1
|
Mochizuki A, Shiraishi K, Honda T, Higashiyama RI, Sunami K, Matsuda M, Shimada Y, Miyazaki Y, Yoshida Y, Watanabe SI, Yatabe Y, Hamamoto R, Kohno T. Passive Smoking-Induced Mutagenesis as a Promoter of Lung Carcinogenesis. J Thorac Oncol 2024; 19:984-994. [PMID: 38382595 DOI: 10.1016/j.jtho.2024.02.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 02/06/2024] [Accepted: 02/13/2024] [Indexed: 02/23/2024]
Abstract
INTRODUCTION The International Agency for Research on Cancer has classified passive smoking (PS) or secondhand smoke exposure as a group 1 carcinogen linked to lung cancer. However, in contrast to active smoking, the mutagenic properties of PS remain unclear. METHODS A consecutive cohort of 564 lung adenocarcinoma samples from female never-smokers, who provided detailed information about their exposure to PS during adolescence and in their thirties through a questionnaire, was prepared. Of these, all 291 cases for whom frozen tumor tissues were available were subjected to whole exome sequencing to estimate tumor mutational burden, and the top 84 cases who were exposed daily, or not, to PS during adolescence, in their thirties or in both periods, were further subjected to whole genome sequencing. RESULTS A modest yet statistically significant increase in tumor mutational burden was observed in the group exposed to PS compared with the group not exposed to PS (median values = 1.44 versus 1.29 per megabase, respectively; p = 0.020). Instead of inducing driver oncogene mutations, PS-induced substantial subclonal mutations exhibiting APOBEC-type signatures, including SMAD4 and ADGRG6 hotspot mutations. A polymorphic APOBEC3A/3B allele-specific to the Asian population that leads to up-regulated expression of APOBEC3A accentuated the mutational load in individuals exposed daily to PS during adolescence. CONCLUSIONS This study reveals that PS-induced mutagenesis can promote lung carcinogenesis. The APOBEC3A/3B polymorphism may serve as a biomarker for identifying passive nonsmoking individuals at high risk of developing lung cancer.
Collapse
Affiliation(s)
- Akifumi Mochizuki
- Division of Genome Biology, National Cancer Center Research Institute, Tokyo, Japan; Department of Respiratory Medicine, Tokyo Medical and Dental University, Tokyo, Japan
| | - Kouya Shiraishi
- Division of Genome Biology, National Cancer Center Research Institute, Tokyo, Japan
| | - Takayuki Honda
- Department of Respiratory Medicine, Tokyo Medical and Dental University, Tokyo, Japan
| | | | - Kuniko Sunami
- Division of Genome Biology, National Cancer Center Research Institute, Tokyo, Japan
| | - Maiko Matsuda
- Division of Genome Biology, National Cancer Center Research Institute, Tokyo, Japan
| | - Yoko Shimada
- Division of Genome Biology, National Cancer Center Research Institute, Tokyo, Japan
| | - Yasunari Miyazaki
- Department of Respiratory Medicine, Tokyo Medical and Dental University, Tokyo, Japan
| | - Yukihiro Yoshida
- Department of Thoracic Surgery, National Cancer Center Hospital, Tokyo, Japan
| | - Shun-Ichi Watanabe
- Department of Thoracic Surgery, National Cancer Center Hospital, Tokyo, Japan
| | - Yasushi Yatabe
- Department of Pathology, National Cancer Center Hospital, Tokyo, Japan
| | - Ryuji Hamamoto
- Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, Japan
| | - Takashi Kohno
- Division of Genome Biology, National Cancer Center Research Institute, Tokyo, Japan.
| |
Collapse
|
2
|
Balasooriya ER, Madhusanka D, López-Palacios TP, Eastmond RJ, Jayatunge D, Owen JJ, Gashler JS, Egbert CM, Bulathsinghalage C, Liu L, Piccolo SR, Andersen JL. Integrating Clinical Cancer and PTM Proteomics Data Identifies a Mechanism of ACK1 Kinase Activation. Mol Cancer Res 2024; 22:137-151. [PMID: 37847650 PMCID: PMC10831333 DOI: 10.1158/1541-7786.mcr-23-0153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 08/17/2023] [Accepted: 10/13/2023] [Indexed: 10/19/2023]
Abstract
Beyond the most common oncogenes activated by mutation (mut-drivers), there likely exists a variety of low-frequency mut-drivers, each of which is a possible frontier for targeted therapy. To identify new and understudied mut-drivers, we developed a machine learning (ML) model that integrates curated clinical cancer data and posttranslational modification (PTM) proteomics databases. We applied the approach to 62,746 patient cancers spanning 84 cancer types and predicted 3,964 oncogenic mutations across 1,148 genes, many of which disrupt PTMs of known and unknown function. The list of putative mut-drivers includes established drivers and others with poorly understood roles in cancer. This ML model is available as a web application. As a case study, we focused the approach on nonreceptor tyrosine kinases (NRTK) and found a recurrent mutation in activated CDC42 kinase-1 (ACK1) that disrupts the Mig6 homology region (MHR) and ubiquitin-association (UBA) domains on the ACK1 C-terminus. By studying these domains in cultured cells, we found that disruption of the MHR domain helps activate the kinase while disruption of the UBA increases kinase stability by blocking its lysosomal degradation. This ACK1 mutation is analogous to lymphoma-associated mutations in its sister kinase, TNK1, which also disrupt a C-terminal inhibitory motif and UBA domain. This study establishes a mut-driver discovery tool for the research community and identifies a mechanism of ACK1 hyperactivation shared among ACK family kinases. IMPLICATIONS This research identifies a potentially targetable activating mutation in ACK1 and other possible oncogenic mutations, including PTM-disrupting mutations, for further study.
Collapse
Affiliation(s)
- Eranga R. Balasooriya
- The Fritz B. Burns Cancer Research Laboratory, Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah
- Center for Cancer Research, Massachusetts General Hospital, Boston, Massachusetts
- Dept. of Medicine, Harvard Medical School, Boston, Massachusetts
| | - Deshan Madhusanka
- The Fritz B. Burns Cancer Research Laboratory, Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah
- Department of Oncological Sciences and Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, Utah
| | - Tania P. López-Palacios
- The Fritz B. Burns Cancer Research Laboratory, Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah
- Department of Oncological Sciences and Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, Utah
| | - Riley J. Eastmond
- The Fritz B. Burns Cancer Research Laboratory, Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah
| | - Dasun Jayatunge
- The Fritz B. Burns Cancer Research Laboratory, Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah
- Department of Oncological Sciences and Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, Utah
| | - Jake J. Owen
- The Fritz B. Burns Cancer Research Laboratory, Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah
| | - Jack S. Gashler
- The Fritz B. Burns Cancer Research Laboratory, Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah
| | - Christina M. Egbert
- The Fritz B. Burns Cancer Research Laboratory, Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah
| | | | - Lu Liu
- Department of Computer Science, North Dakota State University, Fargo, North Dakota
| | | | - Joshua L. Andersen
- The Fritz B. Burns Cancer Research Laboratory, Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah
- Department of Oncological Sciences and Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, Utah
| |
Collapse
|
3
|
Cazares TA, Rizvi FW, Iyer B, Chen X, Kotliar M, Bejjani AT, Wayman JA, Donmez O, Wronowski B, Parameswaran S, Kottyan LC, Barski A, Weirauch MT, Prasath VBS, Miraldi ER. maxATAC: Genome-scale transcription-factor binding prediction from ATAC-seq with deep neural networks. PLoS Comput Biol 2023; 19:e1010863. [PMID: 36719906 PMCID: PMC9917285 DOI: 10.1371/journal.pcbi.1010863] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 02/10/2023] [Accepted: 01/10/2023] [Indexed: 02/01/2023] Open
Abstract
Transcription factors read the genome, fundamentally connecting DNA sequence to gene expression across diverse cell types. Determining how, where, and when TFs bind chromatin will advance our understanding of gene regulatory networks and cellular behavior. The 2017 ENCODE-DREAM in vivo Transcription-Factor Binding Site (TFBS) Prediction Challenge highlighted the value of chromatin accessibility data to TFBS prediction, establishing state-of-the-art methods for TFBS prediction from DNase-seq. However, the more recent Assay-for-Transposase-Accessible-Chromatin (ATAC)-seq has surpassed DNase-seq as the most widely-used chromatin accessibility profiling method. Furthermore, ATAC-seq is the only such technique available at single-cell resolution from standard commercial platforms. While ATAC-seq datasets grow exponentially, suboptimal motif scanning is unfortunately the most common method for TFBS prediction from ATAC-seq. To enable community access to state-of-the-art TFBS prediction from ATAC-seq, we (1) curated an extensive benchmark dataset (127 TFs) for ATAC-seq model training and (2) built "maxATAC", a suite of user-friendly, deep neural network models for genome-wide TFBS prediction from ATAC-seq in any cell type. With models available for 127 human TFs, maxATAC is the largest collection of high-performance TFBS prediction models for ATAC-seq. maxATAC performance extends to primary cells and single-cell ATAC-seq, enabling improved TFBS prediction in vivo. We demonstrate maxATAC's capabilities by identifying TFBS associated with allele-dependent chromatin accessibility at atopic dermatitis genetic risk loci.
Collapse
Affiliation(s)
- Tareian A. Cazares
- Immunology Graduate Program, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States of America
| | - Faiz W. Rizvi
- Systems Biology and Physiology Graduate Program, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States of America
| | - Balaji Iyer
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, Ohio, United States of America
| | - Xiaoting Chen
- The Center for Autoimmune Genetics and Etiology (CAGE), Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
| | - Michael Kotliar
- Division of Allergy and Immunology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
| | - Anthony T. Bejjani
- Molecular and Developmental Biology Graduate Program, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States of America
| | - Joseph A. Wayman
- Division of Immunobiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
| | - Omer Donmez
- The Center for Autoimmune Genetics and Etiology (CAGE), Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
| | - Benjamin Wronowski
- Division of Allergy and Immunology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
| | - Sreeja Parameswaran
- The Center for Autoimmune Genetics and Etiology (CAGE), Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
| | - Leah C. Kottyan
- The Center for Autoimmune Genetics and Etiology (CAGE), Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States of America
- Division of Human Genetics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
| | - Artem Barski
- Division of Allergy and Immunology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States of America
- Division of Human Genetics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
| | - Matthew T. Weirauch
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
- The Center for Autoimmune Genetics and Etiology (CAGE), Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States of America
- Division of Human Genetics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
- Division of Developmental Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
| | - V. B. Surya Prasath
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, Ohio, United States of America
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States of America
| | - Emily R. Miraldi
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, Ohio, United States of America
- Division of Immunobiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States of America
| |
Collapse
|
4
|
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.
Collapse
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;
| |
Collapse
|
5
|
Alterations in transcriptional networks in cancer: the role of noncoding somatic driver mutations. Curr Opin Genet Dev 2022; 75:101919. [PMID: 35609422 DOI: 10.1016/j.gde.2022.101919] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 04/18/2022] [Accepted: 04/20/2022] [Indexed: 11/21/2022]
Abstract
Aberrant gene expression is a cancer hallmark and it is known that almost every tumor acquires somatic mutations in transcription factors, chromatin regulators, or the DNA regulatory elements that are critical for transcriptional control and cell phenotype. While the role of transcription factors and chromatin regulators has been widely studied, relatively few noncoding driver mutations have been identified and functionally characterized to date. Here, we review the current understanding of somatic variants in noncoding regions of the cancer genome and their impact on chromatin architecture and transcriptional networks. We also discuss approaches and ongoing challenges for noncoding driver discovery, and highlight insights gained from recent studies exploring the nature and impact of noncoding drivers on tumor formation.
Collapse
|
6
|
Kikutake C, Suyama M. Pan-cancer analysis of mutations in open chromatin regions and their possible association with cancer pathogenesis. Cancer Med 2022; 11:3902-3916. [PMID: 35416406 PMCID: PMC9582691 DOI: 10.1002/cam4.4749] [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: 12/01/2021] [Revised: 03/29/2022] [Accepted: 04/01/2022] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND Open chromatin is associated with gene transcription. Previous studies have shown that the density of mutations in open chromatin regions is lower than that in flanking regions because of the higher accessibility of DNA repair machinery. However, in several cancer types, open chromatin regions show an increased local density of mutations in activated regulatory regions. Although the mutation distribution within open chromatin regions in cancer cells has been investigated, only few studies have focused on their functional implications in cancer. To reveal the impact of highly mutated open chromatin regions on cancer, we investigated the association between mutations in open chromatin regions and their possible functions. METHODS Whole-genome sequencing data of 18 cancer types were downloaded from the PanCancer Analysis of Whole Genomes and Catalog of Somatic Mutations in Cancer. We quantified the mutations located in open chromatin regions defined by The Cancer Genome Atlas and classified open chromatin regions into three categories based on the number of mutations. Then, we investigated the chromatin state, amplification, and possible target genes of the open chromatin regions with a high number of mutations. We also analyzed the association between the number of mutations in open chromatin regions and patient prognosis. RESULTS In some cancer types, the proportion of promoter or enhancer chromatin state in open chromatin regions with a high number of mutations was significantly higher than that in the regions with a low number of mutations. The possible target genes of open chromatin regions with a high number of mutations were more strongly associated with cancer than those of other open chromatin regions. Moreover, a high number of mutations in open chromatin regions was significantly associated with a poor prognosis in some cancer types. CONCLUSIONS These results suggest that highly mutated open chromatin regions play an important role in cancer pathogenesis and can be effectively used to predict patient prognosis.
Collapse
Affiliation(s)
- Chie Kikutake
- Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan
| | - Mikita Suyama
- Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan
| |
Collapse
|
7
|
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.
Collapse
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.)
| |
Collapse
|
8
|
Andrades R, Recamonde-Mendoza M. Machine learning methods for prediction of cancer driver genes: a survey paper. Brief Bioinform 2022; 23:6551145. [PMID: 35323900 DOI: 10.1093/bib/bbac062] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 02/06/2022] [Accepted: 02/08/2022] [Indexed: 12/21/2022] Open
Abstract
Identifying the genes and mutations that drive the emergence of tumors is a critical step to improving our understanding of cancer and identifying new directions for disease diagnosis and treatment. Despite the large volume of genomics data, the precise detection of driver mutations and their carrying genes, known as cancer driver genes, from the millions of possible somatic mutations remains a challenge. Computational methods play an increasingly important role in discovering genomic patterns associated with cancer drivers and developing predictive models to identify these elements. Machine learning (ML), including deep learning, has been the engine behind many of these efforts and provides excellent opportunities for tackling remaining gaps in the field. Thus, this survey aims to perform a comprehensive analysis of ML-based computational approaches to identify cancer driver mutations and genes, providing an integrated, panoramic view of the broad data and algorithmic landscape within this scientific problem. We discuss how the interactions among data types and ML algorithms have been explored in previous solutions and outline current analytical limitations that deserve further attention from the scientific community. We hope that by helping readers become more familiar with significant developments in the field brought by ML, we may inspire new researchers to address open problems and advance our knowledge towards cancer driver discovery.
Collapse
Affiliation(s)
- Renan Andrades
- Institute of Informatics, Universidade Federal do Rio Grande do Sul, Porto Alegre/RS, Brazil.,Bioinformatics Core, Hospital de Clínicas de Porto Alegre, Porto Alegre/RS, Brazil
| | - Mariana Recamonde-Mendoza
- Institute of Informatics, Universidade Federal do Rio Grande do Sul, Porto Alegre/RS, Brazil.,Bioinformatics Core, Hospital de Clínicas de Porto Alegre, Porto Alegre/RS, Brazil
| |
Collapse
|
9
|
Multi-omic profiling of peritoneal metastases in gastric cancer identifies molecular subtypes and therapeutic vulnerabilities. NATURE CANCER 2022; 2:962-977. [PMID: 35121863 DOI: 10.1038/s43018-021-00240-6] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2021] [Accepted: 06/25/2021] [Indexed: 12/24/2022]
Abstract
Peritoneal metastasis, a hallmark of incurable advanced gastric cancer (GC), presently has no curative therapy and its molecular features have not been examined extensively. Here we present a comprehensive multi-omic analysis of malignant ascitic fluid samples and their corresponding tumor cell lines from 98 patients, including whole-genome sequencing, RNA sequencing, DNA methylation and enhancer landscape. We identify a higher frequency of receptor tyrosine kinase and mitogen-activated protein kinase pathway alterations compared to primary GC; moreover, approximately half of the gene alterations are potentially treatable with targeted therapy. Our analyses also stratify ascites-disseminated GC into two distinct molecular subtypes: one displaying active super enhancers (SEs) at the ELF3, KLF5 and EHF loci, and a second subtype bearing transforming growth factor-β (TGF-β) pathway activation through SMAD3 SE activation and high expression of transcriptional enhancer factor TEF-1 (TEAD1). In the TGF-β subtype, inhibition of the TEAD pathway circumvents therapy resistance, suggesting a potential molecular-guided therapeutic strategy for this subtype of intractable GC.
Collapse
|
10
|
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.
Collapse
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.
| |
Collapse
|
11
|
Kim T. Nucleic Acids in Cancer Diagnosis and Therapy. Cancers (Basel) 2020; 12:cancers12092597. [PMID: 32932912 PMCID: PMC7564611 DOI: 10.3390/cancers12092597] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2020] [Accepted: 09/10/2020] [Indexed: 11/16/2022] Open
Abstract
Cancer research has been focused on the coding genes occupying 1-2% of the human genome [...].
Collapse
Affiliation(s)
- Taewan Kim
- Department of Anatomy, Histology and Developmental Biology, Base for International Science and Technology Cooperation, Carson Cancer Stem Cell Vaccines R&D Center, International Cancer Center, Shenzhen University Health Science Center, Shenzhen 518055, China; or
- The Ohio State University Comprehensive Cancer Center, Columbus, OH 43210, USA
| |
Collapse
|