1
|
Lin-Rahardja K, Weaver DT, Scarborough JA, Scott JG. Evolution-Informed Strategies for Combating Drug Resistance in Cancer. Int J Mol Sci 2023; 24:6738. [PMID: 37047714 PMCID: PMC10095117 DOI: 10.3390/ijms24076738] [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: 02/27/2023] [Revised: 04/01/2023] [Accepted: 04/03/2023] [Indexed: 04/14/2023] Open
Abstract
The ever-changing nature of cancer poses the most difficult challenge oncologists face today. Cancer's remarkable adaptability has inspired many to work toward understanding the evolutionary dynamics that underlie this disease in hopes of learning new ways to fight it. Eco-evolutionary dynamics of a tumor are not accounted for in most standard treatment regimens, but exploiting them would help us combat treatment-resistant effectively. Here, we outline several notable efforts to exploit these dynamics and circumvent drug resistance in cancer.
Collapse
Affiliation(s)
- Kristi Lin-Rahardja
- Systems Biology & Bioinformatics, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Davis T. Weaver
- Systems Biology & Bioinformatics, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Jessica A. Scarborough
- Systems Biology & Bioinformatics, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Jacob G. Scott
- Systems Biology & Bioinformatics, Case Western Reserve University, Cleveland, OH 44106, USA
- Department of Translational Hematology & Oncology, Cleveland Clinic Lerner Research Institute, Cleveland, OH 44106, USA
| |
Collapse
|
2
|
Imparato G, Urciuolo F, Mazio C, Netti PA. Capturing the spatial and temporal dynamics of tumor stroma for on-chip optimization of microenvironmental targeting nanomedicine. LAB ON A CHIP 2022; 23:25-43. [PMID: 36305728 DOI: 10.1039/d2lc00611a] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Malignant cells grow in a complex microenvironment that plays a key role in cancer progression. The "dynamic reciprocity" existing between cancer cells and their microenvironment is involved in cancer differentiation, proliferation, invasion, metastasis, and drug response. Therefore, understanding the molecular mechanisms underlying the crosstalk between cancer cells and their surrounding tissue (i.e., tumor stroma) and how this interplay affects the disease progression is fundamental to design and validate novel nanotherapeutic approaches. As an important regulator of tumor progression, metastasis and therapy resistance, the extracellular matrix of tumors, the acellular component of the tumor microenvironment, has been identified as very promising target of anticancer treatment, revolutionizing the traditional therapeutic paradigm that sees the neoplastic cells as the preferential objective to fight cancer. To design and to validate such a target therapy, advanced 3D preclinical models are necessary to correctly mimic the complex, dynamic and heterogeneous tumor microenvironment. In addition, the recent advancement in microfluidic technology allows fine-tuning and controlling microenvironmental parameters in tissue-on-chip devices in order to emulate the in vivo conditions. In this review, after a brief description of the origin of tumor microenvironment heterogeneity, some examples of nanomedicine approaches targeting the tumor microenvironment have been reported. Further, how advanced 3D bioengineered tumor models coupled with a microfluidic device can improve the design and testing of anti-cancer nanomedicine targeting the tumor microenvironment has been discussed. We highlight that the presence of a dynamic extracellular matrix, able to capture the spatiotemporal heterogeneity of tumor stroma, is an indispensable requisite for tumor-on-chip model and nanomedicine testing.
Collapse
Affiliation(s)
- Giorgia Imparato
- Center for Advanced Biomaterials for Health Care@CRIB Istituto Italiano di Tecnologia, Largo Barsanti e Matteucci n. 53, 80125 Napoli, Italy.
| | - Francesco Urciuolo
- Department of Chemical, Materials and Industrial Production Engineering (DICMAPI) and Interdisciplinary Research Centre on Biomaterials (CRIB), University of Napoli Federico II, P.le Tecchio 80, 80125 Napoli, Italy
| | - Claudia Mazio
- Center for Advanced Biomaterials for Health Care@CRIB Istituto Italiano di Tecnologia, Largo Barsanti e Matteucci n. 53, 80125 Napoli, Italy.
| | - Paolo A Netti
- Center for Advanced Biomaterials for Health Care@CRIB Istituto Italiano di Tecnologia, Largo Barsanti e Matteucci n. 53, 80125 Napoli, Italy.
- Department of Chemical, Materials and Industrial Production Engineering (DICMAPI) and Interdisciplinary Research Centre on Biomaterials (CRIB), University of Napoli Federico II, P.le Tecchio 80, 80125 Napoli, Italy
| |
Collapse
|
3
|
Levenson AS. Dietary stilbenes as modulators of specific miRNAs in prostate cancer. Front Pharmacol 2022; 13:970280. [PMID: 36091792 PMCID: PMC9449421 DOI: 10.3389/fphar.2022.970280] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 07/20/2022] [Indexed: 11/13/2022] Open
Abstract
Accumulated experimental data have suggested that natural plant products may be effective miRNA-modulating chemopreventive and therapeutic agents. Dietary polyphenols such as flavonoids, stilbenes, and lignans, among others, have been intensively studied for their miRNA-mediated cardioprotective, antioxidant, anti-inflammatory and anticancer properties. The aim of this review is to outline known stilbene-regulated miRNAs in cancer, with a special focus on the interplay between various miRNAs and MTA1 signaling in prostate cancer. MTA1 is an epigenetic reader and an oncogenic transcription factor that is overexpressed in advanced prostate cancer and metastasis. Not surprisingly, miRNAs that are linked to MTA1 affect cancer progression and the metastatic potential of cells. Studies led to the identification of MTA1-associated pro-oncogenic miRNAs, which are regulated by stilbenes such as resveratrol and pterostilbene. Specifically, it has been shown that inhibition of the activity of the MTA1 regulated oncogenic miR-17 family of miRNAs, miR-22, and miR-34a by stilbenes leads to inhibition of prostatic hyperplasia and tumor progression in mice and reduction of proliferation, survival and invasion of prostate cancer cells in vitro. Taken together, these findings implicate the use of resveratrol and its analogs as an attractive miRNA-mediated chemopreventive and therapeutic strategy in prostate cancer and the use of circulating miRNAs as potential predictive biomarkers for clinical development.
Collapse
|
4
|
Hemani R, Patel I, Inamdar N, Campanelli G, Donovan V, Kumar A, Levenson AS. Dietary Pterostilbene for MTA1-Targeted Interception in High-Risk Premalignant Prostate Cancer. Cancer Prev Res (Phila) 2022; 15:87-100. [PMID: 34675064 PMCID: PMC8828670 DOI: 10.1158/1940-6207.capr-21-0242] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 07/28/2021] [Accepted: 10/20/2021] [Indexed: 11/16/2022]
Abstract
Prostate cancer remains one of the most prevalent cancers in aging men. Active surveillance subpopulation of patients with prostate cancer includes men with varying cancer risk categories of precancerous disease due to prostatic intraepithelial neoplasia (PIN) heterogeneity. Identifying molecular alterations associated with PIN can provide preventable measures through finding novel pharmacologic targets for cancer interception. Targeted nutritional interception may prove to be the most appropriate chemoprevention for intermediate- and high-risk active surveillance patients. Here, we have generated two prostate-specific transgenic mouse models, one overexpressing MTA1 (R26MTA1 ) and the other overexpressing MTA1 on the background of Pten heterozygosity (R26MTA1 ; Pten+/f ), in which we examined the potential chemopreventive efficacy of dietary pterostilbene. We show that MTA1 promotes neoplastic transformation of prostate epithelial cells by activating cell proliferation and survival, leading to PIN development. Moreover, MTA1 cooperates with PTEN deficiency to accelerate PIN development by increasing cell proliferation and MTA1-associated signaling. Further, we show that mice fed with a pterostilbene-supplemented diet exhibited more favorable histopathology with decreased severity and number of PIN foci accompanied by reduced proliferation, angiogenesis, and inflammation concomitant to reduction in MTA1 and MTA1-associated CyclinD1, Notch2, and oncogenic miR-34a and miR-22 levels. PREVENTION RELEVANCE: Developing novel interceptive strategies for prostate cancer chemoprevention is a paramount goal in clinical oncology. We offer preclinical evidence for the potential of pterostilbene as a promising natural agent for MTA1-targeted interceptive strategy in future cancer prevention trials towards protecting select patients with prostate cancer under active surveillance from developing cancer.
Collapse
Affiliation(s)
- Rutu Hemani
- Arnold & Marie Schwartz College of Pharmacy and Health Sciences, Long Island University, Brooklyn, New York
| | - Ishani Patel
- Arnold & Marie Schwartz College of Pharmacy and Health Sciences, Long Island University, Brooklyn, New York
| | - Ninad Inamdar
- Arnold & Marie Schwartz College of Pharmacy and Health Sciences, Long Island University, Brooklyn, New York
| | - Gisella Campanelli
- Arnold & Marie Schwartz College of Pharmacy and Health Sciences, Long Island University, Brooklyn, New York
| | | | - Avinash Kumar
- Arnold & Marie Schwartz College of Pharmacy and Health Sciences, Long Island University, Brooklyn, New York.
- Cancer Institute, University of Mississippi Medical Center, Jackson, Mississippi
| | - Anait S Levenson
- Cancer Institute, University of Mississippi Medical Center, Jackson, Mississippi.
- College of Veterinary Medicine, Long Island University, Brookville, New York
| |
Collapse
|
5
|
Dujon AM, Aktipis A, Alix‐Panabières C, Amend SR, Boddy AM, Brown JS, Capp J, DeGregori J, Ewald P, Gatenby R, Gerlinger M, Giraudeau M, Hamede RK, Hansen E, Kareva I, Maley CC, Marusyk A, McGranahan N, Metzger MJ, Nedelcu AM, Noble R, Nunney L, Pienta KJ, Polyak K, Pujol P, Read AF, Roche B, Sebens S, Solary E, Staňková K, Swain Ewald H, Thomas F, Ujvari B. Identifying key questions in the ecology and evolution of cancer. Evol Appl 2021; 14:877-892. [PMID: 33897809 PMCID: PMC8061275 DOI: 10.1111/eva.13190] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 12/24/2020] [Accepted: 12/26/2020] [Indexed: 12/17/2022] Open
Abstract
The application of evolutionary and ecological principles to cancer prevention and treatment, as well as recognizing cancer as a selection force in nature, has gained impetus over the last 50 years. Following the initial theoretical approaches that combined knowledge from interdisciplinary fields, it became clear that using the eco-evolutionary framework is of key importance to understand cancer. We are now at a pivotal point where accumulating evidence starts to steer the future directions of the discipline and allows us to underpin the key challenges that remain to be addressed. Here, we aim to assess current advancements in the field and to suggest future directions for research. First, we summarize cancer research areas that, so far, have assimilated ecological and evolutionary principles into their approaches and illustrate their key importance. Then, we assembled 33 experts and identified 84 key questions, organized around nine major themes, to pave the foundations for research to come. We highlight the urgent need for broadening the portfolio of research directions to stimulate novel approaches at the interface of oncology and ecological and evolutionary sciences. We conclude that progressive and efficient cross-disciplinary collaborations that draw on the expertise of the fields of ecology, evolution and cancer are essential in order to efficiently address current and future questions about cancer.
Collapse
Affiliation(s)
- Antoine M. Dujon
- School of Life and Environmental SciencesCentre for Integrative EcologyDeakin UniversityWaurn PondsVic.Australia
- CREEC/CANECEV, MIVEGEC (CREES), University of Montpellier, CNRS, IRDMontpellierFrance
| | - Athena Aktipis
- Biodesign InstituteDepartment of PsychologyArizona State UniversityTempeAZUSA
| | - Catherine Alix‐Panabières
- Laboratory of Rare Human Circulating Cells (LCCRH)University Medical Center of MontpellierMontpellierFrance
| | - Sarah R. Amend
- Brady Urological InstituteThe Johns Hopkins School of MedicineBaltimoreMDUSA
| | - Amy M. Boddy
- Department of AnthropologyUniversity of California Santa BarbaraSanta BarbaraCAUSA
| | - Joel S. Brown
- Department of Integrated MathematicsMoffitt Cancer CenterTampaFLUSA
| | - Jean‐Pascal Capp
- Toulouse Biotechnology InstituteINSA/University of ToulouseCNRSINRAEToulouseFrance
| | - James DeGregori
- Department of Biochemistry and Molecular GeneticsIntegrated Department of ImmunologyDepartment of PaediatricsDepartment of Medicine (Section of Hematology)University of Colorado School of MedicineAuroraCOUSA
| | - Paul Ewald
- Department of BiologyUniversity of LouisvilleLouisvilleKYUSA
| | - Robert Gatenby
- Department of RadiologyH. Lee Moffitt Cancer Center & Research InstituteTampaFLUSA
| | - Marco Gerlinger
- Translational Oncogenomics LabThe Institute of Cancer ResearchLondonUK
| | - Mathieu Giraudeau
- CREEC/CANECEV, MIVEGEC (CREES), University of Montpellier, CNRS, IRDMontpellierFrance
- Littoral Environnement et Sociétés (LIENSs)UMR 7266CNRS‐Université de La RochelleLa RochelleFrance
| | | | - Elsa Hansen
- Center for Infectious Disease Dynamics, Biology DepartmentPennsylvania State UniversityUniversity ParkPAUSA
| | - Irina Kareva
- Mathematical and Computational Sciences CenterSchool of Human Evolution and Social ChangeArizona State UniversityTempeAZUSA
| | - Carlo C. Maley
- Arizona Cancer Evolution CenterBiodesign Institute and School of Life SciencesArizona State UniversityTempeAZUSA
| | - Andriy Marusyk
- Department of Cancer PhysiologyH Lee Moffitt Cancer Centre and Research InstituteTampaFLUSA
| | - Nicholas McGranahan
- Translational Cancer Therapeutics LaboratoryThe Francis Crick InstituteLondonUK
- Cancer Research UK Lung Cancer Centre of ExcellenceUniversity College London Cancer InstituteLondonUK
| | | | | | - Robert Noble
- Department of Biosystems Science and EngineeringETH ZurichBaselSwitzerland
- Department of Evolutionary Biology and Environmental StudiesUniversity of ZurichZurichSwitzerland
| | - Leonard Nunney
- Department of Evolution, Ecology, and Organismal BiologyUniversity of California RiversideRiversideCAUSA
| | - Kenneth J. Pienta
- Brady Urological InstituteThe Johns Hopkins School of MedicineBaltimoreMDUSA
| | - Kornelia Polyak
- Department of Medical OncologyDana‐Farber Cancer InstituteBostonMAUSA
- Department of MedicineHarvard Medical SchoolBostonMAUSA
| | - Pascal Pujol
- CREEC/CANECEV, MIVEGEC (CREES), University of Montpellier, CNRS, IRDMontpellierFrance
- Centre Hospitalier Universitaire Arnaud de VilleneuveMontpellierFrance
| | - Andrew F. Read
- Center for Infectious Disease DynamicsHuck Institutes of the Life SciencesDepartments of Biology and EntomologyPennsylvania State UniversityUniversity ParkPAUSA
| | - Benjamin Roche
- CREEC/CANECEV, MIVEGEC (CREES), University of Montpellier, CNRS, IRDMontpellierFrance
- Unité Mixte Internationale de Modélisation Mathématique et Informatique des Systèmes ComplexesUMI IRD/Sorbonne UniversitéUMMISCOBondyFrance
| | - Susanne Sebens
- Institute for Experimental Cancer Research Kiel University and University Hospital Schleswig‐HolsteinKielGermany
| | - Eric Solary
- INSERM U1287Gustave RoussyVillejuifFrance
- Faculté de MédecineUniversité Paris‐SaclayLe Kremlin‐BicêtreFrance
| | - Kateřina Staňková
- Department of Data Science and Knowledge EngineeringMaastricht UniversityMaastrichtThe Netherlands
- Delft Institute of Applied MathematicsDelft University of TechnologyDelftThe Netherlands
| | | | - Frédéric Thomas
- CREEC/CANECEV, MIVEGEC (CREES), University of Montpellier, CNRS, IRDMontpellierFrance
| | - Beata Ujvari
- School of Life and Environmental SciencesCentre for Integrative EcologyDeakin UniversityWaurn PondsVic.Australia
| |
Collapse
|
6
|
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: 9] [Impact Index Per Article: 3.0] [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.].
Collapse
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
| |
Collapse
|
7
|
Curtius K, Dewanji A, Hazelton WD, Rubenstein JH, Luebeck GE. Optimal Timing for Cancer Screening and Adaptive Surveillance Using Mathematical Modeling. Cancer Res 2020; 81:1123-1134. [PMID: 33293425 DOI: 10.1158/0008-5472.can-20-0335] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 09/08/2020] [Accepted: 12/03/2020] [Indexed: 02/06/2023]
Abstract
Cancer screening and early detection efforts have been partially successful in reducing incidence and mortality, but many improvements are needed. Although current medical practice is informed by epidemiologic studies and experts, the decisions for guidelines are ultimately ad hoc. We propose here that quantitative optimization of protocols can potentially increase screening success and reduce overdiagnosis. Mathematical modeling of the stochastic process of cancer evolution can be used to derive and optimize the timing of clinical screens so that the probability is maximal that a patient is screened within a certain "window of opportunity" for intervention when early cancer development may be observable. Alternative to a strictly empirical approach or microsimulations of a multitude of possible scenarios, biologically based mechanistic modeling can be used for predicting when best to screen and begin adaptive surveillance. We introduce a methodology for optimizing screening, assessing potential risks, and quantifying associated costs to healthcare using multiscale models. As a case study in Barrett's esophagus, these methods were applied for a model of esophageal adenocarcinoma that was previously calibrated to U.S. cancer registry data. Optimal screening ages for patients with symptomatic gastroesophageal reflux disease were older (58 for men and 64 for women) than what is currently recommended (age > 50 years). These ages are in a cost-effective range to start screening and were independently validated by data used in current guidelines. Collectively, our framework captures critical aspects of cancer evolution within patients with Barrett's esophagus for a more personalized screening design. SIGNIFICANCE: This study demonstrates how mathematical modeling of cancer evolution can be used to optimize screening regimes, with the added potential to improve surveillance regimes. GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/81/4/1123/F1.large.jpg.
Collapse
Affiliation(s)
- Kit Curtius
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, United Kingdom. .,Division of Biomedical Informatics, Department of Medicine, University of California, San Diego, San Diego, California
| | - Anup Dewanji
- Applied Statistics Unit, Indian Statistical Institute, Kolkata, India
| | - William D Hazelton
- Program in Computational Biology, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Joel H Rubenstein
- Division of Gastroenterology, University of Michigan Medical School, Ann Arbor, Michigan.,Center for Clinical Management Research, Ann Arbor Veterans Affairs Medical Center, Ann Arbor, Michigan
| | - Georg E Luebeck
- Program in Computational Biology, Fred Hutchinson Cancer Research Center, Seattle, Washington
| |
Collapse
|
8
|
Multicompartment modeling of protein shedding kinetics during vascularized tumor growth. Sci Rep 2020; 10:16709. [PMID: 33028917 PMCID: PMC7542472 DOI: 10.1038/s41598-020-73866-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Accepted: 09/10/2020] [Indexed: 02/07/2023] Open
Abstract
Identification of protein biomarkers for cancer diagnosis and prognosis remains a critical unmet clinical need. A major reason is that the dynamic relationship between proliferating and necrotic cell populations during vascularized tumor growth, and the associated extra- and intra-cellular protein outflux from these populations into blood circulation remains poorly understood. Complementary to experimental efforts, mathematical approaches have been employed to effectively simulate the kinetics of detectable surface proteins (e.g., CA-125) shed into the bloodstream. However, existing models can be difficult to tune and may be unable to capture the dynamics of non-extracellular proteins, such as those shed from necrotic and apoptosing cells. The models may also fail to account for intra-tumoral spatial and microenvironmental heterogeneity. We present a new multi-compartment model to simulate heterogeneously vascularized growing tumors and the corresponding protein outflux. Model parameters can be tuned from histology data, including relative vascular volume, mean vessel diameter, and distance from vasculature to necrotic tissue. The model enables evaluating the difference in shedding rates between extra- and non-extracellular proteins from viable and necrosing cells as a function of heterogeneous vascularization. Simulation results indicate that under certain conditions it is possible for non-extracellular proteins to have superior outflux relative to extracellular proteins. This work contributes towards the goal of cancer biomarker identification by enabling simulation of protein shedding kinetics based on tumor tissue-specific characteristics. Ultimately, we anticipate that models like the one introduced herein will enable examining origins and circulating dynamics of candidate biomarkers, thus facilitating marker selection for validation studies.
Collapse
|
9
|
Noble R, Burley JT, Le Sueur C, Hochberg ME. When, why and how tumour clonal diversity predicts survival. Evol Appl 2020; 13:1558-1568. [PMID: 32821272 PMCID: PMC7428820 DOI: 10.1111/eva.13057] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 06/24/2020] [Accepted: 06/25/2020] [Indexed: 12/23/2022] Open
Abstract
The utility of intratumour heterogeneity as a prognostic biomarker is the subject of ongoing clinical investigation. However, the relationship between this marker and its clinical impact is mediated by an evolutionary process that is not well understood. Here, we employ a spatial computational model of tumour evolution to assess when, why and how intratumour heterogeneity can be used to forecast tumour growth rate and progression-free survival. We identify three conditions that can lead to a positive correlation between clonal diversity and subsequent growth rate: diversity is measured early in tumour development; selective sweeps are rare; and/or tumours vary in the rate at which they acquire driver mutations. Opposite conditions typically lead to negative correlation. In cohorts of tumours with diverse evolutionary parameters, we find that clonal diversity is a reliable predictor of both growth rate and progression-free survival. We thus offer explanations-grounded in evolutionary theory-for empirical findings in various cancers, including survival analyses reported in the recent TRACERx Renal study of clear-cell renal cell carcinoma. Our work informs the search for new prognostic biomarkers and contributes to the development of predictive oncology.
Collapse
Affiliation(s)
- Robert Noble
- Department of Biosystems Science and EngineeringETH ZurichBaselSwitzerland
- SIB Swiss Institute of BioinformaticsBaselSwitzerland
- Department of Evolutionary Biology and Environmental StudiesUniversity of ZurichZurichSwitzerland
- Present address:
Department of MathematicsCity, University of LondonLondonUK
| | - John T. Burley
- Department of Ecology and Evolutionary BiologyBrown UniversityProvidenceRIUSA
- Institute at Brown for Environment and SocietyBrown UniversityProvidenceRIUSA
| | - Cécile Le Sueur
- Department of Biosystems Science and EngineeringETH ZurichBaselSwitzerland
| | - Michael E. Hochberg
- Institut des Sciences de l’EvolutionUniversity of MontpellierMontpellierFrance
- Santa Fe InstituteNMUSA
| |
Collapse
|
10
|
Yang L, Zhang J, Yang G, Xu H, Lin J, Shao L, Li J, Guo C, Du Y, Guo L, Li X, Han-Zhang H, Wang C, Chuai S, Ye J, Kang Q, Liu H, Ying J, Wang Y. The prognostic value of a Methylome-based Malignancy Density Scoring System to predict recurrence risk in early-stage Lung Adenocarcinoma. Theranostics 2020; 10:7635-7644. [PMID: 32685009 PMCID: PMC7359091 DOI: 10.7150/thno.44229] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Accepted: 06/01/2020] [Indexed: 12/11/2022] Open
Abstract
Current NCCN guidelines do not recommend the use of adjuvant chemotherapy for stage IA lung adenocarcinoma patients with R0 surgery. However, 25% to 40% of patients with stage IA disease experience recurrence. Stratifying patients according to the recurrence risk may tailor adjuvant therapy and surveillance imaging for those with a higher risk. However, prognostic markers are often identified by comparing high-risk and low-risk cases which might introduce bias due to the widespread interpatient heterogeneity. Here, we developed a scoring system quantifying the degree of field cancerization in adjacent normal tissues and revealed its association with disease-free survival (DFS). Methods: We recruited a cohort of 44 patients with resected stage IA lung adenocarcinoma who did not receive adjuvant therapy. Both tumor and adjacent normal tissues were obtained from each patient and subjected to capture-based targeted genomic and epigenomic profiling. A novel methylome-based scoring system namely malignancy density ratio (MD ratio) was developed based on 39 patients by comparing tumor and corresponding adjacent normal tissues of each patient. A MD score was then obtained by Wald statistics. The correlations of MD ratio, MD score, and genomic features with clinical outcome were investigated. Results: Patients with a high-risk MD ratio showed a significantly shorter postsurgical DFS compared with those with a low-risk MD ratio (HR=4.47, P=0.01). The MD ratio was not associated with T stage (P=1), tumor cell fraction (P=0.748) nor inflammatory status (p=0.548). Patients with a high-risk MD score also demonstrated an inferior DFS (HR=4.69, P=0.039). In addition, multivariate analysis revealed EGFR 19 del (HR=5.39, P=0.012) and MD score (HR= 7.90, P=0.01) were independent prognostic markers. Conclusion: The novel methylome-based scoring system, developed by comparing the signatures between tumor and corresponding adjacent normal tissues of individual patients, largely minimizes the bias of interpatient heterogeneity and reveals a robust prognostic value in patients with resected lung adenocarcinoma.
Collapse
|
11
|
Baker KT, Salk JJ, Brentnall TA, Risques RA. Precancer in ulcerative colitis: the role of the field effect and its clinical implications. Carcinogenesis 2018; 39:11-20. [PMID: 29087436 PMCID: PMC6248676 DOI: 10.1093/carcin/bgx117] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2017] [Revised: 09/22/2017] [Accepted: 10/26/2017] [Indexed: 12/13/2022] Open
Abstract
Cumulative evidence indicates that a significant proportion of cancer evolution may occur before the development of histological abnormalities. While recent improvements in DNA sequencing technology have begun to reveal the presence of these early preneoplastic clones, the concept of 'premalignant field' was already introduced by Slaughter more than half a century ago. Also referred to as 'field effect', 'field defect' or 'field cancerization', these terms describe the phenomenon by which molecular alterations develop in normal-appearing tissue and expand to form premalignant patches with the potential to progress to dysplasia and cancer. Field effects have been well-characterized in ulcerative colitis, an inflammatory bowel disease that increases the risk of colorectal cancer. The study of the molecular alterations that define these fields is informative of mechanisms of tumor initiation and progression and has provided potential targets for early cancer detection. Herein, we summarize the current knowledge about the molecular alterations that comprise the field effect in ulcerative colitis and the clinical utility of these fields for cancer screening and prevention.
Collapse
Affiliation(s)
- Kathryn T Baker
- Department of Pathology, University of Washington, Seattle, WA, USA
| | - Jesse J Salk
- Division of Hematology and Oncology, Department of Medicine, University of
Washington, Seattle, WA, USA
- TwinStrand Biosciences Seattle, WA, USA
| | - Teresa A Brentnall
- Division of Gasteroenterology, Department of Medicine, University of
Washington, Seattle, WA, USA
| | - Rosa Ana Risques
- To whom correspondence should be addressed. Tel: +206-616-4976; Fax:
+206-543-1140;
| |
Collapse
|
12
|
Abstract
Tumorigenesis begins long before the growth of a clinically detectable lesion and, indeed, even before any of the usual morphological correlates of pre-malignancy are recognizable. Field cancerization, which is the replacement of the normal cell population by a cancer-primed cell population that may show no morphological change, is now recognized to underlie the development of many types of cancer, including the common carcinomas of the lung, colon, skin, prostate and bladder. Field cancerization is the consequence of the evolution of somatic cells in the body that results in cells that carry some but not all phenotypes required for malignancy. Here, we review the evidence of field cancerization across organs and examine the biological mechanisms that drive the evolutionary process that results in field creation. We discuss the clinical implications, principally, how measurements of the cancerized field could improve cancer risk prediction in patients with pre-malignant disease.
Collapse
Affiliation(s)
- Kit Curtius
- Centre for Tumour Biology, Barts Cancer Institute, EC1M 6BQ London, UK
| | - Nicholas A Wright
- Centre for Tumour Biology, Barts Cancer Institute, EC1M 6BQ London, UK
| | - Trevor A Graham
- Centre for Tumour Biology, Barts Cancer Institute, EC1M 6BQ London, UK
| |
Collapse
|
13
|
Abstract
Where does cancer come from? Although the cell-of-origin is difficult to pinpoint, cancer clones harbor information about their clonal ancestries. In an effort to find cells before they evolve into a life-threatening cancer, physicians currently diagnose premalignant diseases at frequencies that substantially exceed those of clinical cancers. Cancer risk prediction relies on our ability to distinguish between which premalignant features will lead to cancer mortality and which are characteristic of inconsequential disease. Here, we review the evolution of cancer from premalignant disease, and discuss the concept that even phenotypically normal cell progenies inherently gain more malignant potential with age. We describe the hurdles of prognosticating cancer risk in premalignant disease by making reference to the underlying continuous and multivariate natures of genotypes and phenotypes and the particular challenge inherent in defining a cell lineage as "cancerized."
Collapse
Affiliation(s)
- Kit Curtius
- Centre for Tumor Biology, Barts Cancer Institute, EC1M 6BQ London, United Kingdom
| | - Nicholas A Wright
- Centre for Tumor Biology, Barts Cancer Institute, EC1M 6BQ London, United Kingdom
| | - Trevor A Graham
- Centre for Tumor Biology, Barts Cancer Institute, EC1M 6BQ London, United Kingdom
| |
Collapse
|
14
|
Storey K, Ryser MD, Leder K, Foo J. Spatial Measures of Genetic Heterogeneity During Carcinogenesis. Bull Math Biol 2016; 79:237-276. [PMID: 27905065 DOI: 10.1007/s11538-016-0234-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2015] [Accepted: 11/18/2016] [Indexed: 12/20/2022]
Abstract
In this work we explore the temporal dynamics of spatial heterogeneity during the process of tumorigenesis from healthy tissue. We utilize a spatial stochastic model of mutation accumulation and clonal expansion in a structured tissue to describe this process. Under a two-step tumorigenesis model, we first derive estimates of a non-spatial measure of diversity: Simpson's Index, which is the probability that two individuals sampled at random from the population are identical, in the premalignant population. We next analyze two new measures of spatial population heterogeneity. In particular we study the typical length scale of genetic heterogeneity during the carcinogenesis process and estimate the extent of a surrounding premalignant clone given a clinical observation of a premalignant point biopsy. This evolutionary framework contributes to a growing literature focused on developing a better understanding of the spatial population dynamics of cancer initiation and progression.
Collapse
Affiliation(s)
- K Storey
- University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - M D Ryser
- Department of Surgery, Division of Oncologic and GI Surgery, Duke University Medical Center, Durham, NC, USA
- Department of Mathematics, Duke University, Durham, NC, USA
| | - K Leder
- University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - J Foo
- University of Minnesota Twin Cities, Minneapolis, MN, USA.
| |
Collapse
|