1
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Lin YJ, Menon AS, Hu Z, Brenner SE. Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors. Hum Genomics 2024; 18:90. [PMID: 39198917 PMCID: PMC11360829 DOI: 10.1186/s40246-024-00663-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2024] [Accepted: 08/19/2024] [Indexed: 09/01/2024] Open
Abstract
BACKGROUND Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb). RESULTS The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past three decades, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 190 VIPs, resulting in a total of 407 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods. CONCLUSIONS VIPdb version 2 summarizes 407 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications. VIPdb is available at https://genomeinterpretation.org/vipdb.
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Affiliation(s)
- Yu-Jen Lin
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, 94720, USA
- Center for Computational Biology, University of California, Berkeley, CA, 94720, USA
| | - Arul S Menon
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, 94720, USA
- College of Computing, Data Science, and Society, University of California, Berkeley, CA, 94720, USA
| | - Zhiqiang Hu
- Department of Plant and Microbial Biology, University of California, 111 Koshland Hall #3102, Berkeley, CA, 94720-3102, USA
- Illumina, Foster City, CA, 94404, USA
| | - Steven E Brenner
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, 94720, USA.
- Center for Computational Biology, University of California, Berkeley, CA, 94720, USA.
- College of Computing, Data Science, and Society, University of California, Berkeley, CA, 94720, USA.
- Department of Plant and Microbial Biology, University of California, 111 Koshland Hall #3102, Berkeley, CA, 94720-3102, USA.
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2
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Wooller SK, Pearl LH, Pearl FMG. Identifying actionable synthetically lethal cancer gene pairs using mutual exclusivity. FEBS Lett 2024; 598:2028-2039. [PMID: 38977941 DOI: 10.1002/1873-3468.14950] [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: 08/03/2023] [Revised: 04/25/2024] [Accepted: 05/09/2024] [Indexed: 07/10/2024]
Abstract
Mutually exclusive loss-of-function alterations in gene pairs are those that occur together less frequently than may be expected and may denote a synthetically lethal relationship (SSL) between the genes. SSLs can be exploited therapeutically to selectively kill cancer cells. Here, we analysed mutation, copy number variation, and methylation levels in samples from The Cancer Genome Atlas, using the hypergeometric and the Poisson binomial tests to identify mutually exclusive inactivated genes. We focused on gene pairs where one is an inactivated tumour suppressor and the other a gene whose protein product can be inhibited by known drugs. This provided an abundance of potential targeted therapeutics and repositioning opportunities for several cancers. These data are available on the MexDrugs website, https://bioinformaticslab.sussex.ac.uk/mexdrugs.
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Affiliation(s)
- Sarah K Wooller
- Bioinformatics Lab, School of Life Sciences, University of Sussex, Brighton, UK
| | - Laurence H Pearl
- Genome Damage Stability Centre, School of Life Sciences, University of Sussex, Brighton, UK
| | - Frances M G Pearl
- Bioinformatics Lab, School of Life Sciences, University of Sussex, Brighton, UK
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3
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Atzeni R, Massidda M, Pieroni E, Rallo V, Pisu M, Angius A. A Novel Affordable and Reliable Framework for Accurate Detection and Comprehensive Analysis of Somatic Mutations in Cancer. Int J Mol Sci 2024; 25:8044. [PMID: 39125613 PMCID: PMC11311285 DOI: 10.3390/ijms25158044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2024] [Revised: 07/11/2024] [Accepted: 07/22/2024] [Indexed: 08/12/2024] Open
Abstract
Accurate detection and analysis of somatic variants in cancer involve multiple third-party tools with complex dependencies and configurations, leading to laborious, error-prone, and time-consuming data conversions. This approach lacks accuracy, reproducibility, and portability, limiting clinical application. Musta was developed to address these issues as an end-to-end pipeline for detecting, classifying, and interpreting cancer mutations. Musta is based on a Python command-line tool designed to manage tumor-normal samples for precise somatic mutation analysis. The core is a Snakemake-based workflow that covers all key cancer genomics steps, including variant calling, mutational signature deconvolution, variant annotation, driver gene detection, pathway analysis, and tumor heterogeneity estimation. Musta is easy to install on any system via Docker, with a Makefile handling installation, configuration, and execution, allowing for full or partial pipeline runs. Musta has been validated at the CRS4-NGS Core facility and tested on large datasets from The Cancer Genome Atlas and the Beijing Institute of Genomics. Musta has proven robust and flexible for somatic variant analysis in cancer. It is user-friendly, requiring no specialized programming skills, and enables data processing with a single command line. Its reproducibility ensures consistent results across users following the same protocol.
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Affiliation(s)
- Rossano Atzeni
- Center for Advanced Studies, Research and Development in Sardinia (CRS4), 09050 Pula, Italy; (R.A.); (E.P.); (M.P.)
| | - Matteo Massidda
- Department of Medical, Surgical and Experimental Sciences, University of Sassari, 07100 Sassari, Italy;
| | - Enrico Pieroni
- Center for Advanced Studies, Research and Development in Sardinia (CRS4), 09050 Pula, Italy; (R.A.); (E.P.); (M.P.)
| | - Vincenzo Rallo
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), Cittadella Universitaria di Cagliari, 09042 Monserrato, Italy;
| | - Massimo Pisu
- Center for Advanced Studies, Research and Development in Sardinia (CRS4), 09050 Pula, Italy; (R.A.); (E.P.); (M.P.)
| | - Andrea Angius
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), Cittadella Universitaria di Cagliari, 09042 Monserrato, Italy;
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4
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Lin YJ, Menon AS, Hu Z, Brenner SE. Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.25.600283. [PMID: 38979289 PMCID: PMC11230257 DOI: 10.1101/2024.06.25.600283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Background Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb). Results The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past 25 years, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 186 VIPs, resulting in a total of 403 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods. Conclusions VIPdb version 2 summarizes 403 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications. Availability VIPdb version 2 is available at https://genomeinterpretation.org/vipdb.
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Affiliation(s)
- Yu-Jen Lin
- Department of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA
- Center for Computational Biology, University of California, Berkeley, California 94720, USA
| | - Arul S. Menon
- Department of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA
- College of Computing, Data Science, and Society, University of California, Berkeley, California 94720, USA
| | - Zhiqiang Hu
- Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA
- Currently at: Illumina, Foster City, California 94404, USA
| | - Steven E. Brenner
- Department of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA
- Center for Computational Biology, University of California, Berkeley, California 94720, USA
- College of Computing, Data Science, and Society, University of California, Berkeley, California 94720, USA
- Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA
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5
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Johnson SM, Haberberger J, Galeotti J, Ramkissoon L, Coombs CC, Richardson DR, Foster MC, Duncan D, Montgomery ND, Ferguson NL, Zeidner JF. Comprehensive genomic profiling reveals molecular subsets of ASXL1-mutated myeloid neoplasms. Leuk Lymphoma 2024; 65:209-218. [PMID: 37921062 DOI: 10.1080/10428194.2023.2277672] [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: 07/11/2023] [Accepted: 10/24/2023] [Indexed: 11/04/2023]
Abstract
A large-scale genomic analysis of patients with ASXL1-mutated myeloid disease has not been performed to date. We reviewed comprehensive genomic profiling results from 6043 adults to characterize clinicopathologic features and co-mutation patterns by ASXL1 mutation status. ASXL1 mutations occurred in 1414 patients (23%). Mutation co-occurrence testing revealed strong co-occurrence (p < 0.01) between mutations in ASXL1 and nine genes (SRSF2, U2AF1, RUNX1, SETBP1, EZH2, STAG2, CUX1, CSF3R, CBL). Further analysis of patients with these co-mutations yielded several novel findings. Co-mutation patterns supported that ASXL1/SF3B1 co-mutation may be biologically distinct from ASXL1/non-SF3B1 spliceosome co-mutation. In AML, ASXL1/SRSF2 co-mutated patients frequently harbored STAG2 mutations (42%), which were dependent on the presence of both ASXL1 and SRSF2 mutation (p < 0.05). STAG2 and SETBP1 mutations were also exclusive in ASXL1/SRSF2 co-mutated patients and associated with divergent chronic myeloid phenotypes. Our findings support that certain multi-mutant genotypes may be biologically relevant in ASXL1-mutated myeloid disease.
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Affiliation(s)
- Steven M Johnson
- Department of Pathology and Laboratory Medicine, The University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
| | | | - Jonathan Galeotti
- Department of Pathology and Laboratory Medicine, The University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
| | - Lori Ramkissoon
- Department of Pathology and Laboratory Medicine, The University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
| | - Catherine C Coombs
- Division of Hematology, Department of Medicine, The University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA
- UC Irvine, Irvine, CA, USA
| | - Daniel R Richardson
- Division of Hematology, Department of Medicine, The University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA
| | - Matthew C Foster
- Division of Hematology, Department of Medicine, The University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA
- Novartis Pharmaceuticals, Cambridge, MA, USA
| | - Daniel Duncan
- Foundation Medicine, Inc, Cambridge, MA, USA
- GRAIL, Inc, Durham, NC, USA
| | - Nathan D Montgomery
- Department of Pathology and Laboratory Medicine, The University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
- NeoGenomics Laboratories, Aliso Viejo, CA, USA
| | | | - Joshua F Zeidner
- Division of Hematology, Department of Medicine, The University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA
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6
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Wang X, Kostrzewa C, Reiner A, Shen R, Begg C. Adaptation of a mutual exclusivity framework to identify driver mutations within oncogenic pathways. Am J Hum Genet 2024; 111:227-241. [PMID: 38232729 PMCID: PMC10870134 DOI: 10.1016/j.ajhg.2023.12.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 12/05/2023] [Accepted: 12/05/2023] [Indexed: 01/19/2024] Open
Abstract
Distinguishing genomic alterations in cancer-associated genes that have functional impact on tumor growth and disease progression from the ones that are passengers and confer no fitness advantage have important clinical implications. Evidence-based methods for nominating drivers are limited by existing knowledge on the oncogenic effects and therapeutic benefits of specific variants from clinical trials or experimental settings. As clinical sequencing becomes a mainstay of patient care, applying computational methods to mine the rapidly growing clinical genomic data holds promise in uncovering functional candidates beyond the existing knowledge base and expanding the patient population that could potentially benefit from genetically targeted therapies. We propose a statistical and computational method (MAGPIE) that builds on a likelihood approach leveraging the mutual exclusivity pattern within an oncogenic pathway for identifying probabilistically both the specific genes within a pathway and the individual mutations within such genes that are truly the drivers. Alterations in a cancer-associated gene are assumed to be a mixture of driver and passenger mutations with the passenger rates modeled in relationship to tumor mutational burden. We use simulations to study the operating characteristics of the method and assess false-positive and false-negative rates in driver nomination. When applied to a large study of primary melanomas, the method accurately identifies the known driver genes within the RTK-RAS pathway and nominates several rare variants as prime candidates for functional validation. A comprehensive evaluation of MAGPIE against existing tools has also been conducted leveraging the Cancer Genome Atlas data.
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Affiliation(s)
- Xinjun Wang
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| | - Caroline Kostrzewa
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Allison Reiner
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ronglai Shen
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Colin Begg
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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7
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Liu CH, Lai YL, Shen PC, Liu HC, Tsai MH, Wang YD, Lin WJ, Chen FH, Li CY, Wang SC, Hung MC, Cheng WC. DriverDBv4: a multi-omics integration database for cancer driver gene research. Nucleic Acids Res 2024; 52:D1246-D1252. [PMID: 37956338 PMCID: PMC10767848 DOI: 10.1093/nar/gkad1060] [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: 09/14/2023] [Revised: 10/12/2023] [Accepted: 10/25/2023] [Indexed: 11/15/2023] Open
Abstract
Advancements in high-throughput technology offer researchers an extensive range of multi-omics data that provide deep insights into the complex landscape of cancer biology. However, traditional statistical models and databases are inadequate to interpret these high-dimensional data within a multi-omics framework. To address this limitation, we introduce DriverDBv4, an updated iteration of the DriverDB cancer driver gene database (http://driverdb.bioinfomics.org/). This updated version offers several significant enhancements: (i) an increase in the number of cohorts from 33 to 70, encompassing approximately 24 000 samples; (ii) inclusion of proteomics data, augmenting the existing types of omics data and thus expanding the analytical scope; (iii) implementation of multiple multi-omics algorithms for identification of cancer drivers; (iv) new visualization features designed to succinctly summarize high-context data and redesigned existing sections to accommodate the increased volume of datasets and (v) two new functions in Customized Analysis, specifically designed for multi-omics driver identification and subgroup expression analysis. DriverDBv4 facilitates comprehensive interpretation of multi-omics data across diverse cancer types, thereby enriching the understanding of cancer heterogeneity and aiding in the development of personalized clinical approaches. The database is designed to foster a more nuanced understanding of the multi-faceted nature of cancer.
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Affiliation(s)
- Chia-Hsin Liu
- Cancer Biology and Precision Therapeutics Center, China Medical University, Taichung 404328, Taiwan
| | - Yo-Liang Lai
- Department of Radiation Oncology, China Medical University, Taichung 404328, Taiwan
| | - Pei-Chun Shen
- Cancer Biology and Precision Therapeutics Center, China Medical University, Taichung 404328, Taiwan
| | - Hsiu-Cheng Liu
- Cancer Biology and Precision Therapeutics Center, China Medical University, Taichung 404328, Taiwan
| | - Meng-Hsin Tsai
- Cancer Biology and Precision Therapeutics Center, China Medical University, Taichung 404328, Taiwan
| | - Yu-De Wang
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 404328, Taiwan
- Department of Urology, China Medical University, Taichung 404328, Taiwan
| | - Wen-Jen Lin
- Cancer Biology and Precision Therapeutics Center, China Medical University, Taichung 404328, Taiwan
- School of Medicine, China Medical University, Taichung 404328, Taiwan
| | - Fang-Hsin Chen
- Institute of Nuclear Engineering and Science, National Tsing Hua University, Hsinchu 300044, Taiwan
| | - Chia-Yang Li
- Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
| | - Shu-Chi Wang
- Department of Medical Laboratory Science and Biotechnology, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
| | - Mien-Chie Hung
- Cancer Biology and Precision Therapeutics Center, China Medical University, Taichung 404328, Taiwan
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 404328, Taiwan
- Institute of Biochemistry and Molecular Biology, China Medical University, Taichung 404328, Taiwan
- Molecular Medicine Center, China Medical University Hospital, China Medical University, Taichung 404328, Taiwan
- Department of Biotechnology, Asia University, Taichung 413305, Taiwan
| | - Wei-Chung Cheng
- Cancer Biology and Precision Therapeutics Center, China Medical University, Taichung 404328, Taiwan
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 404328, Taiwan
- The Ph.D. program for Cancer Biology and Drug Discovery, China Medical University and Academia Sinica, Taichung 404328, Taiwan
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8
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Luo Q, Li X, Meng Z, Rong H, Li Y, Zhao G, Zhu H, Cen L, Liao Q. Identification of hypoxia-related gene signatures based on multi-omics analysis in lung adenocarcinoma. J Cell Mol Med 2024; 28:e18032. [PMID: 38013642 PMCID: PMC10826438 DOI: 10.1111/jcmm.18032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 10/25/2023] [Accepted: 10/27/2023] [Indexed: 11/29/2023] Open
Abstract
Lung adenocarcinoma (LUAD) is the most common type of lung cancer and one of the malignancies with the highest incidence rate and mortality worldwide. Hypoxia is a typical feature of tumour microenvironment (TME), which affects the progression of LUAD from multiple molecular levels. However, the underlying molecular mechanisms behind LUAD hypoxia are not fully understood. In this study, we estimated the level of hypoxia by calculating a score based on 15 hypoxia genes. The hypoxia scores were relatively high in LUAD patients with poor prognosis and were bound up with tumour node metastasis (TNM) stage, tumour size, lymph node, age and gender. By comparison of high hypoxia score group and low hypoxia score group, 1820 differentially expressed genes were identified, among which up-regulated genes were mainly about cell division and proliferation while down-regulated genes were primarily involved in cilium-related biological processes. Besides, LUAD patients with high hypoxia scores had higher frequencies of gene mutations, among which TP53, TTN and MUC16 had the highest mutation rates. As for DNA methylation, 1015 differentially methylated probes-related genes were found and may play potential roles in tumour-related neurobiological processes and cell signal transduction. Finally, a prognostic model with 25 multi-omics features was constructed and showed good predictive performance. The area under curve (AUC) values of 1-, 3- and 5-year survival reached 0.863, 0.826 and 0.846, respectively. Above all, our findings are helpful in understanding the impact and molecular mechanisms of hypoxia in LUAD.
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Affiliation(s)
- Qineng Luo
- School of Public HealthHealth Science CenterNingbo UniversityNingboZhejiangP. R. China
| | - Xing Li
- School of Public HealthHealth Science CenterNingbo UniversityNingboZhejiangP. R. China
| | - Zixing Meng
- School of Public HealthHealth Science CenterNingbo UniversityNingboZhejiangP. R. China
| | - Hao Rong
- School of Public HealthHealth Science CenterNingbo UniversityNingboZhejiangP. R. China
| | - Yanguo Li
- School of Public HealthHealth Science CenterNingbo UniversityNingboZhejiangP. R. China
| | - Guofang Zhao
- Department of Thoracic SurgeryHwa Mei HospitalUniversity of Chinese Academy of SciencesNingboZhejiangP. R. China
| | - Huangkai Zhu
- Department of Thoracic SurgeryHwa Mei HospitalUniversity of Chinese Academy of SciencesNingboZhejiangP. R. China
| | - Lvjun Cen
- The First Affiliated HospitalNingbo UniversityNingboZhejiangP. R. China
| | - Qi Liao
- School of Public HealthHealth Science CenterNingbo UniversityNingboZhejiangP. R. China
- The First Affiliated HospitalNingbo UniversityNingboZhejiangP. R. China
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9
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Wang Y, Zhou B, Ru J, Meng X, Wang Y, Liu W. Advances in computational methods for identifying cancer driver genes. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:21643-21669. [PMID: 38124614 DOI: 10.3934/mbe.2023958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Cancer driver genes (CDGs) are crucial in cancer prevention, diagnosis and treatment. This study employed computational methods for identifying CDGs, categorizing them into four groups. The major frameworks for each of these four categories were summarized. Additionally, we systematically gathered data from public databases and biological networks, and we elaborated on computational methods for identifying CDGs using the aforementioned databases. Further, we summarized the algorithms, mainly involving statistics and machine learning, used for identifying CDGs. Notably, the performances of nine typical identification methods for eight types of cancer were compared to analyze the applicability areas of these methods. Finally, we discussed the challenges and prospects associated with methods for identifying CDGs. The present study revealed that the network-based algorithms and machine learning-based methods demonstrated superior performance.
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Affiliation(s)
- Ying Wang
- School of Computer Science and Engineering, Changshu Institute of Technology, Changshu 215500, China
| | - Bohao Zhou
- School of Computer Science and Engineering, Changshu Institute of Technology, Changshu 215500, China
| | - Jidong Ru
- School of Textile Garment and Design, Changshu Institute of Technology, Changshu 215500, China
| | - Xianglian Meng
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou 213032, China
| | - Yundong Wang
- School of Computer Science and Engineering, Changshu Institute of Technology, Changshu 215500, China
| | - Wenjie Liu
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou 213032, China
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10
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Weigelt B, Marra A, Selenica P, Rios-Doria E, Momeni-Boroujeni A, Berger MF, Arora K, Nemirovsky D, Iasonos A, Chakravarty D, Abu-Rustum NR, Da Cruz Paula A, Dessources K, Ellenson LH, Liu YL, Aghajanian C, Brown CL. Molecular Characterization of Endometrial Carcinomas in Black and White Patients Reveals Disparate Drivers with Therapeutic Implications. Cancer Discov 2023; 13:2356-2369. [PMID: 37651310 PMCID: PMC11149479 DOI: 10.1158/2159-8290.cd-23-0546] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 08/09/2023] [Accepted: 08/29/2023] [Indexed: 09/02/2023]
Abstract
Although the incidence of endometrial carcinoma (EC) is similar in Black and White women, racial disparities are stark, with the highest mortality rates observed among Black patients. Here, analysis of 1,882 prospectively sequenced ECs using a clinical FDA-authorized tumor-normal panel revealed a significantly higher prevalence of high-risk histologic and molecular EC subtypes in self-identified Black (n = 259) compared with White (n = 1,623) patients. Clinically actionable alterations, including high tumor mutational burden/microsatellite instability, which confer benefit from immunotherapy, were less frequent in ECs from Black than from White patients. Ultramutated POLE molecular subtype ECs associated with favorable outcomes were rare in Black patients. Results were confirmed by genetic ancestry analysis. CCNE1 gene amplification, which is associated with aggressive clinical behavior, was more prevalent in carcinosarcomas occurring in Black than in White patients. ECs from Black and White patients display important differences in their histologic types, molecular subtypes, driver genetic alterations, and therapeutic targets. SIGNIFICANCE Our comprehensive analysis of prospectively clinically sequenced ECs revealed significant differences in their histologic and molecular composition and in the presence of therapeutic targets in Black versus White patients. These findings emphasize the importance of incorporating diverse populations into molecular studies and clinical trials to address EC disparities. This article is featured in Selected Articles from This Issue, p. 2293.
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Affiliation(s)
- Britta Weigelt
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Antonio Marra
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Pier Selenica
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Eric Rios-Doria
- Gynecology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Amir Momeni-Boroujeni
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Michael F Berger
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Kanika Arora
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - David Nemirovsky
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Alexia Iasonos
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Debyani Chakravarty
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Nadeem R Abu-Rustum
- Gynecology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
- Department of Obstetrics and Gynecology, Weill Cornell Medical College, New York, New York
| | - Arnaud Da Cruz Paula
- Gynecology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Kimberly Dessources
- Gynecology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Lora H Ellenson
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Ying L Liu
- Gynecologic Medical Oncology, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
- Department of Medicine, Weill Cornell Medical College, New York, New York
| | - Carol Aghajanian
- Gynecologic Medical Oncology, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
- Department of Medicine, Weill Cornell Medical College, New York, New York
| | - Carol L Brown
- Gynecology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
- Department of Obstetrics and Gynecology, Weill Cornell Medical College, New York, New York
- Office of Health Equity, Memorial Sloan Kettering Cancer Center, New York, New York
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11
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Mumphrey MB, Hosseini N, Parolia A, Geng J, Zou W, Raghavan M, Chinnaiyan A, Cieslik M. Distinct mutational processes shape selection of MHC class I and class II mutations across primary and metastatic tumors. Cell Rep 2023; 42:112965. [PMID: 37597185 DOI: 10.1016/j.celrep.2023.112965] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 05/15/2023] [Accepted: 07/26/2023] [Indexed: 08/21/2023] Open
Abstract
Disruption of antigen presentation via loss of major histocompatibility complex (MHC) expression is a strategy whereby cancer cells escape immune surveillance and develop resistance to immunotherapy. Here, we develop the personalized genomics algorithm Hapster and accurately call somatic mutations within the MHC genes of 10,001 primary and 2,199 metastatic tumors, creating a catalog of 1,663 non-synonymous mutations that provide key insights into MHC mutagenesis. We find that MHC class I genes are among the most frequently mutated genes in both primary and metastatic tumors, while MHC class II mutations are more restricted. Recurrent deleterious mutations are found within haplotype- and cancer-type-specific hotspots associated with distinct mutational processes. Functional classification of MHC residues reveals significant positive selection for mutations disruptive to the B2M, peptide, and T cell binding interfaces, as well as to MHC chaperones.
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Affiliation(s)
- Michael B Mumphrey
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Noshad Hosseini
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Abhijit Parolia
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Jie Geng
- Department of Microbiology & Immunology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Weiping Zou
- Department of Microbiology & Immunology, University of Michigan, Ann Arbor, MI 48109, USA; Center of Excellence for Cancer Immunology and Immunotherapy, University of Michigan, Ann Arbor, MI 48109, USA; University of Michigan Rogel Cancer Center, Ann Arbor, MI 48109, USA
| | - Malini Raghavan
- Department of Microbiology & Immunology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Arul Chinnaiyan
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Department of Urology, University of Michigan, Ann Arbor, MI 48109, USA; Howard Hughes Medical Institute, Ann Arbor, MI 48109, USA; University of Michigan Rogel Cancer Center, Ann Arbor, MI 48109, USA
| | - Marcin Cieslik
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA; University of Michigan Rogel Cancer Center, Ann Arbor, MI 48109, USA.
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12
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Ivanovic S, El-Kebir M. Modeling and predicting cancer clonal evolution with reinforcement learning. Genome Res 2023; 33:1078-1088. [PMID: 37344104 PMCID: PMC10538496 DOI: 10.1101/gr.277672.123] [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: 01/06/2023] [Accepted: 06/09/2023] [Indexed: 06/23/2023]
Abstract
Cancer results from an evolutionary process that typically yields multiple clones with varying sets of mutations within the same tumor. Accurately modeling this process is key to understanding and predicting cancer evolution. Here, we introduce clone to mutation (CloMu), a flexible and low-parameter tree generative model of cancer evolution. CloMu uses a two-layer neural network trained via reinforcement learning to determine the probability of new mutations based on the existing mutations on a clone. CloMu supports several prediction tasks, including the determination of evolutionary trajectories, tree selection, causality and interchangeability between mutations, and mutation fitness. Importantly, previous methods support only some of these tasks, and many suffer from overfitting on data sets with a large number of mutations. Using simulations, we show that CloMu either matches or outperforms current methods on a wide variety of prediction tasks. In particular, for simulated data with interchangeable mutations, current methods are unable to uncover causal relationships as effectively as CloMu. On breast cancer and leukemia cohorts, we show that CloMu determines similarities and causal relationships between mutations as well as the fitness of mutations. We validate CloMu's inferred mutation fitness values for the leukemia cohort by comparing them to clonal proportion data not used during training, showing high concordance. In summary, CloMu's low-parameter model facilitates a wide range of prediction tasks regarding cancer evolution on increasingly available cohort-level data sets.
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Affiliation(s)
- Stefan Ivanovic
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Mohammed El-Kebir
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA;
- Cancer Center at Illinois, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, USA
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13
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Ma S, Wang F, Liu Q, Geng X, Wang Z, Yi M, Jiang F, Zhang D, Cao J, Yan X, Zhang J, Wang N, Zhang H, Peng L, Liu Z, Hu S, Tao S. Systematic analysis of the necroptosis index in pan-cancer and classification in discriminating the prognosis and immunotherapy responses of 1716 glioma patients. Front Pharmacol 2023; 14:1170240. [PMID: 37351504 PMCID: PMC10282546 DOI: 10.3389/fphar.2023.1170240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 05/22/2023] [Indexed: 06/24/2023] Open
Abstract
Necroptosis is a programmed form of necrotic cell death that serves as a host gatekeeper for defense against invasion by certain pathogens. Previous studies have uncovered the essential role of necroptosis in tumor progression and implied the potential for novel therapies targeting necroptosis. However, no comprehensive analysis of multi-omics data has been conducted to better understand the relationship between necroptosis and tumor. We developed the necroptosis index (NI) to uncover the effect of necroptosis in most cancers. NI not only correlated with clinical characteristics of multiple tumors, but also could influence drug sensitivity in glioma. Based on necroptosis-related differentially expressed genes, the consensus clustering was used to classify glioma patients into two NI subgroups. Then, we revealed NI subgroup I were more sensitive to immunotherapy, particularly anti-PD1 therapy. This new NI-based classification may have prospective predictive factors for prognosis and guide physicians in prioritizing immunotherapy for potential responders.
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Affiliation(s)
- Shuai Ma
- Department of Neurosurgery, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Cancer Center, Department of Neurosurgery, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
- Department of Neurosurgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Fang Wang
- Cancer Center, Department of Neurosurgery, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Qingzhen Liu
- Institute of Psychiatry and Neuroscience, Xinxiang Medical University, Xinxiang, China
| | - Xiaoteng Geng
- Department of Neurosurgery, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zaibin Wang
- Department of Neurosurgery, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Menglei Yi
- Department of Neurosurgery, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Fan Jiang
- Department of Neurosurgery, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Dongtao Zhang
- Department of Neurosurgery, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Junzheng Cao
- Department of Neurosurgery, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiuwei Yan
- Cancer Center, Department of Neurosurgery, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Jiheng Zhang
- Cancer Center, Department of Neurosurgery, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Nan Wang
- Cancer Center, Department of Neurosurgery, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Heng Zhang
- Department of Neurosurgery, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Lulu Peng
- Department of Neurosurgery, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zhan Liu
- Department of Neurosurgery, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Shaoshan Hu
- Cancer Center, Department of Neurosurgery, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Shengzhong Tao
- Department of Neurosurgery, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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14
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Xu X, Qi Z, Zhang D, Zhang M, Ren Y, Geng Z. DriverGenePathway: Identifying driver genes and driver pathways in cancer based on MutSigCV and statistical methods. Comput Struct Biotechnol J 2023; 21:3124-3135. [PMID: 37293242 PMCID: PMC10244682 DOI: 10.1016/j.csbj.2023.05.019] [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: 02/18/2023] [Revised: 05/18/2023] [Accepted: 05/18/2023] [Indexed: 06/10/2023] Open
Abstract
Although computational methods for driver gene identification have progressed rapidly, it is far from the goal of obtaining widely recognized driver genes for all cancer types. The driver gene lists predicted by these methods often lack consistency and stability across different studies or datasets. In addition to analytical performance, some tools may require further improvement regarding operability and system compatibility. Here, we developed a user-friendly R package (DriverGenePathway) integrating MutSigCV and statistical methods to identify cancer driver genes and pathways. The theoretical basis of the MutSigCV program is elaborated and integrated into DriverGenePathway, such as mutation categories discovery based on information entropy. Five methods of hypothesis testing, including the beta-binomial test, Fisher combined p-value test, likelihood ratio test, convolution test, and projection test, are used to identify the minimal core driver genes. Moreover, de novo methods, which can effectively overcome mutational heterogeneity, are introduced to identify driver pathways. Herein, we describe the computational structure and statistical fundamentals of the DriverGenePathway pipeline and demonstrate its performance using eight types of cancer from TCGA. DriverGenePathway correctly confirms many expected driver genes with high overlap with the Cancer Gene Census list and driver pathways associated with cancer development. The DriverGenePathway R package is freely available on GitHub: https://github.com/bioinformatics-xu/DriverGenePathway.
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Affiliation(s)
- Xiaolu Xu
- School of Computer and Information Technology, Liaoning Normal University, Dalian 116029, China
| | - Zitong Qi
- Department of Statistics, University of Washington, Seattle, WA 98195, USA
| | - Dawei Zhang
- School of Computer and Information Technology, Liaoning Normal University, Dalian 116029, China
| | - Meiwei Zhang
- Center for Reproductive and Genetic Medicine, Dalian Women and Children’s Medical Group, Dalian 116037, China
| | - Yonggong Ren
- School of Computer and Information Technology, Liaoning Normal University, Dalian 116029, China
| | - Zhaohong Geng
- Department of Cardiology, Second Affiliated Hospital of Dalian Medical University, Dalian 116023, China
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15
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Zhang W, Xiang X, Zhao B, Huang J, Yang L, Zeng Y. Identifying Cancer Driver Pathways Based on the Mouth Brooding Fish Algorithm. ENTROPY (BASEL, SWITZERLAND) 2023; 25:841. [PMID: 37372185 DOI: 10.3390/e25060841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 05/05/2023] [Accepted: 05/23/2023] [Indexed: 06/29/2023]
Abstract
Identifying the driver genes of cancer progression is of great significance in improving our understanding of the causes of cancer and promoting the development of personalized treatment. In this paper, we identify the driver genes at the pathway level via an existing intelligent optimization algorithm, named the Mouth Brooding Fish (MBF) algorithm. Many methods based on the maximum weight submatrix model to identify driver pathways attach equal importance to coverage and exclusivity and assign them equal weight, but those methods ignore the impact of mutational heterogeneity. Here, we use principal component analysis (PCA) to incorporate covariate data to reduce the complexity of the algorithm and construct a maximum weight submatrix model considering different weights of coverage and exclusivity. Using this strategy, the unfavorable effect of mutational heterogeneity is overcome to some extent. Data involving lung adenocarcinoma and glioblastoma multiforme were tested with this method and the results compared with the MDPFinder, Dendrix, and Mutex methods. When the driver pathway size was 10, the recognition accuracy of the MBF method reached 80% in both datasets, and the weight values of the submatrix were 1.7 and 1.89, respectively, which are better than those of the compared methods. At the same time, in the signal pathway enrichment analysis, the important role of the driver genes identified by our MBF method in the cancer signaling pathway is revealed, and the validity of these driver genes is demonstrated from the perspective of their biological effects.
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Affiliation(s)
- Wei Zhang
- College of Computer Science and Engineering, Changsha University, Changsha 410022, China
- Hunan Province Key Laboratory of Industrial Internet Technology and Security, Changsha University, Changsha 410022, China
| | - Xiaowen Xiang
- College of Computer Science and Engineering, Changsha University, Changsha 410022, China
| | - Bihai Zhao
- College of Computer Science and Engineering, Changsha University, Changsha 410022, China
- Hunan Province Key Laboratory of Industrial Internet Technology and Security, Changsha University, Changsha 410022, China
| | - Jianlin Huang
- College of Computer Science and Engineering, Changsha University, Changsha 410022, China
| | - Lan Yang
- College of Computer Science and Engineering, Changsha University, Changsha 410022, China
| | - Yifu Zeng
- College of Computer Science and Engineering, Changsha University, Changsha 410022, China
- Hunan Province Key Laboratory of Industrial Internet Technology and Security, Changsha University, Changsha 410022, China
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16
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Liu H, Han Y, Liu Z, Gao L, Yi T, Yu Y, Wang Y, Qu P, Xiang L, Li Y. Depiction of neuroendocrine features associated with immunotherapy response using a novel one-class predictor in lung adenocarcinoma. Discov Oncol 2023; 14:71. [PMID: 37199872 DOI: 10.1007/s12672-023-00693-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 05/12/2023] [Indexed: 05/19/2023] Open
Abstract
BACKGROUND Tumours with no evidence of neuroendocrine transformation histologically but harbouring neuroendocrine features are collectively referred to as non-small cell lung cancer (NSCLC) with neuroendocrine differentiation (NED). Investigating the mechanisms underlying NED is conducive to designing appropriate treatment options for NSCLC patients. METHODS In the present study, we integrated multiple lung cancer datasets to identify neuroendocrine features using a one-class logistic regression (OCLR) machine learning algorithm trained on small cell lung cancer (SCLC) cells, a pulmonary neuroendocrine cell type, based on the transcriptome of NSCLC and named the NED index (NEDI). Single-sample gene set enrichment analysis, pathway enrichment analysis, ESTIMATE algorithm analysis, and unsupervised subclass mapping (SubMap) were performed to assess the altered pathways and immune characteristics of lung cancer samples with different NEDI values. RESULTS We developed and validated a novel one-class predictor based on the expression values of 13,279 mRNAs to quantitatively evaluate neuroendocrine features in NSCLC. We observed that a higher NEDI correlated with better prognosis in patients with LUAD. In addition, we observed that a higher NEDI was significantly associated with reduced immune cell infiltration and immune effector molecule expression. Furthermore, we found that etoposide-based chemotherapy might be more effective in the treatment of LUAD with high NEDI values. Moreover, we noted that tumours with low NEDI values had better responses to immunotherapy than those with high NEDI values. CONCLUSIONS Our findings improve the understanding of NED and provide a useful strategy for applying NEDI-based risk stratification to guide decision-making in the treatment of LUAD.
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Affiliation(s)
- Hao Liu
- Department of Oncology, Renmin Hospital, Hubei University of Medicine, Shiyan, 442000, Hubei, People's Republic of China
| | - Yan Han
- Department of Oncology, Renmin Hospital, Hubei University of Medicine, Shiyan, 442000, Hubei, People's Republic of China
| | - Zhantao Liu
- Department of Oncology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, No.136 Jingzhou Street, Xiangyang, 441021, Hubei, People's Republic of China
| | - Liping Gao
- Department of Gastroenterology, Hubei Clinical Center and Key Lab of Intestinal and Colorectal Diseases, Zhongnan Hospital of Wuhan University, Wuhan, 430072, Hubei, People's Republic of China
| | - Tienan Yi
- Department of Oncology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, No.136 Jingzhou Street, Xiangyang, 441021, Hubei, People's Republic of China
| | - Yuandong Yu
- Department of Oncology, Renmin Hospital, Hubei University of Medicine, Shiyan, 442000, Hubei, People's Republic of China
| | - Yu Wang
- Department of Oncology, Renmin Hospital, Hubei University of Medicine, Shiyan, 442000, Hubei, People's Republic of China
| | - Ping Qu
- Department of Science and Education, Renmin Hospital, Hubei University of Medicine, Shiyan, 442000, Hubei, People's Republic of China
| | - Longchao Xiang
- Department of Oncology, Renmin Hospital, Hubei University of Medicine, Shiyan, 442000, Hubei, People's Republic of China
| | - Yong Li
- Department of Oncology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, No.136 Jingzhou Street, Xiangyang, 441021, Hubei, People's Republic of China.
- Institute of Cancer Research, Renmin Hospital, Hubei University of Medicine, Shiyan, 442000, Hubei, People's Republic of China.
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17
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Patterson A, Elbasir A, Tian B, Auslander N. Computational Methods Summarizing Mutational Patterns in Cancer: Promise and Limitations for Clinical Applications. Cancers (Basel) 2023; 15:1958. [PMID: 37046619 PMCID: PMC10093138 DOI: 10.3390/cancers15071958] [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: 10/27/2022] [Revised: 02/24/2023] [Accepted: 03/09/2023] [Indexed: 03/29/2023] Open
Abstract
Since the rise of next-generation sequencing technologies, the catalogue of mutations in cancer has been continuously expanding. To address the complexity of the cancer-genomic landscape and extract meaningful insights, numerous computational approaches have been developed over the last two decades. In this review, we survey the current leading computational methods to derive intricate mutational patterns in the context of clinical relevance. We begin with mutation signatures, explaining first how mutation signatures were developed and then examining the utility of studies using mutation signatures to correlate environmental effects on the cancer genome. Next, we examine current clinical research that employs mutation signatures and discuss the potential use cases and challenges of mutation signatures in clinical decision-making. We then examine computational studies developing tools to investigate complex patterns of mutations beyond the context of mutational signatures. We survey methods to identify cancer-driver genes, from single-driver studies to pathway and network analyses. In addition, we review methods inferring complex combinations of mutations for clinical tasks and using mutations integrated with multi-omics data to better predict cancer phenotypes. We examine the use of these tools for either discovery or prediction, including prediction of tumor origin, treatment outcomes, prognosis, and cancer typing. We further discuss the main limitations preventing widespread clinical integration of computational tools for the diagnosis and treatment of cancer. We end by proposing solutions to address these challenges using recent advances in machine learning.
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Affiliation(s)
- Andrew Patterson
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- The Wistar Institute, Philadelphia, PA 19104, USA
| | | | - Bin Tian
- The Wistar Institute, Philadelphia, PA 19104, USA
| | - Noam Auslander
- The Wistar Institute, Philadelphia, PA 19104, USA
- Department of Cancer Biology, University of Pennsylvania, Philadelphia, PA 19104, USA
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18
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Chen HH, Hsueh CW, Lee CH, Hao TY, Tu TY, Chang LY, Lee JC, Lin CY. SWEET: a single-sample network inference method for deciphering individual features in disease. Brief Bioinform 2023; 24:7017366. [PMID: 36719112 PMCID: PMC10025435 DOI: 10.1093/bib/bbad032] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 01/05/2023] [Accepted: 01/14/2023] [Indexed: 02/01/2023] Open
Abstract
Recently, extracting inherent biological system information (e.g. cellular networks) from genome-wide expression profiles for developing personalized diagnostic and therapeutic strategies has become increasingly important. However, accurately constructing single-sample networks (SINs) to capture individual characteristics and heterogeneity in disease remains challenging. Here, we propose a sample-specific-weighted correlation network (SWEET) method to model SINs by integrating the genome-wide sample-to-sample correlation (i.e. sample weights) with the differential network between perturbed and aggregate networks. For a group of samples, the genome-wide sample weights can be assessed without prior knowledge of intrinsic subpopulations to address the network edge number bias caused by sample size differences. Compared with the state-of-the-art SIN inference methods, the SWEET SINs in 16 cancers more likely fit the scale-free property, display higher overlap with the human interactomes and perform better in identifying three types of cancer-related genes. Moreover, integrating SWEET SINs with a network proximity measure facilitates characterizing individual features and therapy in diseases, such as somatic mutation, mut-driver and essential genes. Biological experiments further validated two candidate repurposable drugs, albendazole for head and neck squamous cell carcinoma (HNSCC) and lung adenocarcinoma (LUAD) and encorafenib for HNSCC. By applying SWEET, we also identified two possible LUAD subtypes that exhibit distinct clinical features and molecular mechanisms. Overall, the SWEET method complements current SIN inference and analysis methods and presents a view of biological systems at the network level to offer numerous clues for further investigation and clinical translation in network medicine and precision medicine.
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Affiliation(s)
- Hsin-Hua Chen
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
| | - Chun-Wei Hsueh
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
| | - Chia-Hwa Lee
- School of Medical Laboratory Science and Biotechnology, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan
- TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taipei 110, Taiwan
- Ph.D. Program in Medical Biotechnology, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Ting-Yi Hao
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
| | - Tzu-Ying Tu
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
| | - Lan-Yun Chang
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
| | - Jih-Chin Lee
- Department of Otolaryngology-Head and Neck Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei 110, Taiwan
| | - Chun-Yu Lin
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
- Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
- Institute of Data Science and Engineering, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
- Center for Intelligent Drug Systems and Smart Bio-devices, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
- School of Dentistry, Kaohsiung Medical University, Kaohsiung 807, Taiwan
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19
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Quan C, Liu F, Qi L, Tie Y. LRT-CLUSTER: A New Clustering Algorithm Based on Likelihood Ratio Test to Identify Driving Genes. Interdiscip Sci 2023; 15:217-230. [PMID: 36848004 DOI: 10.1007/s12539-023-00554-2] [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: 07/01/2022] [Revised: 01/31/2023] [Accepted: 02/01/2023] [Indexed: 03/01/2023]
Abstract
Somatic mutations often occur at high relapse sites in protein sequences, which indicates that the location clustering of somatic missense mutations can be used to identify driving genes. However, the traditional clustering algorithm has such problems as the background signal over-fitting, the clustering algorithm is not suitable for mutation data, and the performance of identifying low-frequency mutation genes needs to be improved. In this paper, we propose a linear clustering algorithm based on likelihood ratio test knowledge to identify driver genes. In this experiment, firstly, the polynucleotide mutation rate is calculated based on the prior knowledge of likelihood ratio test. Then, the simulation data set is obtained through the background mutation rate model. Finally, the unsupervised peak clustering algorithm is used to, respectively, evaluate the somatic mutation data and the simulation data to identify the driver genes. The experimental results show that our method achieves a better balance of precision and sensitivity. It can also identify the driver genes missed by other methods, making it an effective supplement to other methods. We also discover some potential linkages between genes and between genes and mutation sites, which is of great value to target drug therapy research. Method framework: Our proposed model framework is as follows. a. Counting mutation sites and the number of mutations in tumor gene elements. b. The nucleotide context mutation frequency is counted based on the likelihood ratio test knowledge, and the background mutation rate model is obtained. c. Based on Monte Carlo simulation method, data sets with the same number of mutations as gene elements are randomly sampled to obtain simulated mutation data, and the sampling frequency of each mutation site is related to the mutation rate of polynucleotide. d. The original mutation data and the simulated mutation data after random reconstruction are clustered by peak density, respectively, and the corresponding clustering scores are obtained. e. We can obtain the clustering information statistics in each gene segment and score of each gene segment from the original single nucleotide mutation data through step d. f. According to the observed score and the simulated clustering score, the p-value of the corresponding gene fragment is calculated. g. We can obtain the clustering information statistics in each gene segment and score of each gene segment from the simulated single nucleotide mutation data through step d.
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Affiliation(s)
- Chenxu Quan
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China.,Department of Respiratory and Sleep Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Fenghui Liu
- Department of Respiratory and Sleep Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Lin Qi
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China
| | - Yun Tie
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China.
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20
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Li F, Li H, Shang J, Liu JX, Dai L, Liu X, Li Y. A network-based method for identifying cancer driver genes based on node control centrality. Exp Biol Med (Maywood) 2022; 248:232-241. [PMID: 36573462 PMCID: PMC10107394 DOI: 10.1177/15353702221139201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Cancer is one of the major contributors to human mortality and has a serious influence on human survival and health. In biomedical research, the identification of cancer driver genes (cancer drivers for short) is an important task; cancer drivers can promote the progression and generation of cancer. To identify cancer drivers, many methods have been developed. These computational models only identify coding cancer drivers; however, non-coding drivers likewise play significant roles in the progression of cancer. Hence, we propose a Network-based Method for identifying cancer Driver Genes based on node Control Centrality (NMDGCC), which can identify coding and non-coding cancer driver genes. The process of NMDGCC for identifying driver genes mainly includes the following two steps. In the first step, we construct a gene interaction network by using mRNAs and miRNAs expression data in the cancer state. In the second step, the control centrality of the node is used to identify cancer drivers in the constructed network. We use the breast cancer dataset from The Cancer Genome Atlas (TCGA) to verify the effectiveness of NMDGCC. Compared with the existing methods of cancer driver genes identification, NMDGCC has a better performance. NMDGCC also identifies 295 miRNAs as non-coding cancer drivers, of which 158 are related to tumorigenesis of BRCA. We also apply NMDGCC to identify driver genes related to the different breast cancer subtypes. The result shows that NMDGCC detects many cancer drivers of specific cancer subtypes.
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Affiliation(s)
- Feng Li
- School of Computer Science, Qufu Normal University, Rizhao 276826, China
| | - Han Li
- School of Computer Science, Qufu Normal University, Rizhao 276826, China
| | - Junliang Shang
- School of Computer Science, Qufu Normal University, Rizhao 276826, China
| | - Jin-Xing Liu
- School of Computer Science, Qufu Normal University, Rizhao 276826, China
| | - Lingyun Dai
- School of Computer Science, Qufu Normal University, Rizhao 276826, China
| | - Xikui Liu
- Department of Electrical Engineering and Information Technology, Shandong University of Science and Technology, Jinan 250031, China
| | - Yan Li
- Department of Electrical Engineering and Information Technology, Shandong University of Science and Technology, Jinan 250031, China
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ToMExO: A probabilistic tree-structured model for cancer progression. PLoS Comput Biol 2022; 18:e1010732. [DOI: 10.1371/journal.pcbi.1010732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 12/15/2022] [Accepted: 11/14/2022] [Indexed: 12/12/2022] Open
Abstract
Identifying the interrelations among cancer driver genes and the patterns in which the driver genes get mutated is critical for understanding cancer. In this paper, we study cross-sectional data from cohorts of tumors to identify the cancer-type (or subtype) specific process in which the cancer driver genes accumulate critical mutations. We model this mutation accumulation process using a tree, where each node includes a driver gene or a set of driver genes. A mutation in each node enables its children to have a chance of mutating. This model simultaneously explains the mutual exclusivity patterns observed in mutations in specific cancer genes (by its nodes) and the temporal order of events (by its edges). We introduce a computationally efficient dynamic programming procedure for calculating the likelihood of our noisy datasets and use it to build our Markov Chain Monte Carlo (MCMC) inference algorithm, ToMExO. Together with a set of engineered MCMC moves, our fast likelihood calculations enable us to work with datasets with hundreds of genes and thousands of tumors, which cannot be dealt with using available cancer progression analysis methods. We demonstrate our method’s performance on several synthetic datasets covering various scenarios for cancer progression dynamics. Then, a comparison against two state-of-the-art methods on a moderate-size biological dataset shows the merits of our algorithm in identifying significant and valid patterns. Finally, we present our analyses of several large biological datasets, including colorectal cancer, glioblastoma, and pancreatic cancer. In all the analyses, we validate the results using a set of method-independent metrics testing the causality and significance of the relations identified by ToMExO or competing methods.
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22
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Mina M, Iyer A, Ciriello G. Epistasis and evolutionary dependencies in human cancers. Curr Opin Genet Dev 2022; 77:101989. [PMID: 36182742 DOI: 10.1016/j.gde.2022.101989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 08/29/2022] [Accepted: 08/31/2022] [Indexed: 01/27/2023]
Abstract
Cancer evolution is driven by the concerted action of multiple molecular alterations, which emerge and are selected during tumor progression. An alteration is selected when it provides an advantage to the tumor cell. However, the advantage provided by a specific alteration depends on the tumor lineage, cell epigenetic state, and presence of additional alterations. In this case, we say that an evolutionary dependency exists between an alteration and what influences its selection. Epistatic interactions between altered genes lead to evolutionary dependencies (EDs), by favoring or vetoing specific combinations of events. Large-scale cancer genomics studies have discovered examples of such dependencies, and showed that they influence tumor progression, disease phenotypes, and therapeutic response. In the past decade, several algorithmic approaches have been proposed to infer EDs from large-scale genomics datasets. These methods adopt diverse strategies to address common challenges and shed new light on cancer evolutionary trajectories. Here, we review these efforts starting from a simple conceptualization of the problem, presenting the tackled and still unmet needs in the field, and discussing the implications of EDs in cancer biology and precision oncology.
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Affiliation(s)
- Marco Mina
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland; Swiss Cancer Center Leman, Lausanne, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Arvind Iyer
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland; Swiss Cancer Center Leman, Lausanne, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Giovanni Ciriello
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland; Swiss Cancer Center Leman, Lausanne, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland.
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23
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Jiang Y, Shi C, Tian S, Zhi F, Shen X, Shang D, Tian J. Comprehensive molecular characterization of hypertension-related genes in cancer. CARDIO-ONCOLOGY 2022; 8:10. [PMID: 35513851 PMCID: PMC9069779 DOI: 10.1186/s40959-022-00136-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 04/29/2022] [Indexed: 11/14/2022]
Abstract
Background During cancer treatment, patients have a significantly higher risk of developing cardiovascular complications such as hypertension. In this study, we investigated the internal relationships between hypertension and different types of cancer. Methods First, we comprehensively characterized the involvement of 10 hypertension-related genes across 33 types of cancer. The somatic copy number alteration (CNA) and single nucleotide variant (SNV) of each gene were identified for each type of cancer. Then, the expression patterns of hypertension-related genes were analyzed across 14 types of cancer. The hypertension-related genes were aberrantly expressed in different types of cancer, and some were associated with the overall survival of patients or the cancer stage. Subsequently, the interactions between hypertension-related genes and clinically actionable genes (CAGs) were identified by analyzing the co-expressions and protein–protein interactions. Results We found that certain hypertension-related genes were correlated with CAGs. Next, the pathways associated with hypertension-related genes were identified. The positively correlated pathways included epithelial to mesenchymal transition, hormone androgen receptor, and receptor tyrosine kinase, and the negatively correlated pathways included apoptosis, cell cycle, and DNA damage response. Finally, the correlations between hypertension-related genes and drug sensitivity were evaluated for different drugs and different types of cancer. The hypertension-related genes were all positively or negatively correlated with the resistance of cancer to the majority of anti-cancer drugs. These results highlight the importance of hypertension-related genes in cancer. Conclusions This study provides an approach to characterize the relationship between hypertension-related genes and cancers in the post-genomic era. Supplementary Information The online version contains supplementary material available at 10.1186/s40959-022-00136-z.
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A Novel Thrombosis-Related Signature for Predicting Survival and Drug Compounds in Glioblastoma. JOURNAL OF ONCOLOGY 2022; 2022:6792850. [PMID: 35874629 PMCID: PMC9300384 DOI: 10.1155/2022/6792850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 04/12/2022] [Indexed: 12/02/2022]
Abstract
Glioblastoma is the most common primary tumor in the central nervous system, and thrombosis-associated genes are related to its occurrence and progression. Univariate Cox and LASSO regression analysis were utilized to develop a new prognostic signature based on thrombosis-associated genes. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and HALLMARK were used for functional annotation of risk signature. ESTIMATE, MCP-counter, xCell, and TIMER algorithms were used to quantify immune infiltration in the tumor microenvironment. Genomics of Drug Sensitivity in Cancer (GDSC) was used for selecting potential drug compounds. Risk signature based on thrombosis-associated genes shows moderate performance in prognosis prediction. The functional annotation of the risk signature indicates that the signaling pathways related to the cell cycle, apoptosis, tumorigenesis, and immune suppression are rich in the high-risk group. Somatic mutation analysis shows that tumor-suppressive gene TP53 and oncogene PTEN have higher expression in low-risk and high-risk groups, respectively. Potential drug compounds are explored in risk score groups and show higher AUC values in the low-risk score group. A nomogram with valuable prognostic factors exhibits high sensitivity in predicting the survival outcome of GBM patients. Our research screens out multiple thromboses-associated genes with remarkable clinical significance in GBM and further develops a meaningful prognostic risk signature predicting drug sensitivity and survival outcome.
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25
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The genomic and transcriptional landscape of primary central nervous system lymphoma. Nat Commun 2022; 13:2558. [PMID: 35538064 PMCID: PMC9091224 DOI: 10.1038/s41467-022-30050-y] [Citation(s) in RCA: 49] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 04/12/2022] [Indexed: 02/07/2023] Open
Abstract
Primary lymphomas of the central nervous system (PCNSL) are mainly diffuse large B-cell lymphomas (DLBCLs) confined to the central nervous system (CNS). Molecular drivers of PCNSL have not been fully elucidated. Here, we profile and compare the whole-genome and transcriptome landscape of 51 CNS lymphomas (CNSL) to 39 follicular lymphoma and 36 DLBCL cases outside the CNS. We find recurrent mutations in JAK-STAT, NFkB, and B-cell receptor signaling pathways, including hallmark mutations in MYD88 L265P (67%) and CD79B (63%), and CDKN2A deletions (83%). PCNSLs exhibit significantly more focal deletions of HLA-D (6p21) locus as a potential mechanism of immune evasion. Mutational signatures correlating with DNA replication and mitosis are significantly enriched in PCNSL. TERT gene expression is significantly higher in PCNSL compared to activated B-cell (ABC)-DLBCL. Transcriptome analysis clearly distinguishes PCNSL and systemic DLBCL into distinct molecular subtypes. Epstein-Barr virus (EBV)+ CNSL cases lack recurrent mutational hotspots apart from IG and HLA-DRB loci. We show that PCNSL can be clearly distinguished from DLBCL, having distinct expression profiles, IG expression and translocation patterns, as well as specific combinations of genetic alterations.
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26
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Lou Y, Shi Q, Zhang Y, Qi Y, Zhang W, Wang H, Lu J, Han B, Zhong H. Multi-Omics Signatures Identification for LUAD Prognosis Prediction Model Based on the Integrative Analysis of Immune and Hypoxia Signals. Front Cell Dev Biol 2022; 10:840466. [PMID: 35359451 PMCID: PMC8960258 DOI: 10.3389/fcell.2022.840466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 01/21/2022] [Indexed: 11/21/2022] Open
Abstract
Lung adenocarcinoma (LUAD) is the most common histological subtype of lung cancer with heterogeneous outcomes and diverse therapeutic responses. However, the understanding of the potential mechanism behind LUAD initiation and progression remains limited. Increasing evidence shows the clinical significance of the interaction between immune and hypoxia in tumor microenvironment. To mine reliable prognostic signatures related to both immune and hypoxia and provide a more comprehensive landscape of the hypoxia-immune genome map, we investigated the hypoxia-immune-related alteration at the multi-omics level (gene expression, somatic mutation, and DNA methylation). Multiple strategies including lasso regression and multivariate Cox proportional hazards regression were used to screen the signatures with clinical significance and establish an incorporated prognosis prediction model with robust discriminative power on survival status on both the training and test datasets. Finally, combing all the samples, we constructed a robust model comprising 19 signatures for the prognosis prediction of LUAD patients. The results of our study provide a comprehensive landscape of hypoxia-immune related genetic alterations and provide a robust prognosis predictor for LUAD patients.
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Affiliation(s)
- Yuqing Lou
- Department of Pulmonary Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Qin Shi
- Department of Oncology, Baoshan Branch of Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yanwei Zhang
- Department of Pulmonary Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Ying Qi
- School of Basic Medical Science, Hangzhou Normal University, Hangzhou, China
| | - Wei Zhang
- Department of Pulmonary Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Huimin Wang
- Department of Pulmonary Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Jun Lu
- Department of Pulmonary Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Institute of Thoracic Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
- Translational Medical Research Platform for Thoracic Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
- Department of Bio-bank, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
- *Correspondence: Jun Lu, ; Baohui Han, ; Hua Zhong,
| | - Baohui Han
- Department of Pulmonary Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Institute of Thoracic Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
- Translational Medical Research Platform for Thoracic Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
- *Correspondence: Jun Lu, ; Baohui Han, ; Hua Zhong,
| | - Hua Zhong
- Department of Pulmonary Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
- Translational Medical Research Platform for Thoracic Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
- *Correspondence: Jun Lu, ; Baohui Han, ; Hua Zhong,
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27
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Desai S, Dharavath B, Manavalan S, Rane A, Redhu A, Sunder R, Butle A, Mishra R, Joshi A, Togar T, Apte S, Bala P, Chandrani P, Chopra S, Bashyam M, Banerjee A, Prabhash K, Nair S, Dutt A. Fusobacterium nucleatum is associated with inflammation and poor survival in early-stage HPV-negative tongue cancer. NAR Cancer 2022; 4:zcac006. [PMID: 35252868 PMCID: PMC8894079 DOI: 10.1093/narcan/zcac006] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 02/03/2022] [Accepted: 02/16/2022] [Indexed: 02/05/2023] Open
Abstract
Persistent pathogen infection is a known cause of malignancy, although with sparse systematic evaluation across tumor types. We present a comprehensive landscape of 1060 infectious pathogens across 239 whole exomes and 1168 transcriptomes of breast, lung, gallbladder, cervical, colorectal, and head and neck tumors. We identify known cancer-associated pathogens consistent with the literature. In addition, we identify a significant prevalence of Fusobacterium in head and neck tumors, comparable to colorectal tumors. The Fusobacterium-high subgroup of head and neck tumors occurs mutually exclusive to human papillomavirus, and is characterized by overexpression of miRNAs associated with inflammation, elevated innate immune cell fraction and nodal metastases. We validate the association of Fusobacterium with the inflammatory markers IL1B, IL6 and IL8, miRNAs hsa-mir-451a, hsa-mir-675 and hsa-mir-486-1, and MMP10 in the tongue tumor samples. A higher burden of Fusobacterium is also associated with poor survival, nodal metastases and extracapsular spread in tongue tumors defining a distinct subgroup of head and neck cancer.
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Affiliation(s)
- Sanket Desai
- Integrated Cancer Genomics Laboratory, Advanced Centre for Treatment, Research, and Education in Cancer, Kharghar, Navi Mumbai 410210, Maharashtra, India
- Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai 400094, Maharashtra, India
| | - Bhasker Dharavath
- Integrated Cancer Genomics Laboratory, Advanced Centre for Treatment, Research, and Education in Cancer, Kharghar, Navi Mumbai 410210, Maharashtra, India
- Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai 400094, Maharashtra, India
| | - Sujith Manavalan
- Integrated Cancer Genomics Laboratory, Advanced Centre for Treatment, Research, and Education in Cancer, Kharghar, Navi Mumbai 410210, Maharashtra, India
| | - Aishwarya Rane
- Integrated Cancer Genomics Laboratory, Advanced Centre for Treatment, Research, and Education in Cancer, Kharghar, Navi Mumbai 410210, Maharashtra, India
| | - Archana Kumari Redhu
- Integrated Cancer Genomics Laboratory, Advanced Centre for Treatment, Research, and Education in Cancer, Kharghar, Navi Mumbai 410210, Maharashtra, India
| | - Roma Sunder
- Integrated Cancer Genomics Laboratory, Advanced Centre for Treatment, Research, and Education in Cancer, Kharghar, Navi Mumbai 410210, Maharashtra, India
| | - Ashwin Butle
- Integrated Cancer Genomics Laboratory, Advanced Centre for Treatment, Research, and Education in Cancer, Kharghar, Navi Mumbai 410210, Maharashtra, India
| | - Rohit Mishra
- Integrated Cancer Genomics Laboratory, Advanced Centre for Treatment, Research, and Education in Cancer, Kharghar, Navi Mumbai 410210, Maharashtra, India
| | - Asim Joshi
- Integrated Cancer Genomics Laboratory, Advanced Centre for Treatment, Research, and Education in Cancer, Kharghar, Navi Mumbai 410210, Maharashtra, India
- Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai 400094, Maharashtra, India
| | - Trupti Togar
- Integrated Cancer Genomics Laboratory, Advanced Centre for Treatment, Research, and Education in Cancer, Kharghar, Navi Mumbai 410210, Maharashtra, India
- Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai 400094, Maharashtra, India
| | - Shruti Apte
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai 400076, Maharashtra, India
| | - Pratyusha Bala
- Laboratory of Molecular Oncology, Centre for DNA Fingerprinting and Diagnostics, Hyderabad500039, Telangana, India
| | - Pratik Chandrani
- Integrated Cancer Genomics Laboratory, Advanced Centre for Treatment, Research, and Education in Cancer, Kharghar, Navi Mumbai 410210, Maharashtra, India
- Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai 400094, Maharashtra, India
| | - Supriya Chopra
- Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai 400094, Maharashtra, India
- Department of Radiation Oncology, Advanced Centre for Treatment, Research, and Education in Cancer, Kharghar, Navi Mumbai 410210, Maharashtra, India
| | - Murali Dharan Bashyam
- Laboratory of Molecular Oncology, Centre for DNA Fingerprinting and Diagnostics, Hyderabad500039, Telangana, India
| | - Anirban Banerjee
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai 400076, Maharashtra, India
| | - Kumar Prabhash
- Department of Medical Oncology, Tata Memorial Centre, Ernest Borges Marg, Parel, Mumbai 400012, Maharashtra, India
| | - Sudhir Nair
- Division of Head and Neck Oncology, Department of Surgical Oncology, Tata Memorial Hospital, Tata Memorial Centre, Mumbai 400012, Maharashtra, India
| | - Amit Dutt
- To whom correspondence should be addressed. Tel: +91 22 27405056/30435056;
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Park TY, Leiserson MD, Klau GW, Raphael BJ. SuperDendrix algorithm integrates genetic dependencies and genomic alterations across pathways and cancer types. CELL GENOMICS 2022; 2. [PMID: 35382456 PMCID: PMC8979493 DOI: 10.1016/j.xgen.2022.100099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Recent genome-wide CRISPR-Cas9 loss-of-function screens have identified genetic dependencies across many cancer cell lines. Associations between these dependencies and genomic alterations in the same cell lines reveal phenomena such as oncogene addiction and synthetic lethality. However, comprehensive identification of such associations is complicated by complex interactions between genes across genetically heterogeneous cancer types. We introduce and apply the algorithm SuperDendrix to CRISPR-Cas9 loss-of-function screens from 769 cancer cell lines, to identify differential dependencies across cell lines and to find associations between differential dependencies and combinations of genomic alterations and cell-type-specific markers. These associations respect the position and type of interactions within pathways: for example, we observe increased dependencies on downstream activators of pathways, such as NFE2L2, and decreased dependencies on upstream activators of pathways, such as CDK6. SuperDendrix also reveals dozens of dependencies on lineage-specific transcription factors, identifies cancer-type-specific correlations between dependencies, and enables annotation of individual mutated residues. Using SuperDendrix, Park et al. examine associations between genetic dependencies in 769 cancer cell lines. They report 127 genetic dependencies explained by combinations of mutually exclusive somatic mutations congregating into a few oncogenic pathways across cancer subtypes. These present a small number of prominent and highly specific genetic vulnerabilities in cancer. Graphical abstract
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29
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Kuipers J, Moore AL, Jahn K, Schraml P, Wang F, Morita K, Futreal PA, Takahashi K, Beisel C, Moch H, Beerenwinkel N. Statistical tests for intra-tumour clonal co-occurrence and exclusivity. PLoS Comput Biol 2021; 17:e1009036. [PMID: 34910733 PMCID: PMC8716063 DOI: 10.1371/journal.pcbi.1009036] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 12/29/2021] [Accepted: 11/19/2021] [Indexed: 12/31/2022] Open
Abstract
Tumour progression is an evolutionary process in which different clones evolve over time, leading to intra-tumour heterogeneity. Interactions between clones can affect tumour evolution and hence disease progression and treatment outcome. Intra-tumoural pairs of mutations that are overrepresented in a co-occurring or clonally exclusive fashion over a cohort of patient samples may be suggestive of a synergistic effect between the different clones carrying these mutations. We therefore developed a novel statistical testing framework, called GeneAccord, to identify such gene pairs that are altered in distinct subclones of the same tumour. We analysed our framework for calibration and power. By comparing its performance to baseline methods, we demonstrate that to control type I errors, it is essential to account for the evolutionary dependencies among clones. In applying GeneAccord to the single-cell sequencing of a cohort of 123 acute myeloid leukaemia patients, we find 1 clonally co-occurring and 8 clonally exclusive gene pairs. The clonally exclusive pairs mostly involve genes of the key signalling pathways. Tumours typically display high levels of heterogeneity, not only between different tumours but also within a single one. Intra-tumour heterogeneity results from an evolutionary process, giving rise to different populations of cancer cells known as clones. How clones interact may affect tumour evolution, which in turn determines disease progression and treatment outcome. In practice, we may observe pairs of mutations that co-occur in clones or exclude each other more often than we would expect for a given cohort of patient samples. Exclusive pairs are suggestive that clones carrying one or the other mutation may cooperate in the evolutionary process. Targeting only one of them may then suffice to alter the tumour evolution. Therefore it is critical to have statistical methods which allow us to identify such pairs. GeneAccord is a novel statistical testing framework we developed especially to identify pairs of genes altered in distinct clones of the same tumour. Accounting for the evolutionary dependencies among clones emerged as critical to adequately control testing errors. In a cohort of 123 acute myeloid leukaemia patients, GeneAccord identified one clonally co-occurring and eight clonally exclusive gene pairs. The latter predominantly involved genes of key signalling pathways.
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Affiliation(s)
- Jack Kuipers
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Ariane L. Moore
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Katharina Jahn
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Peter Schraml
- Department of Pathology and Molecular Pathology, University and University Hospital Zurich, Zurich, Switzerland
| | - Feng Wang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Kiyomi Morita
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - P. Andrew Futreal
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Koichi Takahashi
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Christian Beisel
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Holger Moch
- Department of Pathology and Molecular Pathology, University and University Hospital Zurich, Zurich, Switzerland
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
- * E-mail:
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Ahmed R, Erten C, Houdjedj A, Kazan H, Yalcin C. A Network-Centric Framework for the Evaluation of Mutual Exclusivity Tests on Cancer Drivers. Front Genet 2021; 12:746495. [PMID: 34899838 PMCID: PMC8664367 DOI: 10.3389/fgene.2021.746495] [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: 07/23/2021] [Accepted: 10/27/2021] [Indexed: 12/03/2022] Open
Abstract
One of the key concepts employed in cancer driver gene identification is that of mutual exclusivity (ME); a driver mutation is less likely to occur in case of an earlier mutation that has common functionality in the same molecular pathway. Several ME tests have been proposed recently, however the current protocols to evaluate ME tests have two main limitations. Firstly the evaluations are mostly with respect to simulated data and secondly the evaluation metrics lack a network-centric view. The latter is especially crucial as the notion of common functionality can be achieved through searching for interaction patterns in relevant networks. We propose a network-centric framework to evaluate the pairwise significances found by statistical ME tests. It has three main components. The first component consists of metrics employed in the network-centric ME evaluations. Such metrics are designed so that network knowledge and the reference set of known cancer genes are incorporated in ME evaluations under a careful definition of proper control groups. The other two components are designed as further mechanisms to avoid confounders inherent in ME detection on top of the network-centric view. To this end, our second objective is to dissect the side effects caused by mutation load artifacts where mutations driving tumor subtypes with low mutation load might be incorrectly diagnosed as mutually exclusive. Finally, as part of the third main component, the confounding issue stemming from the use of nonspecific interaction networks generated as combinations of interactions from different tissues is resolved through the creation and use of tissue-specific networks in the proposed framework. The data, the source code and useful scripts are available at: https://github.com/abu-compbio/NetCentric.
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Affiliation(s)
- Rafsan Ahmed
- Electrical and Computer Engineering Graduate Program, Antalya Bilim University, Antalya, Turkey
| | - Cesim Erten
- Department of Computer Engineering, Antalya Bilim University, Antalya, Turkey
| | - Aissa Houdjedj
- Department of Computer Engineering, Antalya Bilim University, Antalya, Turkey
| | - Hilal Kazan
- Department of Computer Engineering, Antalya Bilim University, Antalya, Turkey
| | - Cansu Yalcin
- Department of Computer Engineering, Antalya Bilim University, Antalya, Turkey
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Molecular drivers of tumor progression in microsatellite stable APC mutation-negative colorectal cancers. Sci Rep 2021; 11:23507. [PMID: 34873211 PMCID: PMC8648784 DOI: 10.1038/s41598-021-02806-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 11/18/2021] [Indexed: 12/23/2022] Open
Abstract
The tumor suppressor gene adenomatous polyposis coli (APC) is the initiating mutation in approximately 80% of all colorectal cancers (CRC), underscoring the importance of aberrant regulation of intracellular WNT signaling in CRC development. Recent studies have found that early-onset CRC exhibits an increased proportion of tumors lacking an APC mutation. We set out to identify mechanisms underlying APC mutation-negative (APCmut-) CRCs. We analyzed data from The Cancer Genome Atlas to compare clinical phenotypes, somatic mutations, copy number variations, gene fusions, RNA expression, and DNA methylation profiles between APCmut- and APC mutation-positive (APCmut+) microsatellite stable CRCs. Transcriptionally, APCmut- CRCs clustered into two approximately equal groups. Cluster One was associated with enhanced mitochondrial activation. Cluster Two was strikingly associated with genetic inactivation or decreased RNA expression of the WNT antagonist RNF43, increased expression of the WNT agonist RSPO3, activating mutation of BRAF, or increased methylation and decreased expression of AXIN2. APCmut- CRCs exhibited evidence of increased immune cell infiltration, with significant correlation between M2 macrophages and RSPO3. APCmut- CRCs comprise two groups of tumors characterized by enhanced mitochondrial activation or increased sensitivity to extracellular WNT, suggesting that they could be respectively susceptible to inhibition of these pathways.
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The Cancer Surfaceome Atlas integrates genomic, functional and drug response data to identify actionable targets. NATURE CANCER 2021; 2:1406-1422. [PMID: 35121907 PMCID: PMC9940627 DOI: 10.1038/s43018-021-00282-w] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 10/01/2021] [Indexed: 01/01/2023]
Abstract
Cell-surface proteins (SPs) are a rich source of immune and targeted therapies. By systematically integrating single-cell and bulk genomics, functional studies and target actionability, in the present study we comprehensively identify and annotate genes encoding SPs (GESPs) pan-cancer. We characterize GESP expression patterns, recurrent genomic alterations, essentiality, receptor-ligand interactions and therapeutic potential. We also find that mRNA expression of GESPs is cancer-type specific and positively correlates with protein expression, and that certain GESP subgroups function as common or specific essential genes for tumor cell growth. We also predict receptor-ligand interactions substantially deregulated in cancer and, using systems biology approaches, we identify cancer-specific GESPs with therapeutic potential. We have made this resource available through the Cancer Surfaceome Atlas ( http://fcgportal.org/TCSA ) within the Functional Cancer Genome data portal.
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Molecular characterization of metabolic subtypes of gastric cancer based on metabolism-related lncRNA. Sci Rep 2021; 11:21491. [PMID: 34728653 PMCID: PMC8563741 DOI: 10.1038/s41598-021-00410-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 09/23/2021] [Indexed: 11/17/2022] Open
Abstract
Increasing evidence has demonstrated that lncRNAs are critical regulators in diverse biological processes, but the function of lncRNA in metabolic regulation remains largely unexplored. In this study, we evaluated the association between lncRNA and metabolic pathways and identified metabolism-related lncRNAs. Gastric cancer can be mainly subdivided into 2 clusters based on these metabolism-related lncRNA regulators. Comparative analysis shows that these subtypes are found to be highly consistent with previously identified subtypes based on other omics data. Functional enrichment analysis shows that they are enriched in distinct biological processes. Mutation analysis shows that ABCA13 is a protective factor in subtype C1 but a risk factor in C2. Analysis of chemotherapeutic and immunotherapeutic sensitivity shows that these subtypes tend to display distinct sensitivity to the same chemical drugs. In conclusion, these findings demonstrated the significance of lncRNA in metabolic regulation. These metabolism-related lncRNA regulators can improve our understanding of the underlying mechanism of lncRNAs and advance the research of immunotherapies in the clinical management of gastric cancer.
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Rong H, Li Y, Hu S, Gao L, Yi T, Xie Y, Cai P, Li J, Dai X, Ye M, Liao Q. Prognostic signatures and potential pathogenesis of eRNAs-related genes in colon adenocarcinoma. Mol Carcinog 2021; 61:59-72. [PMID: 34622496 DOI: 10.1002/mc.23359] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 09/17/2021] [Accepted: 09/29/2021] [Indexed: 12/23/2022]
Abstract
Enhancer RNAs (eRNAs) are a subclass of long noncoding RNAs (lncRNAs) that have a wide effect in human tumors. However, the systematic analysis of potential functions of eRNAs-related genes (eRGs) in colon cancer (CC) remains unexplored. In this study, a total of 8231 eRGs including 6236 protein-coding genes and 1995 lncRNAs were identified in CC based on the multiple resources. These eRGs showed higher expression level and stability compared to other genes. What's more, the functions of these eRGs were closely related to cancer. Then a prognostic prediction model with 12 eRGs signatures were obtained for colon adenocarcinoma (COAD) patients. ROC curves showed the AUCs were 0.81, 0.77, and 0.78 for 1-, 3-, and 5-year survival prediction, respectively. And the prognostic model also manifested good performance in the validation datasets. Besides, the expression levels of two prognostic signatures, TMEM220 and LRRN2, were verified to be significantly lower in CC tissues than in adjacent noncancerous tissues (p < .05). Finally, the distinct molecular features were characterized between the high- and low-risk group through multiomics analysis including DNA mutation and methylation. Our results show eRGs signatures based prognostic model has high accuracy and may provide innovative biomarkers in COAD.
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Affiliation(s)
- Hao Rong
- The Affiliated Hospital of Medical School of Ningbo University, Ningbo, China.,Department of Preventive Medicine, Zhejiang Provincial Key Laboratory of Pathological and Physiological Technology, School of Medicine, Ningbo, China
| | - Yanguo Li
- Institute of Drug Discovery Technology, Ningbo University, Ningbo, China
| | - Shiyun Hu
- The Affiliated Hospital of Medical School of Ningbo University, Ningbo, China
| | - Liuying Gao
- The Affiliated People's Hospital of Ningbo University, Ningbo, China
| | - Tianfei Yi
- Department of Preventive Medicine, Zhejiang Provincial Key Laboratory of Pathological and Physiological Technology, School of Medicine, Ningbo, China
| | - Yangyang Xie
- Hua Mei Hospital, University of Chinese Academy of Science, Ningbo, China
| | - Ping Cai
- Hua Mei Hospital, University of Chinese Academy of Science, Ningbo, China
| | - Jianjiong Li
- Hua Mei Hospital, University of Chinese Academy of Science, Ningbo, China
| | - Xiaoyu Dai
- Hua Mei Hospital, University of Chinese Academy of Science, Ningbo, China
| | - Meng Ye
- The Affiliated Hospital of Medical School of Ningbo University, Ningbo, China
| | - Qi Liao
- The Affiliated Hospital of Medical School of Ningbo University, Ningbo, China.,Department of Preventive Medicine, Zhejiang Provincial Key Laboratory of Pathological and Physiological Technology, School of Medicine, Ningbo, China
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35
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A systematic analysis of genetic interactions and their underlying biology in childhood cancer. Commun Biol 2021; 4:1139. [PMID: 34615983 PMCID: PMC8494736 DOI: 10.1038/s42003-021-02647-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 09/08/2021] [Indexed: 02/08/2023] Open
Abstract
Childhood cancer is a major cause of child death in developed countries. Genetic interactions between mutated genes play an important role in cancer development. They can be detected by searching for pairs of mutated genes that co-occur more (or less) often than expected. Co-occurrence suggests a cooperative role in cancer development, while mutual exclusivity points to synthetic lethality, a phenomenon of interest in cancer treatment research. Little is known about genetic interactions in childhood cancer. We apply a statistical pipeline to detect genetic interactions in a combined dataset comprising over 2,500 tumors from 23 cancer types. The resulting genetic interaction map of childhood cancers comprises 15 co-occurring and 27 mutually exclusive candidates. The biological explanation of most candidates points to either tumor subtype, pathway epistasis or cooperation while synthetic lethality plays a much smaller role. Thus, other explanations beyond synthetic lethality should be considered when interpreting genetic interaction test results.
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36
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Gao B, Zhao Y, Gao Y, Li G, Wu L. Identification of Common Driver Gene Modules and Associations between Cancers through Integrated Network Analysis. GLOBAL CHALLENGES (HOBOKEN, NJ) 2021; 5:2100006. [PMID: 34504716 PMCID: PMC8414517 DOI: 10.1002/gch2.202100006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 04/26/2021] [Indexed: 05/12/2023]
Abstract
High-throughput biological data has created an unprecedented opportunity for illuminating the mechanisms of tumor emergence and evolution. An important and challenging problem in deciphering cancers is to investigate the commonalities of driver genes and pathways and the associations between cancers. Aiming at this problem, a tool ComCovEx is developed to identify common cancer driver gene modules between two cancers by searching for the candidates in local signaling networks using an exclusivity-coverage iteration strategy and outputting those with significant coverage and exclusivity for both cancers. The associations of the cancer pairs are further evaluated by Fisher's exact test. Being applied to 11 TCGA cancer datasets, ComCovEx identifies 13 significantly associated cancer pairs with plenty of biologically significant common gene modules. The novel results of cancer relationship and common gene modules reveal the relevant pathological basis of different cancer types and provide new clues to diagnosis and drug treatment in associated cancers.
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Affiliation(s)
- Bo Gao
- IAMMADISNCMISAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing100190China
- School of MathematicsShandong UniversityJinan250100China
- School of Mathematical SciencesUniversity of Chinese Academy of SciencesBeijing100049China
- School of Public HealthCapital Medical UniversityBeijing100069China
- Beijing Municipal Key Laboratory of Clinical EpidemiologyBeijing100069China
| | - Yue Zhao
- IAMMADISNCMISAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing100190China
- School of Mathematical SciencesUniversity of Chinese Academy of SciencesBeijing100049China
| | - Yonghang Gao
- IAMMADISNCMISAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing100190China
- School of Mathematical SciencesUniversity of Chinese Academy of SciencesBeijing100049China
| | - Guojun Li
- School of MathematicsShandong UniversityJinan250100China
- Research Center for Mathematics and Interdisciplinary SciencesShandong UniversityQingdao266237China
| | - Ling‐Yun Wu
- IAMMADISNCMISAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing100190China
- School of Mathematical SciencesUniversity of Chinese Academy of SciencesBeijing100049China
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37
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Liu S, Liu J, Xie Y, Zhai T, Hinderer EW, Stromberg AJ, Vanderford NL, Kolesar JM, Moseley HNB, Chen L, Liu C, Wang C. MEScan: a powerful statistical framework for genome-scale mutual exclusivity analysis of cancer mutations. Bioinformatics 2021; 37:1189-1197. [PMID: 33165532 PMCID: PMC8189684 DOI: 10.1093/bioinformatics/btaa957] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2019] [Revised: 03/20/2020] [Accepted: 10/31/2020] [Indexed: 12/17/2022] Open
Abstract
MOTIVATION Cancer somatic driver mutations associated with genes within a pathway often show a mutually exclusive pattern across a cohort of patients. This mutually exclusive mutational signal has been frequently used to distinguish driver from passenger mutations and to investigate relationships among driver mutations. Current methods for de novo discovery of mutually exclusive mutational patterns are limited because the heterogeneity in background mutation rate can confound mutational patterns, and the presence of highly mutated genes can lead to spurious patterns. In addition, most methods only focus on a limited number of pre-selected genes and are unable to perform genome-wide analysis due to computational inefficiency. RESULTS We introduce a statistical framework, MEScan, for accurate and efficient mutual exclusivity analysis at the genomic scale. Our framework contains a fast and powerful statistical test for mutual exclusivity with adjustment of the background mutation rate and impact of highly mutated genes, and a multi-step procedure for genome-wide screening with the control of false discovery rate. We demonstrate that MEScan more accurately identifies mutually exclusive gene sets than existing methods and is at least two orders of magnitude faster than most methods. By applying MEScan to data from four different cancer types and pan-cancer, we have identified several biologically meaningful mutually exclusive gene sets. AVAILABILITY AND IMPLEMENTATION MEScan is available as an R package at https://github.com/MarkeyBBSRF/MEScan. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Jinpeng Liu
- Markey Cancer Center, University of Kentucky, Lexington, KY 40536, USA
| | - Yanqi Xie
- Molecular and Cellular Biochemistry, University of Kentucky, Lexington, KY 40536, USA
| | - Tingting Zhai
- Department of Statistics, University of Kentucky, Lexington, KY 40536, USA
| | - Eugene W Hinderer
- Molecular and Cellular Biochemistry, University of Kentucky, Lexington, KY 40536, USA
| | - Arnold J Stromberg
- Department of Statistics, University of Kentucky, Lexington, KY 40536, USA
| | - Nathan L Vanderford
- Markey Cancer Center, University of Kentucky, Lexington, KY 40536, USA.,Department of Toxicology and Cancer Biology, University of Kentucky, Lexington, KY 40536, USA
| | - Jill M Kolesar
- Markey Cancer Center, University of Kentucky, Lexington, KY 40536, USA.,Department of Pharmacy Practice and Science, University of Kentucky, Lexington, KY 40536, USA
| | - Hunter N B Moseley
- Markey Cancer Center, University of Kentucky, Lexington, KY 40536, USA.,Molecular and Cellular Biochemistry, University of Kentucky, Lexington, KY 40536, USA
| | - Li Chen
- Markey Cancer Center, University of Kentucky, Lexington, KY 40536, USA.,Department of Internal Medicine, University of Kentucky, Lexington, KY 40536, USA
| | - Chunming Liu
- Markey Cancer Center, University of Kentucky, Lexington, KY 40536, USA.,Molecular and Cellular Biochemistry, University of Kentucky, Lexington, KY 40536, USA
| | - Chi Wang
- Markey Cancer Center, University of Kentucky, Lexington, KY 40536, USA.,Department of Internal Medicine, University of Kentucky, Lexington, KY 40536, USA
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38
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Nicol PB, Coombes KR, Deaver C, Chkrebtii O, Paul S, Toland AE, Asiaee A. Oncogenetic network estimation with disjunctive Bayesian networks. COMPUTATIONAL AND SYSTEMS ONCOLOGY 2021. [DOI: 10.1002/cso2.1027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Affiliation(s)
| | - Kevin R. Coombes
- Department of Biomedical Informatics Ohio State University Columbus Ohio
| | - Courtney Deaver
- Natural Sciences Division Pepperdine University Malibu California
| | | | - Subhadeep Paul
- Department of Statistics Ohio State University Columbus Ohio
| | - Amanda E. Toland
- Department of Cancer Biology and Genetics and Department of Internal Medicine Division of Human Genetics, Comprehensive Cancer Center Ohio State University Columbus Ohio
| | - Amir Asiaee
- Mathematical Biosciences Institute Ohio State University Columbus Ohio
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39
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Park Y, Heider D, Hauschild AC. Integrative Analysis of Next-Generation Sequencing for Next-Generation Cancer Research toward Artificial Intelligence. Cancers (Basel) 2021; 13:3148. [PMID: 34202427 PMCID: PMC8269018 DOI: 10.3390/cancers13133148] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 06/16/2021] [Accepted: 06/21/2021] [Indexed: 12/18/2022] Open
Abstract
The rapid improvement of next-generation sequencing (NGS) technologies and their application in large-scale cohorts in cancer research led to common challenges of big data. It opened a new research area incorporating systems biology and machine learning. As large-scale NGS data accumulated, sophisticated data analysis methods became indispensable. In addition, NGS data have been integrated with systems biology to build better predictive models to determine the characteristics of tumors and tumor subtypes. Therefore, various machine learning algorithms were introduced to identify underlying biological mechanisms. In this work, we review novel technologies developed for NGS data analysis, and we describe how these computational methodologies integrate systems biology and omics data. Subsequently, we discuss how deep neural networks outperform other approaches, the potential of graph neural networks (GNN) in systems biology, and the limitations in NGS biomedical research. To reflect on the various challenges and corresponding computational solutions, we will discuss the following three topics: (i) molecular characteristics, (ii) tumor heterogeneity, and (iii) drug discovery. We conclude that machine learning and network-based approaches can add valuable insights and build highly accurate models. However, a well-informed choice of learning algorithm and biological network information is crucial for the success of each specific research question.
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Affiliation(s)
- Youngjun Park
- Department of Mathematics and Computer Science, Philipps-University of Marburg, 35032 Marburg, Germany; (Y.P.); (D.H.)
| | - Dominik Heider
- Department of Mathematics and Computer Science, Philipps-University of Marburg, 35032 Marburg, Germany; (Y.P.); (D.H.)
| | - Anne-Christin Hauschild
- Department of Mathematics and Computer Science, Philipps-University of Marburg, 35032 Marburg, Germany; (Y.P.); (D.H.)
- Department of Medical Informatics, University Medical Center Göttingen, 37075 Göttingen, Germany
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40
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Xu F, Shen J, Xu S. Multi-Omics Data Analyses Construct a Six Immune-Related Genes Prognostic Model for Cervical Cancer in Tumor Microenvironment. Front Genet 2021; 12:663617. [PMID: 34108992 PMCID: PMC8181403 DOI: 10.3389/fgene.2021.663617] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 04/15/2021] [Indexed: 12/26/2022] Open
Abstract
The cross-talk between tumor cells and the tumor microenvironment (TME) is an important factor in determining the tumorigenesis and progression of cervical cancer (CC). However, clarifying the potential mechanisms which trigger the above biological processes remains a challenge. The present study focused on immune-relevant differences at the transcriptome and somatic mutation levels through an integrative multi-omics analysis based on The Cancer Genome Atlas database. The objective of the study was to recognize the specific immune-related prognostic factors predicting the survival and response to immunotherapy of patients with CC. Firstly, eight hub immune-related prognostic genes were ultimately identified through construction of a protein–protein interaction network and Cox regression analysis. Secondly, 32 differentially mutated genes were simultaneously identified based on the different levels of immune infiltration. As a result, an immune gene-related prognostic model (IGRPM), including six factors (chemokine receptor 7 [CCR7], CD3d molecule [CD3D], CD3e molecule [CD3E], and integrin subunit beta 2 [ITGB2], family with sequence similarity 133 member A [FAM133A], and tumor protein p53 [TP53]), was finally constructed to forecast clinical outcomes of CC. Its predictive capability was further assessed and validated using the Gene Expression Omnibus validation set. In conclusion, IGRPM may be a promising prognostic signature to predict the prognoses and responses to immunotherapy of patients with CC. Moreover, the multi-omics study showed that IGRPM could be a novel therapeutic target for CC, which is a promising biomarker for indicating the immune-dominant status of the TME and revealing the potential mechanisms responsible for the tumorigenesis and progression of CC.
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Affiliation(s)
- Fangfang Xu
- Department of Gynecology, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Tongji University, Shanghai, China
| | - Jiacheng Shen
- Department of Gynecology, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Tongji University, Shanghai, China
| | - Shaohua Xu
- Department of Gynecology, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Tongji University, Shanghai, China
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41
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Gui CP, Wei JH, Chen YH, Fu LM, Tang YM, Cao JZ, Chen W, Luo JH. A new thinking: extended application of genomic selection to screen multiomics data for development of novel hypoxia-immune biomarkers and target therapy of clear cell renal cell carcinoma. Brief Bioinform 2021; 22:6273240. [PMID: 34237133 DOI: 10.1093/bib/bbab173] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 03/30/2021] [Accepted: 04/11/2021] [Indexed: 12/12/2022] Open
Abstract
Increasing evidences show the clinical significance of the interaction between hypoxia and immune in clear cell renal cell carcinoma (ccRCC) microenvironment. However, reliable prognostic signatures based on a combination of hypoxia and immune have not been well established. Moreover, many studies have only used RNA-seq profiles to screen the prognosis feature of ccRCC. Presently, there is no comprehensive analysis of multiomics data to mine a better one. Thus, we try and get it. First, t-SNE and ssGSEA analysis were used to establish tumor subtypes related to hypoxia-immune, and we investigated the hypoxia-immune-related differences in three types of genetic or epigenetic characteristics (gene expression profiles, somatic mutation, and DNA methylation) by analyzing the multiomics data from The Cancer Genome Atlas (TCGA) portal. Additionally, a four-step strategy based on lasso regression and Cox regression was used to construct a satisfying prognostic model, with average 1-year, 3-year and 5-year areas under the curve (AUCs) equal to 0.806, 0.776 and 0.837. Comparing it with other nine known prognostic biomarkers and clinical prognostic scoring algorithms, the multiomics-based signature performs better. Then, we verified the gene expression differences in two external databases (ICGC and SYSU cohorts). Next, eight hub genes were singled out and seven hub genes were validated as prognostic genes in SYSU cohort. Furthermore, it was indicated high-risk patients have a better response for immunotherapy in immunophenoscore (IPS) analysis and TIDE algorithm. Meanwhile, estimated by GDSC and cMAP database, the high-risk patients showed sensitive responses to six chemotherapy drugs and six candidate small-molecule drugs. In summary, the signature can accurately predict the prognosis of ccRCC and may shed light on the development of novel hypoxia-immune biomarkers and target therapy of ccRCC.
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Affiliation(s)
- Cheng-Peng Gui
- First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Jin-Huan Wei
- First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yu-Hang Chen
- First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Liang-Min Fu
- First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yi-Ming Tang
- First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Jia-Zheng Cao
- Affiliated Jiangmen Hospital, Sun Yat-sen University, Jiangmen, Guangdong, China
| | - Wei Chen
- First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Jun-Hang Luo
- First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
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42
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Haupt S, Zeilmann A, Ahadova A, Bläker H, von Knebel Doeberitz M, Kloor M, Heuveline V. Mathematical modeling of multiple pathways in colorectal carcinogenesis using dynamical systems with Kronecker structure. PLoS Comput Biol 2021; 17:e1008970. [PMID: 34003820 PMCID: PMC8162698 DOI: 10.1371/journal.pcbi.1008970] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 05/28/2021] [Accepted: 04/16/2021] [Indexed: 01/02/2023] Open
Abstract
Like many other types of cancer, colorectal cancer (CRC) develops through multiple pathways of carcinogenesis. This is also true for colorectal carcinogenesis in Lynch syndrome (LS), the most common inherited CRC syndrome. However, a comprehensive understanding of the distribution of these pathways of carcinogenesis, which allows for tailored clinical treatment and even prevention, is still lacking. We suggest a linear dynamical system modeling the evolution of different pathways of colorectal carcinogenesis based on the involved driver mutations. The model consists of different components accounting for independent and dependent mutational processes. We define the driver gene mutation graphs and combine them using the Cartesian graph product. This leads to matrix components built by the Kronecker sum and product of the adjacency matrices of the gene mutation graphs enabling a thorough mathematical analysis and medical interpretation. Using the Kronecker structure, we developed a mathematical model which we applied exemplarily to the three pathways of colorectal carcinogenesis in LS. Beside a pathogenic germline variant in one of the DNA mismatch repair (MMR) genes, driver mutations in APC, CTNNB1, KRAS and TP53 are considered. We exemplarily incorporate mutational dependencies, such as increased point mutation rates after MMR deficiency, and based on recent experimental data, biallelic somatic CTNNB1 mutations as common drivers of LS-associated CRCs. With the model and parameter choice, we obtained simulation results that are in concordance with clinical observations. These include the evolution of MMR-deficient crypts as early precursors in LS carcinogenesis and the influence of variants in MMR genes thereon. The proportions of MMR-deficient and MMR-proficient APC-inactivated crypts as first measure for the distribution among the pathways in LS-associated colorectal carcinogenesis are compatible with clinical observations. The approach provides a modular framework for modeling multiple pathways of carcinogenesis yielding promising results in concordance with clinical observations in LS CRCs.
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Affiliation(s)
- Saskia Haupt
- Engineering Mathematics and Computing Lab (EMCL), Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany
- Data Mining and Uncertainty Quantification (DMQ), Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
| | - Alexander Zeilmann
- Image and Pattern Analysis Group (IPA), Heidelberg University, Heidelberg, Germany
| | - Aysel Ahadova
- Department of Applied Tumor Biology (ATB), Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
| | - Hendrik Bläker
- Institute of Pathology, University Hospital Leipzig, Leipzig, Germany
| | - Magnus von Knebel Doeberitz
- Department of Applied Tumor Biology (ATB), Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
| | - Matthias Kloor
- Department of Applied Tumor Biology (ATB), Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
| | - Vincent Heuveline
- Engineering Mathematics and Computing Lab (EMCL), Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany
- Data Mining and Uncertainty Quantification (DMQ), Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
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43
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Pham VVH, Liu L, Bracken CP, Nguyen T, Goodall GJ, Li J, Le TD. pDriver : A novel method for unravelling personalised coding and miRNA cancer drivers. Bioinformatics 2021; 37:3285-3292. [PMID: 33904576 DOI: 10.1093/bioinformatics/btab262] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 03/19/2021] [Accepted: 04/22/2021] [Indexed: 02/07/2023] Open
Abstract
MOTIVATION Unravelling cancer driver genes is important in cancer research. Although computational methods have been developed to identify cancer drivers, most of them detect cancer drivers at population level. However, two patients who have the same cancer type and receive the same treatment may have different outcomes because each patient has a different genome and their disease might be driven by different driver genes. Therefore new methods are being developed for discovering cancer drivers at individual level, but existing personalised methods only focus on coding drivers while microRNAs (miRNAs) have been shown to drive cancer progression as well. Thus, novel methods are required to discover both coding and miRNA cancer drivers at individual level. RESULTS We propose the novel method, pDriver, to discover personalised cancer drivers. pDriver includes two stages: (1) Constructing gene networks for each cancer patient and (2) Discovering cancer drivers for each patient based on the constructed gene networks. To demonstrate the effectiveness of pDriver, we have applied it to five TCGA cancer datasets and compared it with the state-of-the-art methods. The result indicates that pDriver is more effective than other methods. Furthermore, pDriver can also detect miRNA cancer drivers and most of them have been confirmed to be associated with cancer by literature. We further analyse the predicted personalised drivers for breast cancer patients and the result shows that they are significantly enriched in many GO processes and KEGG pathways involved in breast cancer. AVAILABILITY AND IMPLEMENTATION pDriver is available at https://github.com/pvvhoang/pDriver. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Vu V H Pham
- UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia
| | - Lin Liu
- UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia
| | - Cameron P Bracken
- Centre for Cancer Biology, an alliance of SA Pathology and University of South Australia, Adelaide, SA 5000, Australia.,Department of Medicine, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Thin Nguyen
- Applied Artificial Intelligence Institute, Deakin University, Australia
| | - Gregory J Goodall
- Centre for Cancer Biology, an alliance of SA Pathology and University of South Australia, Adelaide, SA 5000, Australia.,Department of Medicine, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Jiuyong Li
- UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia
| | - Thuc D Le
- UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia
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da Silva EM, Selenica P, Vahdatinia M, Pareja F, Da Cruz Paula A, Ferrando L, Gazzo AM, Dopeso H, Ross DS, Bakhteri A, Riaz N, Chandarlapaty S, Razavi P, Norton L, Wen HY, Brogi E, Weigelt B, Zhang H, Reis-Filho JS. TERT promoter hotspot mutations and gene amplification in metaplastic breast cancer. NPJ Breast Cancer 2021; 7:43. [PMID: 33863915 PMCID: PMC8052452 DOI: 10.1038/s41523-021-00250-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 03/15/2021] [Indexed: 12/22/2022] Open
Abstract
Metaplastic breast cancers (MBCs) are characterized by complex genomes, which seem to vary according to their histologic subtype. TERT promoter hotspot mutations and gene amplification are rare in common forms of breast cancer, but present in a subset of phyllodes tumors. Here, we sought to determine the frequency of genetic alterations affecting TERT in a cohort of 60 MBCs with distinct predominant metaplastic components (squamous, 23%; spindle, 27%; osseous, 8%; chondroid, 42%), and to compare the repertoire of genetic alterations of MBCs according to the presence of TERT promoter hotspot mutations or gene amplification. Forty-four MBCs were subjected to: whole-exome sequencing (WES; n = 27) or targeted sequencing of 341-468 cancer-related genes (n = 17); 16 MBCs were subjected to Sanger sequencing of the TERT promoter, TP53 and selected exons of PIK3CA, HRAS, and BRAF. TERT promoter hotspot mutations (n = 9) and TERT gene amplification (n = 1) were found in 10 of the 60 MBCs analyzed, respectively. These TERT alterations were less frequently found in MBCs with predominant chondroid differentiation than in other MBC subtypes (p = 0.01, Fisher's exact test) and were mutually exclusive with TP53 mutations (p < 0.001, CoMEt). In addition, a comparative analysis of the MBCs subjected to WES or targeted cancer gene sequencing (n = 44) revealed that MBCs harboring TERT promoter hotspot mutations or gene amplification (n = 6) more frequently harbored PIK3CA than TERT wild-type MBCs (n = 38; p = 0.001; Fisher's exact test). In conclusion, TERT somatic genetic alterations are found in a subset of TP53 wild-type MBCs with squamous/spindle differentiation, highlighting the genetic diversity of these cancers.
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Affiliation(s)
- Edaise M da Silva
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Pier Selenica
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Mahsa Vahdatinia
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Fresia Pareja
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Arnaud Da Cruz Paula
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Lorenzo Ferrando
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Internal Medicine, University of Genoa, Genova, Italy
| | - Andrea M Gazzo
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Higinio Dopeso
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Dara S Ross
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ariya Bakhteri
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Nadeem Riaz
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Sarat Chandarlapaty
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Pedram Razavi
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Larry Norton
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Hannah Y Wen
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Edi Brogi
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Britta Weigelt
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Hong Zhang
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| | - Jorge S Reis-Filho
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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Pham VVH, Liu L, Bracken C, Goodall G, Li J, Le TD. Computational methods for cancer driver discovery: A survey. Am J Cancer Res 2021; 11:5553-5568. [PMID: 33859763 PMCID: PMC8039954 DOI: 10.7150/thno.52670] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 01/20/2021] [Indexed: 12/21/2022] Open
Abstract
Identifying the genes responsible for driving cancer is of critical importance for directing treatment. Accordingly, multiple computational tools have been developed to facilitate this task. Due to the different methods employed by these tools, different data considered by the tools, and the rapidly evolving nature of the field, the selection of an appropriate tool for cancer driver discovery is not straightforward. This survey seeks to provide a comprehensive review of the different computational methods for discovering cancer drivers. We categorise the methods into three groups; methods for single driver identification, methods for driver module identification, and methods for identifying personalised cancer drivers. In addition to providing a “one-stop” reference of these methods, by evaluating and comparing their performance, we also provide readers the information about the different capabilities of the methods in identifying biologically significant cancer drivers. The biologically relevant information identified by these tools can be seen through the enrichment of discovered cancer drivers in GO biological processes and KEGG pathways and through our identification of a small cancer-driver cohort that is capable of stratifying patient survival.
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Joshi A, Mishra R, Desai S, Chandrani P, Kore H, Sunder R, Hait S, Iyer P, Trivedi V, Choughule A, Noronha V, Joshi A, Patil V, Menon N, Kumar R, Prabhash K, Dutt A. Molecular characterization of lung squamous cell carcinoma tumors reveals therapeutically relevant alterations. Oncotarget 2021; 12:578-588. [PMID: 33796225 PMCID: PMC7984830 DOI: 10.18632/oncotarget.27905] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 02/15/2021] [Indexed: 02/05/2023] Open
Abstract
INTRODUCTION Unlike lung adenocarcinoma patients, there is no FDA-approved targeted-therapy likely to benefit lung squamous cell carcinoma patients. MATERIALS AND METHODS We performed survival analyses of lung squamous cell carcinoma patients harboring therapeutically relevant alterations identified by whole exome sequencing and mass spectrometry-based validation across 430 lung squamous tumors. RESULTS We report a mean of 11.6 mutations/Mb with a characteristic smoking signature along with mutations in TP53 (65%), CDKN2A (20%), NFE2L2 (20%), FAT1 (15%), KMT2C (15%), LRP1B (15%), FGFR1 (14%), PTEN (10%) and PREX2 (5%) among lung squamous cell carcinoma patients of Indian descent. In addition, therapeutically relevant EGFR mutations occur in 5.8% patients, significantly higher than as reported among Caucasians. In overall, our data suggests 13.5% lung squamous patients harboring druggable mutations have lower median overall survival, and 19% patients with a mutation in at least one gene, known to be associated with cancer, result in significantly shorter median overall survival compared to those without mutations. CONCLUSIONS We present the first comprehensive landscape of genetic alterations underlying Indian lung squamous cell carcinoma patients and identify EGFR, PIK3CA, KRAS and FGFR1 as potentially important therapeutic and prognostic target.
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Affiliation(s)
- Asim Joshi
- 1Integrated Cancer Genomics Laboratory, Advanced Centre for Treatment Research Education in Cancer (ACTREC), Tata Memorial Centre, Navi Mumbai, Maharashtra 410210, India
- 4Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai, Maharashtra 410210, India
| | - Rohit Mishra
- 1Integrated Cancer Genomics Laboratory, Advanced Centre for Treatment Research Education in Cancer (ACTREC), Tata Memorial Centre, Navi Mumbai, Maharashtra 410210, India
| | - Sanket Desai
- 1Integrated Cancer Genomics Laboratory, Advanced Centre for Treatment Research Education in Cancer (ACTREC), Tata Memorial Centre, Navi Mumbai, Maharashtra 410210, India
- 4Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai, Maharashtra 410210, India
| | - Pratik Chandrani
- 2Department of Medical Oncology, Tata Memorial Centre, Ernest Borges Marg, Parel, Mumbai, Maharashtra 400012, India
- 4Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai, Maharashtra 410210, India
- 5Centre for Computational Biology, Bioinformatics and Crosstalk Laboratory, ACTREC, Tata Memorial Centre, Navi Mumbai, Maharashtra 410210, India
| | - Hitesh Kore
- 1Integrated Cancer Genomics Laboratory, Advanced Centre for Treatment Research Education in Cancer (ACTREC), Tata Memorial Centre, Navi Mumbai, Maharashtra 410210, India
| | - Roma Sunder
- 1Integrated Cancer Genomics Laboratory, Advanced Centre for Treatment Research Education in Cancer (ACTREC), Tata Memorial Centre, Navi Mumbai, Maharashtra 410210, India
| | - Supriya Hait
- 1Integrated Cancer Genomics Laboratory, Advanced Centre for Treatment Research Education in Cancer (ACTREC), Tata Memorial Centre, Navi Mumbai, Maharashtra 410210, India
- 4Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai, Maharashtra 410210, India
| | - Prajish Iyer
- 1Integrated Cancer Genomics Laboratory, Advanced Centre for Treatment Research Education in Cancer (ACTREC), Tata Memorial Centre, Navi Mumbai, Maharashtra 410210, India
- 4Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai, Maharashtra 410210, India
| | - Vaishakhi Trivedi
- 2Department of Medical Oncology, Tata Memorial Centre, Ernest Borges Marg, Parel, Mumbai, Maharashtra 400012, India
- 4Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai, Maharashtra 410210, India
| | - Anuradha Choughule
- 2Department of Medical Oncology, Tata Memorial Centre, Ernest Borges Marg, Parel, Mumbai, Maharashtra 400012, India
- 4Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai, Maharashtra 410210, India
| | - Vanita Noronha
- 2Department of Medical Oncology, Tata Memorial Centre, Ernest Borges Marg, Parel, Mumbai, Maharashtra 400012, India
- 4Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai, Maharashtra 410210, India
| | - Amit Joshi
- 2Department of Medical Oncology, Tata Memorial Centre, Ernest Borges Marg, Parel, Mumbai, Maharashtra 400012, India
- 4Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai, Maharashtra 410210, India
| | - Vijay Patil
- 2Department of Medical Oncology, Tata Memorial Centre, Ernest Borges Marg, Parel, Mumbai, Maharashtra 400012, India
- 4Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai, Maharashtra 410210, India
| | - Nandini Menon
- 2Department of Medical Oncology, Tata Memorial Centre, Ernest Borges Marg, Parel, Mumbai, Maharashtra 400012, India
- 4Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai, Maharashtra 410210, India
| | - Rajiv Kumar
- 3Department of Pathology, Tata Memorial Centre, Ernest Borges Marg, Parel, Mumbai, Maharashtra 400012, India
- 4Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai, Maharashtra 410210, India
| | - Kumar Prabhash
- 2Department of Medical Oncology, Tata Memorial Centre, Ernest Borges Marg, Parel, Mumbai, Maharashtra 400012, India
- 4Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai, Maharashtra 410210, India
- Kumar Prabhash, email:
| | - Amit Dutt
- 1Integrated Cancer Genomics Laboratory, Advanced Centre for Treatment Research Education in Cancer (ACTREC), Tata Memorial Centre, Navi Mumbai, Maharashtra 410210, India
- 4Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai, Maharashtra 410210, India
- Correspondence to: Amit Dutt, email:
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Klein MI, Cannataro VL, Townsend JP, Newman S, Stern DF, Zhao H. Identifying modules of cooperating cancer drivers. Mol Syst Biol 2021; 17:e9810. [PMID: 33769711 PMCID: PMC7995435 DOI: 10.15252/msb.20209810] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 01/20/2021] [Accepted: 01/26/2021] [Indexed: 12/22/2022] Open
Abstract
Identifying cooperating modules of driver alterations can provide insights into cancer etiology and advance the development of effective personalized treatments. We present Cancer Rule Set Optimization (CRSO) for inferring the combinations of alterations that cooperate to drive tumor formation in individual patients. Application to 19 TCGA cancer types revealed a mean of 11 core driver combinations per cancer, comprising 2-6 alterations per combination and accounting for a mean of 70% of samples per cancer type. CRSO is distinct from methods based on statistical co-occurrence, which we demonstrate is a suboptimal criterion for investigating driver cooperation. CRSO identified well-studied driver combinations that were not detected by other approaches and nominated novel combinations that correlate with clinical outcomes in multiple cancer types. Novel synergies were identified in NRAS-mutant melanomas that may be therapeutically relevant. Core driver combinations involving NFE2L2 mutations were identified in four cancer types, supporting the therapeutic potential of NRF2 pathway inhibition. CRSO is available at https://github.com/mikekleinsgit/CRSO/.
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Affiliation(s)
- Michael I Klein
- Program in Computational Biology and BioinformaticsYale UniversityNew HavenCTUSA
- Bioinformatics R&DSema4StamfordCTUSA
| | - Vincent L Cannataro
- Department of BiologyEmmanuel CollegeBostonMAUSA
- Department of BiostatisticsYale School of Public HealthNew HavenCTUSA
| | - Jeffrey P Townsend
- Program in Computational Biology and BioinformaticsYale UniversityNew HavenCTUSA
- Department of BiostatisticsYale School of Public HealthNew HavenCTUSA
- Yale Cancer CenterYale UniversityNew HavenCTUSA
| | | | - David F Stern
- Yale Cancer CenterYale UniversityNew HavenCTUSA
- Department of PathologyYale School of MedicineNew HavenCTUSA
| | - Hongyu Zhao
- Program in Computational Biology and BioinformaticsYale UniversityNew HavenCTUSA
- Department of BiostatisticsYale School of Public HealthNew HavenCTUSA
- Yale Cancer CenterYale UniversityNew HavenCTUSA
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Fang H, Zhang Z, Zhou Y, Jin L, Yang Y. A greedy approach for mutual exclusivity analysis in cancer study. Biostatistics 2021; 23:910-925. [PMID: 33634822 DOI: 10.1093/biostatistics/kxab004] [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: 07/15/2020] [Revised: 11/26/2020] [Accepted: 01/13/2021] [Indexed: 11/14/2022] Open
Abstract
The main challenge in cancer genomics is to distinguish the driver genes from passenger or neutral genes. Cancer genomes exhibit extensive mutational heterogeneity that no two genomes contain exactly the same somatic mutations. Such mutual exclusivity (ME) of mutations has been observed in cancer data and is associated with functional pathways. Analysis of ME patterns may provide useful clues to driver genes or pathways and may suggest novel understandings of cancer progression. In this article, we consider a probabilistic, generative model of ME, and propose a powerful and greedy algorithm to select the mutual exclusivity gene sets. The greedy method includes a pre-selection procedure and a stepwise forward algorithm which can significantly reduce computation time. Power calculations suggest that the new method is efficient and powerful for one ME set or multiple ME sets with overlapping genes. We illustrate this approach by analysis of the whole-exome sequencing data of cancer types from TCGA.
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Affiliation(s)
- Hongyan Fang
- School of Mathematical Sciences, Anhui University, Hefei, Anhui, China
| | - Zeyu Zhang
- Department of Statistics and Finance, University of Science and Technology of China, Hefei, Anhui, China
| | - Yinsheng Zhou
- Department of Statistics and Finance, University of Science and Technology of China, Hefei, Anhui, China
| | - Lishuai Jin
- School of Mathematical Sciences, Anhui University, Hefei, Anhui, China
| | - Yaning Yang
- Department of Statistics and Finance, University of Science and Technology of China, Hefei, Anhui, China
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Esai Selvan M, Zauderer MG, Rudin CM, Jones S, Mukherjee S, Offit K, Onel K, Rennert G, Velculescu VE, Lipkin SM, Klein RJ, Gümüş ZH. Inherited Rare, Deleterious Variants in ATM Increase Lung Adenocarcinoma Risk. J Thorac Oncol 2020; 15:1871-1879. [PMID: 32866655 PMCID: PMC8496202 DOI: 10.1016/j.jtho.2020.08.017] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Revised: 07/05/2020] [Accepted: 08/12/2020] [Indexed: 02/06/2023]
Abstract
INTRODUCTION Lung cancer is the leading cause of cancer deaths in the world, and lung adenocarcinoma (LUAD) is its most prevalent subtype. Symptoms are often found in advanced disease in which treatment options are limited. Identifying genetic risk factors will enable better identification of high-risk individuals. METHODS To identify LUAD risk genes, we performed a case-control association study for gene-level burden of rare, deleterious variants (RDVs) in germline whole-exome sequencing data of 1083 patients with LUAD and 7650 controls, split into discovery and validation cohorts. Of these, we performed whole-exome sequencing on 97 patients and acquired the rest from multiple public databases. We annotated all rare variants for pathogenicity conservatively, using the guidelines of the American College of Medical Genetics and Genomics and ClinVar curation, and investigated gene-level RDV burden using penalized logistic regression. All statistical tests were two-sided. RESULTS We discovered and replicated the finding that the burden of germline ATM RDVs was significantly higher in patients with LUAD versus controls (combined cohort OR = 4.6; p = 1.7e-04; 95% confidence interval = 2.2-9.5; 1.21% of cases; 0.24% of controls). Germline ATM RDVs were also enriched in an independent clinical cohort of 1594 patients from the MSK-IMPACT study (0.63%). In addition, we observed that an Ashkenazi Jewish (AJ) founder ATM variant, rs56009889, was statistically significantly more frequent in AJ cases versus AJ controls in our cohort (combined AJ cohort OR = 2.7, p = 6.9e-03, 95% confidence interval = 1.3-5.3). CONCLUSIONS Our results indicate that ATM is a moderate-penetrance LUAD risk gene and that LUAD may be a part of the ATM-related cancer syndrome spectrum. Individuals with ATM RDVs are at an elevated LUAD risk and can benefit from increased surveillance (particularly computed tomography scanning), early detection, and chemoprevention programs, improving prognosis.
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Affiliation(s)
- Myvizhi Esai Selvan
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Marjorie G Zauderer
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Charles M Rudin
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Siân Jones
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Semanti Mukherjee
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Kenneth Offit
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Kenan Onel
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Gad Rennert
- Department of Community Medicine and Epidemiology, Carmel Medical Center, Clalit National Israeli Cancer Control Center, Haifa, Israel
| | - Victor E Velculescu
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Steven M Lipkin
- Departments of Medicine and Genetic Medicine, Weill Cornell Medical College, New York, New York
| | - Robert J Klein
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Zeynep H Gümüş
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, New York.
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50
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Qin K, Zheng Z, He Y, Gao Y, Shi H, Mo S, Zhang J, Rong J. High expression of neutrophil cytosolic factor 2 (NCF2) is associated with aggressive features and poor prognosis of esophageal squamous cell carcinoma. INTERNATIONAL JOURNAL OF CLINICAL AND EXPERIMENTAL PATHOLOGY 2020; 13:3033-3043. [PMID: 33425104 PMCID: PMC7791380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Accepted: 04/28/2019] [Indexed: 06/12/2023]
Abstract
BACKGROUND In the development of several human cancers, it has been established that neutrophil cytosolic factor 2 (NCF2) plays a major part. Therefore, possible functions of NCF2 in ESCC are investigated in this paper. METHODS The mRNA/protein expression of NCF-2 in ESCC cell lines and tissues were found based on quantitative real-time reverse transcription PCR (qRT-PCR), western blotting, and immunohistochemistry (IHC). A large cohort consisting of 194 postoperative ESCC samples was used for IHC. These data were analyzed based on Chi-square test, Kaplan-Meier analysis, and Cox regression modelling. For the purpose of confirming its role in ESCC cells, we used short hairpin RNA (ShRNA) interfering method to suppress endogenous NCF2 expression. RESULTS NCF2 was significantly up-regulated for in ESCC tissues and cell lines in at mRNA and protein levels; and NCF-2 expression was absent for all normal esophageal epithelium detected by IHC. Furthermore, the knockdown of NCF-2 compromised the proliferation and invasion of ESCC cells in vitro. CONCLUSION Positive NCF2 expression in ESCC may facilitate an aggressive phenotype. This may be an independent biomarker in ESCC.
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Affiliation(s)
- Kai Qin
- Department of Extracorporeal Circulation, The First Affiliated Hospital, Sun Yat-sen UniversityGuangzhou 510080, China
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer MedicineGuangzhou 510060, China
| | - Zhousan Zheng
- Department of Oncology, The First Affiliated Hospital, Sun Yat-sen UniversityGuangzhou 510080, China
| | - Ying He
- Department of Oncology, The First Affiliated Hospital, Sun Yat-sen UniversityGuangzhou 510080, China
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer MedicineGuangzhou 510060, China
| | - Ying Gao
- Department of Oncology, The First Affiliated Hospital, Sun Yat-sen UniversityGuangzhou 510080, China
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer MedicineGuangzhou 510060, China
| | - Han Shi
- Department of Extracorporeal Circulation, The First Affiliated Hospital, Sun Yat-sen UniversityGuangzhou 510080, China
| | - Shaoyan Mo
- Department of Extracorporeal Circulation, The First Affiliated Hospital, Sun Yat-sen UniversityGuangzhou 510080, China
| | - Jiaxing Zhang
- Department of Oncology, The First Affiliated Hospital, Sun Yat-sen UniversityGuangzhou 510080, China
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer MedicineGuangzhou 510060, China
| | - Jian Rong
- Department of Extracorporeal Circulation, The First Affiliated Hospital, Sun Yat-sen UniversityGuangzhou 510080, China
- Key Laboratory on Assisted Circulation, Ministry of HealthGuangzhou 510080, China
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