1
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Lewis MT, Caldas C. The Power and Promise of Patient-Derived Xenografts of Human Breast Cancer. Cold Spring Harb Perspect Med 2024; 14:a041329. [PMID: 38052483 PMCID: PMC10982691 DOI: 10.1101/cshperspect.a041329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
In 2016, a group of researchers engaged in the development of patient-derived xenografts (PDXs) of human breast cancer provided a comprehensive review of the state of the field. In that review, they summarized the clinical problem that PDXs might address, the technical approaches to their generation (including a discussion of host animals and transplant conditions tested), and presented transplantation success (take) rates across groups and across transplantation conditions. At the time, there were just over 500 unique PDX models created by these investigators representing all three clinically defined subtypes (ER+, HER2+, and TNBC). Today, many of these PDX resources have at least doubled in size, and several more PDX development groups now exist, such that there may be well upward of 1000 PDX models of human breast cancer in existence worldwide. They also presented a series of open questions for the field. Many of these questions have been addressed. However, several remain open, or only partially addressed. Herein, we revisit these questions, and recount the progress that has been made in a number of areas with respect to generation, characterization, and use of PDXs in translational research, and re-present questions that remain open. These open questions, and others, are now being addressed not only by individual investigators, but also large, well-funded consortia including the PDXNet program of the National Cancer Institute in the United States, and the EuroPDX Consortium, an organization of PDX developers across Europe. Finally, we discuss the new opportunities in PDX-based research.
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Affiliation(s)
- Michael T Lewis
- Baylor College of Medicine, The Lester and Sue Smith Breast Center, Departments of Molecular and Cellular Biology and Radiology, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Carlos Caldas
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge CB2 0RE, United Kingdom
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2
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Mukashyaka P, Kumar P, Mellert DJ, Nicholas S, Noorbakhsh J, Brugiolo M, Courtois ET, Anczukow O, Liu ET, Chuang JH. High-throughput deconvolution of 3D organoid dynamics at cellular resolution for cancer pharmacology with Cellos. Nat Commun 2023; 14:8406. [PMID: 38114489 PMCID: PMC10730814 DOI: 10.1038/s41467-023-44162-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 12/01/2023] [Indexed: 12/21/2023] Open
Abstract
Three-dimensional (3D) organoid cultures are flexible systems to interrogate cellular growth, morphology, multicellular spatial architecture, and cellular interactions in response to treatment. However, computational methods for analysis of 3D organoids with sufficiently high-throughput and cellular resolution are needed. Here we report Cellos, an accurate, high-throughput pipeline for 3D organoid segmentation using classical algorithms and nuclear segmentation using a trained Stardist-3D convolutional neural network. To evaluate Cellos, we analyze ~100,000 organoids with ~2.35 million cells from multiple treatment experiments. Cellos segments dye-stained or fluorescently-labeled nuclei and accurately distinguishes distinct labeled cell populations within organoids. Cellos can recapitulate traditional luminescence-based drug response of cells with complex drug sensitivities, while also quantifying changes in organoid and nuclear morphologies caused by treatment as well as cell-cell spatial relationships that reflect ecological affinity. Cellos provides powerful tools to perform high-throughput analysis for pharmacological testing and biological investigation of organoids based on 3D imaging.
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Affiliation(s)
- Patience Mukashyaka
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
- Department of Genetics and Genome Sciences, University of Connecticut Health Center, Farmington, CT, USA
| | - Pooja Kumar
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - David J Mellert
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Shadae Nicholas
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Javad Noorbakhsh
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Mattia Brugiolo
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Elise T Courtois
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Olga Anczukow
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
- Department of Genetics and Genome Sciences, University of Connecticut Health Center, Farmington, CT, USA
| | - Edison T Liu
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.
| | - Jeffrey H Chuang
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.
- Department of Genetics and Genome Sciences, University of Connecticut Health Center, Farmington, CT, USA.
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3
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Mukashyaka P, Kumar P, Mellert DJ, Nicholas S, Noorbakhsh J, Brugiolo M, Anczukow O, Liu ET, Chuang JH. Cellos: High-throughput deconvolution of 3D organoid dynamics at cellular resolution for cancer pharmacology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.03.531019. [PMID: 36945601 PMCID: PMC10028797 DOI: 10.1101/2023.03.03.531019] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/11/2023]
Abstract
Three-dimensional (3D) culture models, such as organoids, are flexible systems to interrogate cellular growth and morphology, multicellular spatial architecture, and cell interactions in response to drug treatment. However, new computational methods to segment and analyze 3D models at cellular resolution with sufficiently high throughput are needed to realize these possibilities. Here we report Cellos (Cell and Organoid Segmentation), an accurate, high throughput image analysis pipeline for 3D organoid and nuclear segmentation analysis. Cellos segments organoids in 3D using classical algorithms and segments nuclei using a Stardist-3D convolutional neural network which we trained on a manually annotated dataset of 3,862 cells from 36 organoids confocally imaged at 5 μm z-resolution. To evaluate the capabilities of Cellos we then analyzed 74,450 organoids with 1.65 million cells, from multiple experiments on triple negative breast cancer organoids containing clonal mixtures with complex cisplatin sensitivities. Cellos was able to accurately distinguish ratios of distinct fluorescently labelled cell populations in organoids, with ≤3% deviation from the seeding ratios in each well and was effective for both fluorescently labelled nuclei and independent DAPI stained datasets. Cellos was able to recapitulate traditional luminescence-based drug response quantifications by analyzing 3D images, including parallel analysis of multiple cancer clones in the same well. Moreover, Cellos was able to identify organoid and nuclear morphology feature changes associated with treatment. Finally, Cellos enables 3D analysis of cell spatial relationships, which we used to detect ecological affinity between cancer cells beyond what arises from local cell division or organoid composition. Cellos provides powerful tools to perform high throughput analysis for pharmacological testing and biological investigation of organoids based on 3D imaging.
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Affiliation(s)
- Patience Mukashyaka
- The Jackson Laboratory for Genomic Medicine, Farmington, CT
- University of Connecticut Health Center, Department of Genetics and Genome Sciences, Farmington, CT
| | - Pooja Kumar
- The Jackson Laboratory for Genomic Medicine, Farmington, CT
| | | | | | | | | | - Olga Anczukow
- The Jackson Laboratory for Genomic Medicine, Farmington, CT
- University of Connecticut Health Center, Department of Genetics and Genome Sciences, Farmington, CT
| | - Edison T Liu
- The Jackson Laboratory for Genomic Medicine, Farmington, CT
| | - Jeffrey H Chuang
- The Jackson Laboratory for Genomic Medicine, Farmington, CT
- University of Connecticut Health Center, Department of Genetics and Genome Sciences, Farmington, CT
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4
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Menghi F, Banda K, Kumar P, Straub R, Dobrolecki L, Rodriguez IV, Yost SE, Chandok H, Radke MR, Somlo G, Yuan Y, Lewis MT, Swisher EM, Liu ET. Genomic and epigenomic
BRCA
alterations predict adaptive resistance and response to platinum-based therapy in patients with triple-negative breast and ovarian carcinomas. Sci Transl Med 2022; 14:eabn1926. [PMID: 35857626 PMCID: PMC9585706 DOI: 10.1126/scitranslmed.abn1926] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Triple-negative breast cancer (TNBC) and ovarian carcinomas (OvCas) with
BRCA1
promoter methylation (
BRCA1
meth) respond more poorly to alkylating agents compared to those bearing mutations in
BRCA1
and
BRCA2
(
BRCA
mut). This is a conundrum given the biologically equivalent homologous recombination deficiency (HRD) induced by these genetic and epigenetic
BRCA
perturbations. We dissected this problem through detailed genomic analyses of TNBC and OvCa cohorts and experimentation with patient-derived xenografts and genetically engineered cell lines. We found that despite identical downstream genomic mutational signatures associated with
BRCA1
meth and
BRCA
mut states,
BRCA1
meth uniformly associates with poor outcomes. Exposure of
BRCA1
meth TNBCs to platinum chemotherapy, either as clinical treatment of a patient or as experimental in vivo exposure of preclinical patient derived xenografts, resulted in allelic loss of
BRCA1
methylation and increased
BRCA1
expression and platinum resistance. These data suggest that, unlike
BRCA
mut cancers, where
BRCA
loss is a genetically “fixed” deficiency state,
BRCA1
meth cancers are highly adaptive to genotoxin exposure and, through reversal of promoter methylation, recover
BRCA1
expression and become resistant to therapy. We further found a specific augmented immune transcriptional signal associated with enhanced response to platinum chemotherapy but only in patients with BRCA-proficient cancers. We showed how integrating both this cancer immune signature and the presence of
BRCA
mutations results in more accurate predictions of patient response when compared to either HRD status or
BRCA
status alone. This underscores the importance of defining
BRCA
heterogeneity in optimizing the predictive precision of assigning response probabilities in TNBC and OvCa.
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Affiliation(s)
- Francesca Menghi
- Jackson Laboratory for Genomic Medicine, Farmington, CT 06030, USA
| | - Kalyan Banda
- Division of Medical Oncology, UW Medical Center, Seattle, WA 98195, USA
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Pooja Kumar
- Jackson Laboratory for Genomic Medicine, Farmington, CT 06030, USA
| | - Robert Straub
- Jackson Laboratory for Genomic Medicine, Farmington, CT 06030, USA
| | | | - Isabel V. Rodriguez
- Department of Obstetrics and Gynecology, UW Medical Center, Seattle, WA 98195, USA
| | - Susan E. Yost
- Division of Medical Oncology and Therapeutic Research, City of Hope Comprehensive Cancer Center, Duarte, CA 91010, USA
| | | | - Marc R. Radke
- Department of Obstetrics and Gynecology, UW Medical Center, Seattle, WA 98195, USA
| | - George Somlo
- Division of Medical Oncology and Therapeutic Research, City of Hope Comprehensive Cancer Center, Duarte, CA 91010, USA
| | - Yuan Yuan
- Division of Medical Oncology and Therapeutic Research, City of Hope Comprehensive Cancer Center, Duarte, CA 91010, USA
| | - Michael T. Lewis
- Departments of Molecular and Cellular Biology and Radiology, Lester and Sue Smith Breast Center, Dan L Duncan Comprehensive Cancer Center, Houston, TX 77030, USA
| | - Elizabeth M. Swisher
- Department of Obstetrics and Gynecology, UW Medical Center, Seattle, WA 98195, USA
| | - Edison T. Liu
- Jackson Laboratory for Genomic Medicine, Farmington, CT 06030, USA
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5
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M A M, Kim JY, Pan CH, Kim E. The impact of the spatial heterogeneity of resistant cells and fibroblasts on treatment response. PLoS Comput Biol 2022; 18:e1009919. [PMID: 35263336 PMCID: PMC8906648 DOI: 10.1371/journal.pcbi.1009919] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 02/11/2022] [Indexed: 01/03/2023] Open
Abstract
A long-standing practice in the treatment of cancer is that of hitting hard with the maximum tolerated dose to eradicate tumors. This continuous therapy, however, selects for resistant cells, leading to the failure of the treatment. A different type of treatment strategy, adaptive therapy, has recently been shown to have a degree of success in both preclinical xenograft experiments and clinical trials. Adaptive therapy is used to maintain a tumor's volume by exploiting the competition between drug-sensitive and drug-resistant cells with minimum effective drug doses or timed drug holidays. To further understand the role of competition in the outcomes of adaptive therapy, we developed a 2D on-lattice agent-based model. Our simulations show that the superiority of the adaptive strategy over continuous therapy depends on the local competition shaped by the spatial distribution of resistant cells. Intratumor competition can also be affected by fibroblasts, which produce microenvironmental factors that promote cancer cell growth. To this end, we simulated the impact of different fibroblast distributions on treatment outcomes. As a proof of principle, we focused on five types of distribution of fibroblasts characterized by different locations, shapes, and orientations of the fibroblast region with respect to the resistant cells. Our simulation shows that the spatial architecture of fibroblasts modulates tumor progression in both continuous and adaptive therapy. Finally, as a proof of concept, we simulated the outcomes of adaptive therapy of a virtual patient with four metastatic sites composed of different spatial distributions of fibroblasts and drug-resistant cell populations. Our simulation highlights the importance of undetected metastatic lesions on adaptive therapy outcomes.
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Affiliation(s)
- Masud M A
- Natural Product Informatics Research Center, Korea Institute of Science and Technology, Gangneung, Republic of Korea
| | - Jae-Young Kim
- Graduate School of Science and Technology, Chungnam National University, Daejeon, Republic of Korea
| | - Cheol-Ho Pan
- Natural Product Informatics Research Center, Korea Institute of Science and Technology, Gangneung, Republic of Korea
| | - Eunjung Kim
- Natural Product Informatics Research Center, Korea Institute of Science and Technology, Gangneung, Republic of Korea
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6
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Kim E, Brown JS, Eroglu Z, Anderson AR. Adaptive Therapy for Metastatic Melanoma: Predictions from Patient Calibrated Mathematical Models. Cancers (Basel) 2021; 13:823. [PMID: 33669315 PMCID: PMC7920057 DOI: 10.3390/cancers13040823] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 02/11/2021] [Indexed: 02/07/2023] Open
Abstract
Adaptive therapy is an evolution-based treatment approach that aims to maintain tumor volume by employing minimum effective drug doses or timed drug holidays. For successful adaptive therapy outcomes, it is critical to find the optimal timing of treatment switch points in a patient-specific manner. Here we develop a combination of mathematical models that examine interactions between drug-sensitive and resistant cells to facilitate melanoma adaptive therapy dosing and switch time points. The first model assumes genetically fixed drug-sensitive and -resistant popul tions that compete for limited resources. The second model considers phenotypic switching between drug-sensitive and -resistant cells. We calibrated each model to fit melanoma patient biomarker changes over time and predicted patient-specific adaptive therapy schedules. Overall, the models predict that adaptive therapy would have delayed time to progression by 6-25 months compared to continuous therapy with dose rates of 6-74% relative to continuous therapy. We identified predictive factors driving the clinical time gained by adaptive therapy, such as the number of initial sensitive cells, competitive effect, switching rate from resistant to sensitive cells, and sensitive cell growth rate. This study highlights that there is a range of potential patient-specific benefits of adaptive therapy and identifies parameters that modulate this benefit.
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Affiliation(s)
- Eunjung Kim
- Natural Product Research Center, Korea Institute of Science and Technology, Gangneung 25451, Korea
| | - Joel S. Brown
- Integrated Mathematical Oncology, H. Lee Moffitt Cancer and Research Institute, Tampa, FL 33612, USA;
| | - Zeynep Eroglu
- Cutaneous Oncology, H. Lee Moffitt Cancer and Research Institute, Tampa, FL 33612, USA;
| | - Alexander R.A. Anderson
- Integrated Mathematical Oncology, H. Lee Moffitt Cancer and Research Institute, Tampa, FL 33612, USA;
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7
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Woo XY, Giordano J, Srivastava A, Zhao ZM, Lloyd MW, de Bruijn R, Suh YS, Patidar R, Chen L, Scherer S, Bailey MH, Yang CH, Cortes-Sanchez E, Xi Y, Wang J, Wickramasinghe J, Kossenkov AV, Rebecca VW, Sun H, Mashl RJ, Davies SR, Jeon R, Frech C, Randjelovic J, Rosains J, Galimi F, Bertotti A, Lafferty A, O'Farrell AC, Modave E, Lambrechts D, Ter Brugge P, Serra V, Marangoni E, El Botty R, Kim H, Kim JI, Yang HK, Lee C, Dean DA, Davis-Dusenbery B, Evrard YA, Doroshow JH, Welm AL, Welm BE, Lewis MT, Fang B, Roth JA, Meric-Bernstam F, Herlyn M, Davies MA, Ding L, Li S, Govindan R, Isella C, Moscow JA, Trusolino L, Byrne AT, Jonkers J, Bult CJ, Medico E, Chuang JH. Conservation of copy number profiles during engraftment and passaging of patient-derived cancer xenografts. Nat Genet 2021; 53:86-99. [PMID: 33414553 PMCID: PMC7808565 DOI: 10.1038/s41588-020-00750-6] [Citation(s) in RCA: 109] [Impact Index Per Article: 36.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2019] [Accepted: 11/18/2020] [Indexed: 02/03/2023]
Abstract
Patient-derived xenografts (PDXs) are resected human tumors engrafted into mice for preclinical studies and therapeutic testing. It has been proposed that the mouse host affects tumor evolution during PDX engraftment and propagation, affecting the accuracy of PDX modeling of human cancer. Here, we exhaustively analyze copy number alterations (CNAs) in 1,451 PDX and matched patient tumor (PT) samples from 509 PDX models. CNA inferences based on DNA sequencing and microarray data displayed substantially higher resolution and dynamic range than gene expression-based inferences, and they also showed strong CNA conservation from PTs through late-passage PDXs. CNA recurrence analysis of 130 colorectal and breast PT/PDX-early/PDX-late trios confirmed high-resolution CNA retention. We observed no significant enrichment of cancer-related genes in PDX-specific CNAs across models. Moreover, CNA differences between patient and PDX tumors were comparable to variations in multiregion samples within patients. Our study demonstrates the lack of systematic copy number evolution driven by the PDX mouse host.
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Grants
- NC/T001267/1 National Centre for the Replacement, Refinement and Reduction of Animals in Research
- P30 CA016672 NCI NIH HHS
- 29567 Cancer Research UK
- U54 CA233223 NCI NIH HHS
- P30 CA034196 NCI NIH HHS
- P01 CA114046 NCI NIH HHS
- HHSN261201400008C NCI NIH HHS
- P30 CA091842 NCI NIH HHS
- U24 CA224067 NCI NIH HHS
- P50 CA196510 NCI NIH HHS
- U54 CA224070 NCI NIH HHS
- U54 CA224076 NCI NIH HHS
- U54 CA224065 NCI NIH HHS
- U54 CA233306 NCI NIH HHS
- P30 CA010815 NCI NIH HHS
- U24 CA204781 NCI NIH HHS
- U54 CA224083 NCI NIH HHS
- HHSN261201500003C NCI NIH HHS
- HHSN261200800001C NCI NIH HHS
- T32 HG008962 NHGRI NIH HHS
- R50 CA211199 NCI NIH HHS
- P30 CA125123 NCI NIH HHS
- P50 CA070907 NCI NIH HHS
- HHSN261201500003I NCI NIH HHS
- HHSN261200800001E NCI NIH HHS
- P30 CA042014 NCI NIH HHS
- U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
- KWF Kankerbestrijding (Dutch Cancer Society)
- Oncode Institute
- Fondazione AIRC under 5 per Mille 2018 - ID. 21091 EU H2020 Research and Innovation Programme, grant agreement no. 731105 European Research Council Consolidator Grant 724748
- EU H2020 Research and Innovation Programme, grant Agreement No. 754923
- EU H2020 Research and Innovation Programme, grant agreement no. 731105 ISCIII - Miguel Servet program CP14/00228 GHD-Pink/FERO Foundation grant
- Fondazione Piemontese per la Ricerca sul Cancro-ONLUS 5 per mille Ministero della Salute 2015
- Korean Health Industry Development Institute HI13C2148
- Korean Health Industry Development Institute HI13C2148 The First Affiliated Hospital of Xi’an Jiaotong University Ewha Womans University Research Grant
- CPRIT RP170691
- SCU | Ignatian Center for Jesuit Education, Santa Clara University
- Breast Cancer Research Foundation (BCRF)
- Fashion Footwear Charitable Foundation of New York The Foundation for Barnes-Jewish Hospital’s Cancer Frontier Fund
- My First AIRC Grant 19047
- Fondazione AIRC under 5 per Mille 2018 - ID. 21091 AIRC Investigator Grants 18532 and 20697 AIRC/CRUK/FC AECC Accelerator Award 22795 Fondazione Piemontese per la Ricerca sul Cancro-ONLUS 5 per mille Ministero della Salute 2015, 2014, 2016 EU H2020 Research and Innovation Programme, grant Agreement No. 754923 EU H2020 Research and Innovation Programme, grant agreement no. 731105
- Science Foundation Ireland (SFI)
- EU H2020 Research and Innovation Programme, grant agreement no. 731105 EU H2020 Research and Innovation Programme, grant Agreement No. 754923 Irish Health Research Board grant ILP-POR-2019-066
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (Netherlands Organisation for Scientific Research)
- EU H2020 Research and Innovation Programme, grant agreement no. 731105 European Research Council (ERC) Synergy project CombatCancer Oncode Institute
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Affiliation(s)
- Xing Yi Woo
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Jessica Giordano
- Department of Oncology, University of Turin, Turin, Italy
- Candiolo Cancer Institute, FPO-IRCCS, Turin, Italy
| | - Anuj Srivastava
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Zi-Ming Zhao
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Michael W Lloyd
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, ME, USA
| | | | - Yun-Suhk Suh
- College of Medicine, Seoul National University, Seoul, Republic of Korea
| | - Rajesh Patidar
- Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Li Chen
- Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Sandra Scherer
- Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Matthew H Bailey
- Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
| | - Chieh-Hsiang Yang
- Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Emilio Cortes-Sanchez
- Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Yuanxin Xi
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jing Wang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | | | | | - Hua Sun
- Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - R Jay Mashl
- Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Sherri R Davies
- Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Ryan Jeon
- Seven Bridges Genomics, Charlestown, MA, USA
| | | | | | | | - Francesco Galimi
- Department of Oncology, University of Turin, Turin, Italy
- Candiolo Cancer Institute, FPO-IRCCS, Turin, Italy
| | - Andrea Bertotti
- Department of Oncology, University of Turin, Turin, Italy
- Candiolo Cancer Institute, FPO-IRCCS, Turin, Italy
| | - Adam Lafferty
- Department of Physiology and Medical Physics, Centre for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Alice C O'Farrell
- Department of Physiology and Medical Physics, Centre for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Elodie Modave
- Center for Cancer Biology, VIB, Leuven, Belgium
- Laboratory of Translational Genetics, Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Diether Lambrechts
- Center for Cancer Biology, VIB, Leuven, Belgium
- Laboratory of Translational Genetics, Department of Human Genetics, KU Leuven, Leuven, Belgium
| | | | - Violeta Serra
- Vall d´Hebron Institute of Oncology, Barcelona, Spain
| | - Elisabetta Marangoni
- Department of Translational Research, Institut Curie, PSL Research University, Paris, France
| | - Rania El Botty
- Department of Translational Research, Institut Curie, PSL Research University, Paris, France
| | - Hyunsoo Kim
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Jong-Il Kim
- College of Medicine, Seoul National University, Seoul, Republic of Korea
| | - Han-Kwang Yang
- College of Medicine, Seoul National University, Seoul, Republic of Korea
| | - Charles Lee
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
- Precision Medicine Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, People's Republic of China
- Department of Life Sciences, Ewha Womans University, Seoul, Republic of Korea
| | | | | | - Yvonne A Evrard
- Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - James H Doroshow
- Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD, USA
| | - Alana L Welm
- Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Bryan E Welm
- Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
- Department of Surgery, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Michael T Lewis
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, USA
| | - Bingliang Fang
- Department of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jack A Roth
- Department of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Funda Meric-Bernstam
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | - Michael A Davies
- Department of Melanoma Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Li Ding
- Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Shunqiang Li
- Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Ramaswamy Govindan
- Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Claudio Isella
- Department of Oncology, University of Turin, Turin, Italy
- Candiolo Cancer Institute, FPO-IRCCS, Turin, Italy
| | - Jeffrey A Moscow
- Investigational Drug Branch, National Cancer Institute, Bethesda, MD, USA
| | - Livio Trusolino
- Department of Oncology, University of Turin, Turin, Italy
- Candiolo Cancer Institute, FPO-IRCCS, Turin, Italy
| | - Annette T Byrne
- Department of Physiology and Medical Physics, Centre for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Jos Jonkers
- Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Carol J Bult
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, ME, USA
| | - Enzo Medico
- Department of Oncology, University of Turin, Turin, Italy.
- Candiolo Cancer Institute, FPO-IRCCS, Turin, Italy.
| | - Jeffrey H Chuang
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.
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8
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Porter W, Snowden E, Hahn F, Ferguson M, Tong F, Dillmore WS, Blaesius R. High accuracy gene expression profiling of sorted cell subpopulations from breast cancer PDX model tissue. PLoS One 2020; 15:e0238594. [PMID: 32911489 PMCID: PMC7482927 DOI: 10.1371/journal.pone.0238594] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Accepted: 08/19/2020] [Indexed: 01/01/2023] Open
Abstract
Intratumor Heterogeneity (ITH) is a functionally important property of tumor tissue and may be involved in drug resistance mechanisms. Although descriptions of ITH can be traced back to very early reports about cancer tissue, mechanistic investigations are still limited by the precision of analysis methods and access to relevant tissue sources. PDX models have provided a reproducible source of tissue with at least a partial representation of naturally occurring ITH. We investigated the properties of phenotypically distinct cell populations by Fluorescence activated cell sorting (FACS) tissue derived cells from multiple tumors from a triple negative breast cancer patient derived xenograft (PDX) model. We subsequently subjected each population to in depth gene expression analysis. Our findings suggest that process related gene expression changes (caused by tissue dissociation and FACS sorting) are restricted to Immediate Early Genes (IEGs). This allowed us to discover highly reproducible gene expression profiles of distinct cellular compartments identifiable by cell surface markers in this particular tumor model. Within the context of data from a previously published model our work suggests that gene expression profiles associated with hypoxia, stemness and drug resistance may reside in tumor subpopulations predictably growing in PDX models. This approach provides a novel opportunity for prospective mechanistic studies of ITH.
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Affiliation(s)
- Warren Porter
- BD Technologies and Innovation, Research Triangle Park, NC, United States of America
| | - Eileen Snowden
- BD Technologies and Innovation, Research Triangle Park, NC, United States of America
| | - Friedrich Hahn
- BD Technologies and Innovation, Research Triangle Park, NC, United States of America
| | - Mitchell Ferguson
- BD Technologies and Innovation, Research Triangle Park, NC, United States of America
| | - Frances Tong
- BD Technologies and Innovation, Research Triangle Park, NC, United States of America
| | - W. Shannon Dillmore
- BD Technologies and Innovation, Research Triangle Park, NC, United States of America
| | - Rainer Blaesius
- BD Technologies and Innovation, Research Triangle Park, NC, United States of America
- * E-mail:
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9
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Muciño-Olmos EA, Vázquez-Jiménez A, Avila-Ponce de León U, Matadamas-Guzman M, Maldonado V, López-Santaella T, Hernández-Hernández A, Resendis-Antonio O. Unveiling functional heterogeneity in breast cancer multicellular tumor spheroids through single-cell RNA-seq. Sci Rep 2020; 10:12728. [PMID: 32728097 PMCID: PMC7391783 DOI: 10.1038/s41598-020-69026-7] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Accepted: 07/01/2020] [Indexed: 12/31/2022] Open
Abstract
Heterogeneity is an intrinsic characteristic of cancer. Even in isogenic tumors, cell populations exhibit differential cellular programs that overall supply malignancy and decrease treatment efficiency. In this study, we investigated the functional relationship among cell subtypes and how this interdependency can promote tumor development in a cancer cell line. To do so, we performed single-cell RNA-seq of MCF7 Multicellular Tumor Spheroids as a tumor model. Analysis of single-cell transcriptomes at two-time points of the spheroid growth, allowed us to dissect their functional relationship. As a result, three major robust cellular clusters, with a non-redundant complementary composition, were found. Meanwhile, one cluster promotes proliferation, others mainly activate mechanisms to invade other tissues and serve as a reservoir population conserved over time. Our results provide evidence to see cancer as a systemic unit that has cell populations with task stratification with the ultimate goal of preserving the hallmarks in tumors.
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Affiliation(s)
- Erick Andrés Muciño-Olmos
- PhD Program in Biomedical Sciences, UNAM, Mexico City, Mexico.,Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica, Periférico Sur 4809, Arenal Tepepan, 14610, Mexico City, Mexico
| | - Aarón Vázquez-Jiménez
- Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica, Periférico Sur 4809, Arenal Tepepan, 14610, Mexico City, Mexico
| | - Ugo Avila-Ponce de León
- Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica, Periférico Sur 4809, Arenal Tepepan, 14610, Mexico City, Mexico.,PhD Program in Biological Sciences, UNAM, Mexico City, Mexico
| | - Meztli Matadamas-Guzman
- PhD Program in Biomedical Sciences, UNAM, Mexico City, Mexico.,Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica, Periférico Sur 4809, Arenal Tepepan, 14610, Mexico City, Mexico
| | - Vilma Maldonado
- Epigenetic Laboratory, Instituto Nacional de Medicina, Genómica, Periférico Sur 4809, Arenal Tepepan, 14610, Mexico City, Mexico
| | - Tayde López-Santaella
- Biología de Células Individuales (BIOCELIN), Laboratorio de Investigación en Patología Experimental, Hospital Infantil de México Federico Gómez, Mexico City, Mexico
| | - Abrahan Hernández-Hernández
- Biología de Células Individuales (BIOCELIN), Laboratorio de Investigación en Patología Experimental, Hospital Infantil de México Federico Gómez, Mexico City, Mexico.
| | - Osbaldo Resendis-Antonio
- Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica, Periférico Sur 4809, Arenal Tepepan, 14610, Mexico City, Mexico. .,Coordinación de La Investigación Científica -Red de Apoyo a La Investigación, UNAM, Mexico City, Mexico.
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10
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Schafer JM, Lehmann BD, Gonzalez-Ericsson PI, Marshall CB, Beeler JS, Redman LN, Jin H, Sanchez V, Stubbs MC, Scherle P, Johnson KN, Sheng Q, Roland JT, Bauer JA, Shyr Y, Chakravarthy B, Mobley BC, Hiebert SW, Balko JM, Sanders ME, Liu PCC, Pietenpol JA. Targeting MYCN-expressing triple-negative breast cancer with BET and MEK inhibitors. Sci Transl Med 2020; 12:eaaw8275. [PMID: 32161105 PMCID: PMC7427123 DOI: 10.1126/scitranslmed.aaw8275] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Revised: 10/14/2019] [Accepted: 01/28/2020] [Indexed: 12/12/2022]
Abstract
Triple-negative breast cancer (TNBC) is an aggressive form of breast cancer that does not respond to endocrine therapy or human epidermal growth factor receptor 2 (HER2)-targeted therapies. Individuals with TNBC experience higher rates of relapse and shorter overall survival compared to patients with receptor-positive breast cancer subtypes. Preclinical discoveries are needed to identify, develop, and advance new drug targets to improve outcomes for patients with TNBC. Here, we report that MYCN, an oncogene typically overexpressed in tumors of the nervous system or with neuroendocrine features, is heterogeneously expressed within a substantial fraction of primary and recurrent TNBC and is expressed in an even higher fraction of TNBCs that do not display a pathological complete response after neoadjuvant chemotherapy. We performed high-throughput chemical screens on TNBC cell lines with varying amounts of MYCN expression and determined that cells with higher expression of MYCN were more sensitive to bromodomain and extraterminal motif (BET) inhibitors. Combined BET and MEK inhibition resulted in a synergistic decrease in viability, both in vitro and in vivo, using cell lines and patient-derived xenograft (PDX) models. Our preclinical data provide a rationale to advance a combination of BET and MEK inhibitors to clinical investigation for patients with advanced MYCN-expressing TNBC.
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Affiliation(s)
- Johanna M Schafer
- Department of Biochemistry, Vanderbilt University, Nashville, TN 37232, USA
| | - Brian D Lehmann
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | | | - Clayton B Marshall
- Department of Biochemistry, Vanderbilt University, Nashville, TN 37232, USA
- Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - J Scott Beeler
- Department of Biochemistry, Vanderbilt University, Nashville, TN 37232, USA
| | - Lindsay N Redman
- Department of Biochemistry, Vanderbilt University, Nashville, TN 37232, USA
| | - Hailing Jin
- Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Violeta Sanchez
- Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | | | | | - Kimberly N Johnson
- Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Quanhu Sheng
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Joseph T Roland
- Section of Surgical Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Joshua A Bauer
- Department of Biochemistry, Vanderbilt University, Nashville, TN 37232, USA
- Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, TN 37232, USA
| | - Yu Shyr
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Bapsi Chakravarthy
- Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiation Oncology, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Bret C Mobley
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Scott W Hiebert
- Department of Biochemistry, Vanderbilt University, Nashville, TN 37232, USA
- Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Justin M Balko
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Melinda E Sanders
- Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | | | - Jennifer A Pietenpol
- Department of Biochemistry, Vanderbilt University, Nashville, TN 37232, USA.
- Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN 37232, USA
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11
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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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12
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Noorbakhsh J, Zhao ZM, Russell JC, Chuang JH. Treating Cancer as an Invasive Species. Mol Cancer Res 2020; 18:20-26. [PMID: 31527151 PMCID: PMC6942216 DOI: 10.1158/1541-7786.mcr-19-0262] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Revised: 06/26/2019] [Accepted: 09/10/2019] [Indexed: 11/16/2022]
Abstract
To cure a patient's cancer is to eradicate invasive cells from the ecosystem of the body. However, the ecologic complexity of this challenge is not well understood. Here we show how results from eradications of invasive mammalian species from islands-one of the few contexts in which invasive species have been regularly cleared-inform new research directions for treating cancer. We first summarize the epidemiologic characteristics of island invader eradications and cancer treatments by analyzing recent datasets from the Database of Invasive Island Species Eradications and The Cancer Genome Atlas, detailing the superior successes of island eradication projects. Next, we compare how genetic and environmental factors impact success in each system. These comparisons illuminate a number of promising cancer research and treatment directions, such as heterogeneity engineering as motivated by gene drives and adaptive therapy; multiscale analyses of how population heterogeneity potentiates treatment resistance; and application of ecological data mining techniques to high-throughput cancer data. We anticipate that interdisciplinary comparisons between tumor progression and invasive species would inspire development of novel paradigms to cure cancer.
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Affiliation(s)
- Javad Noorbakhsh
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut
| | - Zi-Ming Zhao
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut
| | | | - Jeffrey H Chuang
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut.
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