. This extensively characterized, widely available system, can be modeled and scaled to allow for improved strategies to anticipate resistance in osteosarcoma due to heterogeneity.
Collapse
Affiliation(s)
- Darcy L Welch
- Adolescent and Young Adult Program, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
- Department of Individualized Cancer Management, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Drive, Tampa, FL, 33612, USA
| | - Brooke L Fridley
- Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Ling Cen
- Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Jamie K Teer
- Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Sean J Yoder
- Molecular Genomics Core Facility, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Fredrik Pettersson
- Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Liping Xu
- Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Chia-Ho Cheng
- Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Yonghong Zhang
- Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Mark G Alexandrow
- Molecular Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Shengyan Xiang
- Molecular Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Mark Robertson-Tessi
- Integrative Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
- Cancer Biology and Evolution, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Joel S Brown
- Integrative Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
- Cancer Biology and Evolution, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Jonathan Metts
- Sarcoma Department, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Andrew S Brohl
- Sarcoma Department, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
- Molecular Medicine Program, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Damon R Reed
- Adolescent and Young Adult Program, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
- Department of Individualized Cancer Management, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Drive, Tampa, FL, 33612, USA.
- Integrative Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
- Cancer Biology and Evolution, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
| |
Collapse
|
10
|
Kinnaman MD, Zaccaria S, Makohon-Moore A, Arnold B, Levine MF, Gundem G, Arango Ossa JE, Glodzik D, Rodríguez-Sánchez MI, Bouvier N, Li S, Stockfisch E, Dunigan M, Cobbs C, Bhanot UK, You D, Mullen K, Melchor JP, Ortiz MV, O'Donohue TJ, Slotkin EK, Wexler LH, Dela Cruz FS, Hameed MR, Glade Bender JL, Tap WD, Meyers PA, Papaemmanuil E, Kung AL, Iacobuzio-Donahue CA. Subclonal Somatic Copy-Number Alterations Emerge and Dominate in Recurrent Osteosarcoma. Cancer Res 2023; 83:3796-3812. [PMID: 37812025 PMCID: PMC10646480 DOI: 10.1158/0008-5472.can-23-0385] [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: 02/17/2023] [Revised: 07/14/2023] [Accepted: 09/01/2023] [Indexed: 10/10/2023]
Abstract
Multiple large-scale genomic profiling efforts have been undertaken in osteosarcoma to define the genomic drivers of tumorigenesis, therapeutic response, and disease recurrence. The spatial and temporal intratumor heterogeneity could also play a role in promoting tumor growth and treatment resistance. We conducted longitudinal whole-genome sequencing of 37 tumor samples from 8 patients with relapsed or refractory osteosarcoma. Each patient had at least one sample from a primary site and a metastatic or relapse site. Subclonal copy-number alterations were identified in all patients except one. In 5 patients, subclones from the primary tumor emerged and dominated at subsequent relapses. MYC gain/amplification was enriched in the treatment-resistant clones in 6 of 7 patients with multiple clones. Amplifications in other potential driver genes, such as CCNE1, RAD21, VEGFA, and IGF1R, were also observed in the resistant copy-number clones. A chromosomal duplication timing analysis revealed that complex genomic rearrangements typically occurred prior to diagnosis, supporting a macroevolutionary model of evolution, where a large number of genomic aberrations are acquired over a short period of time followed by clonal selection, as opposed to ongoing evolution. A mutational signature analysis of recurrent tumors revealed that homologous repair deficiency (HRD)-related SBS3 increases at each time point in patients with recurrent disease, suggesting that HRD continues to be an active mutagenic process after diagnosis. Overall, by examining the clonal relationships between temporally and spatially separated samples from patients with relapsed/refractory osteosarcoma, this study sheds light on the intratumor heterogeneity and potential drivers of treatment resistance in this disease. SIGNIFICANCE The chemoresistant population in recurrent osteosarcoma is subclonal at diagnosis, emerges at the time of primary resection due to selective pressure from neoadjuvant chemotherapy, and is characterized by unique oncogenic amplifications.
Collapse
Affiliation(s)
- Michael D. Kinnaman
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York
- Regeneron Pharmaceuticals, Inc., Tarrytown, New York
| | - Simone Zaccaria
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, United Kingdom
- Computational Cancer Genomics Research Group, University College London Cancer Institute, London, United Kingdom
| | - Alvin Makohon-Moore
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York
- David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, New York
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Brian Arnold
- Department of Computer Science, Princeton University, Princeton, New Jersey
- Center for Statistics and Machine Learning, Princeton University, Princeton, New Jersey
| | - Max F. Levine
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Gunes Gundem
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Juan E. Arango Ossa
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Dominik Glodzik
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | | | - Nancy Bouvier
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Shanita Li
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Emily Stockfisch
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Marisa Dunigan
- Integrated Genomics Operation Core, Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Cassidy Cobbs
- Integrated Genomics Operation Core, Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Umesh K. Bhanot
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
- Precision Pathology Biobanking Center, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Daoqi You
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Katelyn Mullen
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York
- Gerstner Sloan Kettering Graduate School of Biomedical Sciences, New York, New York
| | - Jerry P. Melchor
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York
- David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Michael V. Ortiz
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Tara J. O'Donohue
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Emily K. Slotkin
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Leonard H. Wexler
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Filemon S. Dela Cruz
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Meera R. Hameed
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Julia L. Glade Bender
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - William D. Tap
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Paul A. Meyers
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Elli Papaemmanuil
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Andrew L. Kung
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Christine A. Iacobuzio-Donahue
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York
- David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, New York
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| |
Collapse
|
11
|
Strobl MAR, Gallaher J, Robertson-Tessi M, West J, Anderson ARA. Treatment of evolving cancers will require dynamic decision support. Ann Oncol 2023; 34:867-884. [PMID: 37777307 PMCID: PMC10688269 DOI: 10.1016/j.annonc.2023.08.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 08/01/2023] [Accepted: 08/21/2023] [Indexed: 10/02/2023] Open
Abstract
Cancer research has traditionally focused on developing new agents, but an underexplored question is that of the dose and frequency of existing drugs. Based on the modus operandi established in the early days of chemotherapies, most drugs are administered according to predetermined schedules that seek to deliver the maximum tolerated dose and are only adjusted for toxicity. However, we believe that the complex, evolving nature of cancer requires a more dynamic and personalized approach. Chronicling the milestones of the field, we show that the impact of schedule choice crucially depends on processes driving treatment response and failure. As such, cancer heterogeneity and evolution dictate that a one-size-fits-all solution is unlikely-instead, each patient should be mapped to the strategy that best matches their current disease characteristics and treatment objectives (i.e. their 'tumorscape'). To achieve this level of personalization, we need mathematical modeling. In this perspective, we propose a five-step 'Adaptive Dosing Adjusted for Personalized Tumorscapes (ADAPT)' paradigm to integrate data and understanding across scales and derive dynamic and personalized schedules. We conclude with promising examples of model-guided schedule personalization and a call to action to address key outstanding challenges surrounding data collection, model development, and integration.
Collapse
Affiliation(s)
- M A R Strobl
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa; Translational Hematology and Oncology Research, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, USA
| | - J Gallaher
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa
| | - M Robertson-Tessi
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa
| | - J West
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa
| | - A R A Anderson
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa.
| |
Collapse
|
12
|
Ingles Garces AH, Porta N, Graham TA, Banerji U. Clinical trial designs for evaluating and exploiting cancer evolution. Cancer Treat Rev 2023; 118:102583. [PMID: 37331179 DOI: 10.1016/j.ctrv.2023.102583] [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: 04/03/2023] [Revised: 05/21/2023] [Accepted: 05/23/2023] [Indexed: 06/20/2023]
Abstract
The evolution of drug-resistant cell subpopulations causes cancer treatment failure. Current preclinical evidence shows that it is possible to model herding of clonal evolution and collateral sensitivity where an initial treatment could favourably influence the response to a subsequent one. Novel therapy strategies exploiting this understanding are being considered, and clinical trial designs for steering cancer evolution are needed. Furthermore, preclinical evidence suggests that different subsets of drug-sensitive and resistant clones could compete between themselves for nutrients/blood supply, and clones that populate a tumour do so at the expense of other clones. Treatment paradigms based on this clinical application of exploiting cell-cell competition include intermittent dosing regimens or cycling different treatments before progression. This will require clinical trial designs different from the conventional practice of evaluating responses to individual therapy regimens. Next-generation sequencing to assess clonal dynamics longitudinally will improve current radiological assessment of clinical response/resistance and be incorporated into trials exploiting evolution. Furthermore, if understood, clonal evolution can be used to therapeutic advantage, improving patient outcomes based on a new generation of clinical trials.
Collapse
Affiliation(s)
- Alvaro H Ingles Garces
- Drug Development Unit, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, UK
| | - Nuria Porta
- Clinical Trials and Statistical Unit, The Institute of Cancer Research, UK
| | - Trevor A Graham
- Centre for Evolution and Cancer, The Institute of Cancer Research, UK
| | - Udai Banerji
- Drug Development Unit, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, UK.
| |
Collapse
|
13
|
Wood GE, Graves LA, Rubin EM, Reed DR, Riedel RF, Strauss SJ. Bad to the Bone: Emerging Approaches to Aggressive Bone Sarcomas. Am Soc Clin Oncol Educ Book 2023; 43:e390306. [PMID: 37220319 DOI: 10.1200/edbk_390306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Bone sarcomas are rare heterogeneous tumors that affect patients of all ages including children, adolescent young adults, and older adults. They include many aggressive subtypes and patient groups with poor outcomes, poor access to clinical trials, and lack of defined standard therapeutic strategies. Conventional chondrosarcoma remains a surgical disease, with no defined role for cytotoxic therapy and no approved targeted systemic therapies. Here, we discuss promising novel targets and strategies undergoing evaluation in clinical trials. Multiagent chemotherapy has greatly improved outcomes for patients with Ewing sarcoma (ES) and osteosarcoma, but management of those with high-risk or recurrent disease remains challenging and controversial. We describe the impact of international collaborative trials, such as the rEECur study, that aim to define optimal treatment strategies for those with recurrent, refractory ES, and evidence for high-dose chemotherapy with stem-cell support. We also discuss current and emerging strategies for other small round cell sarcomas, such as CIC-rearranged, BCOR-rearranged tumors, and the evaluation of emerging novel therapeutics and trial designs that may offer a new paradigm to improve survival in these aggressive tumors with notoriously bad (to the bone) outcomes.
Collapse
Affiliation(s)
- Georgina E Wood
- Department of Oncology, University College London Hospitals NHS Trust, UCL Cancer Institute, London, United Kingdom
| | - Laurie A Graves
- Division of Hematology/Oncology, Department of Pediatrics, Duke University, Durham, NC
| | - Elyssa M Rubin
- Division of Oncology, Children's Hospital of Orange County, Orange, CA
| | - Damon R Reed
- Department of Individualized Cancer Management, Moffitt Cancer Center, Tampa, FL
| | - Richard F Riedel
- Division of Medical Oncology, Department of Medicine, Duke Cancer Institute, Durham, NC
| | - Sandra J Strauss
- Department of Oncology, University College London Hospitals NHS Trust, UCL Cancer Institute, London, United Kingdom
| |
Collapse
|
14
|
West J, Adler F, Gallaher J, Strobl M, Brady-Nicholls R, Brown J, Roberson-Tessi M, Kim E, Noble R, Viossat Y, Basanta D, Anderson ARA. A survey of open questions in adaptive therapy: Bridging mathematics and clinical translation. eLife 2023; 12:e84263. [PMID: 36952376 PMCID: PMC10036119 DOI: 10.7554/elife.84263] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 02/27/2023] [Indexed: 03/24/2023] Open
Abstract
Adaptive therapy is a dynamic cancer treatment protocol that updates (or 'adapts') treatment decisions in anticipation of evolving tumor dynamics. This broad term encompasses many possible dynamic treatment protocols of patient-specific dose modulation or dose timing. Adaptive therapy maintains high levels of tumor burden to benefit from the competitive suppression of treatment-sensitive subpopulations on treatment-resistant subpopulations. This evolution-based approach to cancer treatment has been integrated into several ongoing or planned clinical trials, including treatment of metastatic castrate resistant prostate cancer, ovarian cancer, and BRAF-mutant melanoma. In the previous few decades, experimental and clinical investigation of adaptive therapy has progressed synergistically with mathematical and computational modeling. In this work, we discuss 11 open questions in cancer adaptive therapy mathematical modeling. The questions are split into three sections: (1) integrating the appropriate components into mathematical models (2) design and validation of dosing protocols, and (3) challenges and opportunities in clinical translation.
Collapse
Affiliation(s)
- Jeffrey West
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research InstituteTampaUnited States
| | - Fred Adler
- Department of Mathematics, University of UtahSalt Lake CityUnited States
- School of Biological Sciences, University of UtahSalt Lake CityUnited States
| | - Jill Gallaher
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research InstituteTampaUnited States
| | - Maximilian Strobl
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research InstituteTampaUnited States
| | - Renee Brady-Nicholls
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research InstituteTampaUnited States
| | - Joel Brown
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research InstituteTampaUnited States
| | - Mark Roberson-Tessi
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research InstituteTampaUnited States
| | - Eunjung Kim
- Natural Product Informatics Research Center, Korea Institute of Science and TechnologyGangneungRepublic of Korea
| | - Robert Noble
- Department of Mathematics, University of LondonLondonUnited Kingdom
| | - Yannick Viossat
- Ceremade, Université Paris-Dauphine, Université Paris Sciences et LettresParisFrance
| | - David Basanta
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research InstituteTampaUnited States
| | - Alexander RA Anderson
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research InstituteTampaUnited States
| |
Collapse
|
15
|
Bukkuri A, Pienta KJ, Hockett I, Austin RH, Hammarlund EU, Amend SR, Brown JS. Modeling cancer's ecological and evolutionary dynamics. Med Oncol 2023; 40:109. [PMID: 36853375 PMCID: PMC9974726 DOI: 10.1007/s12032-023-01968-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 02/05/2023] [Indexed: 03/01/2023]
Abstract
In this didactic paper, we present a theoretical modeling framework, called the G-function, that integrates both the ecology and evolution of cancer to understand oncogenesis. The G-function has been used in evolutionary ecology, but has not been widely applied to problems in cancer. Here, we build the G-function framework from fundamental Darwinian principles and discuss how cancer can be seen through the lens of ecology, evolution, and game theory. We begin with a simple model of cancer growth and add on components of cancer cell competition and drug resistance. To aid in exploration of eco-evolutionary modeling with this approach, we also present a user-friendly software tool. By the end of this paper, we hope that readers will be able to construct basic G function models and grasp the usefulness of the framework to understand the games cancer plays in a biologically mechanistic fashion.
Collapse
Affiliation(s)
- Anuraag Bukkuri
- Cancer Biology and Evolution Program and Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, USA.
- Tissue Development and Evolution Research Group, Department of Laboratory Medicine, Lund University, Lund, Sweden.
| | - Kenneth J Pienta
- The Brady Urological Institute, Johns Hopkins School of Medicine, Baltimore, USA
| | - Ian Hockett
- The Brady Urological Institute, Johns Hopkins School of Medicine, Baltimore, USA
| | | | - Emma U Hammarlund
- Tissue Development and Evolution Research Group, Department of Laboratory Medicine, Lund University, Lund, Sweden
| | - Sarah R Amend
- The Brady Urological Institute, Johns Hopkins School of Medicine, Baltimore, USA
| | - Joel S Brown
- Cancer Biology and Evolution Program and Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, USA
| |
Collapse
|
16
|
Li K, Huo Q, Li BY, Yokota H. Three unconventional maxims in the natural selection of cancer cells: Generation of induced tumor-suppressing cells (iTSCs). Int J Biol Sci 2023; 19:1403-1412. [PMID: 37056934 PMCID: PMC10086743 DOI: 10.7150/ijbs.79155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 02/16/2023] [Indexed: 03/14/2023] Open
Abstract
Induced tumor-suppressing cells (iTSCs) can be generated from cancer and non-cancer cells. Here, three paradoxical maxims for the action of iTSCs are reviewed: the secretion of tumor-suppressing proteins, their role as a "double-edged" sword, and the elimination of lesser-fit cancer cells. "Super-fit" cancer cells secrete an array of proteins, most of which contribute to enhancing their growth and removing "lesser-fit" cancer cells. These maxims explain the potential dilemma with therapeutic agents since the inhibitory agents tend to promote the synthesis of tumor-promoting proteins. The maxims suggest the possibility of a novel treatment option using cancer-guided evolutionary-fit iTSCs.
Collapse
Affiliation(s)
- Kexin Li
- Department of Pharmacology, School of Pharmacy, Harbin Medical University, Harbin 150081, China
- Department of Biomedical Engineering, Indiana University Purdue University Indianapolis, Indianapolis, IN 46202, USA
| | - Qingji Huo
- Department of Pharmacology, School of Pharmacy, Harbin Medical University, Harbin 150081, China
- Department of Biomedical Engineering, Indiana University Purdue University Indianapolis, Indianapolis, IN 46202, USA
| | - Bai-Yan Li
- Department of Pharmacology, School of Pharmacy, Harbin Medical University, Harbin 150081, China
| | - Hiroki Yokota
- Department of Biomedical Engineering, Indiana University Purdue University Indianapolis, Indianapolis, IN 46202, USA
- Indiana Center for Musculoskeletal Health, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- Simon Comprehensive Cancer Center, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| |
Collapse
|
17
|
The CMG helicase and cancer: a tumor "engine" and weakness with missing mutations. Oncogene 2023; 42:473-490. [PMID: 36522488 PMCID: PMC9948756 DOI: 10.1038/s41388-022-02572-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 12/01/2022] [Accepted: 12/07/2022] [Indexed: 12/23/2022]
Abstract
The replicative Cdc45-MCM-GINS (CMG) helicase is a large protein complex that functions in the DNA melting and unwinding steps as a component of replisomes during DNA replication in mammalian cells. Although the CMG performs this important role in cell growth, the CMG is not a simple bystander in cell cycle events. Components of the CMG, specifically the MCM precursors, are also involved in maintaining genomic stability by regulating DNA replication fork speeds, facilitating recovery from replicative stresses, and preventing consequential DNA damage. Given these important functions, MCM/CMG complexes are highly regulated by growth factors such as TGF-ß1 and by signaling factors such as Myc, Cyclin E, and the retinoblastoma protein. Mismanagement of MCM/CMG complexes when these signaling mediators are deregulated, and in the absence of the tumor suppressor protein p53, leads to increased genomic instability and is a contributor to tumorigenic transformation and tumor heterogeneity. The goal of this review is to provide insight into the mechanisms and dynamics by which the CMG is regulated during its assembly and activation in mammalian genomes, and how errors in CMG regulation due to oncogenic changes promote tumorigenesis. Finally, and most importantly, we highlight the emerging understanding of the CMG helicase as an exploitable vulnerability and novel target for therapeutic intervention in cancer.
Collapse
|
18
|
Kinnaman MD, Zaccaria S, Makohon-Moore A, Arnold B, Levine M, Gundem G, Ossa JEA, Glodzik D, Rodríguez-Sánchez MI, Bouvier N, Li S, Stockfisch E, Dunigan M, Cobbs C, Bhanot U, You D, Mullen K, Melchor J, Ortiz MV, O'Donohue T, Slotkin E, Wexler LH, Dela Cruz FS, Hameed M, Glade Bender JL, Tap WD, Meyers PA, Papaemmanuil E, Kung AL, Iacobuzio-Donahue CA. Subclonal somatic copy number alterations emerge and dominate in recurrent osteosarcoma. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.05.522765. [PMID: 36711976 PMCID: PMC9881990 DOI: 10.1101/2023.01.05.522765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Multiple large-scale tumor genomic profiling efforts have been undertaken in osteosarcoma, however, little is known about the spatial and temporal intratumor heterogeneity and how it may drive treatment resistance. We performed whole-genome sequencing of 37 tumor samples from eight patients with relapsed or refractory osteosarcoma. Each patient had at least one sample from a primary site and a metastatic or relapse site. We identified subclonal copy number alterations in all but one patient. We observed that in five patients, a subclonal copy number clone from the primary tumor emerged and dominated at subsequent relapses. MYC gain/amplification was enriched in the treatment-resistant clone in 6 out of 7 patients with more than one clone. Amplifications in other potential driver genes, such as CCNE1, RAD21, VEGFA, and IGF1R, were also observed in the resistant copy number clones. Our study sheds light on intratumor heterogeneity and the potential drivers of treatment resistance in osteosarcoma.
Collapse
Affiliation(s)
- Michael D Kinnaman
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Simone Zaccaria
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Computational Cancer Genomics Research Group, University College London Cancer Institute, London, UK
| | - Alvin Makohon-Moore
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Hackensack Meridian Health Center for Discovery and Innovation, Nutley, NJ, USA (current affiliation)
- Georgetown University Lombardi Comprehensive Cancer Center, Washington, DC, USA (current affiliation)
| | - Brian Arnold
- Department of Computer Science, Princeton University, Princeton, NJ, USA
- Center for Statistics and Machine Learning, Princeton University, Princeton, NJ, USA
| | - Max Levine
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Isabl, New York, NY, USA (current affiliation)
| | - Gunes Gundem
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Juan E Arango Ossa
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Dominik Glodzik
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA (current affiliation)
| | - M Irene Rodríguez-Sánchez
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Wunderman Thompson Health, New York, NY, USA (current affiliation)
| | - Nancy Bouvier
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- IT and Digital Initiatives, Memorial Sloan Kettering Cancer Center, New York, NY, USA (current affiliation)
| | - Shanita Li
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Emily Stockfisch
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Marisa Dunigan
- Integrated Genomics Operation Core, Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Cassidy Cobbs
- Integrated Genomics Operation Core, Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Umesh Bhanot
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Precision Pathology Biobanking Center, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Daoqi You
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Katelyn Mullen
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Gerstner Sloan Kettering Graduate School of Biomedical Sciences, New York, NY, USA
| | - Jerry Melchor
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Michael V Ortiz
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Tara O'Donohue
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Emily Slotkin
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Leonard H Wexler
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Filemon S Dela Cruz
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Meera Hameed
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Julia L Glade Bender
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - William D Tap
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Paul A Meyers
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Elli Papaemmanuil
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Andrew L Kung
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Christine A Iacobuzio-Donahue
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| |
Collapse
|
19
|
Ramisetty S, Kulkarni P, Bhattacharya S, Nam A, Singhal SS, Guo L, Mirzapoiazova T, Mambetsariev B, Mittan S, Malhotra J, Pisick E, Subbiah S, Rajurkar S, Massarelli E, Salgia R, Mohanty A. A Systems Biology Approach for Addressing Cisplatin Resistance in Non-Small Cell Lung Cancer. J Clin Med 2023; 12:599. [PMID: 36675528 PMCID: PMC9861808 DOI: 10.3390/jcm12020599] [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: 12/06/2022] [Revised: 01/06/2023] [Accepted: 01/10/2023] [Indexed: 01/13/2023] Open
Abstract
Translational research in medicine, defined as the transfer of knowledge and discovery from the basic sciences to the clinic, is typically achieved through interactions between members across scientific disciplines to overcome the traditional silos within the community. Thus, translational medicine underscores 'Team Medicine', the partnership between basic science researchers and clinicians focused on addressing a specific goal in medicine. Here, we highlight this concept from a City of Hope perspective. Using cisplatin resistance in non-small cell lung cancer (NSCLC) as a paradigm, we describe how basic research scientists, clinical research scientists, and medical oncologists, in true 'Team Science' spirit, addressed cisplatin resistance in NSCLC and identified a previously approved compound that is able to alleviate cisplatin resistance in NSCLC. Furthermore, we discuss how a 'Team Medicine' approach can help to elucidate the mechanisms of innate and acquired resistance in NSCLC and develop alternative strategies to overcome drug resistance.
Collapse
Affiliation(s)
- Sravani Ramisetty
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Prakash Kulkarni
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA
- Department of Systems Biology, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Supriyo Bhattacharya
- Translational Bioinformatics, Center for Informatics, Department of Computational and Quantitative Medicine, City of Hope National Medical Center, 1500 Duarte Rd, Duarte, CA 91010, USA
| | - Arin Nam
- Department of Pathology, University of California, La Jolla, San Diego, CA 92093, USA
| | - Sharad S. Singhal
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Linlin Guo
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Tamara Mirzapoiazova
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Bolot Mambetsariev
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Sandeep Mittan
- Montefiore Medical Center, The University Hospital for Albert Einstein College of Medicine, Bronx, NY 10467, USA
| | - Jyoti Malhotra
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, 1000 FivePoint, Irvine, CA 92618, USA
| | - Evan Pisick
- Cancer Treatment Centers of America (CTCA) Chicago, 2520 Elisha Avenue, Zion, IL 60099, USA
| | - Shanmuga Subbiah
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, 1250 S. Sunset Ave., Suite 303, West Covina, CA 91790, USA
| | - Swapnil Rajurkar
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, 1100 San Bernardino Road, Suite 1100, Upland, CA 91786, USA
| | - Erminia Massarelli
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Ravi Salgia
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Atish Mohanty
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA
| |
Collapse
|
20
|
Kulkarni P, Mohanty A, Bhattacharya S, Singhal S, Guo L, Ramisetty S, Mirzapoiazova T, Mambetsariev B, Mittan S, Malhotra J, Gupta N, Kim P, Babikian R, Rajurkar S, Subbiah S, Tan T, Nguyen D, Merla A, Kollimuttathuillam SV, Phillips T, Baik P, Tan B, Vashi P, Shrestha S, Leach B, Garg R, Rich PL, Stewart FM, Pisick E, Salgia R. Addressing Drug Resistance in Cancer: A Team Medicine Approach. J Clin Med 2022; 11:5701. [PMID: 36233569 PMCID: PMC9572909 DOI: 10.3390/jcm11195701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 09/16/2022] [Accepted: 09/23/2022] [Indexed: 12/04/2022] Open
Abstract
Drug resistance remains one of the major impediments to treating cancer. Although many patients respond well initially, resistance to therapy typically ensues. Several confounding factors appear to contribute to this challenge. Here, we first discuss some of the challenges associated with drug resistance. We then discuss how a 'Team Medicine' approach, involving an interdisciplinary team of basic scientists working together with clinicians, has uncovered new therapeutic strategies. These strategies, referred to as intermittent or 'adaptive' therapy, which are based on eco-evolutionary principles, have met with remarkable success in potentially precluding or delaying the emergence of drug resistance in several cancers. Incorporating such treatment strategies into clinical protocols could potentially enhance the precision of delivering personalized medicine to patients. Furthermore, reaching out to patients in the network of hospitals affiliated with leading academic centers could help them benefit from such innovative treatment options. Finally, lowering the dose of the drug and its frequency (because of intermittent rather than continuous therapy) can also have a significant impact on lowering the toxicity and undesirable side effects of the drugs while lowering the financial burden carried by the patient and insurance providers.
Collapse
Affiliation(s)
- Prakash Kulkarni
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA
- Department of Systems Biology, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Atish Mohanty
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Supriyo Bhattacharya
- Integrative Genomics Core, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Sharad Singhal
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Linlin Guo
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Sravani Ramisetty
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Tamara Mirzapoiazova
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Bolot Mambetsariev
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Sandeep Mittan
- Montefiore Medical Center, The University Hospital for Albert Einstein College of Medicine, Bronx, NY 10467, USA
| | - Jyoti Malhotra
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, 1000 FivePoint, Irvine, CA 92618, USA
| | - Naveen Gupta
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, 1100 San Bernardino Road, Suite 1100, Upland, CA 91786, USA
| | - Pauline Kim
- Department of Pharmacy, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Razmig Babikian
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Swapnil Rajurkar
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, 1100 San Bernardino Road, Suite 1100, Upland, CA 91786, USA
| | - Shanmuga Subbiah
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, 1250 S. Sunset Ave., Suite 303, West Covina, CA 91790, USA
| | - Tingting Tan
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, 1601 Avocado Ave., Newport Beach, CA 92660, USA
| | - Danny Nguyen
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, 19671 Beach Blvd. #315, Huntington Beach, CA 92648, USA
| | - Amartej Merla
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, 38660 Medical Center Dr, Suite A380, Palmdale, CA 93551, USA
| | - Sudarsan V. Kollimuttathuillam
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, 16300 Sand Canyon Ave., Suite 207, Irvine, CA 92618, USA
| | - Tanyanika Phillips
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, 44151 15th St. West, Lancaster, CA 93534, USA
| | - Peter Baik
- Cancer Treatment Centers of America, CTCA Chicago, 2520 Elisha Avenue, Zion, IL 60099, USA
| | - Bradford Tan
- Cancer Treatment Centers of America, CTCA Chicago, 2520 Elisha Avenue, Zion, IL 60099, USA
| | - Pankaj Vashi
- Cancer Treatment Centers of America, CTCA Chicago, 2520 Elisha Avenue, Zion, IL 60099, USA
| | - Sagun Shrestha
- Cancer Treatment Centers of America, CTCA Phoenix, 14200 West Celebrate Life Way, Goodyear, AZ 85338, USA
| | - Benjamin Leach
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, 15031 Rinaldi St., Suite 150, Mission Hills, CA 91345, USA
| | - Ruchi Garg
- Cancer Treatment Centers of America, CTCA Atlanta, 600 Celebrate Life Parkway, Newnan, GA 30265, USA
| | - Patricia L. Rich
- Cancer Treatment Centers of America, CTCA Atlanta, 600 Celebrate Life Parkway, Newnan, GA 30265, USA
| | - F. Marc Stewart
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Evan Pisick
- Cancer Treatment Centers of America, CTCA Chicago, 2520 Elisha Avenue, Zion, IL 60099, USA
| | - Ravi Salgia
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA
| |
Collapse
|
21
|
Coggan H, Page KM. The role of evolutionary game theory in spatial and non-spatial models of the survival of cooperation in cancer: a review. JOURNAL OF THE ROYAL SOCIETY, INTERFACE 2022; 19:20220346. [PMID: 35975562 PMCID: PMC9382458 DOI: 10.1098/rsif.2022.0346] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Evolutionary game theory (EGT) is a branch of mathematics which considers populations of individuals interacting with each other to receive pay-offs. An individual’s pay-off is dependent on the strategy of its opponent(s) as well as on its own, and the higher its pay-off, the higher its reproductive fitness. Its offspring generally inherit its interaction strategy, subject to random mutation. Over time, the composition of the population shifts as different strategies spread or are driven extinct. In the last 25 years there has been a flood of interest in applying EGT to cancer modelling, with the aim of explaining how cancerous mutations spread through healthy tissue and how intercellular cooperation persists in tumour-cell populations. This review traces this body of work from theoretical analyses of well-mixed infinite populations through to more realistic spatial models of the development of cooperation between epithelial cells. We also consider work in which EGT has been used to make experimental predictions about the evolution of cancer, and discuss work that remains to be done before EGT can make large-scale contributions to clinical treatment and patient outcomes.
Collapse
Affiliation(s)
- Helena Coggan
- Department of Mathematics, University College London, London, UK
| | - Karen M Page
- Department of Mathematics, University College London, London, UK
| |
Collapse
|
22
|
Zhang J, Cunningham J, Brown J, Gatenby R. Evolution-based mathematical models significantly prolong response to abiraterone in metastatic castrate-resistant prostate cancer and identify strategies to further improve outcomes. eLife 2022; 11:e76284. [PMID: 35762577 PMCID: PMC9239688 DOI: 10.7554/elife.76284] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 06/01/2022] [Indexed: 11/15/2022] Open
Abstract
Background Abiraterone acetate is an effective treatment for metastatic castrate-resistant prostate cancer (mCRPC), but evolution of resistance inevitably leads to progression. We present a pilot study in which abiraterone dosing is guided by evolution-informed mathematical models to delay onset of resistance. Methods In the study cohort, abiraterone was stopped when PSA was <50% of pretreatment value and resumed when PSA returned to baseline. Results are compared to a contemporaneous cohort who had >50% PSA decline after initial abiraterone administration and met trial eligibility requirements but chose standard of care (SOC) dosing. Results 17 subjects were enrolled in the adaptive therapy group and 16 in the SOC group. All SOC subjects have progressed, but four patients in the study cohort remain stably cycling (range 53-70 months). The study cohort had significantly improved median time to progression (TTP; 33.5 months; p<0.001) and median overall survival (OS; 58.5 months; hazard ratio, 0.41, 95% confidence interval (CI), 0.20-0.83, p<0.001) compared to 14.3 and 31.3 months in the SOC cohort. On average, study subjects received no abiraterone during 46% of time on trial. Longitudinal trial data demonstrated the competition coefficient ratio (αRS/αSR) of sensitive and resistant populations, a critical factor in intratumoral evolution, was two- to threefold higher than pre-trial estimates. Computer simulations of intratumoral evolutionary dynamics in the four long-term survivors found that, due to the larger value for αRS/αSR, cycled therapy significantly decreased the resistant population. Simulations in subjects who progressed predicted further increases in OS could be achieved with prompt abiraterone withdrawal after achieving 50% PSA reduction. Conclusions Incorporation of evolution-based mathematical models into abiraterone monotherapy for mCRPC significantly increases TTP and OS. Computer simulations with updated parameters from longitudinal trial data can estimate intratumoral evolutionary dynamics in each subject and identify strategies to improve outcomes. Funding Moffitt internal grants and NIH/NCI U54CA143970-05 (Physical Science Oncology Network).
Collapse
Affiliation(s)
- Jingsong Zhang
- Department of Genitourinary Oncology, Moffitt Cancer Center and Research InstituteTampaUnited States
| | - Jessica Cunningham
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center and Research InstituteTampaUnited States
| | - Joel Brown
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center and Research InstituteTampaUnited States
- Department of Biological Sciences, University of Illinois at ChicagoChicagoUnited States
| | - Robert Gatenby
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center and Research InstituteTampaUnited States
- Cancer Biology and Evolution Program, Moffitt Cancer Center and Research InstituteTampaUnited States
| |
Collapse
|
23
|
Cai D, Ma X, Guo H, Zhang H, Bian A, Yu H, Cheng W. Prognostic value of p16, p53, and pcna in sarcoma and an evaluation of immune infiltration. J Orthop Surg Res 2022; 17:305. [PMID: 35689249 PMCID: PMC9185979 DOI: 10.1186/s13018-022-03193-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 05/26/2022] [Indexed: 12/22/2022] Open
Abstract
Background p16, p53, and proliferating cell nuclear antigen (pcna) genes play significant roles in many chromatin modifications and have been found to be highly expressed in a variety of tumor tissues. Therefore, they have been used as target genes for some tumor therapies. However, the differential expressions of the p16, p53, and pcna genes in human sarcomas and their effects on prognosis have not been widely reported. Methods The Oncomine dataset was used to analyze the transcription levels of p16, p53, and pcna genes, and the gene expression profile interactive analysis (GEPIA) dataset was used to analyze the differential expressions of p16, p53, and pcna. The expression levels of p16, p53, and pcna were further analyzed by Western Blotting. GEPIA and Kaplan–Meier analyses were used to analyze the prognostic value of p16, p53, and pcna. Furthermore, p16, p53, and pcna gene mutations and their association with overall survival (OS) and disease-free survival (DFS) were analyzed using cBioPortal datasets. In addition, genes co-expressed with p16, p53, and pcna were analyzed using Oncomine. The DAVID dataset was used to analyze the functional enrichment of p16, p53, pcna, and their co-expressed genes by Gene Ontology (GO) and Metascape were used to construct a network map. Finally, the immune cell infiltration of p16, p53, and pcna in patients with sarcoma was reported by Tumor Immune Estimation Resource (TIMER). Results p16, p53, and pcna were up-regulated in human sarcoma tissues and almost all sarcoma cell lines. Western Blotting showed that the expression of p16, p53, and pcna was elevated in osteosarcoma cell lines. The expression of pcna was correlated with OS, the expression of p16, p53, and pcna was correlated with relapse-free survival, and the genetic mutation of p16 was negatively correlated with OS and DFS. We also found that p16, p53, and pcna genes were positively/negatively correlated with immune cell infiltration in sarcoma. Conclusions The results of this study showed that p16, p53, and pcna can significantly affect the survival and immune status of sarcoma patients. Therefore, p16, p53, and pcna could be used as potential biomarkers of prognosis and immune infiltration in human sarcoma and provide a possible therapeutic target for sarcoma.
Collapse
Affiliation(s)
- Dechao Cai
- Department of Orthopedics, The Second Hospital of Anhui Medical University, 678 Furong Road, Hefei, 230601, China
| | - Xiao Ma
- Department of Orthopedics, The Second Hospital of Anhui Medical University, 678 Furong Road, Hefei, 230601, China
| | - Huihui Guo
- Department of Orthopedics, The Second Hospital of Anhui Medical University, 678 Furong Road, Hefei, 230601, China
| | - Haotian Zhang
- Department of Orthopedics, The Second Hospital of Anhui Medical University, 678 Furong Road, Hefei, 230601, China
| | - Ashuai Bian
- Department of Orthopedics, The Second Hospital of Anhui Medical University, 678 Furong Road, Hefei, 230601, China
| | - Haoran Yu
- Department of Orthopedics, The Second Hospital of Anhui Medical University, 678 Furong Road, Hefei, 230601, China
| | - Wendan Cheng
- Department of Orthopedics, The Second Hospital of Anhui Medical University, 678 Furong Road, Hefei, 230601, China.
| |
Collapse
|
24
|
Metts J, Russell T, Reed D, Trucco M. A Proposed Trial Design for the Treatment of Widely Metastatic Ewing Sarcoma Inspired by Evolutionary Dynamics. Cancers (Basel) 2022; 14:cancers14030736. [PMID: 35159003 PMCID: PMC8833360 DOI: 10.3390/cancers14030736] [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: 01/06/2022] [Revised: 01/27/2022] [Accepted: 01/28/2022] [Indexed: 12/30/2022] Open
Abstract
Metastatic Ewing sarcoma has dismal long-term survival despite multiple attempts to intensify standard therapy through the addition of new agents to the existing chemotherapy backbone. Here, based on the application of evolutionary dynamics to pediatric sarcoma, we propose an alternative treatment strategy that varies exposure to agents and dosing intensities, termed sequential second-strike therapy (SSST). We announce an upcoming clinical trial to apply these principles to patients with widely metastatic Ewing sarcoma, those with metastatic disease beyond the lungs.
Collapse
Affiliation(s)
- Jonathan Metts
- Cancer and Blood Disorders Institute, Johns Hopkins All Children’s Hospital, St. Petersburg, FL 33701, USA
- Correspondence:
| | - Thomas Russell
- Departments of Pediatrics, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA;
| | - Damon Reed
- Moffitt Cancer Center, Department of Individualized Cancer Management, Tampa, FL 33612, USA;
| | - Matteo Trucco
- Department of Pediatric Hematology Oncology and BMT, Cleveland Clinic Children’s, Cleveland, OH 44106, USA;
| |
Collapse
|
25
|
Optimizing Adaptive Therapy Based on the Reachability to Tumor Resistant Subpopulation. Cancers (Basel) 2021; 13:cancers13215262. [PMID: 34771426 PMCID: PMC8582524 DOI: 10.3390/cancers13215262] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 10/14/2021] [Accepted: 10/18/2021] [Indexed: 11/26/2022] Open
Abstract
Simple Summary The intra-competition among tumor subpopulations is a promising target to modify and control the outgrowth of the resistant subpopulation. Adaptive therapy lives up to this principle well, but the gain of tumors with an aggressive resistant subpopulation is not superior to maximum tolerated dose therapy (MTD). How to integrate these two therapies to maximize the outcome? According to the model and system reachability, the ‘restore index’ is proposed to evaluate the timing of the transition from the treatment cycle of adaptive therapy to high-frequency administration, and to juggle the benefits of intra-competition and killing of the sensitive subpopulation. Based on the simulation and animal experiment, the effectiveness of this method in treating tumors with an aggressive resistant subpopulation has been confirmed. Abstract Adaptive therapy exploits the self-organization of tumor cells to delay the outgrowth of resistant subpopulations successfully. When the tumor has aggressive resistant subpopulations, the outcome of adaptive therapy was not superior to maximum tolerated dose therapy (MTD). To explore methods to improve the adaptive therapy’s performance of this case, the tumor system was constructed by osimertinib-sensitive and resistant cell lines and illustrated by the Lotka-Volterra model in this study. Restore index proposed to assess the system reachability can predict the duration of each treatment cycle. Then the threshold of the restore index was estimated to evaluate the timing of interrupting the treatment cycle and switching to high-frequency administration. The introduced reachability-based adaptive therapy and classic adaptive therapy were compared through simulation and animal experiments. The results suggested that reachability-based adaptive therapy showed advantages when the tumor has an aggressive resistant subpopulation. This study provides a feasible method for evaluating whether to continue the adaptive therapy treatment cycle or switch to high-frequency administration. This method improves the gain of adaptive therapy by taking into account the benefits of tumor intra-competition and the tumor control of killing sensitive subpopulation.
Collapse
|
26
|
Wölfl B, te Rietmole H, Salvioli M, Kaznatcheev A, Thuijsman F, Brown JS, Burgering B, Staňková K. The Contribution of Evolutionary Game Theory to Understanding and Treating Cancer. DYNAMIC GAMES AND APPLICATIONS 2021; 12:313-342. [PMID: 35601872 PMCID: PMC9117378 DOI: 10.1007/s13235-021-00397-w] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 07/05/2021] [Indexed: 05/05/2023]
Abstract
Evolutionary game theory mathematically conceptualizes and analyzes biological interactions where one's fitness not only depends on one's own traits, but also on the traits of others. Typically, the individuals are not overtly rational and do not select, but rather inherit their traits. Cancer can be framed as such an evolutionary game, as it is composed of cells of heterogeneous types undergoing frequency-dependent selection. In this article, we first summarize existing works where evolutionary game theory has been employed in modeling cancer and improving its treatment. Some of these game-theoretic models suggest how one could anticipate and steer cancer's eco-evolutionary dynamics into states more desirable for the patient via evolutionary therapies. Such therapies offer great promise for increasing patient survival and decreasing drug toxicity, as demonstrated by some recent studies and clinical trials. We discuss clinical relevance of the existing game-theoretic models of cancer and its treatment, and opportunities for future applications. Moreover, we discuss the developments in cancer biology that are needed to better utilize the full potential of game-theoretic models. Ultimately, we demonstrate that viewing tumors with evolutionary game theory has medically useful implications that can inform and create a lockstep between empirical findings and mathematical modeling. We suggest that cancer progression is an evolutionary competition between different cell types and therefore needs to be viewed as an evolutionary game.
Collapse
Affiliation(s)
- Benjamin Wölfl
- Department of Mathematics, University of Vienna, Vienna, Austria
- Vienna Graduate School of Population Genetics, Vienna, Austria
| | - Hedy te Rietmole
- Department of Molecular Cancer Research, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Monica Salvioli
- Department of Mathematics, University of Trento, Trento, Italy
- Department of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands
| | - Artem Kaznatcheev
- Department of Biology, University of Pennsylvania, Philadelphia, USA
- Department of Computer Science, University of Oxford, Oxford, UK
| | - Frank Thuijsman
- Department of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands
| | - Joel S. Brown
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL USA
- Department of Biological Sciences, University of Illinois at Chicago, Chicago, IL USA
| | - Boudewijn Burgering
- Department of Molecular Cancer Research, University Medical Center Utrecht, Utrecht, The Netherlands
- The Oncode Institute, Utrecht, The Netherlands
| | - Kateřina Staňková
- Department of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands
- Department of Engineering Systems and Services, Faculty of Technology, Policy and Management, Delft University of Technology, Delft, The Netherlands
| |
Collapse
|
27
|
Pressley M, Salvioli M, Lewis DB, Richards CL, Brown JS, Staňková K. Evolutionary Dynamics of Treatment-Induced Resistance in Cancer Informs Understanding of Rapid Evolution in Natural Systems. Front Ecol Evol 2021. [DOI: 10.3389/fevo.2021.681121] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Rapid evolution is ubiquitous in nature. We briefly review some of this quite broadly, particularly in the context of response to anthropogenic disturbances. Nowhere is this more evident, replicated and accessible to study than in cancer. Curiously cancer has been late - relative to fisheries, antibiotic resistance, pest management and evolution in human dominated landscapes - in recognizing the need for evolutionarily informed management strategies. The speed of evolution matters. Here, we employ game-theoretic modeling to compare time to progression with continuous maximum tolerable dose to that of adaptive therapy where treatment is discontinued when the population of cancer cells gets below half of its initial size and re-administered when the cancer cells recover, forming cycles with and without treatment. We show that the success of adaptive therapy relative to continuous maximum tolerable dose therapy is much higher if the population of cancer cells is defined by two cell types (sensitive vs. resistant in a polymorphic population). Additionally, the relative increase in time to progression increases with the speed of evolution. These results hold with and without a cost of resistance in cancer cells. On the other hand, treatment-induced resistance can be modeled as a quantitative trait in a monomorphic population of cancer cells. In that case, when evolution is rapid, there is no advantage to adaptive therapy. Initial responses to therapy are blunted by the cancer cells evolving too quickly. Our study emphasizes how cancer provides a unique system for studying rapid evolutionary changes within tumor ecosystems in response to human interventions; and allows us to contrast and compare this system to other human managed or dominated systems in nature.
Collapse
|
28
|
Gregg C. Starvation and Climate Change—How to Constrain Cancer Cell Epigenetic Diversity and Adaptability to Enhance Treatment Efficacy. Front Ecol Evol 2021. [DOI: 10.3389/fevo.2021.693781] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Advanced metastatic cancer is currently not curable and the major barrier to eliminating the disease in patients is the resistance of subpopulations of tumor cells to drug treatments. These resistant subpopulations can arise stochastically among the billions of tumor cells in a patient or emerge over time during therapy due to adaptive mechanisms and the selective pressures of drug therapies. Epigenetic mechanisms play important roles in tumor cell diversity and adaptability, and are regulated by metabolic pathways. Here, I discuss knowledge from ecology, evolution, infectious disease, species extinction, metabolism and epigenetics to synthesize a roadmap to a clinically feasible approach to help homogenize tumor cells and, in combination with drug treatments, drive their extinction. Specifically, cycles of starvation and hyperthermia could help synchronize tumor cells and constrain epigenetic diversity and adaptability by limiting substrates and impairing the activity of chromatin modifying enzymes. Hyperthermia could also help prevent cancer cells from entering dangerous hibernation-like states. I propose steps to a treatment paradigm to help drive cancer extinction that builds on the successes of fasting, hyperthermia and immunotherapy and is achievable in patients. Finally, I highlight the many unknowns, opportunities for discovery and that stochastic gene and allele level epigenetic mechanisms pose a major barrier to cancer extinction that warrants deeper investigation.
Collapse
|
29
|
Treatment-induced evolutionary dynamics in nonmetastatic locally advanced rectal adenocarcinoma. Adv Cancer Res 2021; 151:39-67. [PMID: 34148619 DOI: 10.1016/bs.acr.2021.02.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Multi-modal treatment of non-metastatic locally advanced rectal adenocarcinoma (LARC) includes chemotherapy, radiation, and life-altering surgery. Although highly effective for local cancer control, metastatic failure remains significant and drives rectal cancer-related mortality. A consistent observation of this tri-modality treatment paradigm is that histologic response of the primary tumor to neoadjuvant treatment(s), which varies across patients, predicts overall oncologic outcome. In this chapter, we will examine this treatment response heterogeneity in the context of evolutionary dynamics. We hypothesize that improved understanding of eco-evolutionary pressures rendering small cancer cell populations vulnerable to extinction may influence treatment strategies and improve patient outcomes. Applying effective treatment(s) to cancer populations causes a "race to extinction." We explore principles of eco-evolutionary extinction in the context of these small cancer cell populations, evaluating how treatment(s) aim to eradicate the cancer populations to ultimately result in cure. In this chapter, we provide an evolutionary rationale for limiting continuous treatment(s) with the same agent or combination of agents to avoid selection of resistant cancer subpopulation phenotypes, allowing "evolutionary rescue." We draw upon evidence from nature demonstrating species extinction rarely occurring as a single event phenomenon, but rather a series of events in the slide to extinction. We posit that eradicating small cancer populations, similar to small populations in natural extinctions, will usually require a sequence of different external perturbations that produce negative, synergistic dynamics termed the "extinction vortex." By exploiting these unique extinction vulnerabilities of small cancer populations, the optimal therapeutic sequences may be informed by evolution-informed strategies for patients with LARC.
Collapse
|
30
|
Jardim-Perassi BV, Mu W, Huang S, Tomaszewski MR, Poleszczuk J, Abdalah MA, Budzevich MM, Dominguez-Viqueira W, Reed DR, Bui MM, Johnson JO, Martinez GV, Gillies RJ. Deep-learning and MR images to target hypoxic habitats with evofosfamide in preclinical models of sarcoma. Theranostics 2021; 11:5313-5329. [PMID: 33859749 PMCID: PMC8039958 DOI: 10.7150/thno.56595] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 02/03/2021] [Indexed: 11/05/2022] Open
Abstract
Rationale: Hypoxic regions (habitats) within tumors are heterogeneously distributed and can be widely variant. Hypoxic habitats are generally pan-therapy resistant. For this reason, hypoxia-activated prodrugs (HAPs) have been developed to target these resistant volumes. The HAP evofosfamide (TH-302) has shown promise in preclinical and early clinical trials of sarcoma. However, in a phase III clinical trial of non-resectable soft tissue sarcomas, TH-302 did not improve survival in combination with doxorubicin (Dox), possibly due to a lack of patient stratification based on hypoxic status. Therefore, we used magnetic resonance imaging (MRI) to identify hypoxic habitats and non-invasively follow therapies response in sarcoma mouse models. Methods: We developed deep-learning (DL) models to identify hypoxia, using multiparametric MRI and co-registered histology, and monitored response to TH-302 in a patient-derived xenograft (PDX) of rhabdomyosarcoma and a syngeneic model of fibrosarcoma (radiation-induced fibrosarcoma, RIF-1). Results: A DL convolutional neural network showed strong correlations (>0.76) between the true hypoxia fraction in histology and the predicted hypoxia fraction in multiparametric MRI. TH-302 monotherapy or in combination with Dox delayed tumor growth and increased survival in the hypoxic PDX model (p<0.05), but not in the RIF-1 model, which had a lower volume of hypoxic habitats. Control studies showed that RIF-1 resistance was due to hypoxia and not other causes. Notably, PDX tumors developed resistance to TH-302 under prolonged treatment that was not due to a reduction in hypoxic volumes. Conclusion: Artificial intelligence analysis of pre-therapy MR images can predict hypoxia and subsequent response to HAPs. This approach can be used to monitor therapy response and adapt schedules to forestall the emergence of resistance.
Collapse
|
31
|
Abstract
Despite the continuous deployment of new treatment strategies and agents over many decades, most disseminated cancers remain fatal. Cancer cells, through their access to the vast information of the human genome, have a remarkable capacity to deploy adaptive strategies for even the most effective treatments. We note there are two critical steps in the clinical manifestation of treatment resistance. The first, which is widely investigated, requires molecular machinery necessary to eliminate the cytotoxic effect of the treatment. However, the emergence of a resistant phenotype is not in itself clinically significant. That is, resistant cells affect patient outcomes only when they succeed in the second step of resistance by proliferating into a sufficiently large population to allow tumor progression and treatment failure. Importantly, proliferation of the resistant phenotype is by no means certain and, in fact, depends on complex Darwinian dynamics governed by the costs and benefits of the resistance mechanisms in the context of the local environment and competing populations. Attempts to target the molecular machinery of resistance have had little clinical success largely because of the diversity within the human genome-therapeutic interruption of one mechanism simply results in its replacement by an alternative. Here we explore evolutionarily informed strategies (adaptive, double-bind, and extinction therapies) for overcoming treatment resistance that seek to understand and exploit the critical evolutionary dynamics that govern proliferation of the resistant phenotypes. In general, this approach has demonstrated that, while emergence of resistance mechanisms in cancer cells to every current therapy is inevitable, proliferation of the resistant phenotypes is not and can be delayed and even prevented with sufficient understanding of the underlying eco-evolutionary dynamics.
Collapse
Affiliation(s)
- Robert A Gatenby
- Cancer Biology and Evolution Program
- Department of Radiology, Moffitt Cancer Center, Tampa, Florida 33612 USA
| | | |
Collapse
|