1
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Pinheiro M, Wentzensen N, Dean M, Yeager M, Chen Z, Shastry A, Boland JF, Bass S, Burdett L, Lorey T, Mishra S, Castle PE, Schiffman M, Burk RD, Zhu B, Mirabello L. Somatic mutations in 3929 HPV positive cervical cells associated with infection outcome and HPV type. Nat Commun 2024; 15:7895. [PMID: 39266536 PMCID: PMC11393421 DOI: 10.1038/s41467-024-51713-y] [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: 12/05/2023] [Accepted: 08/13/2024] [Indexed: 09/14/2024] Open
Abstract
Invasive cervical cancers (ICC), caused by HPV infections, have a heterogeneous molecular landscape. We investigate the detection, timing, and HPV type specificity of somatic mutations in 3929 HPV-positive exfoliated cervical cell samples from individuals undergoing cervical screening in the U.S. using deep targeted sequencing in ICC cases, precancers, and HPV-positive controls. We discover a subset of hotspot mutations rare in controls (2.6%) but significantly more prevalent in precancers, particularly glandular precancer lesions (10.2%), and cancers (25.7%), supporting their involvement in ICC carcinogenesis. Hotspot mutations differ by HPV type, and HPV18/45-positive ICC are more likely to have multiple hotspot mutations compared to HPV16-positive ICC. The proportion of cells containing hotspot mutations is higher (i.e., higher variant allele fraction) in ICC and mutations are detectable up to 6 years prior to cancer diagnosis. Our findings demonstrate the feasibility of using exfoliated cervical cells for detection of somatic mutations as potential diagnostic biomarkers.
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Affiliation(s)
- Maisa Pinheiro
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - Nicolas Wentzensen
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - Michael Dean
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
- Cancer Genomics Research Laboratory, Leidos Biomedical Research, Inc., Frederick, MD, USA
| | - Meredith Yeager
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
- Cancer Genomics Research Laboratory, Leidos Biomedical Research, Inc., Frederick, MD, USA
- Department of Biology, Hood College, Frederick, MD, USA
| | - Zigui Chen
- Department of Microbiology, The Chinese University of Hong Kong, Hong Kong, China
| | - Amulya Shastry
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
- Cancer Genomics Research Laboratory, Leidos Biomedical Research, Inc., Frederick, MD, USA
| | - Joseph F Boland
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
- Cancer Genomics Research Laboratory, Leidos Biomedical Research, Inc., Frederick, MD, USA
| | - Sara Bass
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
- Cancer Genomics Research Laboratory, Leidos Biomedical Research, Inc., Frederick, MD, USA
| | - Laurie Burdett
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
- Cancer Genomics Research Laboratory, Leidos Biomedical Research, Inc., Frederick, MD, USA
| | - Thomas Lorey
- Regional Laboratory and Women's Health Research Institute, Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Sambit Mishra
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
- Cancer Genomics Research Laboratory, Leidos Biomedical Research, Inc., Frederick, MD, USA
| | - Philip E Castle
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
- Division of Cancer Prevention, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - Mark Schiffman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - Robert D Burk
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Obstetrics & Gynecology and Women's Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Bin Zhu
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - Lisa Mirabello
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA.
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2
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Wang L, Sun H, Yue Z, Xia J, Li X. CDMPred: a tool for predicting cancer driver missense mutations with high-quality passenger mutations. PeerJ 2024; 12:e17991. [PMID: 39253604 PMCID: PMC11382650 DOI: 10.7717/peerj.17991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Accepted: 08/07/2024] [Indexed: 09/11/2024] Open
Abstract
Most computational methods for predicting driver mutations have been trained using positive samples, while negative samples are typically derived from statistical methods or putative samples. The representativeness of these negative samples in capturing the diversity of passenger mutations remains to be determined. To tackle these issues, we curated a balanced dataset comprising driver mutations sourced from the COSMIC database and high-quality passenger mutations obtained from the Cancer Passenger Mutation database. Subsequently, we encoded the distinctive features of these mutations. Utilizing feature correlation analysis, we developed a cancer driver missense mutation predictor called CDMPred employing feature selection through the ensemble learning technique XGBoost. The proposed CDMPred method, utilizing the top 10 features and XGBoost, achieved an area under the receiver operating characteristic curve (AUC) value of 0.83 and 0.80 on the training and independent test sets, respectively. Furthermore, CDMPred demonstrated superior performance compared to existing state-of-the-art methods for cancer-specific and general diseases, as measured by AUC and area under the precision-recall curve. Including high-quality passenger mutations in the training data proves advantageous for CDMPred's prediction performance. We anticipate that CDMPred will be a valuable tool for predicting cancer driver mutations, furthering our understanding of personalized therapy.
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Affiliation(s)
- Lihua Wang
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui, China
- School of Information Engineering, Huangshan University, Huangshan, Anhui, China
| | - Haiyang Sun
- State Key Laboratory of Medicinal Chemical Biology, NanKai University, Tianjin, Tianjin, China
| | - Zhenyu Yue
- School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, Anhui, China
| | - Junfeng Xia
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui, China
| | - Xiaoyan Li
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui, China
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3
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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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4
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Ostroverkhova D, Sheng Y, Panchenko A. Are Next-Generation Pathogenicity Predictors Applicable to Cancer? J Mol Biol 2024; 436:168644. [PMID: 38848867 DOI: 10.1016/j.jmb.2024.168644] [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: 05/06/2024] [Revised: 06/01/2024] [Accepted: 06/03/2024] [Indexed: 06/09/2024]
Abstract
Next-generation pathogenicity predictors are designed to identify pathogenic mutations in genetic disorders but are increasingly used to detect driver mutations in cancer. Despite this, their suitability for cancer is not fully established. Here we have assessed the effectiveness of next-generation pathogenicity predictors when applied to cancer by using a comprehensive experimental benchmark of cancer driver and neutral mutations. Our findings indicate that state-of-the-art methods AlphaMissense and VARITY demonstrate commendable performance despite generally underperforming compared to cancer-specific methods. This is notable considering that these methods do not explicitly incorporate cancer-related data in their training and have made concerted efforts to prevent data leakage from the human-curated training and test sets. Nevertheless, it should be mentioned that a significant limitation of using pathogenicity predictors for cancer arises from their inability to detect cancer potential driver mutations specific for a particular cancer type.
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Affiliation(s)
| | - Yiru Sheng
- Department of Biology and Molecular Sciences, Queen's University, Canada
| | - Anna Panchenko
- Department of Pathology and Molecular Medicine, Queen's University, Canada; Department of Biology and Molecular Sciences, Queen's University, Canada; School of Computing, Queen's University, Canada; Ontario Institute of Cancer Research, Canada.
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5
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Sudhakar M, Vignesh H, Natarajan KN. Crosstalk between tumor and microenvironment: Insights from spatial transcriptomics. Adv Cancer Res 2024; 163:187-222. [PMID: 39271263 DOI: 10.1016/bs.acr.2024.06.009] [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] [Indexed: 09/15/2024]
Abstract
Cancer is a dynamic disease, and clonal heterogeneity plays a fundamental role in tumor development, progression, and resistance to therapies. Single-cell and spatial multimodal technologies can provide a high-resolution molecular map of underlying genomic, epigenomic, and transcriptomic alterations involved in inter- and intra-tumor heterogeneity and interactions with the microenvironment. In this review, we provide a perspective on factors driving cancer heterogeneity, tumor evolution, and clonal states. We briefly describe spatial transcriptomic technologies and summarize recent literature that sheds light on the dynamical interactions between tumor states, cell-to-cell communication, and remodeling local microenvironment.
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Affiliation(s)
- Malvika Sudhakar
- DTU Bioengineering, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Harie Vignesh
- DTU Bioengineering, Technical University of Denmark, Kongens Lyngby, Denmark
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6
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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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7
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Wall P, Ideker T. Representing mutations for predicting cancer drug response. Bioinformatics 2024; 40:i160-i168. [PMID: 38940147 PMCID: PMC11256944 DOI: 10.1093/bioinformatics/btae209] [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] [Indexed: 06/29/2024] Open
Abstract
MOTIVATION Predicting cancer drug response requires a comprehensive assessment of many mutations present across a tumor genome. While current drug response models generally use a binary mutated/unmutated indicator for each gene, not all mutations in a gene are equivalent. RESULTS Here, we construct and evaluate a series of predictive models based on leading methods for quantitative mutation scoring. Such methods include VEST4 and CADD, which score the impact of a mutation on gene function, and CHASMplus, which scores the likelihood a mutation drives cancer. The resulting predictive models capture cellular responses to dabrafenib, which targets BRAF-V600 mutations, whereas models based on binary mutation status do not. Performance improvements generalize to other drugs, extending genetic indications for PIK3CA, ERBB2, EGFR, PARP1, and ABL1 inhibitors. Introducing quantitative mutation features in drug response models increases performance and mechanistic understanding. AVAILABILITY AND IMPLEMENTATION Code and example datasets are available at https://github.com/pgwall/qms.
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Affiliation(s)
- Patrick Wall
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, United States
| | - Trey Ideker
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, United States
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, United States
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA 92093, United States
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8
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Brito-Robinson T, Ayinuola YA, Ploplis VA, Castellino FJ. Plasminogen missense variants and their involvement in cardiovascular and inflammatory disease. Front Cardiovasc Med 2024; 11:1406953. [PMID: 38984351 PMCID: PMC11231438 DOI: 10.3389/fcvm.2024.1406953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Accepted: 06/06/2024] [Indexed: 07/11/2024] Open
Abstract
Human plasminogen (PLG), the zymogen of the fibrinolytic protease, plasmin, is a polymorphic protein with two widely distributed codominant alleles, PLG/Asp453 and PLG/Asn453. About 15 other missense or non-synonymous single nucleotide polymorphisms (nsSNPs) of PLG show major, yet different, relative abundances in world populations. Although the existence of these relatively abundant allelic variants is generally acknowledged, they are often overlooked or assumed to be non-pathogenic. In fact, at least half of those major variants are classified as having conflicting pathogenicity, and it is unclear if they contribute to different molecular phenotypes. From those, PLG/K19E and PLG/A601T are examples of two relatively abundant PLG variants that have been associated with PLG deficiencies (PD), but their pathogenic mechanisms are unclear. On the other hand, approximately 50 rare and ultra-rare PLG missense variants have been reported to cause PD as homozygous or compound heterozygous variants, often leading to a debilitating disease known as ligneous conjunctivitis. The true abundance of PD-associated nsSNPs is unknown since they can remain undetected in heterozygous carriers. However, PD variants may also contribute to other diseases. Recently, the ultra-rare autosomal dominant PLG/K311E has been found to be causative of hereditary angioedema (HAE) with normal C1 inhibitor. Two other rare pathogenic PLG missense variants, PLG/R153G and PLG/V709E, appear to affect platelet function and lead to HAE, respectively. Herein, PLG missense variants that are abundant and/or clinically relevant due to association with disease are examined along with their world distribution. Proposed molecular mechanisms are discussed when known or can be reasonably assumed.
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Affiliation(s)
| | | | | | - Francis J. Castellino
- Department of Chemistry and Biochemistry and the W.M. Keck Center for Transgene Research, University of Notre Dame, Notre Dame, IN, United States
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9
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Tandon S, Sharma M, Kasar P, Kala A. A cloud-based precision oncology framework for whole genome sequence analysis. Comput Biol Chem 2024; 110:108062. [PMID: 38554501 DOI: 10.1016/j.compbiolchem.2024.108062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 03/05/2024] [Accepted: 03/25/2024] [Indexed: 04/01/2024]
Abstract
Cancer is one of the wide-ranging diseases which have a high mortality rate impacting globally. This scenario can be switched by early detection and correct precision treatment, a major concern for cancer patients. Clinicians can figure out the best-suited treatments for cancer patients by analyzing the patient's genome, which will treat the patient well and minimize the chances of side effects as well. Therefore, we have developed a fast, robust, and efficient solution as our precision oncology framework based on the whole genome sequencing of the individual's DNA. This platform can perform the entire genomic analysis, starting from the quality assessment of the input file to the variant annotation and functional prediction, followed by a certain level of interpretation. This analysis helps in the molecular profiling of the tumors for the identification of the targetable alterations. It takes in FASTQ or BAM file as an input and provides us with two output reports: a primary report, which consists of the patients' details, a summary of the analysis, and a secondary report, which is an elaborated report comprised of numerous results obtained from the analysis such as base changes, codon changes, amino acid changes, TMB analysis, MSI analysis, the variant frequency with its effects and impacts, affected biomarkers, etc. This framework can be effectively utilized for cancer treatment guidance, identification and validation of novel biomarkers, oncology research & development, genomic analysis, and gene manipulation.
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Affiliation(s)
- Saloni Tandon
- Celebal Technologies Private Limited, 7th Floor Corporate tower, JLN Marg, Near Jawahar Circle, Malviya Nagar, Jaipur, Rajasthan 302017, India.
| | - Medha Sharma
- Celebal Technologies Private Limited, 7th Floor Corporate tower, JLN Marg, Near Jawahar Circle, Malviya Nagar, Jaipur, Rajasthan 302017, India
| | - Pratik Kasar
- Celebal Technologies Private Limited, 7th Floor Corporate tower, JLN Marg, Near Jawahar Circle, Malviya Nagar, Jaipur, Rajasthan 302017, India
| | - Anirudh Kala
- Celebal Technologies Private Limited, 7th Floor Corporate tower, JLN Marg, Near Jawahar Circle, Malviya Nagar, Jaipur, Rajasthan 302017, India
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10
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Wei H, Ren H. Precision treatment of pancreatic ductal adenocarcinoma. Cancer Lett 2024; 585:216636. [PMID: 38278471 DOI: 10.1016/j.canlet.2024.216636] [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: 10/12/2023] [Revised: 12/08/2023] [Accepted: 01/07/2024] [Indexed: 01/28/2024]
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a highly heterogeneous tumor comprising pancreatic cancer cells, fibroblasts, immune cells, vascular epithelial cells, and other cells in the mesenchymal tissue. PDAC is difficult to treat because of the complexity of the tissue components; therefore, achieving therapeutic effects with a single therapeutic method or target is problematic. Recently, precision therapy has provided new directions and opportunities for treating PDAC using genetic information from an individual's disease to guide treatment. It selects and applies appropriate therapeutic methods for each patient, with an aim to minimize medical damage and costs, while maximizing patient benefits. Molecular targeted therapy is effective in most clinical studies; however, it has been ineffective in large-scale randomized controlled trials of PDAC, mainly because the enrolled populations were not stratified on a molecular basis. Molecular stratification allows the identification of the PDAC population being treated, optimizing therapeutic effect. However, a systematic review of precision therapies for patients with highly heterogeneous PDAC backgrounds has not been conducted. Here, we review the molecular background and current potential therapeutic targets related to PDAC and provide new directions for PDAC precision therapy.
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Affiliation(s)
- Hongyun Wei
- Department of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, 266003, China; Key Laboratory of Pancreatic Diseases, Center of Tumor Immunology and Cytotherapy, Medical Research Center, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, 266003, China.
| | - He Ren
- Key Laboratory of Pancreatic Diseases, Center of Tumor Immunology and Cytotherapy, Medical Research Center, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, 266003, China.
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11
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Chitluri KK, Emerson IA. The importance of protein domain mutations in cancer therapy. Heliyon 2024; 10:e27655. [PMID: 38509890 PMCID: PMC10950675 DOI: 10.1016/j.heliyon.2024.e27655] [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: 10/11/2023] [Revised: 02/28/2024] [Accepted: 03/05/2024] [Indexed: 03/22/2024] Open
Abstract
Cancer is a complex disease that is caused by multiple genetic factors. Researchers have been studying protein domain mutations to understand how they affect the progression and treatment of cancer. These mutations can significantly impact the development and spread of cancer by changing the protein structure, function, and signalling pathways. As a result, there is a growing interest in how these mutations can be used as prognostic indicators for cancer prognosis. Recent studies have shown that protein domain mutations can provide valuable information about the severity of the disease and the patient's response to treatment. They may also be used to predict the response and resistance to targeted therapy in cancer treatment. The clinical implications of protein domain mutations in cancer are significant, and they are regarded as essential biomarkers in oncology. However, additional techniques and approaches are required to characterize changes in protein domains and predict their functional effects. Machine learning and other computational tools offer promising solutions to this challenge, enabling the prediction of the impact of mutations on protein structure and function. Such predictions can aid in the clinical interpretation of genetic information. Furthermore, the development of genome editing tools like CRISPR/Cas9 has made it possible to validate the functional significance of mutants more efficiently and accurately. In conclusion, protein domain mutations hold great promise as prognostic and predictive biomarkers in cancer. Overall, considerable research is still needed to better define genetic and molecular heterogeneity and to resolve the challenges that remain, so that their full potential can be realized.
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Affiliation(s)
- Kiran Kumar Chitluri
- Bioinformatics Programming Lab, Department of Bio-Sciences, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, TN, 632014, India
| | - Isaac Arnold Emerson
- Bioinformatics Programming Lab, Department of Bio-Sciences, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, TN, 632014, India
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12
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Dondi A, Borgsmüller N, Ferreira PF, Haas BJ, Jacob F, Heinzelmann-Schwarz V, Beerenwinkel N. De novo detection of somatic variants in long-read single-cell RNA sequencing data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.06.583775. [PMID: 38496441 PMCID: PMC10942462 DOI: 10.1101/2024.03.06.583775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
In cancer, genetic and transcriptomic variations generate clonal heterogeneity, possibly leading to treatment resistance. Long-read single-cell RNA sequencing (LR scRNA-seq) has the potential to detect genetic and transcriptomic variations simultaneously. Here, we present LongSom, a computational workflow leveraging LR scRNA-seq data to call de novo somatic single-nucleotide variants (SNVs), copy-number alterations (CNAs), and gene fusions to reconstruct the tumor clonal heterogeneity. For SNV calling, LongSom distinguishes somatic SNVs from germline polymorphisms by reannotating marker gene expression-based cell types using called variants and applying strict filters. Applying LongSom to ovarian cancer samples, we detected clinically relevant somatic SNVs that were validated against single-cell and bulk panel DNA-seq data and could not be detected with short-read (SR) scRNA-seq. Leveraging somatic SNVs and fusions, LongSom found subclones with different predicted treatment outcomes. In summary, LongSom enables de novo SNVs, CNAs, and fusions detection, thus enabling the study of cancer evolution, clonal heterogeneity, and treatment resistance.
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Affiliation(s)
- Arthur Dondi
- ETH Zurich, Department of Biosystems Science and Engineering, Schanzenstrasse 44, 4056 Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Schanzenstrasse 44, 4056 Basel, Switzerland
| | - Nico Borgsmüller
- ETH Zurich, Department of Biosystems Science and Engineering, Schanzenstrasse 44, 4056 Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Schanzenstrasse 44, 4056 Basel, Switzerland
| | - Pedro F. Ferreira
- ETH Zurich, Department of Biosystems Science and Engineering, Schanzenstrasse 44, 4056 Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Schanzenstrasse 44, 4056 Basel, Switzerland
| | - Brian J. Haas
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts, USA
| | - Francis Jacob
- University Hospital Basel and University of Basel, Ovarian Cancer Research, Department of Biomedicine, Hebelstrasse 20, 4031 Basel, Switzerland
| | - Viola Heinzelmann-Schwarz
- University Hospital Basel and University of Basel, Ovarian Cancer Research, Department of Biomedicine, Hebelstrasse 20, 4031 Basel, Switzerland
| | | | - Niko Beerenwinkel
- ETH Zurich, Department of Biosystems Science and Engineering, Schanzenstrasse 44, 4056 Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Schanzenstrasse 44, 4056 Basel, Switzerland
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Stan A, Bosart K, Kaur M, Vo M, Escorcia W, Yoder RJ, Bouley RA, Petreaca RC. Detection of driver mutations and genomic signatures in endometrial cancers using artificial intelligence algorithms. PLoS One 2024; 19:e0299114. [PMID: 38408048 PMCID: PMC10896512 DOI: 10.1371/journal.pone.0299114] [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: 10/16/2023] [Accepted: 02/05/2024] [Indexed: 02/28/2024] Open
Abstract
Analyzed endometrial cancer (EC) genomes have allowed for the identification of molecular signatures, which enable the classification, and sometimes prognostication, of these cancers. Artificial intelligence algorithms have facilitated the partitioning of mutations into driver and passenger based on a variety of parameters, including gene function and frequency of mutation. Here, we undertook an evaluation of EC cancer genomes deposited on the Catalogue of Somatic Mutations in Cancers (COSMIC), with the goal to classify all mutations as either driver or passenger. Our analysis showed that approximately 2.5% of all mutations are driver and cause cellular transformation and immortalization. We also characterized nucleotide level mutation signatures, gross chromosomal re-arrangements, and gene expression profiles. We observed that endometrial cancers show distinct nucleotide substitution and chromosomal re-arrangement signatures compared to other cancers. We also identified high expression levels of the CLDN18 claudin gene, which is involved in growth, survival, metastasis and proliferation. We then used in silico protein structure analysis to examine the effect of certain previously uncharacterized driver mutations on protein structure. We found that certain mutations in CTNNB1 and TP53 increase protein stability, which may contribute to cellular transformation. While our analysis retrieved previously classified mutations and genomic alterations, which is to be expected, this study also identified new signatures. Additionally, we show that artificial intelligence algorithms can be effectively leveraged to accurately predict key drivers of cancer. This analysis will expand our understanding of ECs and improve the molecular toolbox for classification, diagnosis, or potential treatment of these cancers.
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Affiliation(s)
- Anda Stan
- Biology Program, The Ohio State University, Marion, Ohio, United States of America
| | - Korey Bosart
- Biology Program, The Ohio State University, Marion, Ohio, United States of America
| | - Mehak Kaur
- Biology Program, The Ohio State University, Marion, Ohio, United States of America
| | - Martin Vo
- Biology Department, Xavier University, Cincinnati, Ohio, United States of America
| | - Wilber Escorcia
- Biology Department, Xavier University, Cincinnati, Ohio, United States of America
| | - Ryan J Yoder
- Department of Chemistry and Biochemistry, The Ohio State University, Marion, Ohio, United States of America
| | - Renee A Bouley
- Department of Chemistry and Biochemistry, The Ohio State University, Marion, Ohio, United States of America
| | - Ruben C Petreaca
- Department of Molecular Genetics, The Ohio State University, Marion, Ohio, United States of America
- James Comprehensive Cancer Center, The Ohio State University Columbus, Columbus, Ohio, United States of America
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14
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Nourbakhsh M, Degn K, Saksager A, Tiberti M, Papaleo E. Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks. Brief Bioinform 2024; 25:bbad519. [PMID: 38261338 PMCID: PMC10805075 DOI: 10.1093/bib/bbad519] [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: 06/09/2023] [Revised: 11/27/2023] [Accepted: 12/11/2023] [Indexed: 01/24/2024] Open
Abstract
The vast amount of available sequencing data allows the scientific community to explore different genetic alterations that may drive cancer or favor cancer progression. Software developers have proposed a myriad of predictive tools, allowing researchers and clinicians to compare and prioritize driver genes and mutations and their relative pathogenicity. However, there is little consensus on the computational approach or a golden standard for comparison. Hence, benchmarking the different tools depends highly on the input data, indicating that overfitting is still a massive problem. One of the solutions is to limit the scope and usage of specific tools. However, such limitations force researchers to walk on a tightrope between creating and using high-quality tools for a specific purpose and describing the complex alterations driving cancer. While the knowledge of cancer development increases daily, many bioinformatic pipelines rely on single nucleotide variants or alterations in a vacuum without accounting for cellular compartments, mutational burden or disease progression. Even within bioinformatics and computational cancer biology, the research fields work in silos, risking overlooking potential synergies or breakthroughs. Here, we provide an overview of databases and datasets for building or testing predictive cancer driver tools. Furthermore, we introduce predictive tools for driver genes, driver mutations, and the impact of these based on structural analysis. Additionally, we suggest and recommend directions in the field to avoid silo-research, moving towards integrative frameworks.
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Affiliation(s)
- Mona Nourbakhsh
- Cancer Systems Biology, Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Kristine Degn
- Cancer Systems Biology, Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Astrid Saksager
- Cancer Systems Biology, Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Matteo Tiberti
- Cancer Structural Biology, Danish Cancer Institute, 2100 Copenhagen, Denmark
| | - Elena Papaleo
- Cancer Systems Biology, Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, 2800 Lyngby, Denmark
- Cancer Structural Biology, Danish Cancer Institute, 2100 Copenhagen, Denmark
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15
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Ghanem P, Fatteh M, Kamson DO, Balan A, Chang M, Tao J, Blakeley J, Canzoniero J, Grossman SA, Marrone K, Schreck KC, Anagnostou V. Druggable genomic landscapes of high-grade gliomas. Front Med (Lausanne) 2023; 10:1254955. [PMID: 38143440 PMCID: PMC10749203 DOI: 10.3389/fmed.2023.1254955] [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: 07/07/2023] [Accepted: 11/06/2023] [Indexed: 12/26/2023] Open
Abstract
Background Despite the putatively targetable genomic landscape of high-grade gliomas, the long-term survival benefit of genomically-tailored targeted therapies remains discouraging. Methods Using glioblastoma (GBM) as a representative example of high-grade gliomas, we evaluated the clonal architecture and distribution of hotspot mutations in 388 GBMs from the Cancer Genome Atlas (TCGA). Mutations were matched with 54 targeted therapies, followed by a comprehensive evaluation of drug biochemical properties in reference to the drug's clinical efficacy in high-grade gliomas. We then assessed clinical outcomes of a cohort of patients with high-grade gliomas with targetable mutations reviewed at the Johns Hopkins Molecular Tumor Board (JH MTB; n = 50). Results Among 1,156 sequence alterations evaluated, 28.6% represented hotspots. While the frequency of hotspot mutations in GBM was comparable to cancer types with actionable hotspot alterations, GBMs harbored a higher fraction of subclonal mutations that affected hotspots (7.0%), compared to breast cancer (4.9%), lung cancer (4.4%), and melanoma (1.4%). In investigating the biochemical features of targeted therapies paired with recurring alterations, we identified a trend toward higher lipid solubility and lower IC50 in GBM cell lines among drugs with clinical efficacy. The drugs' half-life, molecular weight, surface area and binding to efflux transporters were not associated with clinical efficacy. Among the JH MTB cohort of patients with IDH1 wild-type high-grade gliomas who received targeted therapies, trametinib monotherapy or in combination with dabrafenib conferred radiographic partial response in 75% of patients harboring BRAF or NF1 actionable mutations. Cabozantinib conferred radiographic partial response in two patients harboring a MET and a PDGFRA/KDR amplification. Patients with IDH1 wild-type gliomas that harbored actionable alterations who received genotype-matched targeted therapy had longer progression-free (PFS) and overall survival (OS; 7.37 and 14.72 respectively) than patients whose actionable alterations were not targeted (2.83 and 4.2 months respectively). Conclusion While multiple host, tumor and drug-related features may limit the delivery and efficacy of targeted therapies for patients with high-grade gliomas, genotype-matched targeted therapies confer favorable clinical outcomes. Further studies are needed to generate more data on the impact of biochemical features of targeted therapies on their clinical efficacy for high-grade gliomas.
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Affiliation(s)
- Paola Ghanem
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- The Johns Hopkins Molecular Tumor Board, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Maria Fatteh
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- The Johns Hopkins Molecular Tumor Board, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - David Olayinka Kamson
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Archana Balan
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- The Johns Hopkins Molecular Tumor Board, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Michael Chang
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Jessica Tao
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- The Johns Hopkins Molecular Tumor Board, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Jaishri Blakeley
- The Johns Hopkins Molecular Tumor Board, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Jenna Canzoniero
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- The Johns Hopkins Molecular Tumor Board, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Stuart A. Grossman
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- The Johns Hopkins Molecular Tumor Board, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Kristen Marrone
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Karisa C. Schreck
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- The Johns Hopkins Molecular Tumor Board, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Valsamo Anagnostou
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- The Johns Hopkins Molecular Tumor Board, Johns Hopkins University School of Medicine, Baltimore, MD, United States
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16
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Hatano N, Kamada M, Kojima R, Okuno Y. Network-based prediction approach for cancer-specific driver missense mutations using a graph neural network. BMC Bioinformatics 2023; 24:383. [PMID: 37817080 PMCID: PMC10565986 DOI: 10.1186/s12859-023-05507-6] [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: 07/06/2023] [Accepted: 10/02/2023] [Indexed: 10/12/2023] Open
Abstract
BACKGROUND In cancer genomic medicine, finding driver mutations involved in cancer development and tumor growth is crucial. Machine-learning methods to predict driver missense mutations have been developed because variants are frequently detected by genomic sequencing. However, even though the abnormalities in molecular networks are associated with cancer, many of these methods focus on individual variants and do not consider molecular networks. Here we propose a new network-based method, Net-DMPred, to predict driver missense mutations considering molecular networks. Net-DMPred consists of the graph part and the prediction part. In the graph part, molecular networks are learned by a graph neural network (GNN). The prediction part learns whether variants are driver variants using features of individual variants combined with the graph features learned in the graph part. RESULTS Net-DMPred, which considers molecular networks, performed better than conventional methods. Furthermore, the prediction performance differed by the molecular network structure used in learning, suggesting that it is important to consider not only the local network related to cancer but also the large-scale network in living organisms. CONCLUSIONS We propose a network-based machine learning method, Net-DMPred, for predicting cancer driver missense mutations. Our method enables us to consider the entire graph architecture representing the molecular network because it uses GNN. Net-DMPred is expected to detect driver mutations from a lot of missense mutations that are not known to be associated with cancer.
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Affiliation(s)
- Narumi Hatano
- Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Mayumi Kamada
- Graduate School of Medicine, Kyoto University, Kyoto, Japan.
| | - Ryosuke Kojima
- Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yasushi Okuno
- Graduate School of Medicine, Kyoto University, Kyoto, Japan.
- HPC- and AI-driven Drug Development Platform Division, RIKEN Center for Computational Science(R-CCS), Kobe, Japan.
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17
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Watson AJ, Shaffer ML, Bouley RA, Petreaca RC. F-box DNA Helicase 1 (FBH1) Contributes to the Destabilization of DNA Damage Repair Machinery in Human Cancers. Cancers (Basel) 2023; 15:4439. [PMID: 37760409 PMCID: PMC10526855 DOI: 10.3390/cancers15184439] [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: 07/31/2023] [Revised: 09/01/2023] [Accepted: 09/03/2023] [Indexed: 09/29/2023] Open
Abstract
Homologous recombination (HR) is the major mechanism of rescue of stalled replication forks or repair of DNA double-strand breaks (DSBs) during S phase or mitosis. In human cells, HR is facilitated by the BRCA2-BRCA1-PALB2 module, which loads the RAD51 recombinase onto a resected single-stranded DNA end to initiate repair. Although the process is essential for error-free repair, unrestrained HR can cause chromosomal rearrangements and genome instability. F-box DNA Helicase 1 (FBH1) antagonizes the role of BRCA2-BRCA1-PALB2 to restrict hyper-recombination and prevent genome instability. Here, we analyzed reported FBH1 mutations in cancer cells using the Catalogue of Somatic Mutations in Cancers (COSMIC) to understand how they interact with the BRCA2-BRCA1-PALB2. Consistent with previous results from yeast, we find that FBH1 mutations co-occur with BRCA2 mutations and to some degree BRCA1 and PALB2. We also describe some co-occurring mutations with RAD52, the accessory RAD51 loader and facilitator of single-strand annealing, which is independent of RAD51. In silico modeling was used to investigate the role of key FBH1 mutations on protein function, and a Q650K mutation was found to destabilize the protein structure. Taken together, this work highlights how mutations in several DNA damage repair genes contribute to cellular transformation and immortalization.
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Affiliation(s)
- Alizhah J. Watson
- Biology Program, The Ohio State University, Marion, OH 433023, USA; (A.J.W.); (M.L.S.)
| | - Michaela L. Shaffer
- Biology Program, The Ohio State University, Marion, OH 433023, USA; (A.J.W.); (M.L.S.)
| | - Renee A. Bouley
- Department of Chemistry and Biochemistry, The Ohio State University, Marion, OH 43302, USA
| | - Ruben C. Petreaca
- Department of Molecular Genetics, The Ohio State University, Marion, OH 43302, USA
- Cancer Biology Program, James Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
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18
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Li Y, Porta-Pardo E, Tokheim C, Bailey MH, Yaron TM, Stathias V, Geffen Y, Imbach KJ, Cao S, Anand S, Akiyama Y, Liu W, Wyczalkowski MA, Song Y, Storrs EP, Wendl MC, Zhang W, Sibai M, Ruiz-Serra V, Liang WW, Terekhanova NV, Rodrigues FM, Clauser KR, Heiman DI, Zhang Q, Aguet F, Calinawan AP, Dhanasekaran SM, Birger C, Satpathy S, Zhou DC, Wang LB, Baral J, Johnson JL, Huntsman EM, Pugliese P, Colaprico A, Iavarone A, Chheda MG, Ricketts CJ, Fenyö D, Payne SH, Rodriguez H, Robles AI, Gillette MA, Kumar-Sinha C, Lazar AJ, Cantley LC, Getz G, Ding L. Pan-cancer proteogenomics connects oncogenic drivers to functional states. Cell 2023; 186:3921-3944.e25. [PMID: 37582357 DOI: 10.1016/j.cell.2023.07.014] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 12/30/2022] [Accepted: 07/10/2023] [Indexed: 08/17/2023]
Abstract
Cancer driver events refer to key genetic aberrations that drive oncogenesis; however, their exact molecular mechanisms remain insufficiently understood. Here, our multi-omics pan-cancer analysis uncovers insights into the impacts of cancer drivers by identifying their significant cis-effects and distal trans-effects quantified at the RNA, protein, and phosphoprotein levels. Salient observations include the association of point mutations and copy-number alterations with the rewiring of protein interaction networks, and notably, most cancer genes converge toward similar molecular states denoted by sequence-based kinase activity profiles. A correlation between predicted neoantigen burden and measured T cell infiltration suggests potential vulnerabilities for immunotherapies. Patterns of cancer hallmarks vary by polygenic protein abundance ranging from uniform to heterogeneous. Overall, our work demonstrates the value of comprehensive proteogenomics in understanding the functional states of oncogenic drivers and their links to cancer development, surpassing the limitations of studying individual cancer types.
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Affiliation(s)
- Yize Li
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Eduard Porta-Pardo
- Josep Carreras Leukaemia Research Institute (IJC), Badalona 08916, Spain; Barcelona Supercomputing Center (BSC), Barcelona 08034, Spain
| | - Collin Tokheim
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Matthew H Bailey
- Department of Biology and Simmons Center for Cancer Research, Brigham Young University, Provo, UT 84602, USA
| | - Tomer M Yaron
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10021, USA; Department of Medicine, Weill Cornell Medicine, New York, NY 10021, USA; Englander Institute for Precision Medicine, Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Vasileios Stathias
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA; Department of Molecular and Cellular Pharmacology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Yifat Geffen
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Cancer Center and Department of Pathology, Massachusetts General Hospital, Boston, MA 02115, USA
| | - Kathleen J Imbach
- Josep Carreras Leukaemia Research Institute (IJC), Badalona 08916, Spain; Barcelona Supercomputing Center (BSC), Barcelona 08034, Spain
| | - Song Cao
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Shankara Anand
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Yo Akiyama
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Wenke Liu
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Matthew A Wyczalkowski
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Yizhe Song
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Erik P Storrs
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Michael C Wendl
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA; Department of Genetics, Washington University in St. Louis, St. Louis, MO 63130, USA; Department of Mathematics, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Wubing Zhang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Mustafa Sibai
- Josep Carreras Leukaemia Research Institute (IJC), Badalona 08916, Spain; Barcelona Supercomputing Center (BSC), Barcelona 08034, Spain
| | - Victoria Ruiz-Serra
- Josep Carreras Leukaemia Research Institute (IJC), Badalona 08916, Spain; Barcelona Supercomputing Center (BSC), Barcelona 08034, Spain
| | - Wen-Wei Liang
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Nadezhda V Terekhanova
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Fernanda Martins Rodrigues
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Karl R Clauser
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - David I Heiman
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Qing Zhang
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Francois Aguet
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Anna P Calinawan
- Department of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Saravana M Dhanasekaran
- Michigan Center for Translational Pathology, Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Chet Birger
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Shankha Satpathy
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Daniel Cui Zhou
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Liang-Bo Wang
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Jessika Baral
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Jared L Johnson
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10021, USA; Department of Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Emily M Huntsman
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10021, USA; Department of Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Pietro Pugliese
- Department of Science and Technology, University of Sannio, 82100 Benevento, Italy
| | - Antonio Colaprico
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA; Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Antonio Iavarone
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA; Department of Neurological Surgery, Department of Biochemistry and Molecular Biology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Milan G Chheda
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63130, USA; Department of Neurology, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Christopher J Ricketts
- Urologic Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - David Fenyö
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Samuel H Payne
- Department of Biology, Brigham Young University, Provo, UT 84602, USA
| | - Henry Rodriguez
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA
| | - Ana I Robles
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA
| | - Michael A Gillette
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Harvard Medical School, Boston, MA 02115, USA
| | - Chandan Kumar-Sinha
- Michigan Center for Translational Pathology, Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Alexander J Lazar
- Departments of Pathology & Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Lewis C Cantley
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10021, USA; Department of Medicine, Weill Cornell Medicine, New York, NY 10021, USA.
| | - Gad Getz
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Cancer Center and Department of Pathology, Massachusetts General Hospital, Boston, MA 02115, USA; Harvard Medical School, Boston, MA 02115, USA.
| | - Li Ding
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA; Department of Genetics, Washington University in St. Louis, St. Louis, MO 63130, USA; Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63130, USA.
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19
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Vellichirammal NN, Tan YD, Xiao P, Eudy J, Shats O, Kelly D, Desler M, Cowan K, Guda C. The mutational landscape of a US Midwestern breast cancer cohort reveals subtype-specific cancer drivers and prognostic markers. Hum Genomics 2023; 17:64. [PMID: 37454130 PMCID: PMC10349437 DOI: 10.1186/s40246-023-00511-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 07/11/2023] [Indexed: 07/18/2023] Open
Abstract
BACKGROUND Female breast cancer remains the second leading cause of cancer-related death in the USA. The heterogeneity in the tumor morphology across the cohort and within patients can lead to unpredictable therapy resistance, metastasis, and clinical outcome. Hence, supplementing classic pathological markers with intrinsic tumor molecular markers can help identify novel molecular subtypes and the discovery of actionable biomarkers. METHODS We conducted a large multi-institutional genomic analysis of paired normal and tumor samples from breast cancer patients to profile the complex genomic architecture of breast tumors. Long-term patient follow-up, therapeutic regimens, and treatment response for this cohort are documented using the Breast Cancer Collaborative Registry. The majority of the patients in this study were at tumor stage 1 (51.4%) and stage 2 (36.3%) at the time of diagnosis. Whole-exome sequencing data from 554 patients were used for mutational profiling and identifying cancer drivers. RESULTS We identified 54 tumors having at least 1000 mutations and 185 tumors with less than 100 mutations. Tumor mutational burden varied across the classified subtypes, and the top ten mutated genes include MUC4, MUC16, PIK3CA, TTN, TP53, NBPF10, NBPF1, CDC27, AHNAK2, and MUC2. Patients were classified based on seven biological and tumor-specific parameters, including grade, stage, hormone receptor status, histological subtype, Ki67 expression, lymph node status, race, and mutational profiles compared across different subtypes. Mutual exclusion of mutations in PIK3CA and TP53 was pronounced across different tumor grades. Cancer drivers specific to each subtype include TP53, PIK3CA, CDC27, CDH1, STK39, CBFB, MAP3K1, and GATA3, and mutations associated with patient survival were identified in our cohort. CONCLUSIONS This extensive study has revealed tumor burden, driver genes, co-occurrence, mutual exclusivity, and survival effects of mutations on a US Midwestern breast cancer cohort, paving the way for developing personalized therapeutic strategies.
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Affiliation(s)
| | - Yuan-De Tan
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Peng Xiao
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - James Eudy
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Oleg Shats
- Eppley Institute for Research in Cancer and Allied Diseases, University of Nebraska Medical Center, Omaha, NE, USA
- Fred and Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, USA
| | - David Kelly
- Eppley Institute for Research in Cancer and Allied Diseases, University of Nebraska Medical Center, Omaha, NE, USA
- Fred and Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, USA
| | - Michelle Desler
- Eppley Institute for Research in Cancer and Allied Diseases, University of Nebraska Medical Center, Omaha, NE, USA
- Fred and Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, USA
| | - Kenneth Cowan
- Eppley Institute for Research in Cancer and Allied Diseases, University of Nebraska Medical Center, Omaha, NE, USA
- Fred and Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, USA
| | - Chittibabu Guda
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE, 68198, USA.
- Center for Biomedical Informatics Research and Innovation, University of Nebraska Medical Center, Omaha, NE, 68198, USA.
- Fred and Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, USA.
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20
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Johnson A, Ng PKS, Kahle M, Castillo J, Amador B, Wang Y, Zeng J, Holla V, Vu T, Su F, Kim SH, Conway T, Jiang X, Chen K, Shaw KRM, Yap TA, Rodon J, Mills GB, Meric-Bernstam F. Actionability classification of variants of unknown significance correlates with functional effect. NPJ Precis Oncol 2023; 7:67. [PMID: 37454202 DOI: 10.1038/s41698-023-00420-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 07/03/2023] [Indexed: 07/18/2023] Open
Abstract
Genomically-informed therapy requires consideration of the functional impact of genomic alterations on protein expression and/or function. However, a substantial number of variants are of unknown significance (VUS). The MD Anderson Precision Oncology Decision Support (PODS) team developed an actionability classification scheme that categorizes VUS as either "Unknown" or "Potentially" actionable based on their location within functional domains and/or proximity to known oncogenic variants. We then compared PODS VUS actionability classification with results from a functional genomics platform consisting of mutant generation and cell viability assays. 106 (24%) of 438 VUS in 20 actionable genes were classified as oncogenic in functional assays. Variants categorized by PODS as Potentially actionable (N = 204) were more likely to be oncogenic than those categorized as Unknown (N = 230) (37% vs 13%, p = 4.08e-09). Our results demonstrate that rule-based actionability classification of VUS can identify patients more likely to have actionable variants for consideration with genomically-matched therapy.
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Affiliation(s)
- Amber Johnson
- Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Patrick Kwok-Shing Ng
- Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Michael Kahle
- Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Julia Castillo
- Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Bianca Amador
- Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yujia Wang
- Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jia Zeng
- Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Vijaykumar Holla
- Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Thuy Vu
- Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Fei Su
- Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Sun-Hee Kim
- Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Tara Conway
- Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Xianli Jiang
- Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ken Chen
- Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Kenna R Mills Shaw
- Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Timothy A Yap
- Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Investigational Cancer Therapeutics (Phase I Program), The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jordi Rodon
- Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Investigational Cancer Therapeutics (Phase I Program), The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Gordon B Mills
- Division of Oncological Sciences, Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA
| | - Funda Meric-Bernstam
- Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
- Investigational Cancer Therapeutics (Phase I Program), The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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21
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Talwar JV, Laub D, Pagadala MS, Castro A, Lewis M, Luebeck GE, Gorman BR, Pan C, Dong FN, Markianos K, Teerlink CC, Lynch J, Hauger R, Pyarajan S, Tsao PS, Morris GP, Salem RM, Thompson WK, Curtius K, Zanetti M, Carter H. Autoimmune alleles at the major histocompatibility locus modify melanoma susceptibility. Am J Hum Genet 2023; 110:1138-1161. [PMID: 37339630 PMCID: PMC10357503 DOI: 10.1016/j.ajhg.2023.05.013] [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: 07/13/2022] [Revised: 05/20/2023] [Accepted: 05/22/2023] [Indexed: 06/22/2023] Open
Abstract
Autoimmunity and cancer represent two different aspects of immune dysfunction. Autoimmunity is characterized by breakdowns in immune self-tolerance, while impaired immune surveillance can allow for tumorigenesis. The class I major histocompatibility complex (MHC-I), which displays derivatives of the cellular peptidome for immune surveillance by CD8+ T cells, serves as a common genetic link between these conditions. As melanoma-specific CD8+ T cells have been shown to target melanocyte-specific peptide antigens more often than melanoma-specific antigens, we investigated whether vitiligo- and psoriasis-predisposing MHC-I alleles conferred a melanoma-protective effect. In individuals with cutaneous melanoma from both The Cancer Genome Atlas (n = 451) and an independent validation set (n = 586), MHC-I autoimmune-allele carrier status was significantly associated with a later age of melanoma diagnosis. Furthermore, MHC-I autoimmune-allele carriers were significantly associated with decreased risk of developing melanoma in the Million Veteran Program (OR = 0.962, p = 0.024). Existing melanoma polygenic risk scores (PRSs) did not predict autoimmune-allele carrier status, suggesting these alleles provide orthogonal risk-relevant information. Mechanisms of autoimmune protection were neither associated with improved melanoma-driver mutation association nor improved gene-level conserved antigen presentation relative to common alleles. However, autoimmune alleles showed higher affinity relative to common alleles for particular windows of melanocyte-conserved antigens and loss of heterozygosity of autoimmune alleles caused the greatest reduction in presentation for several conserved antigens across individuals with loss of HLA alleles. Overall, this study presents evidence that MHC-I autoimmune-risk alleles modulate melanoma risk unaccounted for by current PRSs.
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Affiliation(s)
- James V Talwar
- Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA 92093, USA; Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA
| | - David Laub
- Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA 92093, USA; Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA
| | - Meghana S Pagadala
- Biomedical Science Program, University of California San Diego, La Jolla, CA 92093, USA
| | - Andrea Castro
- Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA 92093, USA; Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA
| | - McKenna Lewis
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Georg E Luebeck
- Public Health Sciences Division, Herbold Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Bryan R Gorman
- Center for Data and Computational Sciences (C-DACS), VA Boston Healthcare System, Boston, MA 02130, USA; Booz Allen Hamilton, Inc., McLean, VA 22102, USA
| | - Cuiping Pan
- Palo Alto Epidemiology Research and Information Center for Genomics, VA Palo Alto, CA, USA
| | - Frederick N Dong
- Center for Data and Computational Sciences (C-DACS), VA Boston Healthcare System, Boston, MA 02130, USA; Booz Allen Hamilton, Inc., McLean, VA 22102, USA
| | - Kyriacos Markianos
- Center for Data and Computational Sciences (C-DACS), VA Boston Healthcare System, Boston, MA 02130, USA; Division of Genetics and Genomics, Department of Pediatrics, Boston Children's Hospital, Boston, MA 02115, USA; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02115, USA
| | - Craig C Teerlink
- Department of Veterans Affairs Informatics and Computing Infrastructure (VINCI), VA Salt Lake City Healthcare System, Salt Lake City, UT, USA; Department of Internal Medicine, Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Julie Lynch
- Department of Veterans Affairs Informatics and Computing Infrastructure (VINCI), VA Salt Lake City Healthcare System, Salt Lake City, UT, USA; Department of Internal Medicine, Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Richard Hauger
- VA San Diego Healthcare System, La Jolla, CA, USA; Center for Behavioral Genetics of Aging, University of California San Diego, La Jolla, CA, USA; Center of Excellence for Stress and Mental Health (CESAMH), VA San Diego Healthcare System, San Diego, CA, USA
| | - Saiju Pyarajan
- Center for Data and Computational Sciences (C-DACS), VA Boston Healthcare System, Boston, MA 02130, USA; Department of Medicine, Brigham Women's Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Philip S Tsao
- Palo Alto Epidemiology Research and Information Center for Genomics, VA Palo Alto, CA, USA; Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Gerald P Morris
- Department of Pathology, University of California San Diego, La Jolla, CA 92093, USA
| | - Rany M Salem
- Division of Epidemiology, Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA 92093, USA
| | - Wesley K Thompson
- Center for Population Neuroscience and Genetics, Laureate Institute for Brain Research, Tulsa, OK 74136, USA
| | - Kit Curtius
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA; Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA; Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Maurizio Zanetti
- Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA; The Laboratory of Immunology, University of California San Diego, La Jolla, CA 92093, USA; Department of Medicine, Division of Hematology and Oncology, University of California San Diego, La Jolla, CA 92093, USA
| | - Hannah Carter
- Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA 92093, USA; Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA; Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA.
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22
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Giarrizzo M, LaComb JF, Bialkowska AB. The Role of Krüppel-like Factors in Pancreatic Physiology and Pathophysiology. Int J Mol Sci 2023; 24:ijms24108589. [PMID: 37239940 DOI: 10.3390/ijms24108589] [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: 04/13/2023] [Revised: 05/04/2023] [Accepted: 05/06/2023] [Indexed: 05/28/2023] Open
Abstract
Krüppel-like factors (KLFs) belong to the family of transcription factors with three highly conserved zinc finger domains in the C-terminus. They regulate homeostasis, development, and disease progression in many tissues. It has been shown that KLFs play an essential role in the endocrine and exocrine compartments of the pancreas. They are necessary to maintain glucose homeostasis and have been implicated in the development of diabetes. Furthermore, they can be a vital tool in enabling pancreas regeneration and disease modeling. Finally, the KLF family contains proteins that act as tumor suppressors and oncogenes. A subset of members has a biphasic function, being upregulated in the early stages of oncogenesis and stimulating its progression and downregulated in the late stages to allow for tumor dissemination. Here, we describe KLFs' function in pancreatic physiology and pathophysiology.
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Affiliation(s)
- Michael Giarrizzo
- Department of Medicine, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY 11794, USA
| | - Joseph F LaComb
- Department of Medicine, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY 11794, USA
| | - Agnieszka B Bialkowska
- Department of Medicine, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY 11794, USA
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23
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Wang L, Sun J, Ma S, Xia J, Li X. PredDSMC: A predictor for driver synonymous mutations in human cancers. Front Genet 2023; 14:1164593. [PMID: 37051593 PMCID: PMC10083435 DOI: 10.3389/fgene.2023.1164593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 03/09/2023] [Indexed: 03/29/2023] Open
Abstract
Introduction: Driver mutations play a critical role in the occurrence and development of human cancers. Most studies have focused on missense mutations that function as drivers in cancer. However, accumulating experimental evidence indicates that synonymous mutations can also act as driver mutations.Methods: Here, we proposed a computational method called PredDSMC to accurately predict driver synonymous mutations in human cancers. We first systematically explored four categories of multimodal features, including sequence features, splicing features, conservation scores, and functional scores. Further feature selection was carried out to remove redundant features and improve the model performance. Finally, we utilized the random forest classifier to build PredDSMC.Results: The results of two independent test sets indicated that PredDSMC outperformed the state-of-the-art methods in differentiating driver synonymous mutations from passenger mutations.Discussion: In conclusion, we expect that PredDSMC, as a driver synonymous mutation prediction method, will be a valuable method for gaining a deeper understanding of synonymous mutations in human cancers.
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24
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Rasheed S, Bouley RA, Yoder RJ, Petreaca RC. Protein Arginine Methyltransferase 5 (PRMT5) Mutations in Cancer Cells. Int J Mol Sci 2023; 24:6042. [PMID: 37047013 PMCID: PMC10094674 DOI: 10.3390/ijms24076042] [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: 01/24/2023] [Revised: 03/15/2023] [Accepted: 03/20/2023] [Indexed: 04/14/2023] Open
Abstract
Arginine methylation is a form of posttranslational modification that regulates many cellular functions such as development, DNA damage repair, inflammatory response, splicing, and signal transduction, among others. Protein arginine methyltransferase 5 (PRMT5) is one of nine identified methyltransferases, and it can methylate both histone and non-histone targets. It has pleiotropic functions, including recruitment of repair machinery to a chromosomal DNA double strand break (DSB) and coordinating the interplay between repair and checkpoint activation. Thus, PRMT5 has been actively studied as a cancer treatment target, and small molecule inhibitors of its enzymatic activity have already been developed. In this report, we analyzed all reported PRMT5 mutations appearing in cancer cells using data from the Catalogue of Somatic Mutations in Cancers (COSMIC). Our goal is to classify mutations as either drivers or passengers to understand which ones are likely to promote cellular transformation. Using gold standard artificial intelligence algorithms, we uncovered several key driver mutations in the active site of the enzyme (D306H, L315P, and N318K). In silico protein modeling shows that these mutations may affect the affinity of PRMT5 for S-adenosylmethionine (SAM), which is required as a methyl donor. Electrostatic analysis of the enzyme active site shows that one of these mutations creates a tunnel in the vicinity of the SAM binding site, which may allow interfering molecules to enter the enzyme active site and decrease its activity. We also identified several non-coding mutations that appear to affect PRMT5 splicing. Our analyses provide insights into the role of PRMT5 mutations in cancer cells. Additionally, since PRMT5 single molecule inhibitors have already been developed, this work may uncover future directions in how mutations can affect targeted inhibition.
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Affiliation(s)
- Shayaan Rasheed
- James Comprehensive Cancer Center, The Ohio State University Columbus, Columbus, OH 43210, USA
- Biology Program, The Ohio State University, Columbus, OH 43210, USA
| | - Renee A. Bouley
- Department of Chemistry and Biochemistry, The Ohio State University, Marion, OH 43302, USA
| | - Ryan J. Yoder
- Department of Chemistry and Biochemistry, The Ohio State University, Marion, OH 43302, USA
| | - Ruben C. Petreaca
- James Comprehensive Cancer Center, The Ohio State University Columbus, Columbus, OH 43210, USA
- Department of Molecular Genetics, The Ohio State University, Marion, OH 43302, USA
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25
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Cotto KC, Feng YY, Ramu A, Richters M, Freshour SL, Skidmore ZL, Xia H, McMichael JF, Kunisaki J, Campbell KM, Chen THP, Rozycki EB, Adkins D, Devarakonda S, Sankararaman S, Lin Y, Chapman WC, Maher CA, Arora V, Dunn GP, Uppaluri R, Govindan R, Griffith OL, Griffith M. Integrated analysis of genomic and transcriptomic data for the discovery of splice-associated variants in cancer. Nat Commun 2023; 14:1589. [PMID: 36949070 PMCID: PMC10033906 DOI: 10.1038/s41467-023-37266-6] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 03/08/2023] [Indexed: 03/24/2023] Open
Abstract
Somatic mutations within non-coding regions and even exons may have unidentified regulatory consequences that are often overlooked in analysis workflows. Here we present RegTools ( www.regtools.org ), a computationally efficient, free, and open-source software package designed to integrate somatic variants from genomic data with splice junctions from bulk or single cell transcriptomic data to identify variants that may cause aberrant splicing. We apply RegTools to over 9000 tumor samples with both tumor DNA and RNA sequence data. RegTools discovers 235,778 events where a splice-associated variant significantly increases the splicing of a particular junction, across 158,200 unique variants and 131,212 unique junctions. To characterize these somatic variants and their associated splice isoforms, we annotate them with the Variant Effect Predictor, SpliceAI, and Genotype-Tissue Expression junction counts and compare our results to other tools that integrate genomic and transcriptomic data. While many events are corroborated by the aforementioned tools, the flexibility of RegTools also allows us to identify splice-associated variants in known cancer drivers, such as TP53, CDKN2A, and B2M, and other genes.
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Affiliation(s)
- Kelsy C Cotto
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Yang-Yang Feng
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Avinash Ramu
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Megan Richters
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Sharon L Freshour
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Zachary L Skidmore
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Huiming Xia
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Joshua F McMichael
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Jason Kunisaki
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Katie M Campbell
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Timothy Hung-Po Chen
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Emily B Rozycki
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Douglas Adkins
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Siddhartha Devarakonda
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Sumithra Sankararaman
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Yiing Lin
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
| | - William C Chapman
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
| | - Christopher A Maher
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Vivek Arora
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Gavin P Dunn
- Department of Neurosurgery, Mass General Hospital, Boston, MA, USA
- Center for Brain Tumor Immunology and Immunotherapy, Mass General Hospital, Boston, MA, USA
| | - Ravindra Uppaluri
- Department of Surgery, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Ramaswamy Govindan
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Obi L Griffith
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA.
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA.
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA.
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA.
| | - Malachi Griffith
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA.
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA.
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA.
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA.
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26
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An Integrative Analysis of Nasopharyngeal Carcinoma Genomes Unraveled Unique Processes Driving a Viral-Positive Cancer. Cancers (Basel) 2023; 15:cancers15041243. [PMID: 36831585 PMCID: PMC9953764 DOI: 10.3390/cancers15041243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 02/03/2023] [Accepted: 02/13/2023] [Indexed: 02/18/2023] Open
Abstract
As one of few viral-positive cancers, nasopharyngeal carcinoma (NPC) is extremely rare across the world but very frequent in several regions of the world, including Southern China (known as the Cantonese cancer). Even though several genomic studies have been conducted for NPC, their sample sizes are relatively small and systematic comparison with other cancer types has not been explored. In this study, we collected four-hundred-thirty-one samples from six previous studies and provided the first integrative analysis of NPC genomes. Combining several statistical methods for detecting driver genes, we identified 25 novel drivers for NPC, including ATG14 and NLRC5. Many of these novel drivers are enriched in several important pathways, such as autophagy and immunity. By comparing NPC with many other cancer types, we found NPC is a unique cancer type in which a high proportion of patients (45.2%) do not have any known driver mutations (termed as "missing driver events") but have a preponderance of deletion events, including chromosome 3p deletion. Through signature analysis, we identified many known and novel signatures, including single-base signatures (n = 12), double-base signatures (n = 1), indel signatures (n = 9) and copy number signatures (n = 8). Many of these new signatures are involved in DNA repair and have unknown etiology and genome instability, implying an unprecedented dynamic mutational process possibly driven by complex interactions between viral and host genomes. By combining clinical, molecular and intra-tumor heterogeneity features, we constructed the first integrative survival model for NPC, providing a strong basis for patient prognosis and stratification. Taken together, we have performed one of the first integrative analyses of NPC genomes and brought unique genomic insights into tumorigenesis of a viral-driven cancer.
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27
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Kim JY, Cha H, Kim K, Sung C, An J, Bang H, Kim H, Yang JO, Chang S, Shin I, Noh SJ, Shin I, Cho DY, Lee SH, Choi JK. MHC II immunogenicity shapes the neoepitope landscape in human tumors. Nat Genet 2023; 55:221-231. [PMID: 36624345 DOI: 10.1038/s41588-022-01273-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 11/30/2022] [Indexed: 01/11/2023]
Abstract
Despite advances in predicting physical peptide-major histocompatibility complex I (pMHC I) binding, it remains challenging to identify functionally immunogenic neoepitopes, especially for MHC II. By using the results of >36,000 immunogenicity assay, we developed a method to identify pMHC whose structural alignment facilitates T cell reaction. Our method predicted neoepitopes for MHC II and MHC I that were responsive to checkpoint blockade when applied to >1,200 samples of various tumor types. To investigate selection by spontaneous immunity at the single epitope level, we analyzed the frequency spectrum of >25 million mutations in >9,000 treatment-naive tumors with >100 immune phenotypes. MHC II immunogenicity specifically lowered variant frequencies in tumors under high immune pressure, particularly with high TCR clonality and MHC II expression. A similar trend was shown for MHC I neoepitopes, but only in particular tissue types. In summary, we report immune selection imposed by MHC II-restricted natural or therapeutic T cell reactivity.
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Affiliation(s)
- Jeong Yeon Kim
- Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea.,Penta Medix Co., Ltd., Seongnam-si, Republic of Korea
| | - Hongui Cha
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Kyeonghui Kim
- Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea
| | - Changhwan Sung
- Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea.,Graduate School of Medical Science and Engineering, KAIST, Daejeon, Republic of Korea
| | - Jinhyeon An
- Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea
| | - Hyoeun Bang
- Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea.,Penta Medix Co., Ltd., Seongnam-si, Republic of Korea
| | - Hyungjoo Kim
- Penta Medix Co., Ltd., Seongnam-si, Republic of Korea.,Department of Life Science, Hanyang University, Seoul, Republic of Korea
| | - Jin Ok Yang
- Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea.,Korea Bioinformation Center, KRIBB, Daejeon, Republic of Korea
| | - Suhwan Chang
- Department of Biomedical Sciences, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Incheol Shin
- Department of Life Science, Hanyang University, Seoul, Republic of Korea.,Natural Science Institute, Hanyang University, Seoul, Republic of Korea
| | - Seung-Jae Noh
- Penta Medix Co., Ltd., Seongnam-si, Republic of Korea
| | - Inkyung Shin
- Penta Medix Co., Ltd., Seongnam-si, Republic of Korea
| | - Dae-Yeon Cho
- Penta Medix Co., Ltd., Seongnam-si, Republic of Korea.
| | - Se-Hoon Lee
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea. .,Department of Health Sciences and Technology, Samsung Advanced Institute of Health Science and Technology, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
| | - Jung Kyoon Choi
- Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea. .,Penta Medix Co., Ltd., Seongnam-si, Republic of Korea.
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28
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Lee SHT, Kim JY, Kim P, Dong Z, Su CY, Ahn EH. Changes of Mutations and Copy-Number and Enhanced Cell Migration during Breast Tumorigenesis. Adv Biol (Weinh) 2023; 7:e2200072. [PMID: 36449747 PMCID: PMC10836759 DOI: 10.1002/adbi.202200072] [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: 03/16/2022] [Revised: 11/14/2022] [Indexed: 12/02/2022]
Abstract
Although cancer stem cells (CSCs) play a major role in tumorigenesis and metastasis, the role of genetic alterations in invasiveness of CSCs is still unclear. Tumor microenvironment signals, such as extracellular matrix (ECM) composition, significantly influence cell behaviors. Unfortunately, these signals are often lost in in vitro cell culture. This study determines putative CSC populations, examines genetic changes during tumorigenesis of human breast epithelial stem cells, and investigates single-cell migration properties on ECM-mimetic platforms. Whole exome sequencing data indicate that tumorigenic cells have a higher somatic mutation burden than non-tumorigenic cells, and that mutations exclusive to tumorigenic cells exhibit higher predictive deleterious scores. Tumorigenic cells exhibit distinct somatic copy number variations (CNVs) including gain of duplications in chromosomes 5 and 8. ECM-mimetic topography selectively enhances migration speed of tumorigenic cells, but not of non-tumorigenic cells, and results in a wide distribution of tumorigenic single-cell migration speeds, suggesting heterogeneity in cellular sensing of contact guidance cues. This study identifies mutations and CNVs acquired during breast tumorigenesis, which can be associated with enhanced migration of breast tumorigenic cells, and demonstrates that a nanotopographically-defined platform can be applied to recapitulate an ECM structure for investigating cellular migration in the simulated tumor microenvironment.
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Affiliation(s)
- Seung Hyuk T. Lee
- Department of Pathology, University of Washington, Seattle,
WA 98195, USA
| | - Joon Yup Kim
- Department of Pathology, University of Washington, Seattle,
WA 98195, USA
| | - Peter Kim
- Department of Bioengineering, University of Washington,
Seattle, WA 98195, USA
| | - Zhipeng Dong
- Department of Biomedical Engineering, Johns Hopkins
University, Baltimore, MD 21205, USA
| | - Chia-Yi Su
- Department of Biomedical Engineering, Johns Hopkins
University, Baltimore, MD 21205, USA
| | - Eun Hyun Ahn
- Department of Biomedical Engineering, Johns Hopkins
University, Baltimore, MD 21205, USA
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29
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Kim D, Ha D, Lee K, Lee H, Kim I, Kim S. An evolution-based machine learning to identify cancer type-specific driver mutations. Brief Bioinform 2023; 24:6961611. [PMID: 36575568 DOI: 10.1093/bib/bbac593] [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: 06/22/2022] [Revised: 11/18/2022] [Accepted: 12/03/2022] [Indexed: 12/29/2022] Open
Abstract
Identifying cancer type-specific driver mutations is crucial for illuminating distinct pathologic mechanisms across various tumors and providing opportunities of patient-specific treatment. However, although many computational methods were developed to predict driver mutations in a type-specific manner, the methods still have room to improve. Here, we devise a novel feature based on sequence co-evolution analysis to identify cancer type-specific driver mutations and construct a machine learning (ML) model with state-of-the-art performance. Specifically, relying on 28 000 tumor samples across 66 cancer types, our ML framework outperformed current leading methods of detecting cancer driver mutations. Interestingly, the cancer mutations identified by sequence co-evolution feature are frequently observed in interfaces mediating tissue-specific protein-protein interactions that are known to associate with shaping tissue-specific oncogenesis. Moreover, we provide pre-calculated potential oncogenicity on available human proteins with prediction scores of all possible residue alterations through user-friendly website (http://sbi.postech.ac.kr/w/cancerCE). This work will facilitate the identification of cancer type-specific driver mutations in newly sequenced tumor samples.
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Affiliation(s)
| | | | | | | | - Inhae Kim
- ImmunoBiome Inc., Pohang, South Korea
| | - Sanguk Kim
- Department of Life Sciences.,Artificial Intelligence Graduate Program, Pohang University of Science and Technology, Pohang 790-784, South Korea.,Institute of Convergence Research and Education in Advanced Technology, Yonsei University, Seoul 120-149, South Korea
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30
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L Hardison K, M Hawk T, A Bouley R, C Petreaca R. KAT5 histone acetyltransferase mutations in cancer cells. MICROPUBLICATION BIOLOGY 2022; 2022:10.17912/micropub.biology.000676. [PMID: 36530474 PMCID: PMC9748724 DOI: 10.17912/micropub.biology.000676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 11/21/2022] [Accepted: 11/21/2022] [Indexed: 01/25/2023]
Abstract
Cancer cells are characterized by accumulation of mutations due to improperly repaired DNA damage. The DNA double strand break is one of the most severe form of damage and several redundant mechanisms have evolved to facilitate accurate repair. During DNA replication and in mitosis, breaks are primarily repaired by homologous recombination which is facilitated by several genes. Key to this process is the breast cancer susceptibility genes BRCA1 and BRCA2 as well as the accessory RAD52 gene. Proper chromatin remodeling is also essential for repair and the KAT5 histone acetyltransferase facilitates histone removal at the break. Here we undertook a pan cancer analysis to investigate mutations within the KAT5 gene in cancer cells. We employed two standard artificial algorithms to classify mutations as either driver (CHASMPlus algorithm) or pathogenic (VEST4 algorithm). We find that most predicted driver and disease-causing mutations occur in the catalytic site or within key regulatory domains. In silico analysis of protein structure using AlphaFold shows that these mutations are likely to destabilize the function of KAT5 or interactions with DNA or its other partners. The data presented here, although preliminary, could be used to inform clinical strategies.
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31
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Nussinov R, Tsai CJ, Jang H. A New View of Activating Mutations in Cancer. Cancer Res 2022; 82:4114-4123. [PMID: 36069825 PMCID: PMC9664134 DOI: 10.1158/0008-5472.can-22-2125] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/16/2022] [Accepted: 09/01/2022] [Indexed: 12/14/2022]
Abstract
A vast effort has been invested in the identification of driver mutations of cancer. However, recent studies and observations call into question whether the activating mutations or the signal strength are the major determinant of tumor development. The data argue that signal strength determines cell fate, not the mutation that initiated it. In addition to activating mutations, factors that can impact signaling strength include (i) homeostatic mechanisms that can block or enhance the signal, (ii) the types and locations of additional mutations, and (iii) the expression levels of specific isoforms of genes and regulators of proteins in the pathway. Because signal levels are largely decided by chromatin structure, they vary across cell types, states, and time windows. A strong activating mutation can be restricted by low expression, whereas a weaker mutation can be strengthened by high expression. Strong signals can be associated with cell proliferation, but too strong a signal may result in oncogene-induced senescence. Beyond cancer, moderate signal strength in embryonic neural cells may be associated with neurodevelopmental disorders, and moderate signals in aging may be associated with neurodegenerative diseases, like Alzheimer's disease. The challenge for improving patient outcomes therefore lies in determining signaling thresholds and predicting signal strength.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, NCI, Frederick, Maryland
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Chung-Jung Tsai
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, NCI, Frederick, Maryland
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, NCI, Frederick, Maryland
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32
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Scharpf RB, Balan A, Ricciuti B, Fiksel J, Cherry C, Wang C, Lenoue-Newton ML, Rizvi HA, White JR, Baras AS, Anaya J, Landon BV, Majcherska-Agrawal M, Ghanem P, Lee J, Raskin L, Park AS, Tu H, Hsu H, Arbour KC, Awad MM, Riely GJ, Lovly CM, Anagnostou V. Genomic Landscapes and Hallmarks of Mutant RAS in Human Cancers. Cancer Res 2022; 82:4058-4078. [PMID: 36074020 PMCID: PMC9627127 DOI: 10.1158/0008-5472.can-22-1731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Revised: 08/12/2022] [Accepted: 09/01/2022] [Indexed: 01/07/2023]
Abstract
The RAS family of small GTPases represents the most commonly activated oncogenes in human cancers. To better understand the prevalence of somatic RAS mutations and the compendium of genes that are coaltered in RAS-mutant tumors, we analyzed targeted next-generation sequencing data of 607,863 mutations from 66,372 tumors in 51 cancer types in the AACR Project GENIE Registry. Bayesian hierarchical models were implemented to estimate the cancer-specific prevalence of RAS and non-RAS somatic mutations, to evaluate co-occurrence and mutual exclusivity, and to model the effects of tumor mutation burden and mutational signatures on comutation patterns. These analyses revealed differential RAS prevalence and comutations with non-RAS genes in a cancer lineage-dependent and context-dependent manner, with differences across age, sex, and ethnic groups. Allele-specific RAS co-mutational patterns included an enrichment in NTRK3 and chromatin-regulating gene mutations in KRAS G12C-mutant non-small cell lung cancer. Integrated multiomic analyses of 10,217 tumors from The Cancer Genome Atlas (TCGA) revealed distinct genotype-driven gene expression programs pointing to differential recruitment of cancer hallmarks as well as phenotypic differences and immune surveillance states in the tumor microenvironment of RAS-mutant tumors. The distinct genomic tracks discovered in RAS-mutant tumors reflected differential clinical outcomes in TCGA cohort and in an independent cohort of patients with KRAS G12C-mutant non-small cell lung cancer that received immunotherapy-containing regimens. The RAS genetic architecture points to cancer lineage-specific therapeutic vulnerabilities that can be leveraged for rationally combining RAS-mutant allele-directed therapies with targeted therapies and immunotherapy. SIGNIFICANCE The complex genomic landscape of RAS-mutant tumors is reflective of selection processes in a cancer lineage-specific and context-dependent manner, highlighting differential therapeutic vulnerabilities that can be clinically translated.
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Affiliation(s)
- Robert B. Scharpf
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Archana Balan
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Biagio Ricciuti
- Department of Medicine, Lowe Center for Thoracic Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Jacob Fiksel
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Christopher Cherry
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Chenguang Wang
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Michele L. Lenoue-Newton
- Division of Hematology-Oncology, Department of Medicine, Vanderbilt University Medical Center and Vanderbilt-Ingram Cancer Center, Nashville, Tennessee
| | - Hira A. Rizvi
- Department of Medicine, Collaborative Research Centers, Memorial Sloan Kettering Cancer Center, New York, New York
| | - James R. White
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Alexander S. Baras
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Jordan Anaya
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Blair V. Landon
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Marta Majcherska-Agrawal
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Paola Ghanem
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Jocelyn Lee
- AACR Project GENIE, American Association for Cancer Research, Pennsylvania
| | - Leon Raskin
- Center for Observational Research, Amgen Inc., Thousand Oaks, California
| | - Andrew S. Park
- Center for Observational Research, Amgen Inc., Thousand Oaks, California
| | - Huakang Tu
- Center for Observational Research, Amgen Inc., Thousand Oaks, California
| | - Hil Hsu
- Center for Observational Research, Amgen Inc., Thousand Oaks, California
| | - Kathryn C. Arbour
- Department of Medicine, Division of Clinical Research, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Mark M. Awad
- Department of Medicine, Lowe Center for Thoracic Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Gregory J. Riely
- Department of Medicine, Division of Clinical Research, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Christine M. Lovly
- Division of Hematology-Oncology, Department of Medicine, Vanderbilt University Medical Center and Vanderbilt-Ingram Cancer Center, Nashville, Tennessee
| | - Valsamo Anagnostou
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland
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33
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Pan-cancer analysis of co-occurring mutations in RAD52 and the BRCA1-BRCA2-PALB2 axis in human cancers. PLoS One 2022; 17:e0273736. [PMID: 36107942 PMCID: PMC9477347 DOI: 10.1371/journal.pone.0273736] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 08/12/2022] [Indexed: 11/19/2022] Open
Abstract
In human cells homologous recombination (HR) is critical for repair of DNA double strand breaks (DSBs) and rescue of stalled or collapsed replication forks. HR is facilitated by RAD51 which is loaded onto DNA by either BRCA2-BRCA1-PALB2 or RAD52. In human culture cells, double-knockdowns of RAD52 and genes in the BRCA1-BRCA2-PALB2 axis are lethal. Mutations in BRCA2, BRCA1 or PALB2 significantly impairs error free HR as RAD51 loading relies on RAD52 which is not as proficient as BRCA2-BRCA1-PALB2. RAD52 also facilitates Single Strand Annealing (SSA) that produces intra-chromosomal deletions. Some RAD52 mutations that affect the SSA function or decrease RAD52 association with DNA can suppress certain BRCA2 associated phenotypes in breast cancers. In this report we did a pan-cancer analysis using data reported on the Catalogue of Somatic Mutations in Cancers (COSMIC) to identify double mutants between RAD52 and BRCA1, BRCA2 or PALB2 that occur in cancer cells. We find that co-occurring mutations are likely in certain cancer tissues but not others. However, all mutations occur in a heterozygous state. Further, using computational and machine learning tools we identified only a handful of pathogenic or driver mutations predicted to significantly affect the function of the proteins. This supports previous findings that co-inactivation of RAD52 with any members of the BRCA2-BRCA1-PALB2 axis is lethal. Molecular modeling also revealed that pathogenic RAD52 mutations co-occurring with mutations in BRCA2-BRCA1-PALB2 axis are either expected to attenuate its SSA function or its interaction with DNA. This study extends previous breast cancer findings to other cancer types and shows that co-occurring mutations likely destabilize HR by similar mechanisms as in breast cancers.
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34
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Oliver TRW, Chappell L, Sanghvi R, Deighton L, Ansari-Pour N, Dentro SC, Young MD, Coorens THH, Jung H, Butler T, Neville MDC, Leongamornlert D, Sanders MA, Hooks Y, Cagan A, Mitchell TJ, Cortes-Ciriano I, Warren AY, Wedge DC, Heer R, Coleman N, Murray MJ, Campbell PJ, Rahbari R, Behjati S. Clonal diversification and histogenesis of malignant germ cell tumours. Nat Commun 2022; 13:4272. [PMID: 35953478 PMCID: PMC9372159 DOI: 10.1038/s41467-022-31375-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 06/13/2022] [Indexed: 12/21/2022] Open
Abstract
Germ cell tumours (GCTs) are a collection of benign and malignant neoplasms derived from primordial germ cells. They are uniquely able to recapitulate embryonic and extraembryonic tissues, which carries prognostic and therapeutic significance. The developmental pathways underpinning GCT initiation and histogenesis are incompletely understood. Here, we study the relationship of histogenesis and clonal diversification in GCTs by analysing the genomes and transcriptomes of 547 microdissected histological units. We find no correlation between genomic and histological heterogeneity. However, we identify unifying features including the retention of fetal developmental transcripts across tissues, expression changes on chromosome 12p, and a conserved somatic evolutionary sequence of whole genome duplication followed by clonal diversification. While this pattern is preserved across all GCTs, the developmental timing of the duplication varies between prepubertal and postpubertal cases. In addition, tumours of younger children exhibit distinct substitution signatures which may lend themselves as potential biomarkers for risk stratification. Our findings portray the extensive diversification of GCT tissues and genetic subclones as randomly distributed, while identifying overarching transcriptional and genomic features.
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Affiliation(s)
- Thomas R W Oliver
- Wellcome Sanger Institute, Hinxton, UK
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | | | | | | | - Naser Ansari-Pour
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- MRC Molecular Haematology Unit, Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Stefan C Dentro
- Wellcome Sanger Institute, Hinxton, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | | | | | | | | | | | | | - Mathijs A Sanders
- Wellcome Sanger Institute, Hinxton, UK
- Department of Hematology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | | | | | - Thomas J Mitchell
- Wellcome Sanger Institute, Hinxton, UK
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Isidro Cortes-Ciriano
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - Anne Y Warren
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - David C Wedge
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Manchester Cancer Research Centre, Division of Cancer Sciences, University of Manchester, Manchester, UK
| | - Rakesh Heer
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- Newcastle Urology, Freeman Hospital, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Nicholas Coleman
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
- Department of Pathology, University of Cambridge, Cambridge, UK
| | - Matthew J Murray
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
- Department of Pathology, University of Cambridge, Cambridge, UK
| | | | | | - Sam Behjati
- Wellcome Sanger Institute, Hinxton, UK.
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
- Department of Paediatrics, University of Cambridge, Cambridge, UK.
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35
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Belikov AV, Vyatkin AD, Leonov SV. Novel Driver Strength Index highlights important cancer genes in TCGA PanCanAtlas patients. PeerJ 2022; 10:e13860. [PMID: 35975235 PMCID: PMC9375969 DOI: 10.7717/peerj.13860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 07/18/2022] [Indexed: 01/18/2023] Open
Abstract
Background Cancer driver genes are usually ranked by mutation frequency, which does not necessarily reflect their driver strength. We hypothesize that driver strength is higher for genes preferentially mutated in patients with few driver mutations overall, because these few mutations should be strong enough to initiate cancer. Methods We propose formulas for the Driver Strength Index (DSI) and the Normalized Driver Strength Index (NDSI), the latter independent of gene mutation frequency. We validate them using TCGA PanCanAtlas datasets, established driver prediction algorithms and custom computational pipelines integrating SNA, CNA and aneuploidy driver contributions at the patient-level resolution. Results DSI and especially NDSI provide substantially different gene rankings compared to the frequency approach. E.g., NDSI prioritized members of specific protein families, including G proteins GNAQ, GNA11 and GNAS, isocitrate dehydrogenases IDH1 and IDH2, and fibroblast growth factor receptors FGFR2 and FGFR3. KEGG analysis shows that top NDSI-ranked genes comprise EGFR/FGFR2/GNAQ/GNA11-NRAS/HRAS/KRAS-BRAF pathway, AKT1-MTOR pathway, and TCEB1-VHL-HIF1A pathway. Conclusion Our indices are able to select for driver gene attributes not selected by frequency sorting, potentially for driver strength. Genes and pathways prioritized are likely the strongest contributors to cancer initiation and progression and should become future therapeutic targets.
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36
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Revisiting Epithelial Carcinogenesis. Int J Mol Sci 2022; 23:ijms23137437. [PMID: 35806442 PMCID: PMC9267463 DOI: 10.3390/ijms23137437] [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: 05/10/2022] [Revised: 06/22/2022] [Accepted: 06/22/2022] [Indexed: 12/04/2022] Open
Abstract
The origin of cancer remains one of the most important enigmas in modern biology. This paper presents a hypothesis for the origin of carcinomas in which cellular aging and inflammation enable the recovery of cellular plasticity, which may ultimately result in cancer. The hypothesis describes carcinogenesis as the result of the dedifferentiation undergone by epithelial cells in hyperplasia due to replicative senescence towards a mesenchymal cell state with potentially cancerous behavior. In support of this hypothesis, the molecular, cellular, and histopathological evidence was critically reviewed and reinterpreted when necessary to postulate a plausible generic series of mechanisms for the origin and progression of carcinomas. In addition, the implications of this theoretical framework for the current strategies of cancer treatment are discussed considering recent evidence of the molecular events underlying the epigenetic switches involved in the resistance of breast carcinomas. The hypothesis also proposes an epigenetic landscape for their progression and a potential mechanism for restraining the degree of dedifferentiation and malignant behavior. In addition, the manuscript revisits the gradual degeneration of the nonalcoholic fatty liver disease to propose an integrative generalized mechanistic explanation for the involution and carcinogenesis of tissues associated with aging. The presented hypothesis might serve to understand and structure new findings into a more encompassing view of the genesis of degenerative diseases and may inspire novel approaches for their study and therapy.
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37
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Ozturk K, Carter H. Predicting functional consequences of mutations using molecular interaction network features. Hum Genet 2022; 141:1195-1210. [PMID: 34432150 PMCID: PMC8873243 DOI: 10.1007/s00439-021-02329-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 07/31/2021] [Indexed: 12/13/2022]
Abstract
Variant interpretation remains a central challenge for precision medicine. Missense variants are particularly difficult to understand as they change only a single amino acid in a protein sequence yet can have large and varied effects on protein activity. Numerous tools have been developed to identify missense variants with putative disease consequences from protein sequence and structure. However, biological function arises through higher order interactions among proteins and molecules within cells. We therefore sought to capture information about the potential of missense mutations to perturb protein interaction networks by integrating protein structure and interaction data. We developed 16 network-based annotations for missense mutations that provide orthogonal information to features classically used to prioritize variants. We then evaluated them in the context of a proven machine-learning framework for variant effect prediction across multiple benchmark datasets to demonstrate their potential to improve variant classification. Interestingly, network features resulted in larger performance gains for classifying somatic mutations than for germline variants, possibly due to different constraints on what mutations are tolerated at the cellular versus organismal level. Our results suggest that modeling variant potential to perturb context-specific interactome networks is a fruitful strategy to advance in silico variant effect prediction.
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Affiliation(s)
- Kivilcim Ozturk
- Division of Medical Genetics, Department of Medicine, University of California San Diego, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
| | - Hannah Carter
- Division of Medical Genetics, Department of Medicine, University of California San Diego, La Jolla, CA, USA.
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA.
- Moores Cancer Center, University of California San Diego, La Jolla, CA, USA.
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38
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Sudhakar M, Rengaswamy R, Raman K. Multi-Omic Data Improve Prediction of Personalized Tumor Suppressors and Oncogenes. Front Genet 2022; 13:854190. [PMID: 35620468 PMCID: PMC9127508 DOI: 10.3389/fgene.2022.854190] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 04/04/2022] [Indexed: 12/12/2022] Open
Abstract
The progression of tumorigenesis starts with a few mutational and structural driver events in the cell. Various cohort-based computational tools exist to identify driver genes but require multiple samples to identify less frequently mutated driver genes. Many studies use different methods to identify driver mutations/genes from mutations that have no impact on tumor progression; however, a small fraction of patients show no mutational events in any known driver genes. Current unsupervised methods map somatic and expression data onto a network to identify personalized driver genes based on changes in expression. Our method is the first machine learning model to classify genes as tumor suppressor gene (TSG), oncogene (OG), or neutral, thus assigning the functional impact of the gene in the patient. In this study, we develop a multi-omic approach, PIVOT (Personalized Identification of driVer OGs and TSGs), to train on experimentally or computationally validated mutational and structural driver events. Given the lack of any gold standards for the identification of personalized driver genes, we label the data using four strategies and, based on classification metrics, show gene-based labeling strategies perform best. We build different models using SNV, RNA, and multi-omic features to be used based on the data available. Our models trained on multi-omic data improved predictions compared with mutation and expression data, achieving an accuracy ≥0.99 for BRCA, LUAD, and COAD datasets. We show network and expression-based features contribute the most to PIVOT. Our predictions on BRCA, COAD, and LUAD cancer types reveal commonly altered genes such as TP53 and PIK3CA, which are predicted drivers for multiple cancer types. Along with known driver genes, our models also identify new driver genes such as PRKCA, SOX9, and PSMD4. Our multi-omic model labels both CNV and mutations with a more considerable contribution by CNV alterations. While predicting labels for genes mutated in multiple samples, we also label rare driver events occurring in as few as one sample. We also identify genes with dual roles within the same cancer type. Overall, PIVOT labels personalized driver genes as TSGs and OGs and also identified rare driver genes.
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Affiliation(s)
- Malvika Sudhakar
- Centre for Integrative Biology and Systems mEdicine (IBSE), Indian Institute of Technology (IIT) Madras, Chennai, India.,Robert Bosch Center for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai, India.,Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, IIT Madras, Chennai, India
| | - Raghunathan Rengaswamy
- Centre for Integrative Biology and Systems mEdicine (IBSE), Indian Institute of Technology (IIT) Madras, Chennai, India.,Robert Bosch Center for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai, India.,Department of Chemical Engineering, IIT Madras, Chennai, India
| | - Karthik Raman
- Centre for Integrative Biology and Systems mEdicine (IBSE), Indian Institute of Technology (IIT) Madras, Chennai, India.,Robert Bosch Center for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai, India.,Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, IIT Madras, Chennai, India
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39
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Parvandeh S, Donehower LA, Katsonis P, Hsu TK, Asmussen J, Lee K, Lichtarge O. EPIMUTESTR: a nearest neighbor machine learning approach to predict cancer driver genes from the evolutionary action of coding variants. Nucleic Acids Res 2022; 50:e70. [PMID: 35412634 PMCID: PMC9262594 DOI: 10.1093/nar/gkac215] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 03/17/2022] [Accepted: 03/21/2022] [Indexed: 02/01/2023] Open
Abstract
Discovering rare cancer driver genes is difficult because their mutational frequency is too low for statistical detection by computational methods. EPIMUTESTR is an integrative nearest-neighbor machine learning algorithm that identifies such marginal genes by modeling the fitness of their mutations with the phylogenetic Evolutionary Action (EA) score. Over cohorts of sequenced patients from The Cancer Genome Atlas representing 33 tumor types, EPIMUTESTR detected 214 previously inferred cancer driver genes and 137 new candidates never identified computationally before of which seven genes are supported in the COSMIC Cancer Gene Census. EPIMUTESTR achieved better robustness and specificity than existing methods in a number of benchmark methods and datasets.
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Affiliation(s)
- Saeid Parvandeh
- To whom correspondence should be addressed. Tel: +1 713 798 7677;
| | - Lawrence A Donehower
- Department of Molecular Virology and Microbiology, Houston, TX 77030, USA,Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Panagiotis Katsonis
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Teng-Kuei Hsu
- Department of Biochemistry & Molecular Biology, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
| | - Jennifer K Asmussen
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Kwanghyuk Lee
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Olivier Lichtarge
- Correspondence may also be addressed to Olivier Lichtarge. Tel: +1 713 798 5646;
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40
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Guillen KP, Fujita M, Butterfield AJ, Scherer SD, Bailey MH, Chu Z, DeRose YS, Zhao L, Cortes-Sanchez E, Yang CH, Toner J, Wang G, Qiao Y, Huang X, Greenland JA, Vahrenkamp JM, Lum DH, Factor RE, Nelson EW, Matsen CB, Poretta JM, Rosenthal R, Beck AC, Buys SS, Vaklavas C, Ward JH, Jensen RL, Jones KB, Li Z, Oesterreich S, Dobrolecki LE, Pathi SS, Woo XY, Berrett KC, Wadsworth ME, Chuang JH, Lewis MT, Marth GT, Gertz J, Varley KE, Welm BE, Welm AL. A human breast cancer-derived xenograft and organoid platform for drug discovery and precision oncology. NATURE CANCER 2022; 3:232-250. [PMID: 35221336 PMCID: PMC8882468 DOI: 10.1038/s43018-022-00337-6] [Citation(s) in RCA: 141] [Impact Index Per Article: 70.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 01/12/2022] [Indexed: 12/17/2022]
Abstract
Models that recapitulate the complexity of human tumors are urgently needed to develop more effective cancer therapies. We report a bank of human patient-derived xenografts (PDXs) and matched organoid cultures from tumors that represent the greatest unmet need: endocrine-resistant, treatment-refractory and metastatic breast cancers. We leverage matched PDXs and PDX-derived organoids (PDxO) for drug screening that is feasible and cost-effective with in vivo validation. Moreover, we demonstrate the feasibility of using these models for precision oncology in real time with clinical care in a case of triple-negative breast cancer (TNBC) with early metastatic recurrence. Our results uncovered a Food and Drug Administration (FDA)-approved drug with high efficacy against the models. Treatment with this therapy resulted in a complete response for the individual and a progression-free survival (PFS) period more than three times longer than their previous therapies. This work provides valuable methods and resources for functional precision medicine and drug development for human breast cancer.
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Affiliation(s)
- Katrin P Guillen
- Department of Oncological Sciences, University of Utah, Salt Lake City, UT, USA
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Maihi Fujita
- Department of Oncological Sciences, University of Utah, Salt Lake City, UT, USA
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Andrew J Butterfield
- Department of Oncological Sciences, University of Utah, Salt Lake City, UT, USA
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Sandra D Scherer
- Department of Oncological Sciences, University of Utah, Salt Lake City, UT, USA
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Matthew H Bailey
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
- Eccles Institute of Human Genetics, University of Utah, Salt Lake City, UT, USA
| | - Zhengtao Chu
- Department of Oncological Sciences, University of Utah, Salt Lake City, UT, USA
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Yoko S DeRose
- Department of Oncological Sciences, University of Utah, Salt Lake City, UT, USA
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Ling Zhao
- Department of Oncological Sciences, University of Utah, Salt Lake City, UT, USA
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Emilio Cortes-Sanchez
- Department of Oncological Sciences, University of Utah, Salt Lake City, UT, USA
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Chieh-Hsiang Yang
- Department of Oncological Sciences, University of Utah, Salt Lake City, UT, USA
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Jennifer Toner
- Department of Oncological Sciences, University of Utah, Salt Lake City, UT, USA
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Guoying Wang
- Department of Oncological Sciences, University of Utah, Salt Lake City, UT, USA
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Yi Qiao
- Eccles Institute of Human Genetics, University of Utah, Salt Lake City, UT, USA
| | - Xiaomeng Huang
- Eccles Institute of Human Genetics, University of Utah, Salt Lake City, UT, USA
| | - Jeffery A Greenland
- Department of Oncological Sciences, University of Utah, Salt Lake City, UT, USA
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Jeffery M Vahrenkamp
- Department of Oncological Sciences, University of Utah, Salt Lake City, UT, USA
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - David H Lum
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Rachel E Factor
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
- Department of Pathology, University of Utah, Salt Lake City, UT, USA
| | - Edward W Nelson
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
- Department of Surgery, University of Utah, Salt Lake City, UT, USA
| | - Cindy B Matsen
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
- Department of Surgery, University of Utah, Salt Lake City, UT, USA
| | - Jane M Poretta
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
- Department of Surgery, University of Utah, Salt Lake City, UT, USA
| | - Regina Rosenthal
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
- Department of Surgery, University of Utah, Salt Lake City, UT, USA
| | - Anna C Beck
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
- Department of Internal Medicine, Division of Medical Oncology, University of Utah, Salt Lake City, UT, USA
| | - Saundra S Buys
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
- Department of Internal Medicine, Division of Medical Oncology, University of Utah, Salt Lake City, UT, USA
| | - Christos Vaklavas
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
- Department of Internal Medicine, Division of Medical Oncology, University of Utah, Salt Lake City, UT, USA
| | - John H Ward
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
- Department of Internal Medicine, Division of Medical Oncology, University of Utah, Salt Lake City, UT, USA
| | - Randy L Jensen
- Department of Oncological Sciences, University of Utah, Salt Lake City, UT, USA
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
- Department of Neurosurgery, University of Utah, Salt Lake City, UT, USA
| | - Kevin B Jones
- Department of Oncological Sciences, University of Utah, Salt Lake City, UT, USA
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
- Department of Orthopaedics, University of Utah, Salt Lake City, UT, USA
| | - Zheqi Li
- Department of Pharmacology and Chemical Biology, University of Pittsburgh, UPMC Hillman Cancer Center, Magee Womens Research Institute, Pittsburgh, PA, USA
| | - Steffi Oesterreich
- Department of Pharmacology and Chemical Biology, University of Pittsburgh, UPMC Hillman Cancer Center, Magee Womens Research Institute, Pittsburgh, PA, USA
| | - Lacey E Dobrolecki
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, USA
| | - Satya S Pathi
- Department of Oncological Sciences, University of Utah, Salt Lake City, UT, USA
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Xing Yi Woo
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Kristofer C Berrett
- Department of Oncological Sciences, University of Utah, Salt Lake City, UT, USA
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Mark E Wadsworth
- Department of Oncological Sciences, University of Utah, Salt Lake City, UT, USA
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Jeffrey H Chuang
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
- Department of Genetics and Genome Sciences, UCONN-Health, Farmington, CT, USA
| | - Michael T Lewis
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, USA
| | - Gabor T Marth
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
- Eccles Institute of Human Genetics, University of Utah, Salt Lake City, UT, USA
| | - Jason Gertz
- Department of Oncological Sciences, University of Utah, Salt Lake City, UT, USA
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Katherine E Varley
- Department of Oncological Sciences, University of Utah, Salt Lake City, UT, USA
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Bryan E Welm
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA.
- Department of Surgery, University of Utah, Salt Lake City, UT, USA.
| | - Alana L Welm
- Department of Oncological Sciences, University of Utah, Salt Lake City, UT, USA.
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA.
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Comprehensive patient-level classification and quantification of driver events in TCGA PanCanAtlas cohorts. PLoS Genet 2022; 18:e1009996. [PMID: 35030162 PMCID: PMC8759692 DOI: 10.1371/journal.pgen.1009996] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Accepted: 12/14/2021] [Indexed: 12/14/2022] Open
Abstract
There is a growing need to develop novel therapeutics for targeted treatment of cancer. The prerequisite to success is the knowledge about which types of molecular alterations are predominantly driving tumorigenesis. To shed light onto this subject, we have utilized the largest database of human cancer mutations–TCGA PanCanAtlas, multiple established algorithms for cancer driver prediction (2020plus, CHASMplus, CompositeDriver, dNdScv, DriverNet, HotMAPS, OncodriveCLUSTL, OncodriveFML) and developed four novel computational pipelines: SNADRIF (Single Nucleotide Alteration DRIver Finder), GECNAV (Gene Expression-based Copy Number Alteration Validator), ANDRIF (ANeuploidy DRIver Finder) and PALDRIC (PAtient-Level DRIver Classifier). A unified workflow integrating all these pipelines, algorithms and datasets at cohort and patient levels was created. We have found that there are on average 12 driver events per tumour, of which 0.6 are single nucleotide alterations (SNAs) in oncogenes, 1.5 are amplifications of oncogenes, 1.2 are SNAs in tumour suppressors, 2.1 are deletions of tumour suppressors, 1.5 are driver chromosome losses, 1 is a driver chromosome gain, 2 are driver chromosome arm losses, and 1.5 are driver chromosome arm gains. The average number of driver events per tumour increases with age (from 7 to 15) and cancer stage (from 10 to 15) and varies strongly between cancer types (from 1 to 24). Patients with 1 and 7 driver events per tumour are the most frequent, and there are very few patients with more than 40 events. In tumours having only one driver event, this event is most often an SNA in an oncogene. However, with increasing number of driver events per tumour, the contribution of SNAs decreases, whereas the contribution of copy-number alterations and aneuploidy events increases. By analysing genomic and transcriptomic data from 10000 cancer patients through our custom-built computational pipelines and previously established third-party algorithms, we have found that half of all driver events in a patient’s tumour appear to be gains and losses of chromosomal arms and whole chromosomes. We therefore suggest that future therapeutics development efforts should be focused on targeting aneuploidy. We have also found that approximately a third of driver events in a patient are whole gene amplifications and deletions. Thus, therapies aimed at copy-number alterations also appear very promising. On the other hand, drugs aiming at point mutations are predicted to be less successful, as these alterations are responsible for just a couple of drivers per tumour. One notable exception are patients having only one driver event in their tumours, as this event is almost always a single nucleotide alteration in an oncogene.
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Porta-Pardo E, Ruiz-Serra V, Valentini S, Valencia A. The structural coverage of the human proteome before and after AlphaFold. PLoS Comput Biol 2022; 18:e1009818. [PMID: 35073311 PMCID: PMC8812986 DOI: 10.1371/journal.pcbi.1009818] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 02/03/2022] [Accepted: 01/07/2022] [Indexed: 12/12/2022] Open
Abstract
The protein structure field is experiencing a revolution. From the increased throughput of techniques to determine experimental structures, to developments such as cryo-EM that allow us to find the structures of large protein complexes or, more recently, the development of artificial intelligence tools, such as AlphaFold, that can predict with high accuracy the folding of proteins for which the availability of homology templates is limited. Here we quantify the effect of the recently released AlphaFold database of protein structural models in our knowledge on human proteins. Our results indicate that our current baseline for structural coverage of 48%, considering experimentally-derived or template-based homology models, elevates up to 76% when including AlphaFold predictions. At the same time the fraction of dark proteome is reduced from 26% to just 10% when AlphaFold models are considered. Furthermore, although the coverage of disease-associated genes and mutations was near complete before AlphaFold release (69% of Clinvar pathogenic mutations and 88% of oncogenic mutations), AlphaFold models still provide an additional coverage of 3% to 13% of these critically important sets of biomedical genes and mutations. Finally, we show how the contribution of AlphaFold models to the structural coverage of non-human organisms, including important pathogenic bacteria, is significantly larger than that of the human proteome. Overall, our results show that the sequence-structure gap of human proteins has almost disappeared, an outstanding success of direct consequences for the knowledge on the human genome and the derived medical applications. Protein structures are key to understand many biological phenomena at the molecular scale: from the effects of genetic variation to how different proteins interact with each other to create molecular pathways that, together, have a biological function. Obtaining experimental structures, however, is extremely consuming in terms of both, time and resources. For this and other reasons, scientists have long worked to develop computational approaches that predict the structure of a protein using only its sequence as input. Recently, a group of scientists at Deepmind have developed AlphaFold2, a computational tool that is extremely accurate at this task. Moreover, they have used this tool to predict the structures of all human proteins. In this manuscript we provide an overview of the structural coverage of the human proteome before AlphaFold models were released and how much we have gained thanks to these models. We also show how the gain affects our understanding of human pathogenic variants, both germline and somatic. Finally, we provide evidence suggesting that the gain in non-human organisms is larger than for the human proteome, particularly in the case of bacteria.
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Affiliation(s)
- Eduard Porta-Pardo
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
- Josep Carreras Leukaemia Research Institute (IJC), Badalona, Spain
- * E-mail: (EP-P); (AV)
| | - Victoria Ruiz-Serra
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
- Josep Carreras Leukaemia Research Institute (IJC), Badalona, Spain
| | - Samuel Valentini
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
| | - Alfonso Valencia
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
- Institució Catalana de Recerca Avançada (ICREA), Barcelona, Spain
- * E-mail: (EP-P); (AV)
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43
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Koşaloğlu-Yalçın Z, Blazeska N, Carter H, Nielsen M, Cohen E, Kufe D, Conejo-Garcia J, Robbins P, Schoenberger SP, Peters B, Sette A. The Cancer Epitope Database and Analysis Resource: A Blueprint for the Establishment of a New Bioinformatics Resource for Use by the Cancer Immunology Community. Front Immunol 2021; 12:735609. [PMID: 34504503 PMCID: PMC8421848 DOI: 10.3389/fimmu.2021.735609] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 08/09/2021] [Indexed: 12/17/2022] Open
Abstract
Recent years have witnessed a dramatic rise in interest towards cancer epitopes in general and particularly neoepitopes, antigens that are encoded by somatic mutations that arise as a consequence of tumorigenesis. There is also an interest in the specific T cell and B cell receptors recognizing these epitopes, as they have therapeutic applications. They can also aid in basic studies to infer the specificity of T cells or B cells characterized in bulk and single-cell sequencing data. The resurgence of interest in T cell and B cell epitopes emphasizes the need to catalog all cancer epitope-related data linked to the biological, immunological, and clinical contexts, and most importantly, making this information freely available to the scientific community in a user-friendly format. In parallel, there is also a need to develop resources for epitope prediction and analysis tools that provide researchers access to predictive strategies and provide objective evaluations of their performance. For example, such tools should enable researchers to identify epitopes that can be effectively used for immunotherapy or in defining biomarkers to predict the outcome of checkpoint blockade therapies. We present here a detailed vision, blueprint, and work plan for the development of a new resource, the Cancer Epitope Database and Analysis Resource (CEDAR). CEDAR will provide a freely accessible, comprehensive collection of cancer epitope and receptor data curated from the literature and provide easily accessible epitope and T cell/B cell target prediction and analysis tools. The curated cancer epitope data will provide a transparent benchmark dataset that can be used to assess how well prediction tools perform and to develop new prediction tools relevant to the cancer research community.
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MESH Headings
- Antigens, Neoplasm/genetics
- Antigens, Neoplasm/immunology
- Computational Biology
- Databases, Genetic
- Epitopes, B-Lymphocyte
- Epitopes, T-Lymphocyte
- Humans
- Immunotherapy
- Mutation
- Neoplasms/genetics
- Neoplasms/immunology
- Neoplasms/therapy
- Receptors, Antigen, B-Cell/genetics
- Receptors, Antigen, B-Cell/immunology
- Receptors, Antigen, T-Cell/genetics
- Receptors, Antigen, T-Cell/immunology
- Tumor Microenvironment
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Affiliation(s)
- Zeynep Koşaloğlu-Yalçın
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, United States
| | - Nina Blazeska
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, United States
| | - Hannah Carter
- Department of Medicine, University of California San Diego, La Jolla, CA, United States
- Moore’s Cancer Center, University of California San Diego, La Jolla, CA, United States
| | - Morten Nielsen
- Department of Bio and Health Informatics, Technical University of Denmark, Lyngby, Denmark
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, San Martín, Argentina
| | - Ezra Cohen
- Moore’s Cancer Center, University of California San Diego, La Jolla, CA, United States
| | - Donald Kufe
- Dana Farber Cancer Institute, Harvard Medical School, Boston, MA, United States
| | - Jose Conejo-Garcia
- Department of Gynecologic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States
- Department of Immunology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States
| | - Paul Robbins
- National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| | - Stephen P. Schoenberger
- Laboratory of Cellular Immunology, La Jolla Institute for Immunology, La Jolla, CA, United States
| | - Bjoern Peters
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, United States
- Department of Medicine, University of California San Diego, La Jolla, CA, United States
| | - Alessandro Sette
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, United States
- Department of Medicine, University of California San Diego, La Jolla, CA, United States
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44
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Grillo E, Ravelli C, Corsini M, Zammataro L, Mitola S. Protein domain-based approaches for the identification and prioritization of therapeutically actionable cancer variants. Biochim Biophys Acta Rev Cancer 2021; 1876:188614. [PMID: 34403770 DOI: 10.1016/j.bbcan.2021.188614] [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: 07/22/2021] [Revised: 08/11/2021] [Accepted: 08/11/2021] [Indexed: 01/04/2023]
Abstract
The tremendous number of cancer variants that can be detected by NGS analyses has required the development of computational approaches to prioritize mutations on the basis of their biological and clinical significance. Standard strategies take a gene-centric approach to the problem, allowing exclusively the identification of highly frequent variants. On the contrary, protein domain (PD)-based approaches allow to identify functionally relevant low frequency variants by searching for mutations that recur on analogous residues across homologous proteins (i.e. containing the same PD). Such approaches enable to transfer information about the effects and druggability from one known mutation to unknown ones. Here we describe how PD-based strategies work, and discuss how they could be exploited for mutation prioritization. The principle that mutations clustered on specific residues of PDs have the same functional consequences and are therapeutically actionable in a similar manner could help the choice of patient-specific targeted drugs, eventually improving the management of cancer patients.
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Affiliation(s)
- Elisabetta Grillo
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy.
| | - Cosetta Ravelli
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Michela Corsini
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Luca Zammataro
- Division of Artificial Intelligence Systems for Immunoinformatics, Kiromic BioPharma, Inc., Houston, USA
| | - Stefania Mitola
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy.
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45
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Muiños F, Martínez-Jiménez F, Pich O, Gonzalez-Perez A, Lopez-Bigas N. In silico saturation mutagenesis of cancer genes. Nature 2021; 596:428-432. [PMID: 34321661 DOI: 10.1038/s41586-021-03771-1] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 06/25/2021] [Indexed: 12/24/2022]
Abstract
Despite the existence of good catalogues of cancer genes1,2, identifying the specific mutations of those genes that drive tumorigenesis across tumour types is still a largely unsolved problem. As a result, most mutations identified in cancer genes across tumours are of unknown significance to tumorigenesis3. We propose that the mutations observed in thousands of tumours-natural experiments testing their oncogenic potential replicated across individuals and tissues-can be exploited to solve this problem. From these mutations, features that describe the mechanism of tumorigenesis of each cancer gene and tissue may be computed and used to build machine learning models that encapsulate these mechanisms. Here we demonstrate the feasibility of this solution by building and validating 185 gene-tissue-specific machine learning models that outperform experimental saturation mutagenesis in the identification of driver and passenger mutations. The models and their assessment of each mutation are designed to be interpretable, thus avoiding a black-box prediction device. Using these models, we outline the blueprints of potential driver mutations in cancer genes, and demonstrate the role of mutation probability in shaping the landscape of observed driver mutations. These blueprints will support the interpretation of newly sequenced tumours in patients and the study of the mechanisms of tumorigenesis of cancer genes across tissues.
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Affiliation(s)
- Ferran Muiños
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain.
| | - Francisco Martínez-Jiménez
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Oriol Pich
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Abel Gonzalez-Perez
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain. .,Research Program on Biomedical Informatics, Universitat Pompeu Fabra, Barcelona, Spain.
| | - Nuria Lopez-Bigas
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain. .,Research Program on Biomedical Informatics, Universitat Pompeu Fabra, Barcelona, Spain. .,Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain.
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46
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Rogers MF, Gaunt TR, Campbell C. Prediction of driver variants in the cancer genome via machine learning methodologies. Brief Bioinform 2021; 22:bbaa250. [PMID: 33094325 PMCID: PMC8293831 DOI: 10.1093/bib/bbaa250] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 09/04/2020] [Accepted: 09/06/2020] [Indexed: 01/18/2023] Open
Abstract
Sequencing technologies have led to the identification of many variants in the human genome which could act as disease-drivers. As a consequence, a variety of bioinformatics tools have been proposed for predicting which variants may drive disease, and which may be causatively neutral. After briefly reviewing generic tools, we focus on a subset of these methods specifically geared toward predicting which variants in the human cancer genome may act as enablers of unregulated cell proliferation. We consider the resultant view of the cancer genome indicated by these predictors and discuss ways in which these types of prediction tools may be progressed by further research.
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Affiliation(s)
| | - Tom R Gaunt
- MRC Integrative Epidemiology Unit, University of Bristol
| | - Colin Campbell
- University of Bristol with interests in machine learning and medical bioinformatics
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47
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Abstract
Pancreatic cancer is a genetic disease, and the recurrent genetic alterations characteristic of pancreatic cancer indicate the cellular processes that are targeted for malignant transformation. In addition to somatic alterations in the most common driver genes (KRAS, CDKN2A, TP53 and SMAD4), large-scale studies have revealed major roles for genetic alterations of the SWI/SNF and COMPASS complexes, copy number alterations in GATA6 and MYC that partially define phenotypes of pancreatic cancer, and the role(s) of polyploidy and chromothripsis as factors contributing to pancreatic cancer biology and progression. Germline variants that increase the risk of pancreatic cancer continue to be discovered along with a greater appreciation of the features of pancreatic cancers with mismatch repair deficiencies and homologous recombination deficiencies that confer sensitivity to therapeutic targeting. Wild-type KRAS pancreatic cancers, some of which are driven by alternative oncogenic events affecting NRG1 or NTRK1 - for which targeted therapies exist - further underscore that pancreatic cancer is formally entering the era of precision medicine. Given the vast developments within this field, here we review the wide-ranging and most current information related to pancreatic cancer genomics with the goal of integrating this information into a unifying description of the life history of pancreatic cancer.
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48
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Melocchi V, Dama E, Mazzarelli F, Cuttano R, Colangelo T, Di Candia L, Lugli E, Veronesi G, Pelosi G, Ferretti GM, Taurchini M, Graziano P, Bianchi F. Aggressive early-stage lung adenocarcinoma is characterized by epithelial cell plasticity with acquirement of stem-like traits and immune evasion phenotype. Oncogene 2021; 40:4980-4991. [PMID: 34172935 DOI: 10.1038/s41388-021-01909-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Revised: 06/07/2021] [Accepted: 06/14/2021] [Indexed: 12/31/2022]
Abstract
Lung adenocarcinoma (LUAD) is the main non-small-cell lung cancer diagnosed in ~40-50% of all lung cancer cases. Despite the improvements in early detection and personalized medicine, even a sizable fraction of patients with early-stage LUAD would experience disease relapses and adverse prognosis. Previous reports indicated the existence of LUAD molecular subtypes characterized by specific gene expression and mutational profiles, and correlating with prognosis. However, the biological and molecular features of such subtypes have not been further explored. Consequently, the mechanisms driving the emergence of aggressive LUAD remained unclear. Here, we adopted a multi-tiered approach ranging from molecular to functional characterization of LUAD and used it on multiple cohorts of patients (for a total of 1227 patients) and LUAD cell lines. We investigated the tumor transcriptome and the mutational and immune gene expression profiles, and we used LUAD cell lines for cancer cell phenotypic screening. We found that loss of lung cell lineage and gain of stem cell-like characteristics, along with mutator and immune evasion phenotypes, explain the aggressive behavior of a specific subset of lung adenocarcinoma that we called C1-LUAD, including early-stage disease. This subset can be identified using a 10-gene prognostic signature. Poor prognosis patients appear to have this specific molecular lung adenocarcinoma subtype which is characterized by peculiar molecular and biological features. Our data support the hypothesis that transformed lung stem/progenitor cells and/or reprogrammed epithelial cells with CSC characteristics are hallmarks of this aggressive disease. Such discoveries suggest alternative, more aggressive, therapeutic strategies for early-stage C1-LUAD.
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Affiliation(s)
- Valentina Melocchi
- Cancer Biomarkers Unit, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Elisa Dama
- Cancer Biomarkers Unit, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Francesco Mazzarelli
- Cancer Biomarkers Unit, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Roberto Cuttano
- Cancer Biomarkers Unit, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Tommaso Colangelo
- Cancer Biomarkers Unit, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Leonarda Di Candia
- Pathology Unit, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Enrico Lugli
- Laboratory of Translational Immunology, Humanitas Clinical and Research Center-IRCCS, Milan, Italy
| | - Giulia Veronesi
- Division of Thoracic Surgery, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Giuseppe Pelosi
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy.,Inter-Hospital Pathology Division, IRCCS MultiMedica, Milan, Italy
| | - Gian Maria Ferretti
- Thoracic Surgical Unit, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Marco Taurchini
- Thoracic Surgical Unit, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Paolo Graziano
- Pathology Unit, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Fabrizio Bianchi
- Cancer Biomarkers Unit, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy.
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49
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Li L, Halpert G, Lerner MG, Hu H, Dimitrion P, Weiss MJ, He J, Philosophe B, Burkhart R, Burns WR, Wesson RN, MacGregor Cameron A, Wolfgang CL, Georgiades C, Kawamoto S, Azad NS, Yarchoan M, Meltzer SJ, Oshima K, Ensign LM, Bader JS, Selaru FM. Protein synthesis inhibitor omacetaxine is effective against hepatocellular carcinoma. JCI Insight 2021; 6:138197. [PMID: 34003798 PMCID: PMC8262474 DOI: 10.1172/jci.insight.138197] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Accepted: 05/12/2021] [Indexed: 12/24/2022] Open
Abstract
Hepatocellular carcinoma (HCC) is the sixth most common and the fourth most deadly cancer worldwide. The development cost of new therapeutics is a major limitation in patient outcomes. Importantly, there is a paucity of preclinical HCC models in which to test new small molecules. Herein, we implemented potentially novel patient-derived organoid (PDO) and patient-derived xenografts (PDX) strategies for high-throughput drug screening. Omacetaxine, an FDA-approved drug for chronic myelogenous leukemia (CML), was found to be a top effective small molecule in HCC PDOs. Next, omacetaxine was tested against a larger cohort of 40 human HCC PDOs. Serial dilution experiments demonstrated that omacetaxine is effective at low (nanomolar) concentrations. Mechanistic studies established that omacetaxine inhibits global protein synthesis, with a disproportionate effect on short–half-life proteins. High-throughput expression screening identified molecular targets for omacetaxine, including key oncogenes, such as PLK1. In conclusion, by using an innovative strategy, we report — for the first time to our knowledge — the effectiveness of omacetaxine in HCC. In addition, we elucidate key mechanisms of omacetaxine action. Finally, we provide a proof-of-principle basis for future studies applying drug screening PDOs sequenced with candidate validation in PDX models. Clinical trials could be considered to evaluate omacetaxine in patients with HCC.
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Affiliation(s)
- Ling Li
- Division of Gastroenterology and Hepatology and
| | - Gilad Halpert
- Center for Nanomedicine at the Wilmer Eye Institute, Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Michael G Lerner
- Department of Physics and Astronomy, Earlham College, Richmond, Indiana, USA
| | - Haijie Hu
- Division of Gastroenterology and Hepatology and
| | - Peter Dimitrion
- Center for Nanomedicine at the Wilmer Eye Institute, Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Matthew J Weiss
- Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Jin He
- Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Benjamin Philosophe
- Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Richard Burkhart
- Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - William R Burns
- Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Russell N Wesson
- Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | | | | | | | | | - Nilofer S Azad
- Department of Oncology, Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Mark Yarchoan
- Department of Oncology, Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Stephen J Meltzer
- Division of Gastroenterology and Hepatology and.,Department of Oncology, Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | | | - Laura M Ensign
- Center for Nanomedicine at the Wilmer Eye Institute, Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Department of Oncology, Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Joel S Bader
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Florin M Selaru
- Division of Gastroenterology and Hepatology and.,Department of Oncology, Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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50
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Dragomir I, Akbar A, Cassidy JW, Patel N, Clifford HW, Contino G. Identifying Cancer Drivers Using DRIVE: A Feature-Based Machine Learning Model for a Pan-Cancer Assessment of Somatic Missense Mutations. Cancers (Basel) 2021; 13:cancers13112779. [PMID: 34205004 PMCID: PMC8199862 DOI: 10.3390/cancers13112779] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 04/14/2021] [Accepted: 04/21/2021] [Indexed: 11/16/2022] Open
Abstract
Sporadic cancer develops from the accrual of somatic mutations. Out of all small-scale somatic aberrations in coding regions, 95% are base substitutions, with 90% being missense mutations. While multiple studies focused on the importance of this mutation type, a machine learning method based on the number of protein-protein interactions (PPIs) has not been fully explored. This study aims to develop an improved computational method for driver identification, validation and evaluation (DRIVE), which is compared to other methods for assessing its performance. DRIVE aims at distinguishing between driver and passenger mutations using a feature-based learning approach comprising two levels of biological classification for a pan-cancer assessment of somatic mutations. Gene-level features include the maximum number of protein-protein interactions, the biological process and the type of post-translational modifications (PTMs) while mutation-level features are based on pathogenicity scores. Multiple supervised classification algorithms were trained on Genomics Evidence Neoplasia Information Exchange (GENIE) project data and then tested on an independent dataset from The Cancer Genome Atlas (TCGA) study. Finally, the most powerful classifier using DRIVE was evaluated on a benchmark dataset, which showed a better overall performance compared to other state-of-the-art methodologies, however, considerable care must be taken due to the reduced size of the dataset. DRIVE outlines the outstanding potential that multiple levels of a feature-based learning model will play in the future of oncology-based precision medicine.
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Affiliation(s)
- Ionut Dragomir
- Institute of Cancer and Genomic Sciences, College of Medical and Dental Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK;
- Centre for Computational Biology, College of Medical and Dental Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
| | - Adnan Akbar
- Cambridge Cancer Genomics, Cambridge CB2 1QN, UK; (A.A.); (J.W.C.); (N.P.); (H.W.C.)
| | - John W. Cassidy
- Cambridge Cancer Genomics, Cambridge CB2 1QN, UK; (A.A.); (J.W.C.); (N.P.); (H.W.C.)
| | - Nirmesh Patel
- Cambridge Cancer Genomics, Cambridge CB2 1QN, UK; (A.A.); (J.W.C.); (N.P.); (H.W.C.)
| | - Harry W. Clifford
- Cambridge Cancer Genomics, Cambridge CB2 1QN, UK; (A.A.); (J.W.C.); (N.P.); (H.W.C.)
| | - Gianmarco Contino
- Institute of Cancer and Genomic Sciences, College of Medical and Dental Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK;
- Von Hügel Institute, St Edmund College, University of Cambridge, Cambridge CB3 0BN, UK
- Queen Elizabeth Hospital, University of Birmingham Hospital Trust, Edgbaston, Birmingham B15 2GW, UK
- Correspondence:
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