1
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Bhattacharya R, Brown JS, Gatenby RA, Ibrahim-Hashim A. A gene for all seasons: The evolutionary consequences of HIF-1 in carcinogenesis, tumor growth and metastasis. Semin Cancer Biol 2024; 102-103:17-24. [PMID: 38969311 DOI: 10.1016/j.semcancer.2024.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 04/23/2024] [Accepted: 06/06/2024] [Indexed: 07/07/2024]
Abstract
Oxygen played a pivotal role in the evolution of multicellularity during the Cambrian Explosion. Not surprisingly, responses to fluctuating oxygen concentrations are integral to the evolution of cancer-a disease characterized by the breakdown of multicellularity. Poorly organized tumor vasculature results in chaotic patterns of blood flow characterized by large spatial and temporal variations in intra-tumoral oxygen concentrations. Hypoxia-inducible growth factor (HIF-1) plays a pivotal role in enabling cells to adapt, metabolize, and proliferate in low oxygen conditions. HIF-1 is often constitutively activated in cancers, underscoring its importance in cancer progression. Here, we argue that the phenotypic changes mediated by HIF-1, in addition to adapting the cancer cells to their local environment, also "pre-adapt" them for proliferation at distant, metastatic sites. HIF-1-mediated adaptations include a metabolic shift towards anaerobic respiration or glycolysis, activation of cell survival mechanisms like phenotypic plasticity and epigenetic reprogramming, and formation of tumor vasculature through angiogenesis. Hypoxia induced epigenetic reprogramming can trigger epithelial to mesenchymal transition in cancer cells-the first step in the metastatic cascade. Highly glycolytic cells facilitate local invasion by acidifying the tumor microenvironment. New blood vessels, formed due to angiogenesis, provide cancer cells a conduit to the circulatory system. Moreover, survival mechanisms acquired by cancer cells in the primary site allow them to remodel tissue at the metastatic site generating tumor promoting microenvironment. Thus, hypoxia in the primary tumor promoted adaptations conducive to all stages of the metastatic cascade from the initial escape entry into a blood vessel, intravascular survival, extravasation into distant tissues, and establishment of secondary tumors.
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Affiliation(s)
- Ranjini Bhattacharya
- Department of Cancer Biology, University of South Florida, United States; Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center, United States
| | - Joel S Brown
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center, United States; Department of Evolutionary Biology, University of Illinois, at Chicago, United States
| | - Robert A Gatenby
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center, United States; Department of Radiology, H. Lee Moffitt Cancer Center, United States.
| | - Arig Ibrahim-Hashim
- Department of Metabolism and Physiology, H. Lee Moffitt Cancer Center, United States.
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2
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Roddur MS, Snir S, El-Kebir M. Enforcing Temporal Consistency in Migration History Inference. J Comput Biol 2024; 31:396-415. [PMID: 38754138 DOI: 10.1089/cmb.2023.0352] [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] [Indexed: 05/18/2024] Open
Abstract
In addition to undergoing evolution, members of biological populations may also migrate between locations. Examples include the spread of tumor cells from the primary tumor to distant metastases or the spread of pathogens from one host to another. One may represent migration histories by assigning a location label to each vertex of a given phylogenetic tree such that an edge connecting vertices with distinct locations represents a migration. Some biological populations undergo comigration, a phenomenon where multiple taxa from distinct lineages simultaneously comigrate from one location to another. In this work, we show that a previous problem statement for inferring migration histories that are parsimonious in terms of migrations and comigrations may lead to temporally inconsistent solutions. To remedy this deficiency, we introduce precise definitions of temporal consistency of comigrations in a phylogenetic tree, leading to three successive problems. First, we formulate the temporally consistent comigration problem to check if a set of comigrations is temporally consistent and provide a linear time algorithm for solving this problem. Second, we formulate the parsimonious consistent comigrations (PCC) problem, which aims to find comigrations given a location labeling of a phylogenetic tree. We show that PCC is NP-hard. Third, we formulate the parsimonious consistent comigration history (PCCH) problem, which infers the migration history given a phylogenetic tree and locations of its extant vertices only. We show that PCCH is NP-hard as well. On the positive side, we propose integer linear programming models to solve the PCC and PCCH problems. We demonstrate our algorithms on simulated and real data.
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Affiliation(s)
- Mrinmoy Saha Roddur
- Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
| | - Sagi Snir
- Department of Evolutionary Biology, University of Haifa, Haifa, Israel
| | - Mohammed El-Kebir
- Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
- Cancer Center at Illinois, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
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3
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Wang C, Ma A, Li Y, McNutt ME, Zhang S, Zhu J, Hoyd R, Wheeler CE, Robinson LA, Chan CH, Zakharia Y, Dodd RD, Ulrich CM, Hardikar S, Churchman ML, Tarhini AA, Singer EA, Ikeguchi AP, McCarter MD, Denko N, Tinoco G, Husain M, Jin N, Osman AE, Eljilany I, Tan AC, Coleman SS, Denko L, Riedlinger G, Schneider BP, Spakowicz D, Ma Q. A Bioinformatics Tool for Identifying Intratumoral Microbes from the ORIEN Dataset. CANCER RESEARCH COMMUNICATIONS 2024; 4:293-302. [PMID: 38259095 PMCID: PMC10840455 DOI: 10.1158/2767-9764.crc-23-0213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 09/26/2023] [Accepted: 01/04/2024] [Indexed: 01/24/2024]
Abstract
Evidence supports significant interactions among microbes, immune cells, and tumor cells in at least 10%-20% of human cancers, emphasizing the importance of further investigating these complex relationships. However, the implications and significance of tumor-related microbes remain largely unknown. Studies have demonstrated the critical roles of host microbes in cancer prevention and treatment responses. Understanding interactions between host microbes and cancer can drive cancer diagnosis and microbial therapeutics (bugs as drugs). Computational identification of cancer-specific microbes and their associations is still challenging due to the high dimensionality and high sparsity of intratumoral microbiome data, which requires large datasets containing sufficient event observations to identify relationships, and the interactions within microbial communities, the heterogeneity in microbial composition, and other confounding effects that can lead to spurious associations. To solve these issues, we present a bioinformatics tool, microbial graph attention (MEGA), to identify the microbes most strongly associated with 12 cancer types. We demonstrate its utility on a dataset from a consortium of nine cancer centers in the Oncology Research Information Exchange Network. This package has three unique features: species-sample relations are represented in a heterogeneous graph and learned by a graph attention network; it incorporates metabolic and phylogenetic information to reflect intricate relationships within microbial communities; and it provides multiple functionalities for association interpretations and visualizations. We analyzed 2,704 tumor RNA sequencing samples and MEGA interpreted the tissue-resident microbial signatures of each of 12 cancer types. MEGA can effectively identify cancer-associated microbial signatures and refine their interactions with tumors. SIGNIFICANCE Studying the tumor microbiome in high-throughput sequencing data is challenging because of the extremely sparse data matrices, heterogeneity, and high likelihood of contamination. We present a new deep learning tool, MEGA, to refine the organisms that interact with tumors.
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Affiliation(s)
- Cankun Wang
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio
| | - Anjun Ma
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio
- Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio
| | - Yingjie Li
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio
| | - Megan E. McNutt
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio
| | - Shiqi Zhang
- Department of Human Sciences, College of Education and Human Ecology, The Ohio State University, Columbus, Ohio
| | - Jiangjiang Zhu
- Department of Human Sciences, College of Education and Human Ecology, The Ohio State University, Columbus, Ohio
| | - Rebecca Hoyd
- Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio
| | - Caroline E. Wheeler
- Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio
| | - Lary A. Robinson
- Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Carlos H.F. Chan
- University of Iowa, Holden Comprehensive Cancer Center, Iowa City, Iowa
| | - Yousef Zakharia
- Division of Oncology, Hematology and Blood & Marrow Transplantation, University of Iowa, Holden Comprehensive Cancer Center, Iowa City, Iowa
| | - Rebecca D. Dodd
- Department of Internal Medicine, University of Iowa, Iowa City, Iowa
| | - Cornelia M. Ulrich
- Department of Population Health Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
| | - Sheetal Hardikar
- Department of Population Health Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
| | | | - Ahmad A. Tarhini
- Departments of Cutaneous Oncology and Immunology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Eric A. Singer
- Department of Urologic Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio
| | - Alexandra P. Ikeguchi
- Department of Hematology/Oncology, Stephenson Cancer Center of University of Oklahoma, Oklahoma City, Oklahoma
| | - Martin D. McCarter
- Department of Surgery, University of Colorado School of Medicine, Aurora, Colorado
| | - Nicholas Denko
- Department of Radiation Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio
| | - Gabriel Tinoco
- Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio
| | - Marium Husain
- Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio
| | - Ning Jin
- Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio
| | - Afaf E.G. Osman
- Department of Internal Medicine, University of Utah, Salt Lake City, Utah
| | - Islam Eljilany
- Clinical Science Lab – Cutaneous Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Aik Choon Tan
- Departments of Oncological Science and Biomedical Informatics, Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
| | - Samuel S. Coleman
- Departments of Oncological Science and Biomedical Informatics, Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
| | - Louis Denko
- Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio
- Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio
| | - Gregory Riedlinger
- Department of Precision Medicine, Rutgers Cancer Institute of New Jersey, New Brunswick, New Jersey
| | - Bryan P. Schneider
- Indiana University Simon Comprehensive Cancer Center, Indianapolis, Indiana
| | - Daniel Spakowicz
- Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio
- Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio
| | - Qin Ma
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio
- Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio
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4
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Zhu X, Zhao W, Zhou Z, Gu X. Unraveling the Drivers of Tumorigenesis in the Context of Evolution: Theoretical Models and Bioinformatics Tools. J Mol Evol 2023:10.1007/s00239-023-10117-0. [PMID: 37246992 DOI: 10.1007/s00239-023-10117-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Accepted: 05/09/2023] [Indexed: 05/30/2023]
Abstract
Cancer originates from somatic cells that have accumulated mutations. These mutations alter the phenotype of the cells, allowing them to escape homeostatic regulation that maintains normal cell numbers. The emergence of malignancies is an evolutionary process in which the random accumulation of somatic mutations and sequential selection of dominant clones cause cancer cells to proliferate. The development of technologies such as high-throughput sequencing has provided a powerful means to measure subclonal evolutionary dynamics across space and time. Here, we review the patterns that may be observed in cancer evolution and the methods available for quantifying the evolutionary dynamics of cancer. An improved understanding of the evolutionary trajectories of cancer will enable us to explore the molecular mechanism of tumorigenesis and to design tailored treatment strategies.
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Affiliation(s)
- Xunuo Zhu
- Innovation Institute for Artificial Intelligence in Medicine, Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Wenyi Zhao
- Innovation Institute for Artificial Intelligence in Medicine, Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Zhan Zhou
- Innovation Institute for Artificial Intelligence in Medicine, Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
- The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, 322000, China.
- Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 310058, China.
| | - Xun Gu
- Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA, 50011, USA.
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5
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Cordier BA, Sawaya NPD, Guerreschi GG, McWeeney SK. Biology and medicine in the landscape of quantum advantages. J R Soc Interface 2022; 19:20220541. [PMID: 36448288 PMCID: PMC9709576 DOI: 10.1098/rsif.2022.0541] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Quantum computing holds substantial potential for applications in biology and medicine, spanning from the simulation of biomolecules to machine learning methods for subtyping cancers on the basis of clinical features. This potential is encapsulated by the concept of a quantum advantage, which is contingent on a reduction in the consumption of a computational resource, such as time, space or data. Here, we distill the concept of a quantum advantage into a simple framework to aid researchers in biology and medicine pursuing the development of quantum applications. We then apply this framework to a wide variety of computational problems relevant to these domains in an effort to (i) assess the potential of practical advantages in specific application areas and (ii) identify gaps that may be addressed with novel quantum approaches. In doing so, we provide an extensive survey of the intersection of biology and medicine with the current landscape of quantum algorithms and their potential advantages. While we endeavour to identify specific computational problems that may admit practical advantages throughout this work, the rapid pace of change in the fields of quantum computing, classical algorithms and biological research implies that this intersection will remain highly dynamic for the foreseeable future.
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Affiliation(s)
- Benjamin A. Cordier
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR 97202, USA
| | | | | | - Shannon K. McWeeney
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR 97202, USA,Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97202, USA,Oregon Clinical and Translational Research Institute, Oregon Health and Science University, Portland, OR 97202, USA
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6
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Pinheiro D, Santander-Jimenéz S, Ilic A. PhyloMissForest: a random forest framework to construct phylogenetic trees with missing data. BMC Genomics 2022; 23:377. [PMID: 35585494 PMCID: PMC9116704 DOI: 10.1186/s12864-022-08540-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 04/01/2022] [Indexed: 11/10/2022] Open
Abstract
Background In the pursuit of a better understanding of biodiversity, evolutionary biologists rely on the study of phylogenetic relationships to illustrate the course of evolution. The relationships among natural organisms, depicted in the shape of phylogenetic trees, not only help to understand evolutionary history but also have a wide range of additional applications in science. One of the most challenging problems that arise when building phylogenetic trees is the presence of missing biological data. More specifically, the possibility of inferring wrong phylogenetic trees increases proportionally to the amount of missing values in the input data. Although there are methods proposed to deal with this issue, their applicability and accuracy is often restricted by different constraints. Results We propose a framework, called PhyloMissForest, to impute missing entries in phylogenetic distance matrices and infer accurate evolutionary relationships. PhyloMissForest is built upon a random forest structure that infers the missing entries of the input data, based on the known parts of it. PhyloMissForest contributes with a robust and configurable framework that incorporates multiple search strategies and machine learning, complemented by phylogenetic techniques, to provide a more accurate inference of lost phylogenetic distances. We evaluate our framework by examining three real-world datasets, two DNA-based sequence alignments and one containing amino acid data, and two additional instances with simulated DNA data. Moreover, we follow a design of experiments methodology to define the hyperparameter values of our algorithm, which is a concise method, preferable in comparison to the well-known exhaustive parameters search. By varying the percentages of missing data from 5% to 60%, we generally outperform the state-of-the-art alternative imputation techniques in the tests conducted on real DNA data. In addition, significant improvements in execution time are observed for the amino acid instance. The results observed on simulated data also denote the attainment of improved imputations when dealing with large percentages of missing data. Conclusions By merging multiple search strategies, machine learning, and phylogenetic techniques, PhyloMissForest provides a highly customizable and robust framework for phylogenetic missing data imputation, with significant topological accuracy and effective speedups over the state of the art. Supplementary Information The online version contains supplementary material available at (10.1186/s12864-022-08540-6).
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Affiliation(s)
- Diogo Pinheiro
- INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Rua Alves Redol 9, Lisboa, 1000-029, Portugal
| | - Sergio Santander-Jimenéz
- Department of Computer and Communications Technologies, University of Extremadura, Campus universitario s/n, Cáceres, 10003, Spain
| | - Aleksandar Ilic
- INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Rua Alves Redol 9, Lisboa, 1000-029, Portugal.
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7
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Gui P, Bivona TG. Evolution of metastasis: new tools and insights. Trends Cancer 2021; 8:98-109. [PMID: 34872888 DOI: 10.1016/j.trecan.2021.11.002] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 11/01/2021] [Accepted: 11/05/2021] [Indexed: 02/07/2023]
Abstract
Metastasis is an evolutionary process occurring across multiple organs and timescales. Due to its continuous and dynamic nature, this multifaceted process has been challenging to investigate and remains incompletely understood, in part due to the lack of tools capable of probing genomic evolution at high enough resolution. However, technological advances in genetic sequencing and editing have provided new and powerful methods to refine our understanding of the complex series of events that lead to metastatic dissemination. In this review, we summarize the latest genetic and lineage-tracing approaches developed to unravel the genetic evolution of metastasis. The findings that have emerged have enhanced our comprehension of the mechanistic trajectories and timescales of metastasis and could provide new strategies for therapy.
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Affiliation(s)
- Philippe Gui
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA.
| | - Trever G Bivona
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA.
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8
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Abstract
Ecological fitness is the ability of individuals in a population to survive and reproduce. Individuals with increased fitness are better equipped to withstand the selective pressures of their environments. This paradigm pertains to all organismal life as we know it; however, it is also becoming increasingly clear that within multicellular organisms exist highly complex, competitive, and cooperative populations of cells under many of the same ecological and evolutionary constraints as populations of individuals in nature. In this review I discuss the parallels between populations of cancer cells and populations of individuals in the wild, highlighting how individuals in either context are constrained by their environments to converge on a small number of critical phenotypes to ensure survival and future reproductive success. I argue that the hallmarks of cancer can be distilled into key phenotypes necessary for cancer cell fitness: survival and reproduction. I posit that for therapeutic strategies to be maximally beneficial, they should seek to subvert these ecologically driven phenotypic responses.
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9
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Nalbantoglu S, Karadag A. Metabolomics bridging proteomics along metabolites/oncometabolites and protein modifications: Paving the way toward integrative multiomics. J Pharm Biomed Anal 2021; 199:114031. [PMID: 33857836 DOI: 10.1016/j.jpba.2021.114031] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 03/02/2021] [Accepted: 03/16/2021] [Indexed: 02/08/2023]
Abstract
Systems biology adopted functional and integrative multiomics approaches enable to discover the whole set of interacting regulatory components such as genes, transcripts, proteins, metabolites, and metabolite dependent protein modifications. This interactome build up the midpoint of protein-protein/PTM, protein-DNA/RNA, and protein-metabolite network in a cell. As the key drivers in cellular metabolism, metabolites are precursors and regulators of protein post-translational modifications [PTMs] that affect protein diversity and functionality. The precisely orchestrated core pattern of metabolic networks refer to paradigm 'metabolites regulate PTMs, PTMs regulate enzymes, and enzymes modulate metabolites' through a multitude of feedback and feed-forward pathway loops. The concept represents a flawless PTM-metabolite-enzyme(protein) regulomics underlined in reprogramming cancer metabolism. Immense interconnectivity of those biomolecules in their spectacular network of intertwined metabolic pathways makes integrated proteomics and metabolomics an excellent opportunity, and the central component of integrative multiomics framework. It will therefore be of significant interest to integrate global proteome and PTM-based proteomics with metabolomics to achieve disease related altered levels of those molecules. Thereby, present update aims to highlight role and analysis of interacting metabolites/oncometabolites, and metabolite-regulated PTMs loop which may function as translational monitoring biomarkers along the reprogramming continuum of oncometabolism.
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Affiliation(s)
- Sinem Nalbantoglu
- TUBITAK Marmara Research Center, Gene Engineering and Biotechnology Institute, Molecular, Oncology Laboratory, Gebze, Kocaeli, Turkey.
| | - Abdullah Karadag
- TUBITAK Marmara Research Center, Gene Engineering and Biotechnology Institute, Molecular, Oncology Laboratory, Gebze, Kocaeli, Turkey
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10
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Scott JG, Maini PK, Anderson ARA, Fletcher AG. Inferring Tumor Proliferative Organization from Phylogenetic Tree Measures in a Computational Model. Syst Biol 2021; 69:623-637. [PMID: 31665523 DOI: 10.1093/sysbio/syz070] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2018] [Revised: 09/23/2019] [Accepted: 10/02/2019] [Indexed: 01/15/2023] Open
Abstract
We use a computational modeling approach to explore whether it is possible to infer a solid tumor's cellular proliferative hierarchy under the assumptions of the cancer stem cell hypothesis and neutral evolution. We work towards inferring the symmetric division probability for cancer stem cells, since this is believed to be a key driver of progression and therapeutic response. Motivated by the advent of multiregion sampling and resulting opportunities to infer tumor evolutionary history, we focus on a suite of statistical measures of the phylogenetic trees resulting from the tumor's evolution in different regions of parameter space and through time. We find strikingly different patterns in these measures for changing symmetric division probability which hinge on the inclusion of spatial constraints. These results give us a starting point to begin stratifying tumors by this biological parameter and also generate a number of actionable clinical and biological hypotheses regarding changes during therapy, and through tumor evolutionary time. [Cancer; evolution; phylogenetics.].
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Affiliation(s)
- Jacob G Scott
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, UK.,Departments of Translational Hematology and Oncology Research and Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Philip K Maini
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, UK
| | - Alexander R A Anderson
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Alexander G Fletcher
- School of Mathematics and Statistics, University of Sheffield, Sheffield, UK.,Bateson Centre, University of Sheffield, Sheffield, UK
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11
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Murphy WJ, Foley NM, Bredemeyer KR, Gatesy J, Springer MS. Phylogenomics and the Genetic Architecture of the Placental Mammal Radiation. Annu Rev Anim Biosci 2020; 9:29-53. [PMID: 33228377 DOI: 10.1146/annurev-animal-061220-023149] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The genomes of placental mammals are being sequenced at an unprecedented rate. Alignments of hundreds, and one day thousands, of genomes spanning the rich living and extinct diversity of species offer unparalleled power to resolve phylogenetic controversies, identify genomic innovations of adaptation, and dissect the genetic architecture of reproductive isolation. We highlight outstanding questions about the earliest phases of placental mammal diversification and the promise of newer methods, as well as remaining challenges, toward using whole genome data to resolve placental mammal phylogeny. The next phase of mammalian comparative genomics will see the completion and application of finished-quality, gapless genome assemblies from many ordinal lineages and closely related species. Interspecific comparisons between the most hypervariable genomic loci will likely reveal large, but heretofore mostly underappreciated, effects on population divergence, morphological innovation, and the origin of new species.
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Affiliation(s)
- William J Murphy
- Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas 77843, USA;
| | - Nicole M Foley
- Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas 77843, USA;
| | - Kevin R Bredemeyer
- Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas 77843, USA;
| | - John Gatesy
- Division of Vertebrate Zoology, American Museum of Natural History, New York, NY 10024, USA
| | - Mark S Springer
- Department of Evolution, Ecology and Organismal Biology, University of California, Riverside, California 92521, USA
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12
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Miura S, Vu T, Deng J, Buturla T, Oladeinde O, Choi J, Kumar S. Power and pitfalls of computational methods for inferring clone phylogenies and mutation orders from bulk sequencing data. Sci Rep 2020; 10:3498. [PMID: 32103044 PMCID: PMC7044161 DOI: 10.1038/s41598-020-59006-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Accepted: 01/23/2020] [Indexed: 12/13/2022] Open
Abstract
Tumors harbor extensive genetic heterogeneity in the form of distinct clone genotypes that arise over time and across different tissues and regions in cancer. Many computational methods produce clone phylogenies from population bulk sequencing data collected from multiple tumor samples from a patient. These clone phylogenies are used to infer mutation order and clone origins during tumor progression, rendering the selection of the appropriate clonal deconvolution method critical. Surprisingly, absolute and relative accuracies of these methods in correctly inferring clone phylogenies are yet to consistently assessed. Therefore, we evaluated the performance of seven computational methods. The accuracy of the reconstructed mutation order and inferred clone groupings varied extensively among methods. All the tested methods showed limited ability to identify ancestral clone sequences present in tumor samples correctly. The presence of copy number alterations, the occurrence of multiple seeding events among tumor sites during metastatic tumor evolution, and extensive intermixture of cancer cells among tumors hindered the detection of clones and the inference of clone phylogenies for all methods tested. Overall, CloneFinder, MACHINA, and LICHeE showed the highest overall accuracy, but none of the methods performed well for all simulated datasets. So, we present guidelines for selecting methods for data analysis.
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Affiliation(s)
- Sayaka Miura
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, 19122, USA.,Department of Biology, Temple University, Philadelphia, PA, 19122, USA
| | - Tracy Vu
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, 19122, USA.,Department of Biology, Temple University, Philadelphia, PA, 19122, USA
| | - Jiamin Deng
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, 19122, USA.,Department of Biology, Temple University, Philadelphia, PA, 19122, USA
| | - Tiffany Buturla
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, 19122, USA.,Department of Biology, Temple University, Philadelphia, PA, 19122, USA
| | - Olumide Oladeinde
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, 19122, USA.,Department of Biology, Temple University, Philadelphia, PA, 19122, USA
| | - Jiyeong Choi
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, 19122, USA.,Department of Biology, Temple University, Philadelphia, PA, 19122, USA
| | - Sudhir Kumar
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, 19122, USA. .,Department of Biology, Temple University, Philadelphia, PA, 19122, USA. .,Center for Excellence in Genome Medicine and Research, King Abdulaziz University, Jeddah, Saudi Arabia.
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13
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Somarelli JA, Gardner H, Cannataro VL, Gunady EF, Boddy AM, Johnson NA, Fisk JN, Gaffney SG, Chuang JH, Li S, Ciccarelli FD, Panchenko AR, Megquier K, Kumar S, Dornburg A, DeGregori J, Townsend JP. Molecular Biology and Evolution of Cancer: From Discovery to Action. Mol Biol Evol 2020; 37:320-326. [PMID: 31642480 PMCID: PMC6993850 DOI: 10.1093/molbev/msz242] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Cancer progression is an evolutionary process. During this process, evolving cancer cell populations encounter restrictive ecological niches within the body, such as the primary tumor, circulatory system, and diverse metastatic sites. Efforts to prevent or delay cancer evolution-and progression-require a deep understanding of the underlying molecular evolutionary processes. Herein we discuss a suite of concepts and tools from evolutionary and ecological theory that can inform cancer biology in new and meaningful ways. We also highlight current challenges to applying these concepts, and propose ways in which incorporating these concepts could identify new therapeutic modes and vulnerabilities in cancer.
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Affiliation(s)
- Jason A Somarelli
- Department of Medicine, Duke University Medical Center, Durham, NC
- Duke Cancer Institute, Duke University Medical Center, Durham, NC
| | - Heather Gardner
- Sackler School of Graduate Biomedical Sciences, Tufts University, Medford, MA
| | | | - Ella F Gunady
- Department of Medicine, Duke University Medical Center, Durham, NC
| | - Amy M Boddy
- Department of Anthropology, University of California, Santa Barbara, CA
| | | | | | - Stephen G Gaffney
- Department of Biostatistics, Yale School of Public Health, New Haven, CT
| | | | - Sheng Li
- The Jackson Laboratory for Genomic Medicine, Farmington, CT
| | - Francesca D Ciccarelli
- Cancer Systems Biology Laboratory, The Francis Crick Institute, London, United Kingdom
- King’s College London, London, United Kingdom
| | - Anna R Panchenko
- Department of Pathology and Molecular Medicine, School of Medicine, Queen’s University, Kingston, ON, Canada
- Ontario Institute of Cancer Research, Toronto, ON, Canada
| | - Kate Megquier
- Broad Institute, Massachusettes Institute of Technology and Harvard University
| | - Sudhir Kumar
- Institute for Genomics and Evolutionary Medicine, and Department of Biology, Temple University, Philadelphia, PA
| | - Alex Dornburg
- North Carolina Museum of Natural Sciences, Raleigh, NC
| | - James DeGregori
- Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Jeffrey P Townsend
- Department of Biostatistics, Yale School of Public Health, New Haven, CT
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT
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14
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Chroni A, Vu T, Miura S, Kumar S. Delineation of Tumor Migration Paths by Using a Bayesian Biogeographic Approach. Cancers (Basel) 2019; 11:E1880. [PMID: 31783570 PMCID: PMC6966534 DOI: 10.3390/cancers11121880] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Revised: 11/20/2019] [Accepted: 11/26/2019] [Indexed: 12/20/2022] Open
Abstract
Understanding tumor progression and metastatic potential are important in cancer biology. Metastasis is the migration and colonization of clones in secondary tissues. Here, we posit that clone migration events between tumors resemble the dispersal of individuals between distinct geographic regions. This similarity makes Bayesian biogeographic analysis suitable for inferring cancer cell migration paths. We evaluated the accuracy of a Bayesian biogeography method (BBM) in inferring metastatic patterns and compared it with the accuracy of a parsimony-based approach (metastatic and clonal history integrative analysis, MACHINA) that has been specifically developed to infer clone migration patterns among tumors. We used computer-simulated datasets in which simple to complex migration patterns were modeled. BBM and MACHINA were effective in reliably reconstructing simple migration patterns from primary tumors to metastases. However, both of them exhibited a limited ability to accurately infer complex migration paths that involve the migration of clones from one metastatic tumor to another and from metastasis to the primary tumor. Therefore, advanced computational methods are still needed for the biologically realistic tracing of migration paths and to assess the relative preponderance of different types of seeding and reseeding events during cancer progression in patients.
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Affiliation(s)
- Antonia Chroni
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA; (T.V.); (S.M.); (S.K.)
- Department of Biology, Temple University, Philadelphia, PA 19122, USA
| | - Tracy Vu
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA; (T.V.); (S.M.); (S.K.)
- Department of Biology, Temple University, Philadelphia, PA 19122, USA
| | - Sayaka Miura
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA; (T.V.); (S.M.); (S.K.)
- Department of Biology, Temple University, Philadelphia, PA 19122, USA
| | - Sudhir Kumar
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA; (T.V.); (S.M.); (S.K.)
- Department of Biology, Temple University, Philadelphia, PA 19122, USA
- Center for Excellence in Genome Medicine and Research, King Abdulaziz University, Jeddah 21589, Saudi Arabia
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15
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Loh JW, Khiabanian H. Leukemia’s Clonal Evolution in Development, Progression, and Relapse. CURRENT STEM CELL REPORTS 2019. [DOI: 10.1007/s40778-019-00157-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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16
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Xu S, Ware KE, Ding Y, Kim SY, Sheth MU, Rao S, Chan W, Armstrong AJ, Eward WC, Jolly MK, Somarelli JA. An Integrative Systems Biology and Experimental Approach Identifies Convergence of Epithelial Plasticity, Metabolism, and Autophagy to Promote Chemoresistance. J Clin Med 2019; 8:jcm8020205. [PMID: 30736412 PMCID: PMC6406733 DOI: 10.3390/jcm8020205] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Revised: 01/29/2019] [Accepted: 02/04/2019] [Indexed: 01/09/2023] Open
Abstract
The evolution of therapeutic resistance is a major cause of death for cancer patients. The development of therapy resistance is shaped by the ecological dynamics within the tumor microenvironment and the selective pressure of the host immune system. These selective forces often lead to evolutionary convergence on pathways or hallmarks that drive progression. Thus, a deeper understanding of the evolutionary convergences that occur could reveal vulnerabilities to treat therapy-resistant cancer. To this end, we combined phylogenetic clustering, systems biology analyses, and molecular experimentation to identify convergences in gene expression data onto common signaling pathways. We applied these methods to derive new insights about the networks at play during transforming growth factor-β (TGF-β)-mediated epithelial–mesenchymal transition in lung cancer. Phylogenetic analyses of gene expression data from TGF-β-treated cells revealed convergence of cells toward amine metabolic pathways and autophagy during TGF-β treatment. Knockdown of the autophagy regulatory, ATG16L1, re-sensitized lung cancer cells to cancer therapies following TGF-β-induced resistance, implicating autophagy as a TGF-β-mediated chemoresistance mechanism. In addition, high ATG16L expression was found to be a poor prognostic marker in multiple cancer types. These analyses reveal the usefulness of combining evolutionary and systems biology methods with experimental validation to illuminate new therapeutic vulnerabilities for cancer.
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Affiliation(s)
- Shengnan Xu
- Duke Cancer Institute and the Department of Medicine, Duke University Medical Center, Durham, NC 27710, USA.
| | - Kathryn E Ware
- Duke Cancer Institute and the Department of Medicine, Duke University Medical Center, Durham, NC 27710, USA.
| | - Yuantong Ding
- Department of Biology, Duke University Medical Center, Durham, NC 27710, USA.
| | - So Young Kim
- Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, NC 2 7710, USA.
| | - Maya U Sheth
- Duke Cancer Institute and the Department of Medicine, Duke University Medical Center, Durham, NC 27710, USA.
| | - Sneha Rao
- Department of Orthopaedic Surgery, Duke University Medical Center, Durham, NC, 27710, USA.
| | - Wesley Chan
- Duke Cancer Institute and the Department of Medicine, Duke University Medical Center, Durham, NC 27710, USA.
| | - Andrew J Armstrong
- Duke Cancer Institute and the Department of Medicine, Duke University Medical Center, Durham, NC 27710, USA.
- Solid Tumor Program and the Duke Prostate and Urologic Cancer Center, Duke University Medical Center, Durham, NC 27710, USA.
| | - William C Eward
- Department of Orthopaedic Surgery, Duke University Medical Center, Durham, NC, 27710, USA.
| | - Mohit Kumar Jolly
- Center for Theoretical Biological Physics, Rice University, Houston, TX 77005-1827, USA.
- Current address: Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore, 560012, India.
| | - Jason A Somarelli
- Duke Cancer Institute and the Department of Medicine, Duke University Medical Center, Durham, NC 27710, USA.
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Abstract
Kimura's neutral theory argued that positive selection was not responsible for an appreciable fraction of molecular substitutions. Correspondingly, quantitative analysis reveals that the vast majority of substitutions in cancer genomes are not detectably under selection. Insights from the somatic evolution of cancer reveal that beneficial substitutions in cancer constitute a small but important fraction of the molecular variants. The molecular evolution of cancer community will benefit by incorporating the neutral theory of molecular evolution into their understanding and analysis of cancer evolution-and accepting the use of tractable, predictive models, even when there is some evidence that they are not perfect.
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Affiliation(s)
| | - Jeffrey P Townsend
- Department of Biostatistics, Yale University, New Haven, CT
- Program in Computational Biology and Bioinformatics
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT
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18
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Wilkins JF, Cannataro VL, Shuch B, Townsend JP. Analysis of mutation, selection, and epistasis: an informed approach to cancer clinical trials. Oncotarget 2018; 9:22243-22253. [PMID: 29854275 PMCID: PMC5976461 DOI: 10.18632/oncotarget.25155] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2018] [Accepted: 04/02/2018] [Indexed: 12/30/2022] Open
Abstract
Currently, drug development efforts and clinical trials to test them are often prioritized by targeting genes with high frequencies of somatic variants among tumors. However, differences in oncogenic mutation rate-not necessarily the effect the variant has on tumor growth-contribute enormously to somatic variant frequency. We argue that decoupling the contributions of mutation and cancer lineage selection to the frequency of somatic variants among tumors is critical to understanding-and predicting-the therapeutic potential of different interventions. To provide an indicator of that strength of selection and therapeutic potential, the frequency at which we observe a given variant across patients must be modulated by our expectation given the mutation rate and target size to provide an indicator of that strength of selection and therapeutic potential. Additionally, antagonistic and synergistic epistasis among mutations also impacts the potential therapeutic benefit of targeted drug development. Quantitative approaches should be fostered that use the known genetic architectures of cancer types, decouple mutation rate, and provide rigorous guidance regarding investment in targeted drug development. By integrating evolutionary principles and detailed mechanistic knowledge into those approaches, we can maximize our ability to identify those targeted therapies most likely to yield substantial clinical benefit.
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Affiliation(s)
| | | | - Brian Shuch
- Department of Urology, Yale School of Medicine, New Haven, CT, USA
- Department of Radiology, Yale School of Medicine, New Haven, CT, USA
| | - Jeffrey P. Townsend
- Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, CT, USA
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
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