151
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Niida A, Nagayama S, Miyano S, Mimori K. Understanding intratumor heterogeneity by combining genome analysis and mathematical modeling. Cancer Sci 2018; 109:884-892. [PMID: 29352488 PMCID: PMC5891172 DOI: 10.1111/cas.13510] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2017] [Revised: 01/15/2018] [Accepted: 01/16/2018] [Indexed: 01/06/2023] Open
Abstract
Cancer is composed of multiple cell populations with different genomes. This phenomenon called intratumor heterogeneity (ITH) is supposed to be a fundamental cause of therapeutic failure. Therefore, its principle‐level understanding is a clinically important issue. To achieve this goal, an interdisciplinary approach combining genome analysis and mathematical modeling is essential. For example, we have recently performed multiregion sequencing to unveil extensive ITH in colorectal cancer. Moreover, by employing mathematical modeling of cancer evolution, we demonstrated that it is possible that this ITH is generated by neutral evolution. In this review, we introduce recent advances in a research field related to ITH and also discuss strategies for exploiting novel findings on ITH in a clinical setting.
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Affiliation(s)
- Atsushi Niida
- Division of Health Medical Computational Science, Health Intelligence Center, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Satoshi Nagayama
- Department of Gastroenterological Surgery, Gastroenterological Center, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Satoru Miyano
- Division of Health Medical Computational Science, Health Intelligence Center, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Koshi Mimori
- Department of Surgery, Kyushu University Beppu Hospital, Beppu, Japan
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152
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Li R, Zhou M, Yue C, Zhang W, Ma Y, Peng H, Hu Z, Wei Z. Multiple single cell screening and DNA MDA amplification chip for oncogenic mutation profiling. LAB ON A CHIP 2018; 18:723-734. [PMID: 29360118 DOI: 10.1039/c7lc00924k] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
The oncogenic mutation heterogeneity of the cancer cell population has been proven to be essential for predicting both drug-response and drug-resistance of targeted therapies, such as tyrosine kinase inhibitors. It is necessary to accurately evaluate the mutation heterogeneity, oncogenic mutation and resistant mutation profiling at a single cell level. However, there are two major hurdles in the process. First, majority of the cells in tumor tissue are non-cancer cells, which cause background noise. Second, the work load and cost of next generation sequencing on dozens of single cells are prohibitive. To address both these issues, we developed a microfluidic chip for profiling of dozens of selected cells. With the help of a novel tri-states valve structure, which performs precise controlling of the cell/reagent movement, as well as active mixing of different reagents, trapping/identification/lysis and in situ MDA amplification was achieved at a single cell level on the same chip. Using a proof-of-concept assay mimicking EGFR targeting drug Gefitinib treatment of lung cancer cells, the new method was validated as capable of not only detecting the existence of multiple mutations, but also providing complete information of the mutation scenario at the single cell level by using cost-effective Sanger's sequencing.
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Affiliation(s)
- Ren Li
- CAS Key Laboratory of Standardization and Measurement for Nanotechnology, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology of China, Beijing 100190, China.
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153
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Engineering chimeric antigen receptor-T cells for cancer treatment. Mol Cancer 2018; 17:32. [PMID: 29448937 PMCID: PMC5815249 DOI: 10.1186/s12943-018-0814-0] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2017] [Accepted: 02/09/2018] [Indexed: 02/07/2023] Open
Abstract
Intratumor heterogeneity of tumor clones and an immunosuppressive microenvironment in cancer ecosystems contribute to inherent difficulties for tumor treatment. Recently, chimeric antigen receptor (CAR) T-cell therapy has been successfully applied in the treatment of B-cell malignancies, underscoring its great potential in antitumor therapy. However, functional challenges of CAR-T cell therapy, especially in solid tumors, remain. Here, we describe cancer-immunity phenotypes from a clonal-stromal-immune perspective and elucidate mechanisms of T-cell exhaustion that contribute to tumor immune evasion. Then we assess the functional challenges of CAR-T cell therapy, including cell trafficking and infiltration, targeted-recognition and killing of tumor cells, T-cell proliferation and persistence, immunosuppressive microenvironment and self-control regulation. Finally, we delineate tumor precision informatics and advancements in engineered CAR-T cells to counteract inherent challenges of the CAR-T cell therapy, either alone or in combination with traditional therapeutics, and highlight the therapeutic potential of this approach in future tumor precision treatment.
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154
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Zhu J, Yu H, Chen S, Yang P, Dong Z, Ling Y, Tang H, Bai S, Yang W, Tang L, Shen F, Wang H, Wen W. Prognostic significance of combining high mobility group Box-1 and OV-6 expression in hepatocellular carcinoma. SCIENCE CHINA-LIFE SCIENCES 2018; 61:912-923. [PMID: 29441453 DOI: 10.1007/s11427-017-9188-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2017] [Accepted: 10/25/2017] [Indexed: 01/02/2023]
Abstract
The inflammatory environment and existence of cancer stem cells are critical for progression and intrahepatic recurrence of hepatocellular carcinoma (HCC) after curative resections. Here, we investigated the prognostic significance of combining high mobility group box 1 (HMGB1) expression and hepatic progenitor marker OV6 in hepatocellular carcinoma. Expression of HMGB1 and OV6 was evaluated using immunohistochemistry profiling in tissue microarrays containing samples from 208 HCC patients. Invasive clinical or pathological factors were found in patients with high expression of HMGB1 or OV6. Higher HMGB1 was associated with poorer clinical outcomes, and independently related to elevated 5-year recurrence incidence (85.5% vs. 62.4%, P<0.001). We also found that more OV6 positive staining was correlated with poor prognosis of HCC patients (P<0.001). Notably, expression of HMGB1 was positively correlated with OV6 in density (R2=0.032, P<0.001) and reversely related to HCC outcomes. Abnormal expression of HMGB1 in combination with positive staining of OV6 displayed poorer prognostic performance than single biomarker alone (area under curve (AUC) survival=0.696). Therefore, HMGB1 and OV6 positive staining are promising prognostic parameters for HCC, and we propose that HMGB1 and OV6 may cooperate with each other and predict poor prognosis of HCC.
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Affiliation(s)
- Jihui Zhu
- National Center for Liver Cancer, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, 200438, China.,International Cooperation Laboratory on Signal Transduction of Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, 200433, China
| | - Han Yu
- National Center for Liver Cancer, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, 200438, China.,International Cooperation Laboratory on Signal Transduction of Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, 200433, China
| | - Shuzhen Chen
- National Center for Liver Cancer, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, 200438, China.,International Cooperation Laboratory on Signal Transduction of Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, 200433, China
| | - Pinghua Yang
- Department of Hepatic Surgery, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, 200433, China
| | - Zihui Dong
- National Center for Liver Cancer, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, 200438, China.,International Cooperation Laboratory on Signal Transduction of Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, 200433, China
| | - Yan Ling
- National Center for Liver Cancer, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, 200438, China.,International Cooperation Laboratory on Signal Transduction of Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, 200433, China
| | - Hao Tang
- Department of Respiratory Medicine, Changzheng Hospital, Second Military Medical University, Shanghai, 200003, China
| | - Shilei Bai
- Department of Hepatic Surgery, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, 200433, China
| | - Wen Yang
- National Center for Liver Cancer, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, 200438, China.,International Cooperation Laboratory on Signal Transduction of Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, 200433, China
| | - Liang Tang
- National Center for Liver Cancer, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, 200438, China.,International Cooperation Laboratory on Signal Transduction of Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, 200433, China
| | - Feng Shen
- Department of Hepatic Surgery, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, 200433, China
| | - Hongyang Wang
- National Center for Liver Cancer, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, 200438, China. .,International Cooperation Laboratory on Signal Transduction of Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, 200433, China. .,Ministry of Education Key Laboratory on Signaling Regulation and Targeting Therapy of Liver Cancer, Shanghai, 200438, China.
| | - Wen Wen
- National Center for Liver Cancer, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, 200438, China. .,International Cooperation Laboratory on Signal Transduction of Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, 200433, China. .,Ministry of Education Key Laboratory on Signaling Regulation and Targeting Therapy of Liver Cancer, Shanghai, 200438, China.
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155
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156
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Williams MJ, Werner B, Barnes CP, Graham TA, Sottoriva A. Reply: Is the evolution of tumors Darwinian or non-Darwinian? Natl Sci Rev 2018. [DOI: 10.1093/nsr/nwx131] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Affiliation(s)
- Marc J Williams
- Evolution and Cancer Laboratory, Barts Cancer Institute, Queen Marry University of London, London, UK
- Department of Cell and Developmental Biology, University College London, London, UK
- Centre for Mathematics and Physics in the Life Sciences and Experimental Biology (CoMPLEX), University College London, London, UK
| | - Benjamin Werner
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Chris P Barnes
- Department of Cell and Developmental Biology, University College London, London, UK
- Department of Genetics, Evolution and Environment, University College London, London, UK
| | - Trevor A Graham
- Evolution and Cancer Laboratory, Barts Cancer Institute, Queen Marry University of London, London, UK
| | - Andrea Sottoriva
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
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157
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Bitsouni V, Trucu D, Chaplain MAJ, Eftimie R. Aggregation and travelling wave dynamics in a two-population model of cancer cell growth and invasion. MATHEMATICAL MEDICINE AND BIOLOGY-A JOURNAL OF THE IMA 2018; 35:541-577. [DOI: 10.1093/imammb/dqx019] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2016] [Accepted: 11/14/2017] [Indexed: 12/25/2022]
Affiliation(s)
- Vasiliki Bitsouni
- Division of Mathematics, University of Dundee, Dundee, DD1 4HN, Scotland, UK
| | - Dumitru Trucu
- Division of Mathematics, University of Dundee, Dundee, DD1 4HN, Scotland, UK
| | - Mark A J Chaplain
- School of Mathematics and Statistics, Mathematical Institute (MI), North Haugh
- University of St Andrews, St Andrews, KY16 9SS, Scotland, UK
| | - Raluca Eftimie
- Division of Mathematics, University of Dundee, Dundee, DD1 4HN, Scotland, UK
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158
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Duan M, Hao J, Cui S, Worthley DL, Zhang S, Wang Z, Shi J, Liu L, Wang X, Ke A, Cao Y, Xi R, Zhang X, Zhou J, Fan J, Li C, Gao Q. Diverse modes of clonal evolution in HBV-related hepatocellular carcinoma revealed by single-cell genome sequencing. Cell Res 2018; 28:359-373. [PMID: 29327728 DOI: 10.1038/cr.2018.11] [Citation(s) in RCA: 81] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2017] [Revised: 07/13/2017] [Accepted: 12/12/2017] [Indexed: 02/07/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is a cancer of substantial morphologic, genetic and phenotypic diversity. Yet we do not understand the relationship between intratumor heterogeneity and the associated morphologic/histological characteristics of the tumor. Using single-cell whole-genome sequencing to profile 96 tumor cells (30-36 each) and 15 normal liver cells (5 each), collected from three male patients with HBV-associated HCC, we confirmed that copy number variations occur early in hepatocarcinogenesis but thereafter remain relatively stable throughout tumor progression. Importantly, we showed that specific HCCs can be of monoclonal or polyclonal origins. Tumors with confluent multinodular morphology are the typical polyclonal tumors and display the highest intratumor heterogeneity. In addition to mutational and copy number profiles, we dissected the clonal origins of HCC using HBV-derived foreign genomic markers. In monoclonal HCC, all the tumor single cells exhibit the same HBV integrations, indicating that HBV integration is an early driver event and remains extremely stable during tumor progression. In addition, our results indicated that both models of metastasis, late dissemination and early seeding, have a role in HCC progression. Notably, early intrahepatic spreading of the initiating clone leads to the formation of synchronous multifocal tumors. Meanwhile, we identified a potential driver gene ZNF717 in HCC, which exhibits a high frequency of mutation at both single-cell and population levels, as a tumor suppressor acting through regulating the IL-6/STAT3 pathway. These findings highlight multiple distinct tumor evolutionary mechanisms in HCC, which suggests the need for specific treatment strategies.
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Affiliation(s)
- Meng Duan
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai 200032, China
| | - Junfeng Hao
- Core Facility for Protein Research, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Sijia Cui
- Hangzhou Cancer Institute, Hangzhou Cancer Hospital, Hangzhou, Zhejiang 310002, China
| | - Daniel L Worthley
- Cancer Theme, South Australian Health and Medical Research Institute and Department of Medicine, University of Adelaide, Adelaide, SA, Australia
| | - Shu Zhang
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai 200032, China
| | - Zhichao Wang
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai 200032, China
| | - Jieyi Shi
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai 200032, China
| | - Longzi Liu
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai 200032, China
| | - Xiaoying Wang
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai 200032, China
| | - Aiwu Ke
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai 200032, China
| | - Ya Cao
- Cancer Research Institute, Xiangya School of Medicine, Central South University, Changsha, Hunan 410013, China
| | - Ruibin Xi
- School of Mathematical Sciences and Center for Statistical Science, Peking University, Beijing 100871, China
| | - Xiaoming Zhang
- Key Laboratory of Molecular Virology & Immunology, Institute Pasteur of Shanghai, Chinese Academy of Sciences, Shanghai 200032, China
| | - Jian Zhou
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai 200032, China.,Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China
| | - Jia Fan
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai 200032, China.,Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China
| | - Chong Li
- Core Facility for Protein Research, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Qiang Gao
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai 200032, China.,State Key Laboratory of Genetic Engineering, Fudan University, Shanghai 200433, China
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159
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Liu J, Dang H, Wang XW. The significance of intertumor and intratumor heterogeneity in liver cancer. Exp Mol Med 2018; 50:e416. [PMID: 29303512 PMCID: PMC5992990 DOI: 10.1038/emm.2017.165] [Citation(s) in RCA: 138] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2017] [Accepted: 05/11/2017] [Indexed: 02/06/2023] Open
Abstract
Genomic analyses of primary liver cancer samples reveal a complex mutational landscape with vast intertumor and intratumor heterogeneity. Different primary liver tumors and subclones within each tumor display striking molecular and biological variations. Consequently, tumor molecular heterogeneity contributes to drug resistance and tumor relapse following therapy, which poses a substantial obstruction to improving outcomes of patients with liver cancer. There is an urgent need to the compositional and functional understanding of tumor heterogeneity. In this review, we summarize genomic and non-genomic diversities, which include stemness and microenvironmental causes of the functional heterogeneity of the primary liver cancer ecosystem. We discuss the importance and intricacy of intratumor heterogeneity in the context of cancer cell evolution. We also discuss methodologies applicable to determine intratumor heterogeneity and highlight the best-fit patient-derived in vivo and in vitro models to recapture the functional heterogeneity of primary liver cancer with the aim to improve future therapeutic strategies.
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Affiliation(s)
- Jinping Liu
- Liver Carcinogenesis Section, Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Hien Dang
- Liver Carcinogenesis Section, Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Xin Wei Wang
- Liver Carcinogenesis Section, Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
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160
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Lin DC, Wang MR, Koeffler HP. Genomic and Epigenomic Aberrations in Esophageal Squamous Cell Carcinoma and Implications for Patients. Gastroenterology 2018; 154:374-389. [PMID: 28757263 PMCID: PMC5951382 DOI: 10.1053/j.gastro.2017.06.066] [Citation(s) in RCA: 172] [Impact Index Per Article: 28.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2017] [Revised: 06/05/2017] [Accepted: 06/07/2017] [Indexed: 12/28/2022]
Abstract
Esophageal squamous cell carcinoma (ESCC) is a common malignancy without effective therapy. The exomes of more than 600 ESCCs have been sequenced in the past 4 years, and numerous key aberrations have been identified. Recently, researchers reported both inter- and intratumor heterogeneity. Although these are interesting observations, their clinical implications are unclear due to the limited number of samples profiled. Epigenomic alterations, such as changes in DNA methylation, histone acetylation, and RNA editing, also have been observed in ESCCs. However, it is not clear what proportion of ESCC cells carry these epigenomic aberrations or how they contribute to tumor development. We review the genomic and epigenomic characteristics of ESCCs, with a focus on emerging themes. We discuss their clinical implications and future research directions.
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Affiliation(s)
- De-Chen Lin
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California.
| | - Ming-Rong Wang
- State Key Laboratory of Molecular Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| | - H. Phillip Koeffler
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California,Cancer Science Institute of Singapore, National University of Singapore, Singapore,National University Cancer Institute, National University Hospital Singapore, Singapore
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161
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Sievers CK, Zou LS, Pickhardt PJ, Matkowskyj KA, Albrecht DM, Clipson L, Bacher JW, Pooler BD, Moawad FJ, Cash BD, Reichelderfer M, Vo TN, Newton MA, Larget BR, Halberg RB. Subclonal diversity arises early even in small colorectal tumours and contributes to differential growth fates. Gut 2017; 66:2132-2140. [PMID: 27609830 PMCID: PMC5342955 DOI: 10.1136/gutjnl-2016-312232] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2016] [Revised: 08/10/2016] [Accepted: 08/12/2016] [Indexed: 12/18/2022]
Abstract
OBJECTIVE AND DESIGN The goal of the study was to determine whether the mutational profile of early colorectal polyps correlated with growth behaviour. The growth of small polyps (6-9 mm) that were first identified during routine screening of patients was monitored over time by interval imaging with CT colonography. Mutations in these lesions with known growth rates were identified by targeted next-generation sequencing. The timing of mutational events was estimated using computer modelling and statistical inference considering several parameters including allele frequency and fitness. RESULTS The mutational landscape of small polyps is varied both within individual polyps and among the group as a whole but no single alteration was correlated with growth behaviour. Polyps carried 0-3 pathogenic mutations with the most frequent being in APC, KRAS/NRAS, BRAF, FBXW7 and TP53. In polyps with two or more pathogenic mutations, allele frequencies were often variable, indicating the presence of multiple populations within a single tumour. Based on computer modelling, detectable mutations occurred at a mean polyp size of 30±35 crypts, well before the tumour is of a clinically detectable size. CONCLUSIONS These data indicate that small colon polyps can have multiple pathogenic mutations in crucial driver genes that arise early in the existence of a tumour. Understanding the molecular pathway of tumourigenesis and clonal evolution in polyps that are at risk for progressing to invasive cancers will allow us to begin to better predict which polyps are more likely to progress into adenocarcinomas and which patients are at greater risk of developing advanced disease.
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Affiliation(s)
- Chelsie K Sievers
- Department of Oncology, McArdle Laboratory for Cancer Research, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA,Division of Gastroenterology and Hepatology, Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Luli S Zou
- Division of Gastroenterology and Hepatology, Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Perry J Pickhardt
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Kristina A Matkowskyj
- Department of Pathology and Laboratory Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA,US Department of Veterans Affairs, William S. Middleton Memorial Veterans Hospital, Madison, Wisconsin, USA
| | - Dawn M Albrecht
- Division of Gastroenterology and Hepatology, Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Linda Clipson
- Department of Oncology, McArdle Laboratory for Cancer Research, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Jeffery W Bacher
- Genetic Analysis Group, Promega Corporation, Madison, Wisconsin, USA
| | - B Dustin Pooler
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Fouad J Moawad
- Gastroenterology Service, Department of Medicine, Walter Reed National Military Medical Center, Bethesda, Maryland, USA
| | - Brooks D Cash
- Gastroenterology Service, Department of Medicine, Walter Reed National Military Medical Center, Bethesda, Maryland, USA,Gastroenterology Division, Department of Medicine, University of South Alabama, Mobile, Alabama, USA
| | - Mark Reichelderfer
- Division of Gastroenterology and Hepatology, Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Tien N Vo
- Department of Statistics, University of Wisconsin–Madison, Madison, Wisconsin, USA
| | - Michael A Newton
- Department of Statistics, University of Wisconsin–Madison, Madison, Wisconsin, USA,Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Madison, Wisconsin, USA
| | - Bret R Larget
- Department of Statistics, University of Wisconsin–Madison, Madison, Wisconsin, USA,Department of Botany, University of Wisconsin–Madison, Madison, Wisconsin, USA
| | - Richard B Halberg
- Department of Oncology, McArdle Laboratory for Cancer Research, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA,Division of Gastroenterology and Hepatology, Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA,Carbone Cancer Center, University of Wisconsin–Madison, Madison, Wisconsin, USA
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162
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Brock A, Huang S. Precision Oncology: Between Vaguely Right and Precisely Wrong. Cancer Res 2017; 77:6473-6479. [PMID: 29162615 DOI: 10.1158/0008-5472.can-17-0448] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2017] [Revised: 08/28/2017] [Accepted: 09/25/2017] [Indexed: 11/16/2022]
Abstract
Precision Oncology seeks to identify and target the mutation that drives a tumor. Despite its straightforward rationale, concerns about its effectiveness are mounting. What is the biological explanation for the "imprecision?" First, Precision Oncology relies on indiscriminate sequencing of genomes in biopsies that barely represent the heterogeneous mix of tumor cells. Second, findings that defy the orthodoxy of oncogenic "driver mutations" are now accumulating: the ubiquitous presence of oncogenic mutations in silent premalignancies or the dynamic switching without mutations between various cell phenotypes that promote progression. Most troublesome is the observation that cancer cells that survive treatment still will have suffered cytotoxic stress and thereby enter a stem cell-like state, the seeds for recurrence. The benefit of "precision targeting" of mutations is inherently limited by this counterproductive effect. These findings confirm that there is no precise linear causal relationship between tumor genotype and phenotype, a reminder of logician Carveth Read's caution that being vaguely right may be preferable to being precisely wrong. An open-minded embrace of the latest inconvenient findings indicating nongenetic and "imprecise" phenotype dynamics of tumors as summarized in this review will be paramount if Precision Oncology is ultimately to lead to clinical benefits. Cancer Res; 77(23); 6473-9. ©2017 AACR.
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Affiliation(s)
- Amy Brock
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas.
| | - Sui Huang
- Institute for Systems Biology, Seattle, Washington.
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163
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Huang L, Liu D, Wang N, Ling S, Tang Y, Wu J, Hao L, Luo H, Hu X, Sheng L, Zhu L, Wang D, Luo Y, Shang Z, Xiao M, Mao X, Zhou K, Cao L, Dong L, Zheng X, Sui P, He J, Mo S, Yan J, Ao Q, Qiu L, Zhou H, Liu Q, Zhang H, Li J, Jin J, Fu L, Zhao W, Chen J, Du X, Qing G, Liu H, Liu X, Huang G, Ma D, Zhou J, Wang QF. Integrated genomic analysis identifies deregulated JAK/STAT-MYC-biosynthesis axis in aggressive NK-cell leukemia. Cell Res 2017; 28:172-186. [PMID: 29148541 DOI: 10.1038/cr.2017.146] [Citation(s) in RCA: 62] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2017] [Revised: 07/16/2017] [Accepted: 08/30/2017] [Indexed: 12/21/2022] Open
Abstract
Aggressive NK-cell leukemia (ANKL) is a rare form of NK cell neoplasm that is more prevalent among people from Asia and Central and South America. Patients usually die within days to months, even after receiving prompt therapeutic management. Here we performed the first comprehensive study of ANKL by integrating whole genome, transcriptome and targeted sequencing, cytokine array as well as functional assays. Mutations in the JAK-STAT pathway were identified in 48% (14/29) of ANKL patients, while the extracellular STAT3 stimulator IL10 was elevated by an average of 56-fold (P < 0.0001) in the plasma of all patients examined. Additional frequently mutated genes included TP53 (34%), TET2 (28%), CREBBP (21%) and MLL2 (21%). Patient NK leukemia cells showed prominent activation of STAT3 phosphorylation, MYC expression and transcriptional activities in multiple metabolic pathways. Functionally, STAT3 activation and MYC expression were critical for the proliferation and survival of ANKL cells. STAT signaling regulated the MYC transcription program, and both STAT signaling and MYC transcription were required to maintain the activation of nucleotide synthesis and glycolysis. Collectively, the JAK-STAT pathway represents a major target for genomic alterations and IL10 stimulation in ANKL. This newly discovered JAK/STAT-MYC-biosynthesis axis may provide opportunities for the development of novel therapeutic strategies in treating this subtype of leukemia.
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Affiliation(s)
- Liang Huang
- Department of Hematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Dan Liu
- Key Laboratory of Genomic and Precision Medicine, Collaborative Innovation Center of Genetics and Development, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Na Wang
- Department of Hematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Shaoping Ling
- Key Laboratory of Genomic and Precision Medicine, Collaborative Innovation Center of Genetics and Development, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,Genome Wisdom Inc., Beijing 100195, China
| | - Yuting Tang
- Department of Hematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Jun Wu
- Key Laboratory of Genomic and Precision Medicine, Collaborative Innovation Center of Genetics and Development, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Lingtong Hao
- Key Laboratory of Genomic and Precision Medicine, Collaborative Innovation Center of Genetics and Development, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100049, China.,Genome Wisdom Inc., Beijing 100195, China
| | - Hui Luo
- Department of Hematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Xuelian Hu
- Department of Hematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Lingshuang Sheng
- Department of Hematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Lijun Zhu
- Department of Hematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Di Wang
- Department of Hematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Yi Luo
- Department of Hematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Zhen Shang
- Department of Hematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Min Xiao
- Department of Hematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Xia Mao
- Department of Hematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Kuangguo Zhou
- Department of Hematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Lihua Cao
- Key Laboratory of Genomic and Precision Medicine, Collaborative Innovation Center of Genetics and Development, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,Genome Wisdom Inc., Beijing 100195, China
| | - Lili Dong
- Key Laboratory of Genomic and Precision Medicine, Collaborative Innovation Center of Genetics and Development, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Xinchang Zheng
- Key Laboratory of Genomic and Precision Medicine, Collaborative Innovation Center of Genetics and Development, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Pinpin Sui
- Key Laboratory of Genomic and Precision Medicine, Collaborative Innovation Center of Genetics and Development, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jianlin He
- Key Laboratory of Genomic and Precision Medicine, Collaborative Innovation Center of Genetics and Development, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Shanlan Mo
- Key Laboratory of Genomic and Precision Medicine, Collaborative Innovation Center of Genetics and Development, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jin Yan
- Key Laboratory of Genomic and Precision Medicine, Collaborative Innovation Center of Genetics and Development, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Qilin Ao
- Department of Pathology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Lugui Qiu
- Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China
| | - Hongsheng Zhou
- Department of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong 510515, China
| | - Qifa Liu
- Department of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong 510515, China
| | - Hongyu Zhang
- Department of Hematology, Peking University Shenzhen Hospital, Shenzhen, Guangdong 518036, China
| | - Jianyong Li
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University and Jiangsu Province Hospital, Nanjing, Jiangsu 210029, China
| | - Jie Jin
- Department of Hematology, the First Affiliated Hospital, Zhejiang University College of Medicine, Hangzhou, Zhejiang 310003, China
| | - Li Fu
- Department of Hematology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Weili Zhao
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, Shanghai Rui Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jieping Chen
- Department of Hematology, Southwest Hospital, Third Military Medical University, Chongqing 400038, China
| | - Xin Du
- Department of Hematology, Guangdong General Hospital and Guangdong Academy of Medical Sciences, Guangzhou, Guangdong 510080, China
| | - Guoliang Qing
- Medical Research Institute, Wuhan University, Wuhan, Hubei 430071, China
| | - Hudan Liu
- Medical Research Institute, Wuhan University, Wuhan, Hubei 430071, China
| | - Xin Liu
- Key Laboratory of Genomic and Precision Medicine, Collaborative Innovation Center of Genetics and Development, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Gang Huang
- Division of Experimental Hematology and Cancer Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA.,Division of Pathology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Ding Ma
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China.,Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Jianfeng Zhou
- Department of Hematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China.,Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China.,Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Qian-Fei Wang
- Key Laboratory of Genomic and Precision Medicine, Collaborative Innovation Center of Genetics and Development, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China.,University of Chinese Academy of Sciences, Beijing 100049, China
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164
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Yang Z, Jia M, Liu G, Hao H, Chen L, Li G, Liu S, Li Y, Wu CI, Lu X, Wang S. Genomic sequencing identifies a few mutations driving the independent origin of primary liver tumors in a chronic hepatitis murine model. PLoS One 2017; 12:e0187551. [PMID: 29117265 PMCID: PMC5678715 DOI: 10.1371/journal.pone.0187551] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2017] [Accepted: 10/20/2017] [Indexed: 12/11/2022] Open
Abstract
With the development of high-throughput genomic analysis, sequencing a mouse primary cancer model provides a new opportunity to understand fundamental mechanisms of tumorigenesis and progression. Here, we characterized the genomic variations in a hepatitis-related primary hepatocellular carcinoma (HCC) mouse model. A total of 12 tumor sections and four adjacent non-tumor tissues from four mice were used for whole exome and/or whole genome sequencing and validation of genotyping. The functions of the mutated genes in tumorigenesis were studied by analyzing their mutation frequency and expression in clinical HCC samples. A total of 46 single nucleotide variations (SNVs) were detected within coding regions. All SNVs were only validated in the sequencing samples, except the Hras mutation, which was shared by three tumors in the M1 mouse. However, the mutated allele frequency varied from high (0.4) to low (0.1), and low frequency (0.1-0.2) mutations existed in almost every tumor. Together with a diploid karyotype and an equal distribution pattern of these SNVs within the tumor, these results suggest the existence of subclones within tumors. A total of 26 mutated genes were mapped to 17 terms describing different molecular and cellular functions. All 41 human homologs of the mutated genes were mutated in the clinical samples, and some mutations were associated with clinical outcomes, suggesting a high probability of cancer driver genes in the spontaneous tumors of the mouse model. Genomic sequencing shows that a few mutations can drive the independent origin of primary liver tumors and reveals high heterogeneity among tumors in the early stage of hepatitis-related primary hepatocellular carcinoma.
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Affiliation(s)
- Zuyu Yang
- CAS Key Laboratory of Genomics and Precision Medicine, China Gastrointestinal Cancer Research Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Mingming Jia
- Key Laboratory of Infection and Immunity, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China, University of Chinese Academy of Sciences, Beijing, China
| | - Guojing Liu
- CAS Key Laboratory of Genomics and Precision Medicine, China Gastrointestinal Cancer Research Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Huaining Hao
- CAS Key Laboratory of Genomics and Precision Medicine, China Gastrointestinal Cancer Research Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Li Chen
- Key Laboratory of Infection and Immunity, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China, University of Chinese Academy of Sciences, Beijing, China
| | - Guanghao Li
- CAS Key Laboratory of Genomics and Precision Medicine, China Gastrointestinal Cancer Research Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Sixue Liu
- CAS Key Laboratory of Genomics and Precision Medicine, China Gastrointestinal Cancer Research Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Yawei Li
- CAS Key Laboratory of Genomics and Precision Medicine, China Gastrointestinal Cancer Research Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Chung-I Wu
- CAS Key Laboratory of Genomics and Precision Medicine, China Gastrointestinal Cancer Research Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
- State Key Laboratory of Biocontrol, College of Ecology and Evolution, Sun Yat-Sen University, Guangzhou, China
- Department of Ecology and Evolution, University of Chicago, Chicago, Illinois, United States of America
| | - Xuemei Lu
- CAS Key Laboratory of Genomics and Precision Medicine, China Gastrointestinal Cancer Research Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
- * E-mail: (SW); (XL)
| | - Shengdian Wang
- Key Laboratory of Infection and Immunity, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China, University of Chinese Academy of Sciences, Beijing, China
- * E-mail: (SW); (XL)
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165
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Díaz-Castillo C. Transcriptome dynamics along axolotl regenerative development are consistent with an extensive reduction in gene expression heterogeneity in dedifferentiated cells. PeerJ 2017; 5:e4004. [PMID: 29134148 PMCID: PMC5678507 DOI: 10.7717/peerj.4004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2017] [Accepted: 10/18/2017] [Indexed: 12/13/2022] Open
Abstract
Although in recent years the study of gene expression variation in the absence of genetic or environmental cues or gene expression heterogeneity has intensified considerably, many basic and applied biological fields still remain unaware of how useful the study of gene expression heterogeneity patterns might be for the characterization of biological systems and/or processes. Largely based on the modulator effect chromatin compaction has for gene expression heterogeneity and the extensive changes in chromatin compaction known to occur for specialized cells that are naturally or artificially induced to revert to less specialized states or dedifferentiate, I recently hypothesized that processes that concur with cell dedifferentiation would show an extensive reduction in gene expression heterogeneity. The confirmation of the existence of such trend could be of wide interest because of the biomedical and biotechnological relevance of cell dedifferentiation-based processes, i.e., regenerative development, cancer, human induced pluripotent stem cells, or plant somatic embryogenesis. Here, I report the first empirical evidence consistent with the existence of an extensive reduction in gene expression heterogeneity for processes that concur with cell dedifferentiation by analyzing transcriptome dynamics along forearm regenerative development in Ambystoma mexicanum or axolotl. Also, I briefly discuss on the utility of the study of gene expression heterogeneity dynamics might have for the characterization of cell dedifferentiation-based processes, and the engineering of tools that afforded better monitoring and modulating such processes. Finally, I reflect on how a transitional reduction in gene expression heterogeneity for dedifferentiated cells can promote a long-term increase in phenotypic heterogeneity following cell dedifferentiation with potential adverse effects for biomedical and biotechnological applications.
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166
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Li C, Hou Y, Xu J, Zhang A, Liu Z, Qi F, Yang Z, Chen K, Liu S, Huang H, Wang Q, Dong J, Wu CI, Lu X. A Direct Test of Selection in Cell Populations Using the Diversity in Gene Expression within Tumors. Mol Biol Evol 2017; 34:1730-1742. [PMID: 28369576 DOI: 10.1093/molbev/msx115] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Although intratumor diversity driven by selection has been the prevailing view in cancer biology, recent population genetic analyses have been unable to reject the neutral interpretation. As the power to reject neutrality in tumors is often low, it will be desirable to have an alternative means to test selection directly. Here, we utilize gene expression data as a surrogate for functional significance in intra- and intertumor comparisons. The expression divergence between samples known to be driven by selection (e.g., between tumor and normal tissues) is always higher than the divergence between normal samples, which should be close to the neutral level of divergence. In contrast, the expression differentiation between regions of the same tumor, being lower than the neutral divergence, is incompatible with the hypothesis of selectively driven divergence. To further test the hypothesis of neutral evolution, we select a hepatocellular carcinoma tumor that has large intratumor SNV and CNV (single nucleotide variation and copy number variation, respectively) diversity. This tumor enables us to calibrate the level of expression divergence against that of genetic divergence. We observe that intratumor divergence in gene expression profile lags far behind genetic divergence, indicating insufficient phenotypic differences for selection to operate. All these expression analyses corroborate that natural selection does not operate effectively within tumors, supporting recent interpretations of within-tumor diversity. As the expected level of genetic diversity, hence the potential for drug resistance, would be much higher under neutrality than under selection, the issue is of both theoretical and clinical significance.
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Affiliation(s)
- Chunyan Li
- Key Laboratory of Genomic and Precision Medicine, China Gastrointestinal Cancer Research Center, Beijing Institute of Genomics, Chinese Academy of Sciences and University of Chinese Academy of Sciences, Beijing, China
| | - Yali Hou
- Key Laboratory of Genomic and Precision Medicine, China Gastrointestinal Cancer Research Center, Beijing Institute of Genomics, Chinese Academy of Sciences and University of Chinese Academy of Sciences, Beijing, China
| | - Jin Xu
- Key Laboratory of Genomic and Precision Medicine, China Gastrointestinal Cancer Research Center, Beijing Institute of Genomics, Chinese Academy of Sciences and University of Chinese Academy of Sciences, Beijing, China
| | - Aiqun Zhang
- Hospital and Institute of Hepatobiliary Surgery, Chinese PLA General Hospital, Beijing, China
| | - Zhenzhen Liu
- Key Laboratory of Genomic and Precision Medicine, China Gastrointestinal Cancer Research Center, Beijing Institute of Genomics, Chinese Academy of Sciences and University of Chinese Academy of Sciences, Beijing, China
| | - Furong Qi
- Key Laboratory of Genomic and Precision Medicine, China Gastrointestinal Cancer Research Center, Beijing Institute of Genomics, Chinese Academy of Sciences and University of Chinese Academy of Sciences, Beijing, China
| | - Zuyu Yang
- Key Laboratory of Genomic and Precision Medicine, China Gastrointestinal Cancer Research Center, Beijing Institute of Genomics, Chinese Academy of Sciences and University of Chinese Academy of Sciences, Beijing, China
| | - Ke Chen
- Key Laboratory of Genomic and Precision Medicine, China Gastrointestinal Cancer Research Center, Beijing Institute of Genomics, Chinese Academy of Sciences and University of Chinese Academy of Sciences, Beijing, China
| | - Sixue Liu
- Key Laboratory of Genomic and Precision Medicine, China Gastrointestinal Cancer Research Center, Beijing Institute of Genomics, Chinese Academy of Sciences and University of Chinese Academy of Sciences, Beijing, China
| | - Huanwei Huang
- Key Laboratory of Genomic and Precision Medicine, China Gastrointestinal Cancer Research Center, Beijing Institute of Genomics, Chinese Academy of Sciences and University of Chinese Academy of Sciences, Beijing, China
| | - Qianfei Wang
- Key Laboratory of Genomic and Precision Medicine, China Gastrointestinal Cancer Research Center, Beijing Institute of Genomics, Chinese Academy of Sciences and University of Chinese Academy of Sciences, Beijing, China
| | - Jiahong Dong
- Hospital and Institute of Hepatobiliary Surgery, Chinese PLA General Hospital, Beijing, China.,Department of Hepatopancreatobiliary Surgery, Beijing Tsinghua Changgung Hospital, Medical Center, Tsinghua University, Beijing, China
| | - Chung-I Wu
- Key Laboratory of Genomic and Precision Medicine, China Gastrointestinal Cancer Research Center, Beijing Institute of Genomics, Chinese Academy of Sciences and University of Chinese Academy of Sciences, Beijing, China.,Department of Ecology and Evolution, University of Chicago, Chicago, IL
| | - Xuemei Lu
- Key Laboratory of Genomic and Precision Medicine, China Gastrointestinal Cancer Research Center, Beijing Institute of Genomics, Chinese Academy of Sciences and University of Chinese Academy of Sciences, Beijing, China
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167
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Roman T, Xie L, Schwartz R. Automated deconvolution of structured mixtures from heterogeneous tumor genomic data. PLoS Comput Biol 2017; 13:e1005815. [PMID: 29059177 PMCID: PMC5695636 DOI: 10.1371/journal.pcbi.1005815] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2017] [Revised: 11/02/2017] [Accepted: 10/10/2017] [Indexed: 11/23/2022] Open
Abstract
With increasing appreciation for the extent and importance of intratumor heterogeneity, much attention in cancer research has focused on profiling heterogeneity on a single patient level. Although true single-cell genomic technologies are rapidly improving, they remain too noisy and costly at present for population-level studies. Bulk sequencing remains the standard for population-scale tumor genomics, creating a need for computational tools to separate contributions of multiple tumor clones and assorted stromal and infiltrating cell populations to pooled genomic data. All such methods are limited to coarse approximations of only a few cell subpopulations, however. In prior work, we demonstrated the feasibility of improving cell type deconvolution by taking advantage of substructure in genomic mixtures via a strategy called simplicial complex unmixing. We improve on past work by introducing enhancements to automate learning of substructured genomic mixtures, with specific emphasis on genome-wide copy number variation (CNV) data, as well as the ability to process quantitative RNA expression data, and heterogeneous combinations of RNA and CNV data. We introduce methods for dimensionality estimation to better decompose mixture model substructure; fuzzy clustering to better identify substructure in sparse, noisy data; and automated model inference methods for other key model parameters. We further demonstrate their effectiveness in identifying mixture substructure in true breast cancer CNV data from the Cancer Genome Atlas (TCGA). Source code is available at https://github.com/tedroman/WSCUnmix.
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Affiliation(s)
- Theodore Roman
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Lu Xie
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Russell Schwartz
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- Biological Sciences Department, Mellon College of Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
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168
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Stem cell self-renewal in regeneration and cancer: Insights from mathematical modeling. ACTA ACUST UNITED AC 2017. [DOI: 10.1016/j.coisb.2017.09.006] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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169
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Tissot T, Thomas F, Roche B. Non-cell-autonomous effects yield lower clonal diversity in expanding tumors. Sci Rep 2017; 7:11157. [PMID: 28894191 PMCID: PMC5593982 DOI: 10.1038/s41598-017-11562-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2017] [Accepted: 08/21/2017] [Indexed: 01/07/2023] Open
Abstract
Recent cancer research has investigated the possibility that non-cell-autonomous (NCA) driving tumor growth can support clonal diversity (CD). Indeed, mutations can affect the phenotypes not only of their carriers (“cell-autonomous”, CA effects), but also sometimes of other cells (NCA effects). However, models that have investigated this phenomenon have only considered a restricted number of clones. Here, we designed an individual-based model of tumor evolution, where clones grow and mutate to yield new clones, among which a given frequency have NCA effects on other clones’ growth. Unlike previously observed for smaller assemblages, most of our simulations yield lower CD with high frequency of mutations with NCA effects. Owing to NCA effects increasing competition in the tumor, clones being already dominant are more likely to stay dominant, and emergent clones not to thrive. These results may help personalized medicine to predict intratumor heterogeneity across different cancer types for which frequency of NCA effects could be quantified.
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Affiliation(s)
- Tazzio Tissot
- CREEC/MIVEGEC, UMR IRD/CNRS/UM 5290, 911 Avenue Agropolis, BP 64501, 34394, Montpellier, Cedex 5, France.
| | - Frédéric Thomas
- CREEC/MIVEGEC, UMR IRD/CNRS/UM 5290, 911 Avenue Agropolis, BP 64501, 34394, Montpellier, Cedex 5, France
| | - Benjamin Roche
- CREEC/MIVEGEC, UMR IRD/CNRS/UM 5290, 911 Avenue Agropolis, BP 64501, 34394, Montpellier, Cedex 5, France.,Unité mixte internationale de Modélisation Mathématique et Informatique des Systèmes Complexes. (UMI IRD/UPMC UMMISCO), 32 Avenue Henri Varagnat, 93143, Bondy Cedex, France
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170
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Mechanisms and clinical implications of tumor heterogeneity and convergence on recurrent phenotypes. J Mol Med (Berl) 2017; 95:1167-1178. [PMID: 28871446 DOI: 10.1007/s00109-017-1587-4] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2017] [Revised: 08/11/2017] [Accepted: 08/20/2017] [Indexed: 10/18/2022]
Abstract
Tumor heterogeneity has been identified at various -omic levels. The tumor genome, transcriptome, proteome, and phenome can vary widely across cells in patient tumors and are influenced by tumor cell interactions with heterogeneous physical conditions and cellular components of the tumor microenvironment. Here, we explore the concept that while variation exists at multiple -omic levels, changes at each of these levels converge on the same pathways and lead to convergent phenotypes in tumors that can provide common drug targets. These phenotypes include cellular growth and proliferation, sustained oncogenic signaling, and immune avoidance, among others. Tumor heterogeneity complicates treatment of patient cancers as it leads to varied response to therapies. Identification of convergent cellular phenotypes arising in patient cancers and targeted therapies that reverse them has the potential to transform the way clinicians treat these cancers and to improve patient outcome.
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171
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Ohtsuki H, Innan H. Forward and backward evolutionary processes and allele frequency spectrum in a cancer cell population. Theor Popul Biol 2017; 117:43-50. [PMID: 28866007 DOI: 10.1016/j.tpb.2017.08.006] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2017] [Revised: 08/08/2017] [Accepted: 08/23/2017] [Indexed: 01/04/2023]
Abstract
A cancer grows from a single cell, thereby constituting a large cell population. In this work, we are interested in how mutations accumulate in a cancer cell population. We provide a theoretical framework of the stochastic process in a cancer cell population and obtain near exact expressions of allele frequency spectrum or AFS (only continuous approximation is involved) from both forward and backward treatments under a simple setting; all cells undergo cell divisions and die at constant rates, b and d, respectively, such that the entire population grows exponentially. This setting means that once a parental cancer cell is established, in the following growth phase, all mutations are assumed to have no effect on b or d (i.e., neutral or passengers). Our theoretical results show that the difference from organismal population genetics is mainly in the coalescent time scale, and the mutation rate is defined per cell division, not per time unit (e.g., generation). Except for these two factors, the basic logic is very similar between organismal and cancer population genetics, indicating that a number of well established theories of organismal population genetics could be translated to cancer population genetics with simple modifications.
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Affiliation(s)
- Hisashi Ohtsuki
- SOKENDAI, The Graduate University for Advanced Studies, Hayama, Kanagawa 240-0193, Japan
| | - Hideki Innan
- SOKENDAI, The Graduate University for Advanced Studies, Hayama, Kanagawa 240-0193, Japan.
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172
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Shpak M, Ni Y, Lu J, Müller P. Variance in estimated pairwise genetic distance under high versus low coverage sequencing: The contribution of linkage disequilibrium. Theor Popul Biol 2017; 117:51-63. [PMID: 28842178 DOI: 10.1016/j.tpb.2017.08.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2017] [Revised: 05/30/2017] [Accepted: 08/07/2017] [Indexed: 10/19/2022]
Abstract
The mean pairwise genetic distance among haplotypes is an estimator of the population mutation rate θ and a standard measure of variation in a population. With the advent of next-generation sequencing (NGS) methods, this and other population parameters can be estimated under different modes of sampling. One approach is to sequence individual genomes with high coverage, and to calculate genetic distance over all sample pairs. The second approach, typically used for microbial samples or for tumor cells, is sequencing a large number of pooled genomes with very low individual coverage. With low coverage, pairwise genetic distances are calculated across independently sampled sites rather than across individual genomes. In this study, we show that the variance in genetic distance estimates is reduced with low coverage sampling if the mean pairwise linkage disequilibrium weighted by allele frequencies is positive. Practically, this means that if on average the most frequent alleles over pairs of loci are in positive linkage disequilibrium, low coverage sequencing results in improved estimates of θ, assuming similar per-site read depths. We show that this result holds under the expected distribution of allele frequencies and linkage disequilibria for an infinite sites model at mutation-drift equilibrium. From simulations, we find that the conditions for reduced variance only fail to hold in cases where variant alleles are few and at very low frequency. These results are applied to haplotype frequencies from a lung cancer tumor to compute the weighted linkage disequilibria and the expected error in estimated genetic distance using high versus low coverage.
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Affiliation(s)
- Max Shpak
- Sarah Cannon Research Institute, Nashville TN 37203, USA; Center for Systems and Synthetic Biology, University of Texas, Austin TX 78712, USA; Fresh Pond Research Institute, Cambridge MA 02140, USA.
| | - Yang Ni
- Department of Statistics and Data Science, University of Texas, Austin TX 78712, USA
| | - Jie Lu
- Genetics Division, Fisher Scientific, Austin TX 78744, USA
| | - Peter Müller
- Department of Statistics and Data Science, University of Texas, Austin TX 78712, USA; Department of Mathematics, University of Texas, Austin TX 78712, USA
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173
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Multisite tumor sampling enhances the detection of intratumor heterogeneity at all different temporal stages of tumor evolution. Virchows Arch 2017; 472:187-194. [DOI: 10.1007/s00428-017-2223-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2017] [Revised: 08/01/2017] [Accepted: 08/20/2017] [Indexed: 12/11/2022]
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174
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Neutral tumor evolution in myeloma is associated with poor prognosis. Blood 2017; 130:1639-1643. [PMID: 28827410 DOI: 10.1182/blood-2016-11-750612] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2016] [Accepted: 08/07/2017] [Indexed: 12/24/2022] Open
Abstract
Recent studies suggest that the evolutionary history of a cancer is important in forecasting clinical outlook. To gain insight into the clonal dynamics of multiple myeloma (MM) and its possible influence on patient outcomes, we analyzed whole exome sequencing tumor data for 333 patients from Myeloma XI, a UK phase 3 trial and 434 patients from the CoMMpass study, all of which had received immunomodulatory drug (IMiD) therapy. By analyzing mutant allele frequency distributions in tumors, we found that 17% to 20% of MM is under neutral evolutionary dynamics. These tumors are associated with poorer patient survival in nonintensively treated patients, which is consistent with the reduced therapeutic efficacy of microenvironment-modulating IMiDs. Our findings provide evidence that knowledge of the evolutionary history of MM has relevance for predicting patient outcomes and personalizing therapy.
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175
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Spatial genomic heterogeneity in multiple myeloma revealed by multi-region sequencing. Nat Commun 2017; 8:268. [PMID: 28814763 PMCID: PMC5559527 DOI: 10.1038/s41467-017-00296-y] [Citation(s) in RCA: 251] [Impact Index Per Article: 35.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2016] [Accepted: 06/20/2017] [Indexed: 12/27/2022] Open
Abstract
In multiple myeloma malignant plasma cells expand within the bone marrow. Since this site is well-perfused, a rapid dissemination of "fitter" clones may be anticipated. However, an imbalanced distribution of multiple myeloma is frequently observed in medical imaging. Here, we perform multi-region sequencing, including iliac crest and radiology-guided focal lesion specimens from 51 patients to gain insight into the spatial clonal architecture. We demonstrate spatial genomic heterogeneity in more than 75% of patients, including inactivation of CDKN2C and TP53, and mutations affecting mitogen-activated protein kinase genes. We show that the extent of spatial heterogeneity is positively associated with the size of biopsied focal lesions consistent with regional outgrowth of advanced clones. The results support a model for multiple myeloma progression with clonal sweeps in the early phase and regional evolution in advanced disease. We suggest that multi-region investigations are critical to understanding intra-patient heterogeneity and the evolutionary processes in multiple myeloma.In multiple myeloma, malignant cells expand within bone marrow. Here, the authors use multi-region sequencing in patient samples to analyse spatial clonal architecture and heterogeneity, providing novel insight into multiple myeloma progression and evolution.
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176
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Huang A, Zhao X, Yang XR, Li FQ, Zhou XL, Wu K, Zhang X, Sun QM, Cao Y, Zhu HM, Wang XD, Yang HM, Wang J, Tang ZY, Hou Y, Fan J, Zhou J. Circumventing intratumoral heterogeneity to identify potential therapeutic targets in hepatocellular carcinoma. J Hepatol 2017; 67:293-301. [PMID: 28323123 DOI: 10.1016/j.jhep.2017.03.005] [Citation(s) in RCA: 72] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2016] [Revised: 03/07/2017] [Accepted: 03/07/2017] [Indexed: 02/07/2023]
Abstract
BACKGROUND & AIMS Identifying target genetic mutations in hepatocellular carcinoma (HCC) for therapy is made challenging by intratumoral heterogeneity. Circulating cell-free DNAs (cfDNA) may contain a more complete mutational spectrum compared to a single tumor sample. This study aimed to identify the most efficient strategy to identify all the mutations within heterogeneous HCCs. METHODS Whole exome sequencing (WES) and targeted deep sequencing (TDS) were carried out in 32 multi-regional tumor samples from five patients. Matched preoperative cfDNAs were sequenced accordingly. Intratumoral heterogeneity was measured using the average percentage of non-ubiquitous mutations (present in parts of tumor regions). Profiling efficiencies of single tumor specimen and cfDNA were compared. The strategy with the highest performance was used to screen for actionable mutations. RESULTS Variable levels of heterogeneity with branched and parallel evolution patterns were observed. The heterogeneity decreased at higher sequencing depth of TDS compared to measurements by WES (28.1% vs. 34.9%, p<0.01) but remained unchanged when additional samples were analyzed. TDS of single tumor specimen identified an average of 70% of the total mutations from multi-regional tissues. Although genome profiling efficiency of cfDNA increased with sequencing depth, an average of 47.2% total mutations were identified using TDS, suggesting that tissue samples outperformed it. TDS of single tumor specimen in 66 patients and cfDNAs in four unresectable HCCs showed that 38.6% (26/66 and 1/4) of patients carried mutations that were potential therapeutic targets. CONCLUSIONS TDS of single tumor specimen could identify actionable mutations targets for therapy in HCC. cfDNA may serve as secondary alternative in profiling HCC genome. LAY SUMMARY Targeted deep sequencing of single tumor specimen is a more efficient method to identify mutations in hepatocellular carcinoma made from mixed subtypes compared to circulating cell-free DNA in blood. cfDNA may serve as secondary alternative in profiling HCC genome. Identifying mutations may help clinicians choose targeted therapy for better individual treatments.
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Affiliation(s)
- Ao Huang
- Liver Surgery Department, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion (Fudan University), Ministry of Education, Shanghai 200032, China
| | - Xin Zhao
- BGI-Shenzhen, Shenzhen 518083, China; China National Genebank-Shenzhen, BGI-Shenzhen, Shenzhen 518083, China
| | - Xin-Rong Yang
- Liver Surgery Department, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion (Fudan University), Ministry of Education, Shanghai 200032, China
| | - Fu-Qiang Li
- BGI-Shenzhen, Shenzhen 518083, China; China National Genebank-Shenzhen, BGI-Shenzhen, Shenzhen 518083, China
| | - Xin-Lan Zhou
- BGI-Shenzhen, Shenzhen 518083, China; China National Genebank-Shenzhen, BGI-Shenzhen, Shenzhen 518083, China
| | - Kui Wu
- BGI-Shenzhen, Shenzhen 518083, China; China National Genebank-Shenzhen, BGI-Shenzhen, Shenzhen 518083, China
| | - Xin Zhang
- Liver Surgery Department, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion (Fudan University), Ministry of Education, Shanghai 200032, China
| | - Qi-Man Sun
- Liver Surgery Department, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion (Fudan University), Ministry of Education, Shanghai 200032, China
| | - Ya Cao
- Cancer Research Institute, Central South University, Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Changsha 410078, China
| | - Hong-Mei Zhu
- BGI-Shenzhen, Shenzhen 518083, China; BGI-Tianjin, Tianjin 300308, China
| | - Xiang-Dong Wang
- Zhongshan Hospital Institute of Clinical Science, Shanghai Institute of Clinical Bioinformatics, Fudan University Center for Clinical Bioinformatics, Shanghai 200032, China
| | - Huan-Ming Yang
- BGI-Shenzhen, Shenzhen 518083, China; James D. Watson Institute of Genome Sciences, Hangzhou 310058, China
| | - Jian Wang
- BGI-Shenzhen, Shenzhen 518083, China; James D. Watson Institute of Genome Sciences, Hangzhou 310058, China
| | - Zhao-You Tang
- Liver Surgery Department, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion (Fudan University), Ministry of Education, Shanghai 200032, China
| | - Yong Hou
- BGI-Shenzhen, Shenzhen 518083, China; China National Genebank-Shenzhen, BGI-Shenzhen, Shenzhen 518083, China.
| | - Jia Fan
- Liver Surgery Department, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion (Fudan University), Ministry of Education, Shanghai 200032, China; Institute of Biomedical Sciences, Fudan University, Shanghai 200032, China; State Key Laboratory of Genetic Engineering, Fudan University, Shanghai 200433, China.
| | - Jian Zhou
- Liver Surgery Department, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion (Fudan University), Ministry of Education, Shanghai 200032, China; Institute of Biomedical Sciences, Fudan University, Shanghai 200032, China; State Key Laboratory of Genetic Engineering, Fudan University, Shanghai 200433, China.
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177
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Sievers CK, Grady WM, Halberg RB, Pickhardt PJ. New insights into the earliest stages of colorectal tumorigenesis. Expert Rev Gastroenterol Hepatol 2017; 11:723-729. [PMID: 28503955 PMCID: PMC5859121 DOI: 10.1080/17474124.2017.1330150] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Tumors in the large intestine have been postulated to arise via a stepwise accumulation of mutations, a process that takes up to 20 years. Recent advances in lineage tracing and DNA sequencing, however, are revealing new evolutionary models that better explain the vast amount of heterogeneity observed within and across colorectal tumors. Areas covered: A review of the literature supporting a novel model of colorectal tumor evolution was conducted. The following commentary examines the basic science and clinical evidence supporting a modified view of tumor initiation and progression in the colon. Expert commentary: The proposed 'cancer punctuated equilibrium' model of tumor evolution better explains the variability seen within and across polyps of the colon and rectum. Small colorectal polyps (6-9mm) followed longitudinally by interval imaging with CT colonography have been reported to have multiple fates: some growing, some remaining static in size, and others regressing in size over time. This new model allows for this variability in growth behavior and supports the hypothesis that some tumors can be 'born to be bad' as originally postulated by Sottoriva and colleagues, with very early molecular events impacting tumor fitness and growth behavior in the later stages of the disease process.
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Affiliation(s)
- Chelsie K. Sievers
- Department of Oncology, McArdle Laboratory for Cancer Research, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA; Division of Gastroenterology and Hepatology, Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - William M. Grady
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA; Department of Medicine, University of Washington Medical School, Seattle, WA
| | - Richard B. Halberg
- Department of Oncology, McArdle Laboratory for Cancer Research, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA; Division of Gastroenterology and Hepatology, Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA,Carbone Cancer Center, University of Wisconsin - Madison, Madison, Wisconsin, USA
| | - Perry J. Pickhardt
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
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178
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Wang HY, Chen Y, Tong D, Ling S, Hu Z, Tao Y, Lu X, Wu CI. Is the evolution in tumors Darwinian or non-Darwinian? Natl Sci Rev 2017. [DOI: 10.1093/nsr/nwx076] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Hurng-Yi Wang
- Graduate Institute of Clinical Medicine, Taiwan University, Taipei
| | - Yuxin Chen
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou
| | - Ding Tong
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou
| | - Shaoping Ling
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou
- Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing
| | - Zheng Hu
- Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford
| | - Yong Tao
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore
| | - Xuemei Lu
- Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing
| | - Chung-I Wu
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou
- Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing
- Department of Ecology and Evolution, University of Chicago, Chicago
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179
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Evaluating optimal therapy robustness by virtual expansion of a sample population, with a case study in cancer immunotherapy. Proc Natl Acad Sci U S A 2017; 114:E6277-E6286. [PMID: 28716945 DOI: 10.1073/pnas.1703355114] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Cancer is a highly heterogeneous disease, exhibiting spatial and temporal variations that pose challenges for designing robust therapies. Here, we propose the VEPART (Virtual Expansion of Populations for Analyzing Robustness of Therapies) technique as a platform that integrates experimental data, mathematical modeling, and statistical analyses for identifying robust optimal treatment protocols. VEPART begins with time course experimental data for a sample population, and a mathematical model fit to aggregate data from that sample population. Using nonparametric statistics, the sample population is amplified and used to create a large number of virtual populations. At the final step of VEPART, robustness is assessed by identifying and analyzing the optimal therapy (perhaps restricted to a set of clinically realizable protocols) across each virtual population. As proof of concept, we have applied the VEPART method to study the robustness of treatment response in a mouse model of melanoma subject to treatment with immunostimulatory oncolytic viruses and dendritic cell vaccines. Our analysis (i) showed that every scheduling variant of the experimentally used treatment protocol is fragile (nonrobust) and (ii) discovered an alternative region of dosing space (lower oncolytic virus dose, higher dendritic cell dose) for which a robust optimal protocol exists.
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180
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Alves JM, Prieto T, Posada D. Multiregional Tumor Trees Are Not Phylogenies. Trends Cancer 2017; 3:546-550. [PMID: 28780931 PMCID: PMC5549612 DOI: 10.1016/j.trecan.2017.06.004] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2017] [Revised: 06/16/2017] [Accepted: 06/19/2017] [Indexed: 02/06/2023]
Abstract
Tumor samples most often comprise a mixture of different cell lineages. Multiregional trees built from bulk mutational profiles do not consider this heterogeneity and can potentially lead to erroneous evolutionary inferences, including biased timing of somatic mutations, spurious parallel mutation events, and/or incorrect chronological ordering of metastatic events.
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Affiliation(s)
- João M Alves
- Department of Biochemistry, Genetics and Immunology and Biomedical Research Center (CINBIO), University of Vigo, Spain; Galicia Sur Health Research Institute, Vigo, Spain
| | - Tamara Prieto
- Department of Biochemistry, Genetics and Immunology and Biomedical Research Center (CINBIO), University of Vigo, Spain; Galicia Sur Health Research Institute, Vigo, Spain
| | - David Posada
- Department of Biochemistry, Genetics and Immunology and Biomedical Research Center (CINBIO), University of Vigo, Spain; Galicia Sur Health Research Institute, Vigo, Spain.
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181
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Zhou Z, Wu S, Lai J, Shi Y, Qiu C, Chen Z, Wang Y, Gu X, Zhou J, Chen S. Identification of trunk mutations in gastric carcinoma: a case study. BMC Med Genomics 2017; 10:49. [PMID: 28716121 PMCID: PMC5520061 DOI: 10.1186/s12920-017-0285-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2017] [Accepted: 07/13/2017] [Indexed: 02/06/2023] Open
Abstract
Background Intratumor heterogeneity (ITH) poses an urgent challenge for cancer precision medicine because it can cause drug resistance against cancer target therapy and immunotherapy. The search for trunk mutations that are present in all cancer cells is therefore critical for each patient. Case presentation In this study, we aimed to evaluate the efficiency of multiregional sequencing for the identification of trunk mutations present in all regions of a tumor as a case study. We applied multiregional whole-exome sequencing (WES) to investigate the genetic heterogeneity and homogeneity of a case of gastric carcinoma. Approximately 83% of common missense mutations present in two samples and approximately 89% of common missense mutations present in three samples were trunk mutations. Notably, trunk mutations appeared to have higher variant allele frequencies (VAFs) than non-trunk mutations. Conclusions Our results indicate that small-scale multiregional sampling and subsequent screening of low VAF somatic mutations might be a cost-effective strategy for identifying the majority of trunk mutations in gastric carcinoma. Electronic supplementary material The online version of this article (doi:10.1186/s12920-017-0285-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Zhan Zhou
- College of Pharmaceutical Sciences, Zhejiang University, Zhejiang, Hangzhou, 310058, China
| | - Shanshan Wu
- College of Pharmaceutical Sciences, Zhejiang University, Zhejiang, Hangzhou, 310058, China
| | - Jun Lai
- College of Pharmaceutical Sciences, Zhejiang University, Zhejiang, Hangzhou, 310058, China
| | - Yuan Shi
- Zhejiang Hospital of Traditional Chinese Medicine, Zhejiang, Hangzhou, 310058, China
| | - Chixiao Qiu
- College of Pharmaceutical Sciences, Zhejiang University, Zhejiang, Hangzhou, 310058, China
| | - Zhe Chen
- Zhejiang Hospital of Traditional Chinese Medicine, Zhejiang, Hangzhou, 310058, China
| | - Yufeng Wang
- Department of Biology and South Texas Center for Emerging Infectious Diseases, University of Texas at San Antonio, San Antonio, TX, 78249, USA
| | - Xun Gu
- Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA, 50011, USA
| | - Jie Zhou
- College of Pharmaceutical Sciences, Zhejiang University, Zhejiang, Hangzhou, 310058, China.
| | - Shuqing Chen
- College of Pharmaceutical Sciences, Zhejiang University, Zhejiang, Hangzhou, 310058, China. .,International Center for Precision Medicine, Zhejiang California International NanoSystems Institute, Zhejiang, Hangzhou, 310058, China.
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182
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Thomas F, Ujvari B, Renaud F, Vincent M. Cancer adaptations: Atavism, de novo selection, or something in between? Bioessays 2017; 39. [DOI: 10.1002/bies.201700039] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Frédéric Thomas
- CREEC/MIVEGEC; UMR IRD/CNRS/UM 5290; Montpellier Cedex France
| | - Beata Ujvari
- Centre for Integrative Ecology, School of Life and Environmental Sciences; DeakinUniversity; Waurn Ponds Australia
| | - François Renaud
- CREEC/MIVEGEC; UMR IRD/CNRS/UM 5290; Montpellier Cedex France
| | - Mark Vincent
- Department of Oncology; University of Western Ontario; London Ontario Canada
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183
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Hanna MG, Howard JD. A key to the backdoor into the castle: The clinical ramifications of immunoediting driven by antigenic competition. Hum Vaccin Immunother 2017; 13:1579-1585. [PMID: 28340323 PMCID: PMC5512768 DOI: 10.1080/21645515.2017.1301337] [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] [Indexed: 11/16/2022] Open
Abstract
Over the last decade the field of cancer biology has gained considerable data on genomic heterogeneity. This situation creates challenges and possibly opportunities for cancer treatment. The evolution of the tumor at all stages also requires the growing malignancy to confront and avoid the immune system. What we describe here is the interaction of two immune phenomena that work together to change the characteristics of the tumor, i.e., antigenic competition and immune editing. These two systems are mutually functional and their interaction is capable of altering the characteristics of the tumor for protection and survival in an immune competent host as well as restricting the diversity of the tumor clones. Therefore, the final outcome of these interactions can also become the key to the backdoor into the castle. Through an additional immune manipulation, autologous tumor cell immunization, we can achieve prevention of disease recurrence after surgical resection and by analyzing induced human monoclonal antibodies to the neoantigens, gain in site into the restriction of diversity of the mutant clones. These findings may also open the door for a pathway to immune prevention of cancer.
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Affiliation(s)
- M G Hanna
- a Vaccinogen Inc. , Baltimore , MD , USA
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184
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Sun R, Hu Z, Sottoriva A, Graham TA, Harpak A, Ma Z, Fischer JM, Shibata D, Curtis C. Between-region genetic divergence reflects the mode and tempo of tumor evolution. Nat Genet 2017; 49:1015-1024. [PMID: 28581503 PMCID: PMC5643198 DOI: 10.1038/ng.3891] [Citation(s) in RCA: 98] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2016] [Accepted: 05/05/2017] [Indexed: 12/17/2022]
Abstract
Given the implications of tumor dynamics for precision medicine, there is a need to systematically characterize the mode of evolution across diverse solid tumor types. In particular, methods to infer the role of natural selection within established human tumors are lacking. By simulating spatial tumor growth under different evolutionary modes and examining patterns of between-region subclonal genetic divergence from multiregion sequencing (MRS) data, we demonstrate that it is feasible to distinguish tumors driven by strong positive subclonal selection from those evolving neutrally or under weak selection, as the latter fail to dramatically alter subclonal composition. We developed a classifier based on measures of between-region subclonal genetic divergence and projected patient data into model space, finding different modes of evolution both within and between solid tumor types. Our findings have broad implications for how human tumors progress, how they accumulate intratumoral heterogeneity, and ultimately how they may be more effectively treated.
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Affiliation(s)
- Ruping Sun
- Department of Medicine, Stanford University School of Medicine, Stanford, California, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California, USA
| | - Zheng Hu
- Department of Medicine, Stanford University School of Medicine, Stanford, California, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California, USA
| | - Andrea Sottoriva
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Trevor A. Graham
- Evolution and Cancer Laboratory, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Arbel Harpak
- Department of Biology, Stanford University, Stanford, California, USA
| | - Zhicheng Ma
- Department of Medicine, Stanford University School of Medicine, Stanford, California, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California, USA
| | - Jared M. Fischer
- Oregon Health and Science University, Department of Molecular and Medical Genetics, Portland, Oregon, USA
| | - Darryl Shibata
- Department of Pathology, Keck School of Medicine of the University of Southern California, Los Angeles, California, USA
| | - Christina Curtis
- Department of Medicine, Stanford University School of Medicine, Stanford, California, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California, USA
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185
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Vandin F. Computational Methods for Characterizing Cancer Mutational Heterogeneity. Front Genet 2017; 8:83. [PMID: 28659971 PMCID: PMC5469877 DOI: 10.3389/fgene.2017.00083] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Accepted: 05/30/2017] [Indexed: 12/21/2022] Open
Abstract
Advances in DNA sequencing technologies have allowed the characterization of somatic mutations in a large number of cancer genomes at an unprecedented level of detail, revealing the extreme genetic heterogeneity of cancer at two different levels: inter-tumor, with different patients of the same cancer type presenting different collections of somatic mutations, and intra-tumor, with different clones coexisting within the same tumor. Both inter-tumor and intra-tumor heterogeneity have crucial implications for clinical practices. Here, we review computational methods that use somatic alterations measured through next-generation DNA sequencing technologies for characterizing tumor heterogeneity and its association with clinical variables. We first review computational methods for studying inter-tumor heterogeneity, focusing on methods that attempt to summarize cancer heterogeneity by discovering pathways that are commonly mutated across different patients of the same cancer type. We then review computational methods for characterizing intra-tumor heterogeneity using information from bulk sequencing data or from single cell sequencing data. Finally, we present some of the recent computational methodologies that have been proposed to identify and assess the association between inter- or intra-tumor heterogeneity with clinical variables.
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Affiliation(s)
- Fabio Vandin
- Department of Information Engineering, University of PadovaPadova, Italy
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186
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Ahmed Z, Gravel S. Intratumor Heterogeneity and Circulating Tumor Cell Clusters. Mol Biol Evol 2017; 35:2135-2144. [PMID: 29897504 DOI: 10.1093/molbev/msy115] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2017] [Accepted: 05/23/2018] [Indexed: 12/19/2022] Open
Abstract
Genetic diversity plays a central role in tumor progression, metastasis, and resistance to treatment. Experiments are shedding light on this diversity at ever finer scales, but interpretation is challenging. Using recent progress in numerical models, we simulate macroscopic tumors to investigate the interplay between growth dynamics, microscopic composition, and circulating tumor cell cluster diversity. We find that modest differences in growth parameters can profoundly change microscopic diversity. Simple outwards expansion leads to spatially segregated clones and low diversity, as expected. However, a modest cell turnover can result in an increased number of divisions and mixing among clones resulting in increased microscopic diversity in the tumor core. Using simulations to estimate power to detect such spatial trends, we find that multiregion sequencing data from contemporary studies is marginally powered to detect the predicted effects. Slightly larger samples, improved detection of rare variants, or sequencing of smaller biopsies or circulating tumor cell clusters would allow one to distinguish between leading models of tumor evolution. The genetic composition of circulating tumor cell clusters, which can be obtained from non-invasive blood draws, is therefore informative about tumor evolution and its metastatic potential.
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Affiliation(s)
- Zafarali Ahmed
- School of Computer Science, McGill University, Montréeal, QC, Canada
| | - Simon Gravel
- Department of Human Genetics, McGill University, Montréeal, QC, Canada.,McGill University and Genome Québec Innovation Centre, Montréal, QC, Canada
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187
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Beckman RA, Loeb LA. Evolutionary dynamics and significance of multiple subclonal mutations in cancer. DNA Repair (Amst) 2017; 56:7-15. [PMID: 28652129 DOI: 10.1016/j.dnarep.2017.06.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
For the last 40 years the authors have collaborated on trying to understand the complexities of human cancer by formulating testable mathematical models that are based on mutation accumulation in human malignancies. We summarize the concepts encompassed by multiple mutations in human cancers in the context of source, accumulation during carcinogenesis and tumor progression, and therapeutic consequences. We conclude that the efficacious treatment of human cancer by targeted therapy will involve individualized, uniquely directed specific agents singly and in simultaneous combinations, and take into account the importance of targeting resistant subclonal mutations, particularly those subclones with alterations in DNA repair genes, DNA polymerase, and other genes required to maintain genetic stability.
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Affiliation(s)
- Robert A Beckman
- Departments of Oncology and Biostatistics, Bioinformatics, & Biomathematics, Lombardi Comprehensive Cancer Center and Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC 20007 USA
| | - Lawrence A Loeb
- Joseph Gottstein Memorial Cancer Research Laboratory, Departments of Pathology and Biochemistry, University of Washington School of Medicine, Seattle, WA, 98195 USA.
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188
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Multisite tumor sampling: a new tumor selection method to enhance intratumor heterogeneity detection. Hum Pathol 2017; 64:1-6. [DOI: 10.1016/j.humpath.2017.02.010] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2016] [Revised: 01/14/2017] [Accepted: 02/09/2017] [Indexed: 01/20/2023]
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189
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Abstract
Hepatocellular carcinomas can be further divided into distinct subtypes that provide important clinical information and biological insights. These subtypes are distinct from growth patterns and are on based on morphologic and molecular findings. There are 12 reasonably well-defined subtypes as well as 6 provisional subtypes, together making up 35% of all hepatocellular carcinomas. These subtypes are discussed, with an emphasis on their definitions and the key morphologic findings.
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Affiliation(s)
- Michael S Torbenson
- Department of Laboratory Medicine and Pathology, Mayo Clinic Rochester, Rochester, MN, USA.
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190
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Wang K, Lai S, Yang X, Zhu T, Lu X, Wu CI, Ruan J. Ultrasensitive and high-efficiency screen of de novo low-frequency mutations by o2n-seq. Nat Commun 2017; 8:15335. [PMID: 28530222 PMCID: PMC5458117 DOI: 10.1038/ncomms15335] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2016] [Accepted: 03/21/2017] [Indexed: 12/15/2022] Open
Abstract
Detection of de novo, low-frequency mutations is essential for characterizing cancer genomes and heterogeneous cell populations. However, the screening capacity of current ultrasensitive NGS methods is inadequate owing to either low-efficiency read utilization or severe amplification bias. Here, we present o2n-seq, an ultrasensitive and high-efficiency NGS library preparation method for discovering de novo, low-frequency mutations. O2n-seq reduces the error rate of NGS to 10-5-10-8. The efficiency of its data usage is about 10-30 times higher than that of barcode-based strategies. For detecting mutations with allele frequency (AF) 1% in 4.6 Mb-sized genome, the sensitivity and specificity of o2n-seq reach to 99% and 98.64%, respectively. For mutations with AF around 0.07% in phix174, o2n-seq detects all the mutations with 100% specificity. Moreover, we successfully apply o2n-seq to screen de novo, low-frequency mutations in human tumours. O2n-seq will aid to characterize the landscape of somatic mutations in research and clinical settings.
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Affiliation(s)
- Kaile Wang
- Agricultural Genomics Institute, Chinese Academy of Agricultural Sciences, Pengfei Road No. 7, Dapeng New District, Shenzhen, Guangdong 518120, China
- Key Laboratory of Genomics and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Chaoyang, Beijing 100101, China
- University of Chinese Academy of Sciences, Shijingshan, Beijing 100049, China
| | - Shujuan Lai
- Key Laboratory of Genomics and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Chaoyang, Beijing 100101, China
| | - Xiaoxu Yang
- Center for Bioinformatics, State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Haidian, Beijing 100871, China
| | - Tianqi Zhu
- Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Haidian, Beijing 100190, China
- Key Laboratory of Random Complex Structures and Data Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
| | - Xuemei Lu
- Key Laboratory of Genomics and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Chaoyang, Beijing 100101, China
| | - Chung-I Wu
- Key Laboratory of Genomics and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Chaoyang, Beijing 100101, China
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, Guangdong 510275, China
- Department of Ecology and Evolution, University of Chicago, Chicago, Illinois 60637, USA
| | - Jue Ruan
- Agricultural Genomics Institute, Chinese Academy of Agricultural Sciences, Pengfei Road No. 7, Dapeng New District, Shenzhen, Guangdong 518120, China
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191
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Abstract
Kelch-like ECH-associated protein 1 (Keap1), nuclear factor erythroid 2-like factor 2-related factor 2 (Nrf2), and the antioxidant response element (ARE) are interacting components of a master regulatory signaling pathway that coordinates redox homeostasis, cytoprotective responses, and shifts in stem cell state. This study reexamined detailed dose–response (DR) data reported for in vitro Nrf2-ARE activation in human hepatoblastoma HepG2 cell lines containing either a ARE-bla or ARE-luc reporter at 12 different concentrations of each of 15 chemicals. The normalized study data were combined among chemicals exhibiting a positive response, yielding n = 531 (179) DR data for 9 (7) chemicals using the ARE-bla (ARE-luc) assay. Three-parameter linear/kth-power regression fits obtained to each combined set of ARE-bla- or ARE-luc-assay response data provided good fits (R2 = .99 or .91, respectively, Pfit > .99) that each incorporate a highly significant negative initial linear slope (P = 4 × 10−5 or .00025) and an overall J-shaped DR pattern. Results from this reanalysis of high-resolution ARE response data support the hypothesis that nonlinear ARE-mediated adaptive cellular responses to oxidative stress are governed by an ultrasensitive molecular switch.
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192
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Lin DC, Mayakonda A, Dinh HQ, Huang P, Lin L, Liu X, Ding LW, Wang J, Berman BP, Song EW, Yin D, Koeffler HP. Genomic and Epigenomic Heterogeneity of Hepatocellular Carcinoma. Cancer Res 2017; 77:2255-2265. [PMID: 28302680 PMCID: PMC5413372 DOI: 10.1158/0008-5472.can-16-2822] [Citation(s) in RCA: 129] [Impact Index Per Article: 18.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2016] [Revised: 11/14/2016] [Accepted: 02/04/2017] [Indexed: 02/06/2023]
Abstract
Understanding the intratumoral heterogeneity of hepatocellular carcinoma is instructive for developing personalized therapy and identifying molecular biomarkers. Here we applied whole-exome sequencing to 69 samples from 11 patients to resolve the genetic architecture of subclonal diversification. Spatial genomic diversity was found in all 11 hepatocellular carcinoma cases, with 29% of driver mutations being heterogeneous, including TERT, ARID1A, NOTCH2, and STAG2. Similar with other cancer types, TP53 mutations were always shared between all tumor regions, that is, located on the "trunk" of the evolutionary tree. In addition, we found that variants within several drug targets such as KIT, SYK, and PIK3CA were mutated in a fully clonal manner, indicating their therapeutic potentials for hepatocellular carcinoma. Temporal dissection of mutational signatures suggested that mutagenic processes associated with exposure to aristolochic acid and aflatoxin might play a more important role in early, as opposed to late, stages of hepatocellular carcinoma development. Moreover, we observed extensive intratumoral epigenetic heterogeneity in hepatocellular carcinoma based on multiple independent analytical methods and showed that intratumoral methylation heterogeneity might play important roles in the biology of hepatocellular carcinoma cells. Our results also demonstrated prominent heterogeneity of intratumoral methylation even in a stable hepatocellular carcinoma genome. Together, these findings highlight widespread intratumoral heterogeneity at both the genomic and epigenomic levels in hepatocellular carcinoma and provide an important molecular foundation for better understanding the pathogenesis of this malignancy. Cancer Res; 77(9); 2255-65. ©2017 AACR.
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Affiliation(s)
- De-Chen Lin
- Guangdong Province Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Research Center of Medicine, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.
- Division of Hematology/Oncology, Cedars-Sinai Medical Center, UCLA School of Medicine, Los Angeles, California
| | - Anand Mayakonda
- Cancer Science Institute of Singapore, National University of Singapore, Singapore
| | - Huy Q Dinh
- Center for Bioinformatics and Functional Genomics, Biomedical Sciences, Cedars-Sinai Medical Center, UCLA School of Medicine, Los Angeles, California
| | - Pinbo Huang
- Guangdong Province Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Research Center of Medicine, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
- Department of Hepatobiliary Surgery, Sun-Yat-Sen Memorial Hospital, Sun-Yat-Sen University, Guangzhou, China
| | - Lehang Lin
- Guangdong Province Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Research Center of Medicine, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Xiaoping Liu
- Guangdong Province Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Research Center of Medicine, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Ling-Wen Ding
- Cancer Science Institute of Singapore, National University of Singapore, Singapore
| | - Jie Wang
- Guangdong Province Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Research Center of Medicine, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
- Department of Hepatobiliary Surgery, Sun-Yat-Sen Memorial Hospital, Sun-Yat-Sen University, Guangzhou, China
| | - Benjamin P Berman
- Center for Bioinformatics and Functional Genomics, Biomedical Sciences, Cedars-Sinai Medical Center, UCLA School of Medicine, Los Angeles, California.
| | - Er-Wei Song
- Guangdong Province Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Research Center of Medicine, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.
| | - Dong Yin
- Guangdong Province Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Research Center of Medicine, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.
| | - H Phillip Koeffler
- Division of Hematology/Oncology, Cedars-Sinai Medical Center, UCLA School of Medicine, Los Angeles, California
- Cancer Science Institute of Singapore, National University of Singapore, Singapore
- National University Cancer Institute, National University Hospital Singapore, Singapore
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193
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Martínez-Jiménez F, Overington JP, Al-Lazikani B, Marti-Renom MA. Rational design of non-resistant targeted cancer therapies. Sci Rep 2017; 7:46632. [PMID: 28436422 PMCID: PMC5402386 DOI: 10.1038/srep46632] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2016] [Accepted: 03/22/2017] [Indexed: 12/29/2022] Open
Abstract
Drug resistance is one of the major problems in targeted cancer therapy. A major cause of resistance is changes in the amino acids that form the drug-target binding site. Despite of the numerous efforts made to individually understand and overcome these mutations, there is a lack of comprehensive analysis of the mutational landscape that can prospectively estimate drug-resistance mutations. Here we describe and computationally validate a framework that combines the cancer-specific likelihood with the resistance impact to enable the detection of single point mutations with the highest chance to be responsible of resistance to a particular targeted cancer therapy. Moreover, for these treatment-threatening mutations, the model proposes alternative therapies overcoming the resistance. We exemplified the applicability of the model using EGFR-gefitinib treatment for Lung Adenocarcinoma (LUAD) and Lung Squamous Cell Cancer (LSCC) and the ERK2-VTX11e treatment for melanoma and colorectal cancer. Our model correctly identified the phenotype known resistance mutations, including the classic EGFR-T790M and the ERK2-P58L/S/T mutations. Moreover, the model predicted new previously undescribed mutations as potentially responsible of drug resistance. Finally, we provided a map of the predicted sensitivity of alternative ERK2 and EGFR inhibitors, with a particular highlight of two molecules with a low predicted resistance impact.
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Affiliation(s)
- Francisco Martínez-Jiménez
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Baldiri i Reixac 4, 08028 Barcelona, Spain
- Gene Regulation, Stem Cells and Cancer Program, Centre for Genomic Regulation (CRG), Dr. Aiguader 88, 08003 Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - John P. Overington
- Medicines Discovery Catapult Block 35, Mereside, Alderley Park, Alderley Edge, Cheshire, SK10 4TG, UK
| | | | - Marc A. Marti-Renom
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Baldiri i Reixac 4, 08028 Barcelona, Spain
- Gene Regulation, Stem Cells and Cancer Program, Centre for Genomic Regulation (CRG), Dr. Aiguader 88, 08003 Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- ICREA, Pg. Lluís Companys 23, 08010 Barcelona, Spain
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194
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Abstract
Rapid advances in high-throughput sequencing and a growing realization of the importance of evolutionary theory to cancer genomics have led to a proliferation of phylogenetic studies of tumour progression. These studies have yielded not only new insights but also a plethora of experimental approaches, sometimes reaching conflicting or poorly supported conclusions. Here, we consider this body of work in light of the key computational principles underpinning phylogenetic inference, with the goal of providing practical guidance on the design and analysis of scientifically rigorous tumour phylogeny studies. We survey the range of methods and tools available to the researcher, their key applications, and the various unsolved problems, closing with a perspective on the prospects and broader implications of this field.
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Affiliation(s)
- Russell Schwartz
- Department of Biological Sciences and Computational Biology Department, Carnegie Mellon University, Pittsburgh, Pennsylvania 15217, USA
| | - Alejandro A Schäffer
- Computational Biology Branch, National Center for Biotechnology Information, National Institutes of Health, Bethesda, Maryland 20892, USA
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195
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Sottoriva A, Barnes CP, Graham TA. Catch my drift? Making sense of genomic intra-tumour heterogeneity. Biochim Biophys Acta Rev Cancer 2017; 1867:95-100. [PMID: 28069394 PMCID: PMC5446319 DOI: 10.1016/j.bbcan.2016.12.003] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2016] [Revised: 12/24/2016] [Accepted: 12/27/2016] [Indexed: 01/08/2023]
Abstract
The cancer genome is shaped by three components of the evolutionary process: mutation, selection and drift. While many studies have focused on the first two components, the role of drift in cancer evolution has received little attention. Drift occurs when all individuals in the population have the same likelihood of producing surviving offspring, and so by definition a drifting population is one that is evolving neutrally. Here we focus on how neutral evolution is manifested in the cancer genome. We discuss how neutral passenger mutations provide a magnifying glass that reveals the evolutionary dynamics underpinning cancer development, and outline how statistical inference can be used to quantify these dynamics from sequencing data. We argue that only after we understand the impact of neutral drift on the genome can we begin to make full sense of clonal selection. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer? Edited by Dr. Robert A. Gatenby.
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Affiliation(s)
- Andrea Sottoriva
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, SM2 5NG, UK.
| | - Chris P Barnes
- Department of Cell and Developmental Biology, University College London, London, UK.
| | - Trevor A Graham
- Evolution and Cancer Laboratory, Barts Cancer Institute, Charterhouse Sq, Queen Mary University of London, EC1M 6BQ, UK.
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196
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Kuipers J, Jahn K, Beerenwinkel N. Advances in understanding tumour evolution through single-cell sequencing. Biochim Biophys Acta Rev Cancer 2017; 1867:127-138. [PMID: 28193548 PMCID: PMC5813714 DOI: 10.1016/j.bbcan.2017.02.001] [Citation(s) in RCA: 67] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Revised: 02/02/2017] [Accepted: 02/04/2017] [Indexed: 12/14/2022]
Abstract
The mutational heterogeneity observed within tumours poses additional challenges to the development of effective cancer treatments. A thorough understanding of a tumour's subclonal composition and its mutational history is essential to open up the design of treatments tailored to individual patients. Comparative studies on a large number of tumours permit the identification of mutational patterns which may refine forecasts of cancer progression, response to treatment and metastatic potential. The composition of tumours is shaped by evolutionary processes. Recent advances in next-generation sequencing offer the possibility to analyse the evolutionary history and accompanying heterogeneity of tumours at an unprecedented resolution, by sequencing single cells. New computational challenges arise when moving from bulk to single-cell sequencing data, leading to the development of novel modelling frameworks. In this review, we present the state of the art methods for understanding the phylogeny encoded in bulk or single-cell sequencing data, and highlight future directions for developing more comprehensive and informative pictures of tumour evolution. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby.
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MESH Headings
- Adaptation, Physiological
- Animals
- Biomarkers, Tumor/genetics
- Biomarkers, Tumor/metabolism
- Cell Transformation, Neoplastic/genetics
- Cell Transformation, Neoplastic/metabolism
- Cell Transformation, Neoplastic/pathology
- Evolution, Molecular
- Gene Expression Regulation, Neoplastic
- Genetic Fitness
- Genetic Heterogeneity
- Genetic Predisposition to Disease
- Heredity
- Humans
- Models, Genetic
- Mutation
- Neoplasms/drug therapy
- Neoplasms/genetics
- Neoplasms/metabolism
- Neoplasms/pathology
- Pedigree
- Phenotype
- Phylogeny
- Sequence Analysis, DNA
- Signal Transduction/genetics
- Single-Cell Analysis/methods
- Time Factors
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Affiliation(s)
- Jack Kuipers
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland; Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Katharina Jahn
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland; Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland; Swiss Institute of Bioinformatics, Basel, Switzerland
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197
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Detecting truly clonal alterations from multi-region profiling of tumours. Sci Rep 2017; 7:44991. [PMID: 28344344 PMCID: PMC5366809 DOI: 10.1038/srep44991] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2016] [Accepted: 02/16/2017] [Indexed: 12/12/2022] Open
Abstract
Modern cancer therapies aim at targeting tumour-specific alterations, such as mutations or neo-antigens, and maximal treatment efficacy requires that targeted alterations are present in all tumour cells. Currently, treatment decisions are based on one or a few samples per tumour, creating uncertainty on whether alterations found in those samples are actually present in all tumour cells. The probability of classifying clonal versus sub-clonal alterations from multi-region profiling of tumours depends on the earliest phylogenetic branching event during tumour growth. By analysing 181 samples from 10 renal carcinoma and 11 colorectal cancers we demonstrate that the information gain from additional sampling falls onto a simple universal curve. We found that in colorectal cancers, 30% of alterations identified as clonal with one biopsy proved sub-clonal when 8 samples were considered. The probability to overestimate clonal alterations fell below 1% in 7/11 patients with 8 samples per tumour. In renal cell carcinoma, 8 samples reduced the list of clonal alterations by 40% with respect to a single biopsy. The probability to overestimate clonal alterations remained as high as 92% in 7/10 renal cancer patients. Furthermore, treatment was associated with more unbalanced tumour phylogenetic trees, suggesting the need of denser sampling of tumours at relapse.
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198
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Affiliation(s)
- Ivana Bozic
- Program for Evolutionary Dynamics and
- Department of Mathematics, Harvard University, Cambridge, Massachusetts 02138
- Department of Applied Mathematics, University of Washington, Seattle, Washington 98195
| | - Martin A. Nowak
- Program for Evolutionary Dynamics and
- Department of Mathematics, Harvard University, Cambridge, Massachusetts 02138
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts 02138
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199
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Hu Z, Sun R, Curtis C. A population genetics perspective on the determinants of intra-tumor heterogeneity. Biochim Biophys Acta Rev Cancer 2017; 1867:109-126. [PMID: 28274726 DOI: 10.1016/j.bbcan.2017.03.001] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2016] [Revised: 03/01/2017] [Accepted: 03/02/2017] [Indexed: 12/17/2022]
Abstract
Cancer results from the acquisition of somatic alterations in a microevolutionary process that typically occurs over many years, much of which is occult. Understanding the evolutionary dynamics that are operative at different stages of progression in individual tumors might inform the earlier detection, diagnosis, and treatment of cancer. Although these processes cannot be directly observed, the resultant spatiotemporal patterns of genetic variation amongst tumor cells encode their evolutionary histories. Such intra-tumor heterogeneity is pervasive not only at the genomic level, but also at the transcriptomic, phenotypic, and cellular levels. Given the implications for precision medicine, the accurate quantification of heterogeneity within and between tumors has become a major focus of current research. In this review, we provide a population genetics perspective on the determinants of intra-tumor heterogeneity and approaches to quantify genetic diversity. We summarize evidence for different modes of evolution based on recent cancer genome sequencing studies and discuss emerging evolutionary strategies to therapeutically exploit tumor heterogeneity. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby.
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Affiliation(s)
- Zheng Hu
- Departments of Medicine and Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Ruping Sun
- Departments of Medicine and Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Christina Curtis
- Departments of Medicine and Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA 94305, USA.
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200
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The spatial organization of intra-tumour heterogeneity and evolutionary trajectories of metastases in hepatocellular carcinoma. Nat Commun 2017; 8:4565. [PMID: 28240289 PMCID: PMC5333358 DOI: 10.1038/ncomms14565] [Citation(s) in RCA: 92] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2016] [Accepted: 01/12/2017] [Indexed: 12/12/2022] Open
Abstract
Hepatocellular carcinoma (HCC) has one of the poorest survival rates among cancers. Using multi-regional sampling of nine resected HCC with different aetiologies, here we construct phylogenetic relationships of these sectors, showing diverse levels of genetic sharing, spanning early to late diversification. Unlike the variegated pattern found in colorectal cancers, a large proportion of HCC display a clear isolation-by-distance pattern where spatially closer sectors are genetically more similar. Two resected intra-hepatic metastases showed genetic divergence occurring before and after primary tumour diversification, respectively. Metastatic tumours had much higher variability than their primary tumours, suggesting that intra-hepatic metastasis is accompanied by rapid diversification at the distant location. The presence of co-existing mutations offers the possibility of drug repositioning for HCC treatment. Taken together, these insights into intra-tumour heterogeneity allow for a comprehensive understanding of the evolutionary trajectories of HCC and suggest novel avenues for personalized therapy.
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