1
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Rao Y, Ahmed N, Pritchard J, O'Brien EP. Incorporating mutational heterogeneity to identify genes that are enriched for synonymous mutations in cancer. BMC Bioinformatics 2023; 24:462. [PMID: 38062391 PMCID: PMC10704839 DOI: 10.1186/s12859-023-05521-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 10/05/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Synonymous mutations, which change the DNA sequence but not the encoded protein sequence, can affect protein structure and function, mRNA maturation, and mRNA half-lives. The possibility that synonymous mutations might be enriched in cancer has been explored in several recent studies. However, none of these studies control for all three types of mutational heterogeneity (patient, histology, and gene) that are known to affect the accurate identification of non-synonymous cancer-associated genes. Our goal is to adopt the current standard for non-synonymous mutations in an investigation of synonymous mutations. RESULTS Here, we create an algorithm, MutSigCVsyn, an adaptation of MutSigCV, to identify cancer-associated genes that are enriched for synonymous mutations based on a non-coding background model that takes into account the mutational heterogeneity across these levels. Using MutSigCVsyn, we first analyzed 2572 cancer whole-genome samples from the Pan-cancer Analysis of Whole Genomes (PCAWG) to identify non-synonymous cancer drivers as a quality control. Indicative of the algorithm accuracy we find that 58.6% of these candidate genes were also found in Cancer Census Gene (CGC) list, and 66.2% were found within the PCAWG cancer driver list. We then applied it to identify 30 putative cancer-associated genes that are enriched for synonymous mutations within the same samples. One of the promising gene candidates is the B cell lymphoma 2 (BCL-2) gene. BCL-2 regulates apoptosis by antagonizing the action of proapoptotic BCL-2 family member proteins. The synonymous mutations in BCL2 are enriched in its anti-apoptotic domain and likely play a role in cancer cell proliferation. CONCLUSION Our study introduces MutSigCVsyn, an algorithm that accounts for mutational heterogeneity at patient, histology, and gene levels, to identify cancer-associated genes that are enriched for synonymous mutations using whole genome sequencing data. We identified 30 putative candidate genes that will benefit from future experimental studies on the role of synonymous mutations in cancer biology.
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Affiliation(s)
- Yiyun Rao
- Huck Institute of the Life Sciences, Pennsylvania State University, University Park, State College, PA, 16802, USA
| | - Nabeel Ahmed
- Huck Institute of the Life Sciences, Pennsylvania State University, University Park, State College, PA, 16802, USA
- Moderna, Inc., Cambridge, USA
| | - Justin Pritchard
- Department of Biomedical Engineering, Pennsylvania State University, University Park, State College, PA, 16802, USA.
| | - Edward P O'Brien
- Department of Chemistry, Pennsylvania State University, University Park, State College, PA, 16802, USA.
- Institute for Computational and Data Sciences, Pennsylvania State University, University Park, State College, PA, 16802, USA.
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2
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Huang Y, Pfeiffer SM, Zhang Q. Primary tumor type prediction based on US nationwide genomic profiling data in 13,522 patients. Comput Struct Biotechnol J 2023; 21:3865-3874. [PMID: 37593720 PMCID: PMC10432138 DOI: 10.1016/j.csbj.2023.07.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 07/16/2023] [Accepted: 07/25/2023] [Indexed: 08/19/2023] Open
Abstract
Timely and accurate primary tumor diagnosis is critical, and misdiagnoses and delays may cause undue health and economic burden. To predict primary tumor types based on genomics data from a de-identified US nationwide clinico-genomic database (CGDB), the XGBoost-based Clinico-Genomic Machine Learning Model (XC-GeM) was developed to predict 13 primary tumor types based on data from 12,060 patients in the CGDB, derived from routine clinical comprehensive genomic profiling (CGP) testing and chart-confirmed electronic health records (EHRs). The SHapley Additive exPlanations method was used to interpret model predictions. XC-GeM reached an outstanding area under the curve (AUC) of 0.965 and Matthew's correlation coefficient (MCC) of 0.742 in the holdout validation dataset. In the independent validation cohort of 955 patients, XC-GeM reached 0.954 AUC and 0.733 MCC and made correct predictions in 77% of non-small cell lung cancer (NSCLC), 86% of colorectal cancer, and 84% of breast cancer patients. Top predictors for the overall model (e.g. tumor mutational burden (TMB), gender, and KRAS alteration), and for specific tumor types (e.g., TMB and EGFR alteration for NSCLC) were supported by published studies. XC-GeM also achieved an excellent AUC of 0.880 and positive MCC of 0.540 in 507 patients with missing primary diagnosis. XC-GeM is the first algorithm to predict primary tumor type using US nationwide data from routine CGP testing and chart-confirmed EHRs, showing promising performance. It may enhance the accuracy and efficiency of cancer diagnoses, enabling more timely treatment choices and potentially leading to better outcomes.
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Affiliation(s)
| | | | - Qing Zhang
- Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, United States
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3
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Kumar S, Gerstein M. Unified views on variant impact across many diseases. Trends Genet 2023; 39:442-450. [PMID: 36858880 PMCID: PMC10192142 DOI: 10.1016/j.tig.2023.02.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 02/02/2023] [Accepted: 02/02/2023] [Indexed: 03/03/2023]
Abstract
Genomic studies of human disorders are often performed by distinct research communities (i.e., focused on rare diseases, common diseases, or cancer). Despite underlying differences in the mechanistic origin of different disease categories, these studies share the goal of identifying causal genomic events that are critical for the clinical manifestation of the disease phenotype. Moreover, these studies face common challenges, including understanding the complex genetic architecture of the disease, deciphering the impact of variants on multiple scales, and interpreting noncoding mutations. Here, we highlight these challenges in depth and argue that properly addressing them will require a more unified vocabulary and approach across disease communities. Toward this goal, we present a unified perspective on relating variant impact to various genomic disorders.
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Affiliation(s)
- Sushant Kumar
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.
| | - Mark Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA; Department of Computer Science, Yale University, New Haven, CT 06520, USA; Department of Statistics & Data Science, Yale University, New Haven, CT 06520, USA.
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4
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Zhu X, Zhao W, Zhou Z, Gu X. Unraveling the Drivers of Tumorigenesis in the Context of Evolution: Theoretical Models and Bioinformatics Tools. J Mol Evol 2023:10.1007/s00239-023-10117-0. [PMID: 37246992 DOI: 10.1007/s00239-023-10117-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Accepted: 05/09/2023] [Indexed: 05/30/2023]
Abstract
Cancer originates from somatic cells that have accumulated mutations. These mutations alter the phenotype of the cells, allowing them to escape homeostatic regulation that maintains normal cell numbers. The emergence of malignancies is an evolutionary process in which the random accumulation of somatic mutations and sequential selection of dominant clones cause cancer cells to proliferate. The development of technologies such as high-throughput sequencing has provided a powerful means to measure subclonal evolutionary dynamics across space and time. Here, we review the patterns that may be observed in cancer evolution and the methods available for quantifying the evolutionary dynamics of cancer. An improved understanding of the evolutionary trajectories of cancer will enable us to explore the molecular mechanism of tumorigenesis and to design tailored treatment strategies.
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Affiliation(s)
- Xunuo Zhu
- Innovation Institute for Artificial Intelligence in Medicine, Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Wenyi Zhao
- Innovation Institute for Artificial Intelligence in Medicine, Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Zhan Zhou
- Innovation Institute for Artificial Intelligence in Medicine, Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
- The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, 322000, China.
- Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 310058, China.
| | - Xun Gu
- Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA, 50011, USA.
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5
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Ostroverkhova D, Przytycka TM, Panchenko AR. Cancer driver mutations: predictions and reality. Trends Mol Med 2023:S1471-4914(23)00067-9. [PMID: 37076339 DOI: 10.1016/j.molmed.2023.03.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 03/17/2023] [Accepted: 03/23/2023] [Indexed: 04/21/2023]
Abstract
Cancer cells accumulate many genetic alterations throughout their lifetime, but only a few of them drive cancer progression, termed driver mutations. Driver mutations may vary between cancer types and patients, can remain latent for a long time and become drivers at particular cancer stages, or may drive oncogenesis only in conjunction with other mutations. The high mutational, biochemical, and histological tumor heterogeneity makes driver mutation identification very challenging. In this review we summarize recent efforts to identify driver mutations in cancer and annotate their effects. We underline the success of computational methods to predict driver mutations in finding novel cancer biomarkers, including in circulating tumor DNA (ctDNA). We also report on the boundaries of their applicability in clinical research.
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Affiliation(s)
- Daria Ostroverkhova
- Department of Pathology and Molecular Medicine, Queen's University, Kingston, ON, Canada
| | - Teresa M Przytycka
- National Library of Medicine, National Institutes of Health (NIH), Bethesda, MD, USA.
| | - Anna R Panchenko
- Department of Pathology and Molecular Medicine, Queen's University, Kingston, ON, Canada; Department of Biology and Molecular Sciences, Queen's University, Kingston, ON, Canada; School of Computing, Queen's University, Kingston, ON, Canada; Ontario Institute of Cancer Research, Toronto, ON, Canada.
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6
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Woodward EL, Yang M, Moura-Castro LH, van den Bos H, Gunnarsson R, Olsson-Arvidsson L, Spierings DCJ, Castor A, Duployez N, Zaliova M, Zuna J, Johansson B, Foijer F, Paulsson K. Clonal origin and development of high hyperdiploidy in childhood acute lymphoblastic leukaemia. Nat Commun 2023; 14:1658. [PMID: 36966135 PMCID: PMC10039905 DOI: 10.1038/s41467-023-37356-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 03/14/2023] [Indexed: 03/27/2023] Open
Abstract
High hyperdiploid acute lymphoblastic leukemia (HeH ALL), one of the most common childhood malignancies, is driven by nonrandom aneuploidy (abnormal chromosome numbers) mainly comprising chromosomal gains. In this study, we investigate how aneuploidy in HeH ALL arises. Single cell whole genome sequencing of 2847 cells from nine primary cases and one normal bone marrow reveals that HeH ALL generally display low chromosomal heterogeneity, indicating that they are not characterized by chromosomal instability and showing that aneuploidy-driven malignancies are not necessarily chromosomally heterogeneous. Furthermore, most chromosomal gains are present in all leukemic cells, suggesting that they arose early during leukemogenesis. Copy number data from 577 primary cases reveals selective pressures that were used for in silico modeling of aneuploidy development. This shows that the aneuploidy in HeH ALL likely arises by an initial tripolar mitosis in a diploid cell followed by clonal evolution, in line with a punctuated evolution model.
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Affiliation(s)
- Eleanor L Woodward
- Department of Laboratory Medicine, Division of Clinical Genetics, Lund University, Lund, Sweden
| | - Minjun Yang
- Department of Laboratory Medicine, Division of Clinical Genetics, Lund University, Lund, Sweden
| | - Larissa H Moura-Castro
- Department of Laboratory Medicine, Division of Clinical Genetics, Lund University, Lund, Sweden
| | - Hilda van den Bos
- European Research Institute for the Biology of Ageing (ERIBA), University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Rebeqa Gunnarsson
- Department of Laboratory Medicine, Division of Clinical Genetics, Lund University, Lund, Sweden
| | - Linda Olsson-Arvidsson
- Department of Laboratory Medicine, Division of Clinical Genetics, Lund University, Lund, Sweden
- Department of Clinical Genetics, Pathology, and Molecular Diagnostics, Office for Medical Services, Region Skåne, Lund, Sweden
| | - Diana C J Spierings
- European Research Institute for the Biology of Ageing (ERIBA), University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Anders Castor
- Department of Pediatrics, Skåne University Hospital, Lund University, Lund, Sweden
| | - Nicolas Duployez
- Laboratory of Hematology, Centre Hospitalier Universitaire (CHU) Lille, Lille, France
- Unité Mixte de Recherche en Santé (UMR-S) 1172, INSERM/University of Lille, Lille, France
| | - Marketa Zaliova
- Department of Pediatric Hematology and Oncology, Second Faculty of Medicine, Charles University/University Hospital Motol, Prague, Czech Republic
- Childhood Leukaemia Investigation Prague (CLIP), Prague, Czech Republic
| | - Jan Zuna
- Department of Pediatric Hematology and Oncology, Second Faculty of Medicine, Charles University/University Hospital Motol, Prague, Czech Republic
- Childhood Leukaemia Investigation Prague (CLIP), Prague, Czech Republic
| | - Bertil Johansson
- Department of Laboratory Medicine, Division of Clinical Genetics, Lund University, Lund, Sweden
- Department of Clinical Genetics, Pathology, and Molecular Diagnostics, Office for Medical Services, Region Skåne, Lund, Sweden
| | - Floris Foijer
- European Research Institute for the Biology of Ageing (ERIBA), University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Kajsa Paulsson
- Department of Laboratory Medicine, Division of Clinical Genetics, Lund University, Lund, Sweden.
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7
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Tomkova M, Tomek J, Chow J, McPherson JD, Segal DJ, Hormozdiari F. Dr.Nod: computational framework for discovery of regulatory non-coding drivers in tissue-matched distal regulatory elements. Nucleic Acids Res 2023; 51:e23. [PMID: 36625266 PMCID: PMC9976879 DOI: 10.1093/nar/gkac1251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 12/07/2022] [Accepted: 12/19/2022] [Indexed: 01/11/2023] Open
Abstract
The discovery of cancer driver mutations is a fundamental goal in cancer research. While many cancer driver mutations have been discovered in the protein-coding genome, research into potential cancer drivers in the non-coding regions showed limited success so far. Here, we present a novel comprehensive framework Dr.Nod for detection of non-coding cis-regulatory candidate driver mutations that are associated with dysregulated gene expression using tissue-matched enhancer-gene annotations. Applying the framework to data from over 1500 tumours across eight tissues revealed a 4.4-fold enrichment of candidate driver mutations in regulatory regions of known cancer driver genes. An overarching conclusion that emerges is that the non-coding driver mutations contribute to cancer by significantly altering transcription factor binding sites, leading to upregulation of tissue-matched oncogenes and down-regulation of tumour-suppressor genes. Interestingly, more than half of the detected cancer-promoting non-coding regulatory driver mutations are over 20 kb distant from the cancer-associated genes they regulate. Our results show the importance of tissue-matched enhancer-gene maps, functional impact of mutations, and complex background mutagenesis model for the prediction of non-coding regulatory drivers. In conclusion, our study demonstrates that non-coding mutations in enhancers play a previously underappreciated role in cancer and dysregulation of clinically relevant target genes.
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Affiliation(s)
- Marketa Tomkova
- Department of Biochemistry and Molecular Medicine, University of California, Davis, CA 95616, USA.,Ludwig Cancer Research, University of Oxford, Oxford, OX3 7DQ, UK.,UC Davis Genome Center, University of California, Davis, CA 95616, USA
| | - Jakub Tomek
- Department of Pharmacology, University of California, Davis, CA 95616, USA
| | - Julie Chow
- Department of Biochemistry and Molecular Medicine, University of California, Davis, CA 95616, USA
| | - John D McPherson
- Department of Biochemistry and Molecular Medicine, University of California, Davis, CA 95616, USA
| | - David J Segal
- Department of Biochemistry and Molecular Medicine, University of California, Davis, CA 95616, USA.,UC Davis Genome Center, University of California, Davis, CA 95616, USA.,UC Davis MIND Institute, University of California, Davis, CA 95616, USA
| | - Fereydoun Hormozdiari
- Department of Biochemistry and Molecular Medicine, University of California, Davis, CA 95616, USA.,UC Davis Genome Center, University of California, Davis, CA 95616, USA.,UC Davis MIND Institute, University of California, Davis, CA 95616, USA
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8
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Carrasco Pro S, Hook H, Bray D, Berenzy D, Moyer D, Yin M, Labadorf AT, Tewhey R, Siggers T, Fuxman Bass JI. Widespread perturbation of ETS factor binding sites in cancer. Nat Commun 2023; 14:913. [PMID: 36808133 PMCID: PMC9938127 DOI: 10.1038/s41467-023-36535-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 02/03/2023] [Indexed: 02/19/2023] Open
Abstract
Although >90% of somatic mutations reside in non-coding regions, few have been reported as cancer drivers. To predict driver non-coding variants (NCVs), we present a transcription factor (TF)-aware burden test based on a model of coherent TF function in promoters. We apply this test to NCVs from the Pan-Cancer Analysis of Whole Genomes cohort and predict 2555 driver NCVs in the promoters of 813 genes across 20 cancer types. These genes are enriched in cancer-related gene ontologies, essential genes, and genes associated with cancer prognosis. We find that 765 candidate driver NCVs alter transcriptional activity, 510 lead to differential binding of TF-cofactor regulatory complexes, and that they primarily impact the binding of ETS factors. Finally, we show that different NCVs within a promoter often affect transcriptional activity through shared mechanisms. Our integrated computational and experimental approach shows that cancer NCVs are widespread and that ETS factors are commonly disrupted.
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Affiliation(s)
| | - Heather Hook
- Department of Biology, Boston University, Boston, MA, USA
| | - David Bray
- Bioinformatics Program, Boston University, Boston, MA, USA
| | | | - Devlin Moyer
- Bioinformatics Program, Boston University, Boston, MA, USA
| | - Meimei Yin
- Department of Biology, Boston University, Boston, MA, USA
| | - Adam Thomas Labadorf
- Bioinformatics Hub, Boston University, Boston, MA, USA
- Boston University School of Medicine, Department of Neurology, Boston, MA, USA
| | | | - Trevor Siggers
- Bioinformatics Program, Boston University, Boston, MA, USA.
- Department of Biology, Boston University, Boston, MA, USA.
- Biological Design Center, Boston University, Boston, MA, USA.
| | - Juan Ignacio Fuxman Bass
- Bioinformatics Program, Boston University, Boston, MA, USA.
- Department of Biology, Boston University, Boston, MA, USA.
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9
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Morova T, Ding Y, Huang CCF, Sar F, Schwarz T, Giambartolomei C, Baca S, Grishin D, Hach F, Gusev A, Freedman M, Pasaniuc B, Lack N. Optimized high-throughput screening of non-coding variants identified from genome-wide association studies. Nucleic Acids Res 2022; 51:e18. [PMID: 36546757 PMCID: PMC9943666 DOI: 10.1093/nar/gkac1198] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 11/19/2022] [Accepted: 12/06/2022] [Indexed: 12/24/2022] Open
Abstract
The vast majority of disease-associated single nucleotide polymorphisms (SNP) identified from genome-wide association studies (GWAS) are localized in non-coding regions. A significant fraction of these variants impact transcription factors binding to enhancer elements and alter gene expression. To functionally interrogate the activity of such variants we developed snpSTARRseq, a high-throughput experimental method that can interrogate the functional impact of hundreds to thousands of non-coding variants on enhancer activity. snpSTARRseq dramatically improves signal-to-noise by utilizing a novel sequencing and bioinformatic approach that increases both insert size and the number of variants tested per loci. Using this strategy, we interrogated known prostate cancer (PCa) risk-associated loci and demonstrated that 35% of them harbor SNPs that significantly altered enhancer activity. Combining these results with chromosomal looping data we could identify interacting genes and provide a mechanism of action for 20 PCa GWAS risk regions. When benchmarked to orthogonal methods, snpSTARRseq showed a strong correlation with in vivo experimental allelic-imbalance studies whereas there was no correlation with predictive in silico approaches. Overall, snpSTARRseq provides an integrated experimental and computational framework to functionally test non-coding genetic variants.
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Affiliation(s)
- Tunc Morova
- Vancouver Prostate Centre, Vancouver, BC V6H 3Z6, Canada
| | - Yi Ding
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | | | - Funda Sar
- Vancouver Prostate Centre, Vancouver, BC V6H 3Z6, Canada
| | - Tommer Schwarz
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Claudia Giambartolomei
- Central RNA Lab, Istituto Italiano di Tecnologia, Genova 16163, Italy,Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Sylvan C Baca
- Department of Medical Oncology, The Center for Functional Cancer Epigenetics, Dana Farber Cancer Institute, Boston, MA 02215, USA
| | - Dennis Grishin
- Department of Medical Oncology, The Center for Functional Cancer Epigenetics, Dana Farber Cancer Institute, Boston, MA 02215, USA
| | - Faraz Hach
- Vancouver Prostate Centre, Vancouver, BC V6H 3Z6, Canada,Department of Urologic Science, University of British Columbia, Vancouver, BC V5Z 1M9, Canada
| | - Alexander Gusev
- Department of Medical Oncology, The Center for Functional Cancer Epigenetics, Dana Farber Cancer Institute, Boston, MA 02215, USA,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Matthew L Freedman
- Department of Medical Oncology, The Center for Functional Cancer Epigenetics, Dana Farber Cancer Institute, Boston, MA 02215, USA,The Center for Cancer Genome Discovery, Dana Farber Cancer Institute, Boston, MA 02215, USA
| | - Bogdan Pasaniuc
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA,Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA,Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA,Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Nathan A Lack
- To whom correspondence should be addressed. Tel: +1 604 875 4411;
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10
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Castro-Mondragon JA, Aure M, Lingjærde O, Langerød A, Martens JWM, Børresen-Dale AL, Kristensen V, Mathelier A. Cis-regulatory mutations associate with transcriptional and post-transcriptional deregulation of gene regulatory programs in cancers. Nucleic Acids Res 2022; 50:12131-12148. [PMID: 36477895 PMCID: PMC9757053 DOI: 10.1093/nar/gkac1143] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 11/03/2022] [Accepted: 11/17/2022] [Indexed: 12/13/2022] Open
Abstract
Most cancer alterations occur in the noncoding portion of the human genome, where regulatory regions control gene expression. The discovery of noncoding mutations altering the cells' regulatory programs has been limited to few examples with high recurrence or high functional impact. Here, we show that transcription factor binding sites (TFBSs) have similar mutation loads to those in protein-coding exons. By combining cancer somatic mutations in TFBSs and expression data for protein-coding and miRNA genes, we evaluate the combined effects of transcriptional and post-transcriptional alterations on the regulatory programs in cancers. The analysis of seven TCGA cohorts culminates with the identification of protein-coding and miRNA genes linked to mutations at TFBSs that are associated with a cascading trans-effect deregulation on the cells' regulatory programs. Our analyses of cis-regulatory mutations associated with miRNAs recurrently predict 12 mature miRNAs (derived from 7 precursors) associated with the deregulation of their target gene networks. The predictions are enriched for cancer-associated protein-coding and miRNA genes and highlight cis-regulatory mutations associated with the dysregulation of key pathways associated with carcinogenesis. By combining transcriptional and post-transcriptional regulation of gene expression, our method predicts cis-regulatory mutations related to the dysregulation of key gene regulatory networks in cancer patients.
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Affiliation(s)
- Jaime A Castro-Mondragon
- Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo, 0318 Oslo, Norway
| | - Miriam Ragle Aure
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, 0310 Oslo, Norway,Department of Medical Genetics, Institute of Clinical Medicine, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Ole Christian Lingjærde
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, 0310 Oslo, Norway,Centre for Bioinformatics, Department of Informatics, University of Oslo, Gaustadalléen 23 B, N-0373 Oslo, Norway,KG Jebsen Centre for B-cell malignancies, Institute for Clinical Medicine, University of Oslo, Ullernchausseen 70, N-0372 Oslo, Norway
| | - Anita Langerød
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, 0310 Oslo, Norway
| | - John W M Martens
- Erasmus MC Cancer Institute and Cancer Genomics Netherlands, University Medical Center Rotterdam, Department of Medical Oncology, 3015GD Rotterdam, The Netherlands
| | - Anne-Lise Børresen-Dale
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, 0310 Oslo, Norway
| | - Vessela N Kristensen
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, 0310 Oslo, Norway,Department of Medical Genetics, Institute of Clinical Medicine, University of Oslo and Oslo University Hospital, Oslo, Norway
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11
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Newell F, Johansson PA, Wilmott JS, Nones K, Lakis V, Pritchard AL, Lo SN, Rawson RV, Kazakoff SH, Colebatch AJ, Koufariotis LT, Ferguson PM, Wood S, Leonard C, Law MH, Brooks KM, Broit N, Palmer JM, Couts KL, Vergara IA, Long GV, Barbour AP, Nieweg OE, Shivalingam B, Robinson WA, Stretch JR, Spillane AJ, Saw RP, Shannon KF, Thompson JF, Mann GJ, Pearson JV, Scolyer RA, Waddell N, Hayward NK. Comparative Genomics Provides Etiologic and Biological Insight into Melanoma Subtypes. Cancer Discov 2022; 12:2856-2879. [PMID: 36098958 PMCID: PMC9716259 DOI: 10.1158/2159-8290.cd-22-0603] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 08/01/2022] [Accepted: 09/02/2022] [Indexed: 01/12/2023]
Abstract
Melanoma is a cancer of melanocytes, with multiple subtypes based on body site location. Cutaneous melanoma is associated with skin exposed to ultraviolet radiation; uveal melanoma occurs in the eyes; mucosal melanoma occurs in internal mucous membranes; and acral melanoma occurs on the palms, soles, and nail beds. Here, we present the largest whole-genome sequencing study of melanoma to date, with 570 tumors profiled, as well as methylation and RNA sequencing for subsets of tumors. Uveal melanoma is genomically distinct from other melanoma subtypes, harboring the lowest tumor mutation burden and with significantly mutated genes in the G-protein signaling pathway. Most cutaneous, acral, and mucosal melanomas share alterations in components of the MAPK, PI3K, p53, p16, and telomere pathways. However, the mechanism by which these pathways are activated or inactivated varies between melanoma subtypes. Additionally, we identify potential novel germline predisposition genes for some of the less common melanoma subtypes. SIGNIFICANCE This is the largest whole-genome analysis of melanoma to date, comprehensively comparing the genomics of the four major melanoma subtypes. This study highlights both similarities and differences between the subtypes, providing insights into the etiology and biology of melanoma. This article is highlighted in the In This Issue feature, p. 2711.
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Affiliation(s)
- Felicity Newell
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.,Corresponding Authors: Felicity Newell, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, QLD 4006, Australia. Phone: 61-7-3845-3965; E-mail: ; Richard A. Scolyer, Melanoma Institute Australia, 40 Rockland Road, Wollstonecraft, Sydney, NSW 2065, Australia. Phone: 61-2-9515-7011; E-mail: ; and Nicola Waddell, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, QLD 4006, Australia. Phone: 61-7-3845-3538;
| | - Peter A. Johansson
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - James S. Wilmott
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia.,Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.,Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia
| | - Katia Nones
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Vanessa Lakis
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Antonia L. Pritchard
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.,Department of Genetics and Immunology, Division of Biomedical Science, University of the Highlands and Islands, Inverness, Scotland, United Kingdom
| | - Serigne N. Lo
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia
| | - Robert V. Rawson
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia.,Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.,Department of Tissue Pathology and Diagnostic Oncology, Royal Prince Alfred Hospital and NSW Health Pathology, Sydney, New South Wales, Australia
| | | | - Andrew J. Colebatch
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia.,Department of Tissue Pathology and Diagnostic Oncology, Royal Prince Alfred Hospital and NSW Health Pathology, Sydney, New South Wales, Australia
| | | | - Peter M. Ferguson
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia.,Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.,Department of Tissue Pathology and Diagnostic Oncology, Royal Prince Alfred Hospital and NSW Health Pathology, Sydney, New South Wales, Australia
| | - Scott Wood
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Conrad Leonard
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Matthew H. Law
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.,Faculty of Health, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Kelly M. Brooks
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Natasa Broit
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.,Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia.,Q-Gen Cell Therapeutics, Brisbane, Queensland, Australia
| | - Jane M. Palmer
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Kasey L. Couts
- Center for Rare Melanomas, University of Colorado Cancer Center, Aurora, Colorado
| | - Ismael A. Vergara
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia.,Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.,Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia
| | - Georgina V. Long
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia.,Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.,Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia.,Mater Hospital, North Sydney, New South Wales, Australia.,Royal North Shore Hospital, St Leonards, New South Wales, Australia
| | - Andrew P. Barbour
- Diamantina Institute, The University of Queensland, Brisbane, Queensland, Australia
| | - Omgo E. Nieweg
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia.,Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
| | - Brindha Shivalingam
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia.,Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.,Mater Hospital, North Sydney, New South Wales, Australia.,Department of Neurosurgery, Chris O'Brien Lifehouse, Camperdown, New South Wales, Australia.,Department of Neurosurgery, Royal Prince Alfred Hospital, Camperdown, New South Wales, Australia
| | - William A. Robinson
- Center for Rare Melanomas, University of Colorado Cancer Center, Aurora, Colorado
| | - Jonathan R. Stretch
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia.,Mater Hospital, North Sydney, New South Wales, Australia.,Department of Melanoma and Surgical Oncology, Royal Prince Alfred Hospital, Camperdown, New South Wales, Australia
| | - Andrew J. Spillane
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia.,Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.,Mater Hospital, North Sydney, New South Wales, Australia.,Royal North Shore Hospital, St Leonards, New South Wales, Australia
| | - Robyn P.M. Saw
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia.,Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.,Mater Hospital, North Sydney, New South Wales, Australia.,Department of Melanoma and Surgical Oncology, Royal Prince Alfred Hospital, Camperdown, New South Wales, Australia
| | - Kerwin F. Shannon
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia.,Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.,Department of Melanoma and Surgical Oncology, Royal Prince Alfred Hospital, Camperdown, New South Wales, Australia
| | - John F. Thompson
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia.,Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.,Mater Hospital, North Sydney, New South Wales, Australia.,Department of Melanoma and Surgical Oncology, Royal Prince Alfred Hospital, Camperdown, New South Wales, Australia
| | - Graham J. Mann
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia.,Centre for Cancer Research, Westmead Institute for Medical Research, The University of Sydney, Westmead, New South Wales, Australia.,John Curtin School of Medical Research, Australian National University, Canberra, Australian Capital Territory, Australia
| | - John V. Pearson
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Richard A. Scolyer
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia.,Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.,Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia.,Department of Tissue Pathology and Diagnostic Oncology, Royal Prince Alfred Hospital and NSW Health Pathology, Sydney, New South Wales, Australia.,Corresponding Authors: Felicity Newell, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, QLD 4006, Australia. Phone: 61-7-3845-3965; E-mail: ; Richard A. Scolyer, Melanoma Institute Australia, 40 Rockland Road, Wollstonecraft, Sydney, NSW 2065, Australia. Phone: 61-2-9515-7011; E-mail: ; and Nicola Waddell, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, QLD 4006, Australia. Phone: 61-7-3845-3538;
| | - Nicola Waddell
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.,Corresponding Authors: Felicity Newell, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, QLD 4006, Australia. Phone: 61-7-3845-3965; E-mail: ; Richard A. Scolyer, Melanoma Institute Australia, 40 Rockland Road, Wollstonecraft, Sydney, NSW 2065, Australia. Phone: 61-2-9515-7011; E-mail: ; and Nicola Waddell, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, QLD 4006, Australia. Phone: 61-7-3845-3538;
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12
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Sherman MA, Yaari AU, Priebe O, Dietlein F, Loh PR, Berger B. Genome-wide mapping of somatic mutation rates uncovers drivers of cancer. Nat Biotechnol 2022; 40:1634-1643. [PMID: 35726091 PMCID: PMC9646522 DOI: 10.1038/s41587-022-01353-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 05/10/2022] [Indexed: 01/12/2023]
Abstract
Identification of cancer driver mutations that confer a proliferative advantage is central to understanding cancer; however, searches have often been limited to protein-coding sequences and specific non-coding elements (for example, promoters) because of the challenge of modeling the highly variable somatic mutation rates observed across tumor genomes. Here we present Dig, a method to search for driver elements and mutations anywhere in the genome. We use deep neural networks to map cancer-specific mutation rates genome-wide at kilobase-scale resolution. These estimates are then refined to search for evidence of driver mutations under positive selection throughout the genome by comparing observed to expected mutation counts. We mapped mutation rates for 37 cancer types and applied these maps to identify putative drivers within intronic cryptic splice regions, 5' untranslated regions and infrequently mutated genes. Our high-resolution mutation rate maps, available for web-based exploration, are a resource to enable driver discovery genome-wide.
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Affiliation(s)
- Maxwell A Sherman
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Harvard-MIT Health Sciences and Technology Program, Cambridge, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Adam U Yaari
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- The Center for Brains, Minds and Machines of MIT and Harvard, Cambridge, MA, USA
| | - Oliver Priebe
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Physics, University of Pennsylvania, Philadelphia, PA, USA
| | - Felix Dietlein
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA
| | - Po-Ru Loh
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Bonnie Berger
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Harvard-MIT Health Sciences and Technology Program, Cambridge, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA, USA.
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13
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Kuksa PP, Greenfest-Allen E, Cifello J, Ionita M, Wang H, Nicaretta H, Cheng PL, Lee WP, Wang LS, Leung YY. Scalable approaches for functional analyses of whole-genome sequencing non-coding variants. Hum Mol Genet 2022; 31:R62-R72. [PMID: 35943817 PMCID: PMC9585666 DOI: 10.1093/hmg/ddac191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 08/04/2022] [Accepted: 08/08/2022] [Indexed: 11/23/2022] Open
Abstract
Non-coding genetic variants outside of protein-coding genome regions play an important role in genetic and epigenetic regulation. It has become increasingly important to understand their roles, as non-coding variants often make up the majority of top findings of genome-wide association studies (GWAS). In addition, the growing popularity of disease-specific whole-genome sequencing (WGS) efforts expands the library of and offers unique opportunities for investigating both common and rare non-coding variants, which are typically not detected in more limited GWAS approaches. However, the sheer size and breadth of WGS data introduce additional challenges to predicting functional impacts in terms of data analysis and interpretation. This review focuses on the recent approaches developed for efficient, at-scale annotation and prioritization of non-coding variants uncovered in WGS analyses. In particular, we review the latest scalable annotation tools, databases and functional genomic resources for interpreting the variant findings from WGS based on both experimental data and in silico predictive annotations. We also review machine learning-based predictive models for variant scoring and prioritization. We conclude with a discussion of future research directions which will enhance the data and tools necessary for the effective functional analyses of variants identified by WGS to improve our understanding of disease etiology.
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Affiliation(s)
- Pavel P Kuksa
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Emily Greenfest-Allen
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jeffrey Cifello
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Matei Ionita
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Hui Wang
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Heather Nicaretta
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Po-Liang Cheng
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Wan-Ping Lee
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Li-San Wang
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yuk Yee Leung
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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14
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Pudjihartono M, Perry JK, Print C, O'Sullivan JM, Schierding W. Interpretation of the role of germline and somatic non-coding mutations in cancer: expression and chromatin conformation informed analysis. Clin Epigenetics 2022; 14:120. [PMID: 36171609 PMCID: PMC9520844 DOI: 10.1186/s13148-022-01342-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 09/21/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND There has been extensive scrutiny of cancer driving mutations within the exome (especially amino acid altering mutations) as these are more likely to have a clear impact on protein functions, and thus on cell biology. However, this has come at the neglect of systematic identification of regulatory (non-coding) variants, which have recently been identified as putative somatic drivers and key germline risk factors for cancer development. Comprehensive understanding of non-coding mutations requires understanding their role in the disruption of regulatory elements, which then disrupt key biological functions such as gene expression. MAIN BODY We describe how advancements in sequencing technologies have led to the identification of a large number of non-coding mutations with uncharacterized biological significance. We summarize the strategies that have been developed to interpret and prioritize the biological mechanisms impacted by non-coding mutations, focusing on recent annotation of cancer non-coding variants utilizing chromatin states, eQTLs, and chromatin conformation data. CONCLUSION We believe that a better understanding of how to apply different regulatory data types into the study of non-coding mutations will enhance the discovery of novel mechanisms driving cancer.
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Affiliation(s)
| | - Jo K Perry
- Liggins Institute, The University of Auckland, Auckland, New Zealand
- The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand
| | - Cris Print
- The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand
- Department of Molecular Medicine and Pathology, School of Medical Sciences, University of Auckland, Auckland, 1142, New Zealand
| | - Justin M O'Sullivan
- Liggins Institute, The University of Auckland, Auckland, New Zealand
- The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand
- Australian Parkinson's Mission, Garvan Institute of Medical Research, Sydney, NSW, Australia
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
| | - William Schierding
- Liggins Institute, The University of Auckland, Auckland, New Zealand.
- The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand.
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15
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Pich O, Reyes-Salazar I, Gonzalez-Perez A, Lopez-Bigas N. Discovering the drivers of clonal hematopoiesis. Nat Commun 2022; 13:4267. [PMID: 35871184 PMCID: PMC9308779 DOI: 10.1038/s41467-022-31878-0] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 07/06/2022] [Indexed: 12/28/2022] Open
Abstract
Mutations in genes that confer a selective advantage to hematopoietic stem cells (HSCs) drive clonal hematopoiesis (CH). While some CH drivers have been identified, the compendium of all genes able to drive CH upon mutations in HSCs remains incomplete. Exploiting signals of positive selection in blood somatic mutations may be an effective way to identify CH driver genes, analogously to cancer. Using the tumor sample in blood/tumor pairs as reference, we identify blood somatic mutations across more than 12,000 donors from two large cancer genomics cohorts. The application of IntOGen, a driver discovery pipeline, to both cohorts, and more than 24,000 targeted sequenced samples yields a list of close to 70 genes with signals of positive selection in CH, available at http://www.intogen.org/ch . This approach recovers known CH genes, and discovers other candidates.
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Affiliation(s)
- Oriol Pich
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac, 10, 08028, Barcelona, Spain
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Iker Reyes-Salazar
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac, 10, 08028, Barcelona, Spain
| | - Abel Gonzalez-Perez
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac, 10, 08028, Barcelona, Spain.
- Research Program on Biomedical Informatics, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain.
| | - Nuria Lopez-Bigas
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac, 10, 08028, Barcelona, Spain.
- Research Program on Biomedical Informatics, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain.
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain.
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16
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Li Q, Ren Z, Cao K, Li MM, Wang K, Zhou Y. CancerVar: An artificial intelligence-empowered platform for clinical interpretation of somatic mutations in cancer. SCIENCE ADVANCES 2022; 8:eabj1624. [PMID: 35544644 PMCID: PMC9075800 DOI: 10.1126/sciadv.abj1624] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Accepted: 03/21/2022] [Indexed: 05/12/2023]
Abstract
Several knowledgebases are manually curated to support clinical interpretations of thousands of hotspot somatic mutations in cancer. However, discrepancies or even conflicting interpretations are observed among these databases. Furthermore, many previously undocumented mutations may have clinical or functional impacts on cancer but are not systematically interpreted by existing knowledgebases. To address these challenges, we developed CancerVar to facilitate automated and standardized interpretations for 13 million somatic mutations based on the AMP/ASCO/CAP 2017 guidelines. We further introduced a deep learning framework to predict oncogenicity for these variants using both functional and clinical features. CancerVar achieved satisfactory performance when compared to several independent knowledgebases and, using clinically curated datasets, demonstrated practical utility in classifying somatic variants. In summary, by integrating clinical guidelines with a deep learning framework, CancerVar facilitates clinical interpretation of somatic variants, reduces manual work, improves consistency in variant classification, and promotes implementation of the guidelines.
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Affiliation(s)
- Quan Li
- Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G2C1, Canada
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Zilin Ren
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Kajia Cao
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Marilyn M. Li
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kai Wang
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yunyun Zhou
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
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17
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Dietlein F, Wang AB, Fagre C, Tang A, Besselink NJM, Cuppen E, Li C, Sunyaev SR, Neal JT, Van Allen EM. Genome-wide analysis of somatic noncoding mutation patterns in cancer. Science 2022; 376:eabg5601. [PMID: 35389777 PMCID: PMC9092060 DOI: 10.1126/science.abg5601] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
We established a genome-wide compendium of somatic mutation events in 3949 whole cancer genomes representing 19 tumor types. Protein-coding events captured well-established drivers. Noncoding events near tissue-specific genes, such as ALB in the liver or KLK3 in the prostate, characterized localized passenger mutation patterns and may reflect tumor-cell-of-origin imprinting. Noncoding events in regulatory promoter and enhancer regions frequently involved cancer-relevant genes such as BCL6, FGFR2, RAD51B, SMC6, TERT, and XBP1 and represent possible drivers. Unlike most noncoding regulatory events, XBP1 mutations primarily accumulated outside the gene's promoter, and we validated their effect on gene expression using CRISPR-interference screening and luciferase reporter assays. Broadly, our study provides a blueprint for capturing mutation events across the entire genome to guide advances in biological discovery, therapies, and diagnostics.
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Affiliation(s)
- Felix Dietlein
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, USA.,Cancer Program, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA.,Corresponding author. (E.M.V.A.); (F.D.)
| | - Alex B. Wang
- Cancer Program, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Christian Fagre
- Cancer Program, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Anran Tang
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, USA.,Cancer Program, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Nicolle J. M. Besselink
- Center for Molecular Medicine and Oncode Institute, University Medical Center Utrecht, 3584 CX Utrecht, Netherlands
| | - Edwin Cuppen
- Center for Molecular Medicine and Oncode Institute, University Medical Center Utrecht, 3584 CX Utrecht, Netherlands.,Hartwig Medical Foundation, 1098 XH Amsterdam, Netherlands
| | - Chunliang Li
- Department of Tumor Cell Biology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
| | - Shamil R. Sunyaev
- Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - James T. Neal
- Cancer Program, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Eliezer M. Van Allen
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, USA.,Cancer Program, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA.,Corresponding author. (E.M.V.A.); (F.D.)
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18
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Kogure Y, Kameda T, Koya J, Yoshimitsu M, Nosaka K, Yasunaga JI, Imaizumi Y, Watanabe M, Saito Y, Ito Y, McClure MB, Tabata M, Shingaki S, Yoshifuji K, Chiba K, Okada A, Kakiuchi N, Nannya Y, Kamiunten A, Tahira Y, Akizuki K, Sekine M, Shide K, Hidaka T, Kubuki Y, Kitanaka A, Hidaka M, Nakano N, Utsunomiya A, Sica RA, Acuna-Villaorduna A, Janakiram M, Shah U, Ramos JC, Shibata T, Takeuchi K, Takaori-Kondo A, Miyazaki Y, Matsuoka M, Ishitsuka K, Shiraishi Y, Miyano S, Ogawa S, Ye BH, Shimoda K, Kataoka K. Whole-genome landscape of adult T-cell leukemia/lymphoma. Blood 2022; 139:967-982. [PMID: 34695199 PMCID: PMC8854674 DOI: 10.1182/blood.2021013568] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 10/11/2021] [Indexed: 11/20/2022] Open
Abstract
Adult T-cell leukemia/lymphoma (ATL) is an aggressive neoplasm immunophenotypically resembling regulatory T cells, associated with human T-cell leukemia virus type-1. Here, we performed whole-genome sequencing (WGS) of 150 ATL cases to reveal the overarching landscape of genetic alterations in ATL. We discovered frequent (33%) loss-of-function alterations preferentially targeting the CIC long isoform, which were overlooked by previous exome-centric studies of various cancer types. Long but not short isoform-specific inactivation of Cic selectively increased CD4+CD25+Foxp3+ T cells in vivo. We also found recurrent (13%) 3'-truncations of REL, which induce transcriptional upregulation and generate gain-of-function proteins. More importantly, REL truncations are also common in diffuse large B-cell lymphoma, especially in germinal center B-cell-like subtype (12%). In the non-coding genome, we identified recurrent mutations in regulatory elements, particularly splice sites, of several driver genes. In addition, we characterized the different mutational processes operative in clustered hypermutation sites within and outside immunoglobulin/T-cell receptor genes and identified the mutational enrichment at the binding sites of host and viral transcription factors, suggesting their activities in ATL. By combining the analyses for coding and noncoding mutations, structural variations, and copy number alterations, we discovered 56 recurrently altered driver genes, including 11 novel ones. Finally, ATL cases were classified into 2 molecular groups with distinct clinical and genetic characteristics based on the driver alteration profile. Our findings not only help to improve diagnostic and therapeutic strategies in ATL, but also provide insights into T-cell biology and have implications for genome-wide cancer driver discovery.
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Affiliation(s)
- Yasunori Kogure
- Division of Molecular Oncology, National Cancer Center Research Institute, Tokyo, Japan
| | - Takuro Kameda
- Division of Hematology, Diabetes, and Endocrinology, Department of Internal Medicine, Faculty of Medicine, University of Miyazaki, Miyazaki, Japan
| | - Junji Koya
- Division of Molecular Oncology, National Cancer Center Research Institute, Tokyo, Japan
| | - Makoto Yoshimitsu
- Department of Hematology and Rheumatology, Kagoshima University Hospital, Kagoshima, Japan
| | - Kisato Nosaka
- Department of Hematology, Rheumatology, and Infectious Disease, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | - Jun-Ichirou Yasunaga
- Department of Hematology, Rheumatology, and Infectious Disease, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | - Yoshitaka Imaizumi
- Department of Hematology, Atomic Bomb Disease and Hibakusha Medicine Unit, Atomic Bomb Disease Institute, Nagasaki University, Nagasaki, Japan
| | - Mizuki Watanabe
- Department of Hematology and Oncology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yuki Saito
- Division of Molecular Oncology, National Cancer Center Research Institute, Tokyo, Japan
- Department of Gastroenterology, Keio University School of Medicine, Tokyo, Japan
| | - Yuta Ito
- Division of Molecular Oncology, National Cancer Center Research Institute, Tokyo, Japan
- Division of Clinical Oncology and Hematology, Department of Internal Medicine, The Jikei University School of Medicine, Tokyo, Japan
| | - Marni B McClure
- Division of Molecular Oncology, National Cancer Center Research Institute, Tokyo, Japan
| | - Mariko Tabata
- Division of Molecular Oncology, National Cancer Center Research Institute, Tokyo, Japan
- Department of Urology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Sumito Shingaki
- Division of Molecular Oncology, National Cancer Center Research Institute, Tokyo, Japan
| | - Kota Yoshifuji
- Division of Molecular Oncology, National Cancer Center Research Institute, Tokyo, Japan
- Department of Hematology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Kenichi Chiba
- Division of Genome Analysis Platform Development, National Cancer Center Research Institute, Tokyo, Japan
| | - Ai Okada
- Division of Genome Analysis Platform Development, National Cancer Center Research Institute, Tokyo, Japan
| | - Nobuyuki Kakiuchi
- Department of Pathology and Tumor Biology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yasuhito Nannya
- Department of Pathology and Tumor Biology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Ayako Kamiunten
- Division of Hematology, Diabetes, and Endocrinology, Department of Internal Medicine, Faculty of Medicine, University of Miyazaki, Miyazaki, Japan
| | - Yuki Tahira
- Division of Hematology, Diabetes, and Endocrinology, Department of Internal Medicine, Faculty of Medicine, University of Miyazaki, Miyazaki, Japan
| | - Keiichi Akizuki
- Division of Hematology, Diabetes, and Endocrinology, Department of Internal Medicine, Faculty of Medicine, University of Miyazaki, Miyazaki, Japan
| | - Masaaki Sekine
- Division of Hematology, Diabetes, and Endocrinology, Department of Internal Medicine, Faculty of Medicine, University of Miyazaki, Miyazaki, Japan
| | - Kotaro Shide
- Division of Hematology, Diabetes, and Endocrinology, Department of Internal Medicine, Faculty of Medicine, University of Miyazaki, Miyazaki, Japan
| | - Tomonori Hidaka
- Division of Hematology, Diabetes, and Endocrinology, Department of Internal Medicine, Faculty of Medicine, University of Miyazaki, Miyazaki, Japan
| | - Yoko Kubuki
- Division of Hematology, Diabetes, and Endocrinology, Department of Internal Medicine, Faculty of Medicine, University of Miyazaki, Miyazaki, Japan
| | - Akira Kitanaka
- Department of Laboratory Medicine, Kawasaki Medical School, Kurashiki, Japan
| | - Michihiro Hidaka
- Department of Hematology, National Hospital Organization Kumamoto Medical Center, Kumamoto, Japan
| | - Nobuaki Nakano
- Department of Hematology, Imamura General Hospital, Kagoshima, Japan
| | - Atae Utsunomiya
- Department of Hematology, Imamura General Hospital, Kagoshima, Japan
| | - R Alejandro Sica
- Department of Oncology, Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, NY
| | - Ana Acuna-Villaorduna
- Department of Oncology, Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, NY
| | - Murali Janakiram
- Department of Oncology, Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, NY
| | - Urvi Shah
- Department of Oncology, Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, NY
| | - Juan Carlos Ramos
- Division of Hematology/Oncology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL
| | - Tatsuhiro Shibata
- Division of Cancer Genomics, National Cancer Center Research Institute, Tokyo, Japan
- Laboratory of Molecular Medicine, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Kengo Takeuchi
- Division of Pathology, Cancer Institute, Japanese Foundation for Cancer Research, Tokyo, Japan
- Department of Pathology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
- Pathology Project for Molecular Targets, Cancer Institute, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Akifumi Takaori-Kondo
- Department of Hematology and Oncology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yasushi Miyazaki
- Department of Hematology, Atomic Bomb Disease and Hibakusha Medicine Unit, Atomic Bomb Disease Institute, Nagasaki University, Nagasaki, Japan
| | - Masao Matsuoka
- Department of Hematology, Rheumatology, and Infectious Disease, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | - Kenji Ishitsuka
- Department of Hematology and Rheumatology, Kagoshima University Hospital, Kagoshima, Japan
| | - Yuichi Shiraishi
- Division of Genome Analysis Platform Development, National Cancer Center Research Institute, Tokyo, Japan
| | - Satoru Miyano
- M&D Data Science Center, Tokyo Medical and Dental University, Tokyo, Japan
| | - Seishi Ogawa
- Department of Pathology and Tumor Biology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - B Hilda Ye
- Department of Cell Biology, Albert Einstein College of Medicine, New York, NY; and
| | - Kazuya Shimoda
- Division of Hematology, Diabetes, and Endocrinology, Department of Internal Medicine, Faculty of Medicine, University of Miyazaki, Miyazaki, Japan
| | - Keisuke Kataoka
- Division of Molecular Oncology, National Cancer Center Research Institute, Tokyo, Japan
- Division of Hematology, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
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19
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Wong JKL, Aichmüller C, Schulze M, Hlevnjak M, Elgaafary S, Lichter P, Zapatka M. Association of mutation signature effectuating processes with mutation hotspots in driver genes and non-coding regions. Nat Commun 2022; 13:178. [PMID: 35013316 PMCID: PMC8748499 DOI: 10.1038/s41467-021-27792-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Accepted: 12/09/2021] [Indexed: 02/06/2023] Open
Abstract
Cancer driving mutations are difficult to identify especially in the non-coding part of the genome. Here, we present sigDriver, an algorithm dedicated to call driver mutations. Using 3813 whole-genome sequenced tumors from International Cancer Genome Consortium, The Cancer Genome Atlas Program, and a childhood pan-cancer cohort, we employ mutational signatures based on single-base substitution in the context of tri- and penta-nucleotide motifs for hotspot discovery. Knowledge-based annotations on mutational hotspots reveal enrichment in coding regions and regulatory elements for 6 mutational signatures, including APOBEC and somatic hypermutation signatures. APOBEC activity is associated with 32 hotspots of which 11 are known and 11 are putative regulatory drivers. Somatic single nucleotide variants clusters detected at hypermutation-associated hotspots are distinct from translocation or gene amplifications. Patients carrying APOBEC induced PIK3CA driver mutations show lower occurrence of signature SBS39. In summary, sigDriver uncovers mutational processes associated with known and putative tumor drivers and hotspots particularly in the non-coding regions of the genome.
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Affiliation(s)
- John K L Wong
- Division of Molecular Genetics and German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.
| | - Christian Aichmüller
- Division of Molecular Genetics and German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Markus Schulze
- Computational Oncology Group, Molecular Precision Oncology Program, National Center for Tumor Diseases (NCT) and DKFZ, Heidelberg, Germany
| | - Mario Hlevnjak
- Computational Oncology Group, Molecular Precision Oncology Program, National Center for Tumor Diseases (NCT) and DKFZ, Heidelberg, Germany
| | - Shaymaa Elgaafary
- Gynecologic Oncology, National Center for Tumor Diseases (NCT) and University of Heidelberg, Heidelberg, Germany
- Molecular Precision Oncology Program at the National Center for Tumor Diseases (NCT) and DKFZ, Heidelberg, Germany
| | - Peter Lichter
- Division of Molecular Genetics and German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
- Molecular Precision Oncology Program at the National Center for Tumor Diseases (NCT) and DKFZ, Heidelberg, Germany
| | - Marc Zapatka
- Division of Molecular Genetics and German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.
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20
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Jung S, Lee JL, Kim TW, Lee J, Yoon YS, Lee KY, Song KH, Yu CS, Cho YB. Molecular Characterization of Dysplasia-Initiated Colorectal Cancer With Assessing Matched Tumor and Dysplasia Samples. Ann Coloproctol 2021:ac.2021.00290.0041. [PMID: 34788527 PMCID: PMC8898627 DOI: 10.3393/ac.2021.00290.0041] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 07/20/2021] [Indexed: 10/26/2022] Open
Abstract
Purpose Ulcerative colitis (UC) is known to have an association with the increased risk of colorectal cancer (CRC), and UC-associated CRC does not follow the typical progress pattern of adenoma-carcinoma. The aim of this study is to investigate molecular characteristics of UC-associated CRC and further our understanding of the association between UC and CRC. Methods From 5 patients with UC-associated CRC, matched normal, dysplasia, and tumor specimens were obtained from formalin-fixed paraffin-embedded (FFPE) samples for analysis. Genomic DNA was extracted and whole exome sequencing was conducted to identify somatic variations in dysplasia and tumor samples. Statistical analysis was performed to identify somatic variations with significantly higher frequencies in dysplasia-initiated tumors, and their relevant functions were investigated. Results Total of 104 tumor mutation genes were identified with higher mutation frequencies in dysplasia-initiated tumors. Four of the 5 dysplasia-initiated tumors (80.0%) have TP53 mutations with frequent stop-gain mutations that were originated from matched dysplasia. APC and KRAS are known to be frequently mutated in general CRC, while none of the 5 patients have APC or KRAS mutation in their dysplasia and tumor samples. Glycoproteins including mucins were also frequently mutated in dysplasia-initiated tumors. Conclusion UC-associated CRC tumors have distinct mutational characteristics compared to typical adenoma-carcinoma tumors and may have different cancer-driving molecular mechanisms that are initiated from earlier dysplasia status.
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Affiliation(s)
- Sungwon Jung
- Department of Genome Medicine and Science, Gachon University College of Medicine, Incheon, Korea.,Gachon Institute of Genome Medicine and Science, Gachon University Gil Medical Center, Incheon, Korea
| | - Jong Lyul Lee
- Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Tae Won Kim
- Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Korea
| | - Jongmin Lee
- Department of Genome Medicine and Science, Gachon University College of Medicine, Incheon, Korea
| | - Yong Sik Yoon
- Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Kil Yeon Lee
- Department of Surgery, Kyung Hee University College of Medicine, Seoul, Korea
| | - Ki-Hwan Song
- Department of Surgery, Koo Hospital, Daegu, Korea
| | - Chang Sik Yu
- Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Yong Beom Cho
- Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Korea.,Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
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21
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Kan Y, Jiang L, Guo Y, Tang J, Guo F. Two-stage-vote ensemble framework based on integration of mutation data and gene interaction network for uncovering driver genes. Brief Bioinform 2021; 23:6426028. [PMID: 34791034 DOI: 10.1093/bib/bbab429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Revised: 08/30/2021] [Accepted: 09/18/2021] [Indexed: 11/14/2022] Open
Abstract
Identifying driver genes, exactly from massive genes with mutations, promotes accurate diagnosis and treatment of cancer. In recent years, a lot of works about uncovering driver genes based on integration of mutation data and gene interaction networks is gaining more attention. However, it is in suspense if it is more effective for prioritizing driver genes when integrating various types of mutation information (frequency and functional impact) and gene networks. Hence, we build a two-stage-vote ensemble framework based on somatic mutations and mutual interactions. Specifically, we first represent and combine various kinds of mutation information, which are propagated through networks by an improved iterative framework. The first vote is conducted on iteration results by voting methods, and the second vote is performed to get ensemble results of the first poll for the final driver gene list. Compared with four excellent previous approaches, our method has better performance in identifying driver genes on $33$ types of cancer from The Cancer Genome Atlas. Meanwhile, we also conduct a comparative analysis about two kinds of mutation information, five gene interaction networks and four voting strategies. Our framework offers a new view for data integration and promotes more latent cancer genes to be admitted.
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Affiliation(s)
- Yingxin Kan
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Limin Jiang
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China.,Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yan Guo
- Comprehensive cancer center, Department of Internal Medicine, University of New Mexico, Albuquerque, U.S
| | - Jijun Tang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,School of Computational Science and Engineering, University of South Carolina, Columbia, U.S
| | - Fei Guo
- School of Computer Science and Engineering, Central South University, Changsha, China
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22
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Montazeri H, Coto-Llerena M, Bianco G, Zangene E, Taha-Mehlitz S, Paradiso V, Srivatsa S, de Weck A, Roma G, Lanzafame M, Bolli M, Beerenwinkel N, von Flüe M, Terracciano L, Piscuoglio S, Ng CKY. Systematic identification of novel cancer genes through analysis of deep shRNA perturbation screens. Nucleic Acids Res 2021; 49:8488-8504. [PMID: 34313788 PMCID: PMC8421231 DOI: 10.1093/nar/gkab627] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Revised: 07/07/2021] [Accepted: 07/13/2021] [Indexed: 11/30/2022] Open
Abstract
Systematic perturbation screens provide comprehensive resources for the elucidation of cancer driver genes. The perturbation of many genes in relatively few cell lines in such functional screens necessitates the development of specialized computational tools with sufficient statistical power. Here we developed APSiC (Analysis of Perturbation Screens for identifying novel Cancer genes) to identify genetic drivers and effectors in perturbation screens even with few samples. Applying APSiC to the shRNA screen Project DRIVE, APSiC identified well-known and novel putative mutational and amplified cancer genes across all cancer types and in specific cancer types. Additionally, APSiC discovered tumor-promoting and tumor-suppressive effectors, respectively, for individual cancer types, including genes involved in cell cycle control, Wnt/β-catenin and hippo signalling pathways. We functionally demonstrated that LRRC4B, a putative novel tumor-suppressive effector, suppresses proliferation by delaying cell cycle and modulates apoptosis in breast cancer. We demonstrate APSiC is a robust statistical framework for discovery of novel cancer genes through analysis of large-scale perturbation screens. The analysis of DRIVE using APSiC is provided as a web portal and represents a valuable resource for the discovery of novel cancer genes.
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Affiliation(s)
- Hesam Montazeri
- Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
- Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
| | - Mairene Coto-Llerena
- Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
- Visceral Surgery and Precision Medicine Research laboratory, Department of Biomedicine, University of Basel, Basel, Switzerland
| | - Gaia Bianco
- Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
- Visceral Surgery and Precision Medicine Research laboratory, Department of Biomedicine, University of Basel, Basel, Switzerland
| | - Ehsan Zangene
- Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Stephanie Taha-Mehlitz
- Visceral Surgery and Precision Medicine Research laboratory, Department of Biomedicine, University of Basel, Basel, Switzerland
| | - Viola Paradiso
- Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
| | - Sumana Srivatsa
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Antoine de Weck
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, Basel, Switzerland
| | - Guglielmo Roma
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, Basel, Switzerland
| | - Manuela Lanzafame
- Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
| | - Martin Bolli
- Clarunis, Department of Visceral Surgery, University Centre for Gastrointestinal and Liver Diseases, St. Clara Hospital and University Hospital Basel, Switzerland
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Markus von Flüe
- Clarunis, Department of Visceral Surgery, University Centre for Gastrointestinal and Liver Diseases, St. Clara Hospital and University Hospital Basel, Switzerland
| | - Luigi M Terracciano
- Department of Pathology, Humanitas Clinical and Research Center, IRCCS, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
| | - Salvatore Piscuoglio
- Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
- Visceral Surgery and Precision Medicine Research laboratory, Department of Biomedicine, University of Basel, Basel, Switzerland
| | - Charlotte K Y Ng
- Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
- Department for BioMedical Research, University of Bern, Bern, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
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23
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Giunta S. Decoding human cancer with whole genome sequencing: a review of PCAWG Project studies published in February 2020. Cancer Metastasis Rev 2021; 40:909-924. [PMID: 34097189 PMCID: PMC8180541 DOI: 10.1007/s10555-021-09969-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 04/21/2021] [Indexed: 12/15/2022]
Abstract
Cancer is underlined by genetic changes. In an unprecedented international effort, the Pan-Cancer Analysis of Whole Genomes (PCAWG) of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) sequenced the tumors of over two thousand five hundred patients across 38 different cancer types, as well as the corresponding healthy tissue, with the aim of identifying genome-wide mutations exclusively found in cancer and uncovering new genetic changes that drive tumor formation. What set this project apart from earlier efforts is the use of whole genome sequencing (WGS) that enabled to explore alterations beyond the coding DNA, into cancer's non-coding genome. WGS of the entire cohort allowed to tease apart driving mutations that initiate and support carcinogenesis from passenger mutations that do not play an overt role in the disease. At least one causative mutation was found in 95% of all cancers, with many tumors showing an average of 5 driver mutations. The PCAWG Project also assessed the transcriptional output altered in cancer and rebuilt the evolutionary history of each tumor showing that initial driver mutations can occur years if not decades prior to a diagnosis. Here, I provide a concise review of the Pan-Cancer Project papers published on February 2020, along with key computational tools and the digital framework generated as part of the project. This represents an historic effort by hundreds of international collaborators, which provides a comprehensive understanding of cancer genetics, with publicly available data and resources representing a treasure trove of information to advance cancer research for years to come.
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Affiliation(s)
- Simona Giunta
- Laboratory of Genome Evolution, Department of Biology & Biotechnology "Charles Darwin", University of Rome Sapienza, Rome, Italy.
- The Rockefeller University, 1230 York Avenue, New York, NY, USA.
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24
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Abstract
Tumour formation involves random mutagenic events and positive evolutionary selection acting on a subset of such events, referred to as driver mutations. A decade of careful surveying of tumour DNA using exome-based analyses has revealed a multitude of protein-coding somatic driver mutations, some of which are clinically actionable. Today, a transition towards whole-genome analysis is well under way, technically enabling the discovery of potential driver mutations occurring outside protein-coding sequences. Mutations are abundant in this vast non-coding space, which is more than 50 times larger than the coding exome, but reliable identification of selection signals in non-coding DNA remains a challenge. In this Review, we discuss recent findings in the field, where the emerging landscape is one in which non-coding driver mutations appear to be relatively infrequent. Nevertheless, we highlight several notable discoveries. We consider possible reasons for the relative absence of non-coding driver events, as well as the difficulties associated with detecting signals of positive selection in non-coding DNA.
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Affiliation(s)
- Kerryn Elliott
- Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Erik Larsson
- Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden.
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25
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Abstract
Pancreatic cancer is a genetic disease, and the recurrent genetic alterations characteristic of pancreatic cancer indicate the cellular processes that are targeted for malignant transformation. In addition to somatic alterations in the most common driver genes (KRAS, CDKN2A, TP53 and SMAD4), large-scale studies have revealed major roles for genetic alterations of the SWI/SNF and COMPASS complexes, copy number alterations in GATA6 and MYC that partially define phenotypes of pancreatic cancer, and the role(s) of polyploidy and chromothripsis as factors contributing to pancreatic cancer biology and progression. Germline variants that increase the risk of pancreatic cancer continue to be discovered along with a greater appreciation of the features of pancreatic cancers with mismatch repair deficiencies and homologous recombination deficiencies that confer sensitivity to therapeutic targeting. Wild-type KRAS pancreatic cancers, some of which are driven by alternative oncogenic events affecting NRG1 or NTRK1 - for which targeted therapies exist - further underscore that pancreatic cancer is formally entering the era of precision medicine. Given the vast developments within this field, here we review the wide-ranging and most current information related to pancreatic cancer genomics with the goal of integrating this information into a unifying description of the life history of pancreatic cancer.
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26
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Daga S, Fallerini C, Baldassarri M, Fava F, Valentino F, Doddato G, Benetti E, Furini S, Giliberti A, Tita R, Amitrano S, Bruttini M, Meloni I, Pinto AM, Raimondi F, Stella A, Biscarini F, Picchiotti N, Gori M, Pinoli P, Ceri S, Sanarico M, Crawley FP, Birolo G, Renieri A, Mari F, Frullanti E. Employing a systematic approach to biobanking and analyzing clinical and genetic data for advancing COVID-19 research. Eur J Hum Genet 2021; 29:745-759. [PMID: 33456056 PMCID: PMC7811682 DOI: 10.1038/s41431-020-00793-7] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Revised: 10/27/2020] [Accepted: 11/25/2020] [Indexed: 02/07/2023] Open
Abstract
Within the GEN-COVID Multicenter Study, biospecimens from more than 1000 SARS-CoV-2 positive individuals have thus far been collected in the GEN-COVID Biobank (GCB). Sample types include whole blood, plasma, serum, leukocytes, and DNA. The GCB links samples to detailed clinical data available in the GEN-COVID Patient Registry (GCPR). It includes hospitalized patients (74.25%), broken down into intubated, treated by CPAP-biPAP, treated with O2 supplementation, and without respiratory support (9.5%, 18.4%, 31.55% and 14.8, respectively); and non-hospitalized subjects (25.75%), either pauci- or asymptomatic. More than 150 clinical patient-level data fields have been collected and binarized for further statistics according to the organs/systems primarily affected by COVID-19: heart, liver, pancreas, kidney, chemosensors, innate or adaptive immunity, and clotting system. Hierarchical clustering analysis identified five main clinical categories: (1) severe multisystemic failure with either thromboembolic or pancreatic variant; (2) cytokine storm type, either severe with liver involvement or moderate; (3) moderate heart type, either with or without liver damage; (4) moderate multisystemic involvement, either with or without liver damage; (5) mild, either with or without hyposmia. GCB and GCPR are further linked to the GCGDR, which includes data from whole-exome sequencing and high-density SNP genotyping. The data are available for sharing through the Network for Italian Genomes, found within the COVID-19 dedicated section. The study objective is to systematize this comprehensive data collection and begin identifying multi-organ involvement in COVID-19, defining genetic parameters for infection susceptibility within the population, and mapping genetically COVID-19 severity and clinical complexity among patients.
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Affiliation(s)
- Sergio Daga
- Medical Genetics, University of Siena, Siena, Italy
- Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy
| | - Chiara Fallerini
- Medical Genetics, University of Siena, Siena, Italy
- Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy
| | - Margherita Baldassarri
- Medical Genetics, University of Siena, Siena, Italy
- Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy
| | - Francesca Fava
- Medical Genetics, University of Siena, Siena, Italy
- Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy
- Genetica Medica, Azienda Ospedaliero-Universitaria Senese, Siena, Italy
| | - Floriana Valentino
- Medical Genetics, University of Siena, Siena, Italy
- Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy
| | - Gabriella Doddato
- Medical Genetics, University of Siena, Siena, Italy
- Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy
| | - Elisa Benetti
- Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy
| | - Simone Furini
- Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy
| | - Annarita Giliberti
- Medical Genetics, University of Siena, Siena, Italy
- Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy
| | - Rossella Tita
- Genetica Medica, Azienda Ospedaliero-Universitaria Senese, Siena, Italy
| | - Sara Amitrano
- Genetica Medica, Azienda Ospedaliero-Universitaria Senese, Siena, Italy
| | - Mirella Bruttini
- Medical Genetics, University of Siena, Siena, Italy
- Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy
- Genetica Medica, Azienda Ospedaliero-Universitaria Senese, Siena, Italy
| | - Ilaria Meloni
- Medical Genetics, University of Siena, Siena, Italy
- Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy
| | - Anna Maria Pinto
- Genetica Medica, Azienda Ospedaliero-Universitaria Senese, Siena, Italy
| | | | - Alessandra Stella
- CNR-Consiglio Nazionale delle Ricerche, Istituto di Biologia e Biotecnologia Agraria (IBBA), Milano, Italy
| | - Filippo Biscarini
- CNR-Consiglio Nazionale delle Ricerche, Istituto di Biologia e Biotecnologia Agraria (IBBA), Milano, Italy
- ERCEA (European Research Council Executive Agency), Bruxelles, Belgium
| | - Nicola Picchiotti
- University of Siena, DIISM- SAILAB, Siena, Italy
- Department of Mathematics, University of Pavia, Pavia, Italy
| | - Marco Gori
- University of Siena, DIISM- SAILAB, Siena, Italy
- Université Côte d'Azur, Inria, CNRS, I3S, Maasai, Italy
| | - Pietro Pinoli
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milano, Italy
| | - Stefano Ceri
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milano, Italy
| | | | - Francis P Crawley
- Good Clinical Practice Alliance-Europe (GCPA) and Strategic Initiative for Developing Capacity in Ethical Review-Europe (SIDCER), Brussels, Belgium
- ERCEA (European Research Council Executive Agency), Bruxelles, Belgium
| | - Giovanni Birolo
- Department of Medical Sciences, University of Turin, Turin, Italy
| | - Alessandra Renieri
- Medical Genetics, University of Siena, Siena, Italy.
- Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy.
- Genetica Medica, Azienda Ospedaliero-Universitaria Senese, Siena, Italy.
| | - Francesca Mari
- Medical Genetics, University of Siena, Siena, Italy
- Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy
- Genetica Medica, Azienda Ospedaliero-Universitaria Senese, Siena, Italy
| | - Elisa Frullanti
- Medical Genetics, University of Siena, Siena, Italy
- Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, Siena, Italy
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27
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Cui H, Yu W, Yu M, Luo Y, Yang M, Cong R, Chu X, Gao G, Zhong M. GPR126 regulates colorectal cancer cell proliferation by mediating HDAC2 and GLI2 expression. Cancer Sci 2021; 112:1798-1810. [PMID: 33629464 PMCID: PMC8088945 DOI: 10.1111/cas.14868] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 02/20/2021] [Accepted: 02/20/2021] [Indexed: 12/15/2022] Open
Abstract
The G‐protein‐coupled receptor 126 (GPR126) may play an important role in tumor development, although its role remains poorly understood. We found that GPR126 had higher expression in most colorectal cancer cell lines than in normal colon epithelial cell lines, and higher expression levels in colorectal cancer tissues than in normal adjacent colon tissues. GPR126 knockdown induced by shRNA inhibited cell viability and colony formation in HT‐29, HCT116, and LoVo cells, decreased BrdU incorporation into newly synthesized proliferating HT‐29 cells, led to an arrest of cell cycle progression at the G1 phase in HCT‐116 and HT‐29 cells, and suppressed tumorigenesis of HT‐29, HCT116, and LoVo cells in nude mouse xenograft models. GPR126 knockdown engendered decreased transcription and translation of histone deacetylase 2 (HDAC2), previously implicated in the activation of GLI1 and GLI2 in the Hedgehog signaling pathway. Ectopic expression of HDAC2 in GPR126‐silenced cells restored cell viability and proliferation, GLI2 luciferase reporter activity, partially recovered GLI2 expression, and reduced the cell cycle arrest. HDAC2 regulated GLI2 expression and, along with GLI2, it bound to the PTCH1 promoter, as evidenced by a chip assay with HT‐29 cells. Purmorphamine, a hedgehog agonist, largely restored the cell viability and expression of GLI2 proteins in GPR126‐silenced HT‐29 cells, whereas GANT61, a hedgehog inhibitor, further enhanced the GPR126 knockdown‐induced inhibitory effects. Our findings demonstrate that GPR126 regulates colorectal cancer cell proliferation by mediating the expression of HDAC2 and GLI2, therefore it may represent a suitable therapeutic target for colorectal cancer treatment.
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Affiliation(s)
- Hengxiang Cui
- Medical Research Center, Second Affiliated Hospital of Nantong University, Nantong, China.,Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Wenjie Yu
- Department of Internal Medicine, Carver College of Medicine, The University of Iowa, Iowa City, IA, USA
| | - Minhao Yu
- Department of Gastrointestinal Surgery, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Yang Luo
- Department of Gastrointestinal Surgery, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Mingming Yang
- Department of Biochemistry and Molecular Biology, School of Medicine, Nantong University, Nantong, China
| | - Ruochen Cong
- Medical Research Center, Second Affiliated Hospital of Nantong University, Nantong, China
| | - Xin Chu
- Medical Research Center, Second Affiliated Hospital of Nantong University, Nantong, China
| | - Ganglong Gao
- Department of Gastrointestinal Surgery, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Ming Zhong
- Department of Gastrointestinal Surgery, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, Shanghai, China
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28
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Khalighi S, Singh S, Varadan V. Untangling a complex web: Computational analyses of tumor molecular profiles to decode driver mechanisms. J Genet Genomics 2020; 47:595-609. [PMID: 33423960 PMCID: PMC7902422 DOI: 10.1016/j.jgg.2020.11.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2020] [Revised: 11/04/2020] [Accepted: 11/14/2020] [Indexed: 12/19/2022]
Abstract
Genome-scale studies focusing on molecular profiling of cancers across tissue types have revealed a plethora of aberrations across the genomic, transcriptomic, and epigenomic scales. The significant molecular heterogeneity across individual tumors even within the same tissue context complicates decoding the key etiologic mechanisms of this disease. Furthermore, it is increasingly likely that biologic mechanisms underlying the pathobiology of cancer involve multiple molecular entities interacting across functional scales. This has motivated the development of computational approaches that integrate molecular measurements with prior biological knowledge in increasingly intricate ways to enable the discovery of driver genomic aberrations across cancers. Here, we review diverse methodological approaches that have powered significant advances in our understanding of the genomic underpinnings of cancer at the cohort and at the individual tumor scales. We outline the key advances and challenges in the computational discovery of cancer mechanisms while motivating the development of systems biology approaches to comprehensively decode the biologic drivers of this complex disease.
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Affiliation(s)
- Sirvan Khalighi
- Division of General Medical Sciences-Oncology, Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
| | - Salendra Singh
- Division of General Medical Sciences-Oncology, Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
| | - Vinay Varadan
- Division of General Medical Sciences-Oncology, Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA.
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29
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Kreisberg JF, Ideker T, Meric-Bernstam F, Mills G. Prospecting whole cancer genomes. NATURE CANCER 2020; 1:273-275. [PMID: 35122032 DOI: 10.1038/s43018-020-0045-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Affiliation(s)
- Jason F Kreisberg
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Trey Ideker
- Department of Medicine, University of California San Diego, La Jolla, CA, USA.
| | | | - Gordon Mills
- Knight Cancer Institute Oregon Health and Sciences University, Portland, OR, USA.
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