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Zhang T, Zhang SW, Xie MY, Li Y. Identifying cooperating cancer driver genes in individual patients through hypergraph random walk. J Biomed Inform 2024; 157:104710. [PMID: 39159864 DOI: 10.1016/j.jbi.2024.104710] [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: 04/27/2024] [Revised: 07/30/2024] [Accepted: 08/14/2024] [Indexed: 08/21/2024]
Abstract
OBJECTIVE Identifying cancer driver genes, especially rare or patient-specific cancer driver genes, is a primary goal in cancer therapy. Although researchers have proposed some methods to tackle this problem, these methods mostly identify cancer driver genes at single gene level, overlooking the cooperative relationship among cancer driver genes. Identifying cooperating cancer driver genes in individual patients is pivotal for understanding cancer etiology and advancing the development of personalized therapies. METHODS Here, we propose a novel Personalized Cooperating cancer Driver Genes (PCoDG) method by using hypergraph random walk to identify the cancer driver genes that cooperatively drive individual patient cancer progression. By leveraging the powerful ability of hypergraph in representing multi-way relationships, PCoDG first employs the personalized hypergraph to depict the complex interactions among mutated genes and differentially expressed genes of an individual patient. Then, a hypergraph random walk algorithm based on hyperedge similarity is utilized to calculate the importance scores of mutated genes, integrating these scores with signaling pathway data to identify the cooperating cancer driver genes in individual patients. RESULTS The experimental results on three TCGA cancer datasets (i.e., BRCA, LUAD, and COADREAD) demonstrate the effectiveness of PCoDG in identifying personalized cooperating cancer driver genes. These genes identified by PCoDG not only offer valuable insights into patient stratification correlating with clinical outcomes, but also provide an useful reference resource for tailoring personalized treatments. CONCLUSION We propose a novel method that can effectively identify cooperating cancer driver genes for individual patients, thereby deepening our understanding of the cooperative relationship among personalized cancer driver genes and advancing the development of precision oncology.
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Affiliation(s)
- Tong Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China; School of Electrical and Mechanical Engineering, Pingdingshan University, Pingdingshan 467000, China
| | - Shao-Wu Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China.
| | - Ming-Yu Xie
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Yan Li
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
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Anglada-Girotto M, Ciampi L, Bonnal S, Head SA, Miravet-Verde S, Serrano L. In silico RNA isoform screening to identify potential cancer driver exons with therapeutic applications. Nat Commun 2024; 15:7039. [PMID: 39147755 PMCID: PMC11327330 DOI: 10.1038/s41467-024-51380-z] [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: 07/12/2023] [Accepted: 08/06/2024] [Indexed: 08/17/2024] Open
Abstract
Alternative splicing is crucial for cancer progression and can be targeted pharmacologically, yet identifying driver exons genome-wide remains challenging. We propose identifying such exons by associating statistically gene-level cancer dependencies from knockdown viability screens with splicing profiles and gene expression. Our models predict the effects of splicing perturbations on cell proliferation from transcriptomic data, enabling in silico RNA screening and prioritizing targets for splicing-based therapies. We identified 1,073 exons impacting cell proliferation, many from genes not previously linked to cancer. Experimental validation confirms their influence on proliferation, especially in highly proliferative cancer cell lines. Integrating pharmacological screens with splicing dependencies highlights the potential driver exons affecting drug sensitivity. Our models also allow predicting treatment outcomes from tumor transcriptomes, suggesting applications in precision oncology. This study presents an approach to identifying cancer driver exon and their therapeutic potential, emphasizing alternative splicing as a cancer target.
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Affiliation(s)
- Miquel Anglada-Girotto
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona, 08003, Spain.
| | - Ludovica Ciampi
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona, 08003, Spain
| | - Sophie Bonnal
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona, 08003, Spain
| | - Sarah A Head
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona, 08003, Spain
| | - Samuel Miravet-Verde
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona, 08003, Spain.
- Department of Biology, Institute of Microbiology and Swiss Institute of Bioinformatics, ETH Zurich, Zurich, Switzerland.
| | - Luis Serrano
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona, 08003, Spain.
- Universitat Pompeu Fabra (UPF), Barcelona, Spain.
- ICREA, Pg. Lluís Companys 23, Barcelona, 08010, Spain.
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Kozono D, Hua X, Wu MC, Tolba KA, Waqar SN, Dragnev KH, Cheng H, Hirsch FR, Mack PC, Gray JE, Kelly K, Borghaei H, Herbst RS, Gandara DR, Redman MW. Lung-MAP Next-Generation Sequencing Analysis of Advanced Squamous Cell Lung Cancers (SWOG S1400). J Thorac Oncol 2024:S1556-0864(24)00751-2. [PMID: 39111731 DOI: 10.1016/j.jtho.2024.07.024] [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: 03/13/2024] [Revised: 07/24/2024] [Accepted: 07/27/2024] [Indexed: 08/27/2024]
Abstract
INTRODUCTION Squamous cell cancer (SqCC) is a lung cancer subtype with few targeted therapy options. Molecular characterization, that is, by next-generation sequencing (NGS), is needed to identify potential targets. Lung Cancer Master Protocol Southwest Oncology Group S1400 enrolled patients with previously treated stage IV or recurrent SqCC to assess NGS biomarkers for therapeutic sub-studies. METHODS Tumors underwent NGS using Foundation Medicine's FoundationOne research platform, which sequenced the exons and/or introns of 313 cancer-related genes. Mutually exclusive gene set analysis and Selected Events Linked by Evolutionary Conditions across Human Tumors were performed to identify mutually exclusive and co-occurring gene alterations. Comparisons were performed with data on 495 lung SqCC downloaded from The Cancer Genome Atlas. Cox proportional hazards models were used to assess associations between genetic variants and survival. RESULTS NGS data are reported for 1672 patients enrolled on S1400 between 2014 and 2019. Mutually exclusive gene set analysis identified two non-overlapping sets of mutually exclusive alterations with a false discovery rate of less than 15%: NFE2L2, KEAP1, and PARP4; and CDKN2A and RB1. PARP4, a relatively uncharacterized gene, showed three frequent mutations suggesting functional significance: 3116T>C (I1039T), 3176A>G (Q1059R), and 3509C>T (T1170I). When taken together, NFE2L2 and KEAP1 alterations were associated with poorer survival. CONCLUSIONS As the largest dataset to date of lung SqCC profiled on a clinical trial, the S1400 NGS dataset establishes a rich resource for biomarker discovery. Mutual exclusivity of PARP4 and NFE2L2 or KEAP1 alterations suggests that PARP4 may have an uncharacterized role in a key pathway known to impact oxidative stress response and treatment resistance.
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Affiliation(s)
- David Kozono
- Department of Radiation Oncology, Dana-Farber Brigham Cancer Center, Boston, Massachusetts.
| | - Xing Hua
- SWOG Statistics and Data Management Center, Seattle, Washington; Biostatistics Program, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Michael C Wu
- SWOG Statistics and Data Management Center, Seattle, Washington; Biostatistics Program, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | | | - Saiama N Waqar
- Division of Oncology, Washington University School of Medicine, St. Louis, Missouri
| | | | - Haiying Cheng
- Department of Oncology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York
| | - Fred R Hirsch
- Mt. Sinai Health System Center for Thoracic Oncology, Tisch Cancer Institute, New York, New York
| | - Philip C Mack
- Mt. Sinai Health System Center for Thoracic Oncology, Tisch Cancer Institute, New York, New York
| | - Jhanelle E Gray
- Department of Thoracic Oncology, Moffitt Cancer Center, Tampa, Florida
| | - Karen Kelly
- Division of Hematology and Oncology, UC Davis Comprehensive Cancer Center, Sacramento, California
| | - Hossein Borghaei
- Department of Hematology/Oncology, Fox Chase Cancer Center, Philadelphia, Pennsylvania
| | - Roy S Herbst
- Section of Medical Oncology, Yale University, New Haven, Connecticut
| | - David R Gandara
- Division of Hematology and Oncology, UC Davis Comprehensive Cancer Center, Sacramento, California
| | - Mary W Redman
- SWOG Statistics and Data Management Center, Seattle, Washington; Biostatistics Program, Fred Hutchinson Cancer Research Center, Seattle, Washington
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Sibai DS, Tremblay MG, Lessard F, Tav C, Sabourin-Félix M, Robinson M, Moss T. TTF1 control of LncRNA synthesis delineates a tumor suppressor pathway directly regulating the ribosomal RNA genes. J Cell Physiol 2024; 239:e31303. [PMID: 38764354 DOI: 10.1002/jcp.31303] [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: 03/15/2024] [Revised: 04/29/2024] [Accepted: 05/02/2024] [Indexed: 05/21/2024]
Abstract
The tumor suppressor p14/19ARF regulates ribosomal RNA (rRNA) synthesis by controlling the nucleolar localization of Transcription Termination Factor 1 (TTF1). However, the role played by TTF1 in regulating the rRNA genes and in potentially controlling growth has remained unclear. We now show that TTF1 expression regulates cell growth by determining the cellular complement of ribosomes. Unexpectedly, it achieves this by acting as a "roadblock" to synthesis of the noncoding LncRNA and pRNA that we show are generated from the "Spacer Promoter" duplications present upstream of the 47S pre-rRNA promoter on the mouse and human ribosomal RNA genes. Unexpectedly, the endogenous generation of these noncoding RNAs does not induce CpG methylation or gene silencing. Rather, it acts in cis to suppress 47S preinitiation complex formation and hence de novo pre-rRNA synthesis by a mechanism reminiscent of promoter interference or occlusion. Taken together, our data delineate a pathway from p19ARF to cell growth suppression via the regulation of ribosome biogenesis by noncoding RNAs and validate a key cellular growth law in mammalian cells.
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Affiliation(s)
- Dany S Sibai
- St-Patrick Research Group in Basic Oncology, Cancer Division of the Quebec University Hospital Research Centre, Laval University, Quebec City, Quebec, Canada
- Department of Molecular Biology, Medical Biochemistry and Pathology, Faculty of Medicine, Laval University, Quebec City, Quebec, Canada
- Cancer Research Centre, Laval University, Quebec City, Quebec, Canada
| | - Michel G Tremblay
- St-Patrick Research Group in Basic Oncology, Cancer Division of the Quebec University Hospital Research Centre, Laval University, Quebec City, Quebec, Canada
| | - Frédéric Lessard
- St-Patrick Research Group in Basic Oncology, Cancer Division of the Quebec University Hospital Research Centre, Laval University, Quebec City, Quebec, Canada
| | - Christophe Tav
- St-Patrick Research Group in Basic Oncology, Cancer Division of the Quebec University Hospital Research Centre, Laval University, Quebec City, Quebec, Canada
- Department of Molecular Biology, Medical Biochemistry and Pathology, Faculty of Medicine, Laval University, Quebec City, Quebec, Canada
- Cancer Research Centre, Laval University, Quebec City, Quebec, Canada
| | - Marianne Sabourin-Félix
- St-Patrick Research Group in Basic Oncology, Cancer Division of the Quebec University Hospital Research Centre, Laval University, Quebec City, Quebec, Canada
| | - Mark Robinson
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
| | - Tom Moss
- St-Patrick Research Group in Basic Oncology, Cancer Division of the Quebec University Hospital Research Centre, Laval University, Quebec City, Quebec, Canada
- Department of Molecular Biology, Medical Biochemistry and Pathology, Faculty of Medicine, Laval University, Quebec City, Quebec, Canada
- Cancer Research Centre, Laval University, Quebec City, Quebec, Canada
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Chong Z, Huang F, McLeod M, Irwin R, Smithson M, Yue Z, Gao M, Hardiman K. Molecular differentiation between complete and incomplete responders to neoadjuvant therapy in rectal cancer. RESEARCH SQUARE 2024:rs.3.rs-4456000. [PMID: 39011117 PMCID: PMC11247942 DOI: 10.21203/rs.3.rs-4456000/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/17/2024]
Abstract
Background Neoadjuvant chemoradiotherapy (nCRT) is the standard treatment for locally advanced rectal cancer, but only 20-40% of patients completely respond to this treatment. Methods To define the molecular features that are associated with response to nCRT, we generated and collected genomic and transcriptomic data from 712 cancers prior to treatment from our own data and from publicly available data. Results We found that patients with a complete response have decreased risk of both local recurrence and future metastasis. We identified multiple differences in DNA mutations and transcripts between complete and incomplete responders. Complete responder tumors have a higher tumor mutation burden and more significant co-occurring mutations than the incomplete responder tumors. In addition, mutations in DNA repair genes (across multiple mechanisms of repair) were enriched in complete responders and they also had lower expression of these genes indicating that defective DNA repair is associated with complete response to nCRT. Using logistic regression, we identified three significant predictors of complete response: tumor size, mutations within specific network genes, and the existence of three or more specific co-occurrent mutations. In incompletely responder tumors, abnormal cell-cell interaction and increased cancer associated fibroblasts were associated with recurrence. Additionally, gene expression analysis identified a subset of immune hot tumors with worse outcomes and upregulated of immune checkpoint proteins. Conclusions Overall, our study provides a comprehensive understanding of the molecular features associated with response to nCRT and the molecular differences in non-responder tumors that later reoccur. This knowledge may provide critical insight for the development of precision therapy for rectal cancer.
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Affiliation(s)
| | | | - M McLeod
- University of Alabama at Birmingham
| | | | | | | | - Min Gao
- University of Alabama at Birmingham
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Zong Y, Zhu A, Liu P, Fu P, Li Y, Chen S, Gao X. Pan-cancer analysis of the disulfidptosis-related gene RPN1 and its potential biological function and prognostic significance in gliomas. Heliyon 2024; 10:e31875. [PMID: 38845861 PMCID: PMC11154626 DOI: 10.1016/j.heliyon.2024.e31875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 05/22/2024] [Accepted: 05/23/2024] [Indexed: 06/09/2024] Open
Abstract
Background Numerous studies have shown a strong correlation between disulfidptosis and various cancers. However, the expression and function of RPN1, a crucial gene in disulfidptosis, remain unclear in the context of cancer. Methods Gene expression and clinical information on lung adenocarcinoma were obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. RPN1 expression was analyzed using the Timer2.0 and the Human Protein Atlas (HPA) databases. Prognostic significance was assessed using Cox regression analysis and Kaplan-Meier curves. Genetic mutations and methylation levels were examined using the cBioPortal and UALCAN platforms, respectively. The relationship between RPN1 and tumor mutation burden (TMB) and microsatellite instability (MSI) across different cancer types was analyzed using the Spearman correlation coefficient. The relationship between RPN1 and immune cell infiltration was analyzed using the Timer2.0 database, whereas variations in drug sensitivity were explored using the CellMiner database. Receiver operating characteristic curves validated RPN1's diagnostic potential in glioma, and its correlation with immune checkpoint inhibitors (ICIs) was assessed using Spearman's correlation coefficient. Single-sample gene set enrichment analysis elucidated a link between RPN1 and immune cells and pathways. In addition, a nomogram based on RPN1 was developed to predict patient prognosis. The functional impact of RPN1 on glioma cells was confirmed using scratch and Transwell assays. Result RPN1 was aberrantly expressed in various cancers and affected patient prognosis. The main mutation type of RPN1 in the cancer was amplified. RPN1 exhibited a positive correlation with myeloid-derived suppressor cells, neutrophils, and macrophages, and a negative correlation with CD8+ T cells and hematopoietic stem cells. RPN1 expression was associated with TMB and MSI in various cancers. The expression of RPN1 affected drug sensitivity in cancer cells. RPN1 was positively correlated with multiple ICIs in gliomas. RPN1 also affected immune cell infiltration into the tumor microenvironment. RPN1 was an independent prognostic factor for gliomas, and the nomogram demonstrated excellent predictive performance. Interference with RPN1 expression reduces the migratory and invasive ability of glioma cells. Conclusion RPN1 exerts multifaceted effects on different stages of cancer, including immune infiltration, prognosis, and treatment outcomes. RPN1 expression affects the prognosis and immune microenvironment infiltration in patients with glioma, making RPN1 a potential target for the treatment of glioma.
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Affiliation(s)
- Yan Zong
- Department of Neurology, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Ankang Zhu
- Department of Thoracic Surgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Peipei Liu
- Anhui BioX-Vision Biological Technology Co., Ltd., Anhui, China
| | - Peiji Fu
- Department of Neurology, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Yinuo Li
- Department of Neurology, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Shuai Chen
- Department of Neurology, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Xingcai Gao
- Department of Thoracic Surgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
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Horie S, Saito Y, Kogure Y, Mizuno K, Ito Y, Tabata M, Kanai T, Murakami K, Koya J, Kataoka K. Pan-Cancer Comparative and Integrative Analyses of Driver Alterations Using Japanese and International Genomic Databases. Cancer Discov 2024; 14:786-803. [PMID: 38276885 DOI: 10.1158/2159-8290.cd-23-0902] [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: 08/17/2023] [Revised: 12/02/2023] [Accepted: 01/23/2024] [Indexed: 01/27/2024]
Abstract
Using 48,627 samples from the Center for Cancer Genomics and Advanced Therapeutics (C-CAT), we present a pan-cancer landscape of driver alterations and their clinical actionability in Japanese patients. Comparison with White patients in Genomics Evidence Neoplasia Information Exchange (GENIE) demonstrates high TP53 mutation frequencies in Asian patients across multiple cancer types. Integration of C-CAT, GENIE, and The Cancer Genome Atlas data reveals many cooccurring and mutually exclusive relationships between driver mutations. At pathway level, mutations in epigenetic regulators frequently cooccur with PI3K pathway molecules. Furthermore, we found significant cooccurring mutations within the epigenetic pathway. Accumulation of mutations in epigenetic regulators causes increased proliferation-related transcriptomic signatures. Loss-of-function of many epigenetic drivers inhibits cell proliferation in their wild-type cell lines, but this effect is attenuated in those harboring mutations of not only the same but also different epigenetic drivers. Our analyses dissect various genetic properties and provide valuable resources for precision medicine in cancer. SIGNIFICANCE We present a genetic landscape of 26 principal cancer types/subtypes, including Asian-prevalent ones, in Japanese patients. Multicohort data integration unveils numerous cooccurring and exclusive relationships between driver mutations, identifying cooccurrence of multiple mutations in epigenetic regulators, which coordinately cause transcriptional and phenotypic changes. These findings provide insights into epigenetic regulator-driven oncogenesis. This article is featured in Selected Articles from This Issue, p. 695.
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Affiliation(s)
- Sara Horie
- Division of Molecular Oncology, National Cancer Center Research Institute, Tokyo, Japan
- Department of Gastroenterology, Keio University School of Medicine, Tokyo, Japan
| | - Yuki Saito
- Division of Molecular Oncology, National Cancer Center Research Institute, Tokyo, Japan
- Department of Gastroenterology, Keio University School of Medicine, Tokyo, Japan
| | - Yasunori Kogure
- Division of Molecular Oncology, National Cancer Center Research Institute, Tokyo, Japan
| | - Kota Mizuno
- Division of Molecular Oncology, National Cancer Center Research Institute, Tokyo, Japan
- Division of Hematology, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Yuta Ito
- Division of Molecular Oncology, National Cancer Center Research Institute, Tokyo, Japan
- Division of Clinical Oncology and Hematology, Department of Internal Medicine, The Jikei University School of Medicine, Tokyo, Japan
| | - Mariko Tabata
- Division of Molecular Oncology, National Cancer Center Research Institute, Tokyo, Japan
- Department of Urology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Takanori Kanai
- Department of Gastroenterology, Keio University School of Medicine, Tokyo, Japan
| | - Koichi Murakami
- Division of Molecular Oncology, National Cancer Center Research Institute, Tokyo, Japan
- Division of Hematology, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Junji Koya
- Division of Molecular Oncology, National Cancer Center Research Institute, Tokyo, Japan
| | - Keisuke Kataoka
- Division of Molecular Oncology, National Cancer Center Research Institute, Tokyo, Japan
- Division of Hematology, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
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Lin CJ, Jin X, Ma D, Chen C, Ou-Yang Y, Pei YC, Zhou CZ, Qu FL, Wang YJ, Liu CL, Fan L, Hu X, Shao ZM, Jiang YZ. Genetic interactions reveal distinct biological and therapeutic implications in breast cancer. Cancer Cell 2024; 42:701-719.e12. [PMID: 38593782 DOI: 10.1016/j.ccell.2024.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 01/16/2024] [Accepted: 03/13/2024] [Indexed: 04/11/2024]
Abstract
Co-occurrence and mutual exclusivity of genomic alterations may reflect the existence of genetic interactions, potentially shaping distinct biological phenotypes and impacting therapeutic response in breast cancer. However, our understanding of them remains limited. Herein, we investigate a large-scale multi-omics cohort (n = 873) and a real-world clinical sequencing cohort (n = 4,405) including several clinical trials with detailed treatment outcomes and perform functional validation in patient-derived organoids, tumor fragments, and in vivo models. Through this comprehensive approach, we construct a network comprising co-alterations and mutually exclusive events and characterize their therapeutic potential and underlying biological basis. Notably, we identify associations between TP53mut-AURKAamp and endocrine therapy resistance, germline BRCA1mut-MYCamp and improved sensitivity to PARP inhibitors, and TP53mut-MYBamp and immunotherapy resistance. Furthermore, we reveal that precision treatment strategies informed by co-alterations hold promise to improve patient outcomes. Our study highlights the significance of genetic interactions in guiding genome-informed treatment decisions beyond single driver alterations.
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Affiliation(s)
- Cai-Jin Lin
- Key Laboratory of Breast Cancer, Department of Breast Surgery, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Xi Jin
- Key Laboratory of Breast Cancer, Department of Breast Surgery, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Ding Ma
- Key Laboratory of Breast Cancer, Department of Breast Surgery, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Chao Chen
- Key Laboratory of Breast Cancer, Department of Breast Surgery, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Yang Ou-Yang
- Key Laboratory of Breast Cancer, Department of Breast Surgery, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Yu-Chen Pei
- Precision Cancer Medical Center, Fudan University Shanghai Cancer Center, Shanghai 200032, China
| | - Chao-Zheng Zhou
- Key Laboratory of Breast Cancer, Department of Breast Surgery, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Fei-Lin Qu
- Key Laboratory of Breast Cancer, Department of Breast Surgery, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Yun-Jin Wang
- Precision Cancer Medical Center, Fudan University Shanghai Cancer Center, Shanghai 200032, China
| | - Cheng-Lin Liu
- Key Laboratory of Breast Cancer, Department of Breast Surgery, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Lei Fan
- Key Laboratory of Breast Cancer, Department of Breast Surgery, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Xin Hu
- Precision Cancer Medical Center, Fudan University Shanghai Cancer Center, Shanghai 200032, China.
| | - Zhi-Ming Shao
- Key Laboratory of Breast Cancer, Department of Breast Surgery, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
| | - Yi-Zhou Jiang
- Key Laboratory of Breast Cancer, Department of Breast Surgery, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
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Alejandre C, Calle-Espinosa J, Iranzo J. Synergistic epistasis among cancer drivers can rescue early tumors from the accumulation of deleterious passengers. PLoS Comput Biol 2024; 20:e1012081. [PMID: 38687804 PMCID: PMC11087069 DOI: 10.1371/journal.pcbi.1012081] [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: 08/30/2023] [Revised: 05/10/2024] [Accepted: 04/16/2024] [Indexed: 05/02/2024] Open
Abstract
Epistasis among driver mutations is pervasive and explains relevant features of cancer, such as differential therapy response and convergence towards well-characterized molecular subtypes. Furthermore, a growing body of evidence suggests that tumor development could be hampered by the accumulation of slightly deleterious passenger mutations. In this work, we combined empirical epistasis networks, computer simulations, and mathematical models to explore how synergistic interactions among driver mutations affect cancer progression under the burden of slightly deleterious passengers. We found that epistasis plays a crucial role in tumor development by promoting the transformation of precancerous clones into rapidly growing tumors through a process that is analogous to evolutionary rescue. The triggering of epistasis-driven rescue is strongly dependent on the intensity of epistasis and could be a key rate-limiting step in many tumors, contributing to their unpredictability. As a result, central genes in cancer epistasis networks appear as key intervention targets for cancer therapy.
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Affiliation(s)
- Carla Alejandre
- Centro de Astrobiología (CAB) CSIC-INTA, Torrejón de Ardoz, Madrid, Spain
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)—Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain
| | - Jorge Calle-Espinosa
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)—Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain
| | - Jaime Iranzo
- Centro de Astrobiología (CAB) CSIC-INTA, Torrejón de Ardoz, Madrid, Spain
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)—Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain
- Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, Zaragoza, Spain
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10
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Ciriello G, Magnani L, Aitken SJ, Akkari L, Behjati S, Hanahan D, Landau DA, Lopez-Bigas N, Lupiáñez DG, Marine JC, Martin-Villalba A, Natoli G, Obenauf AC, Oricchio E, Scaffidi P, Sottoriva A, Swarbrick A, Tonon G, Vanharanta S, Zuber J. Cancer Evolution: A Multifaceted Affair. Cancer Discov 2024; 14:36-48. [PMID: 38047596 PMCID: PMC10784746 DOI: 10.1158/2159-8290.cd-23-0530] [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: 05/04/2023] [Revised: 08/29/2023] [Accepted: 10/23/2023] [Indexed: 12/05/2023]
Abstract
Cancer cells adapt and survive through the acquisition and selection of molecular modifications. This process defines cancer evolution. Building on a theoretical framework based on heritable genetic changes has provided insights into the mechanisms supporting cancer evolution. However, cancer hallmarks also emerge via heritable nongenetic mechanisms, including epigenetic and chromatin topological changes, and interactions between tumor cells and the tumor microenvironment. Recent findings on tumor evolutionary mechanisms draw a multifaceted picture where heterogeneous forces interact and influence each other while shaping tumor progression. A comprehensive characterization of the cancer evolutionary toolkit is required to improve personalized medicine and biomarker discovery. SIGNIFICANCE Tumor evolution is fueled by multiple enabling mechanisms. Importantly, genetic instability, epigenetic reprogramming, and interactions with the tumor microenvironment are neither alternative nor independent evolutionary mechanisms. As demonstrated by findings highlighted in this perspective, experimental and theoretical approaches must account for multiple evolutionary mechanisms and their interactions to ultimately understand, predict, and steer tumor evolution.
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Affiliation(s)
- Giovanni Ciriello
- Swiss Cancer Center Leman, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Luca Magnani
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, United Kingdom
- Breast Epigenetic Plasticity and Evolution Laboratory, Division of Breast Cancer Research, The Institute of Cancer Research, London, United Kingdom
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Sarah J. Aitken
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, United Kingdom
- Department of Histopathology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Leila Akkari
- Division of Tumor Biology and Immunology, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Sam Behjati
- Wellcome Sanger Institute, Hinxton, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
- Department of Paediatrics, University of Cambridge, Cambridge, United Kingdom
| | - Douglas Hanahan
- Swiss Cancer Center Leman, Lausanne, Switzerland
- Ludwig Institute for Cancer Research, Lausanne, Switzerland
| | - Dan A. Landau
- New York Genome Center, New York, New York
- Division of Hematology and Medical Oncology, Department of Medicine and Meyer Cancer Center, Weill Cornell Medicine, New York, New York
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, New York
| | - Nuria Lopez-Bigas
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Centro de Investigación Biomédica en Red en Cáncer (CIBERONC), Instituto de Salud Carlos III, Madrid, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - Darío G. Lupiáñez
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany
| | - Jean-Christophe Marine
- Laboratory for Molecular Cancer Biology, Center for Cancer Biology, VIB, Leuven, Belgium
- Laboratory for Molecular Cancer Biology, Department of Oncology, KULeuven, Leuven, Belgium
| | - Ana Martin-Villalba
- Department of Molecular Neurobiology, German Cancer Research Center (DFKZ), Heidelberg, Germany
| | - Gioacchino Natoli
- Department of Experimental Oncology, European Institute of Oncology IRCCS, Milan, Italy
| | - Anna C. Obenauf
- Research Institute of Molecular Pathology, Vienna Biocenter, Vienna, Austria
| | - Elisa Oricchio
- Swiss Cancer Center Leman, Lausanne, Switzerland
- Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland
| | - Paola Scaffidi
- Department of Experimental Oncology, European Institute of Oncology IRCCS, Milan, Italy
- Cancer Epigenetic Laboratory, The Francis Crick Institute, London, United Kingdom
| | - Andrea Sottoriva
- Computational Biology Research Centre, Human Technopole, Milan, Italy
| | - Alexander Swarbrick
- Cancer Ecosystems Program, Garvan Institute of Medical Research, Darlinghurst, Australia
- School of Clinical Medicine, Faculty of Medicine and Health, UNSW Sydney, Sydney, Australia
| | - Giovanni Tonon
- Vita-Salute San Raffaele University, Milan, Italy
- Center for Omics Sciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Sakari Vanharanta
- Translational Cancer Medicine Program, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Biochemistry and Developmental Biology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Johannes Zuber
- Research Institute of Molecular Pathology, Vienna Biocenter, Vienna, Austria
- Medical University of Vienna, Vienna BioCenter (VBC), Vienna, Austria
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11
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Chen C, Lin CJ, Pei YC, Ma D, Liao L, Li SY, Fan L, Di GH, Wu SY, Liu XY, Wang YJ, Hong Q, Zhang GL, Xu LL, Li BB, Huang W, Shi JX, Jiang YZ, Hu X, Shao ZM. Comprehensive genomic profiling of breast cancers characterizes germline-somatic mutation interactions mediating therapeutic vulnerabilities. Cell Discov 2023; 9:125. [PMID: 38114467 PMCID: PMC10730692 DOI: 10.1038/s41421-023-00614-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 10/08/2023] [Indexed: 12/21/2023] Open
Abstract
Germline-somatic mutation interactions are universal and associated with tumorigenesis, but their role in breast cancer, especially in non-Caucasians, remains poorly characterized. We performed large-scale prospective targeted sequencing of matched tumor-blood samples from 4079 Chinese females, coupled with detailed clinical annotation, to map interactions between germline and somatic alterations. We discovered 368 pathogenic germline variants and identified 5 breast cancer DNA repair-associated genes (BCDGs; BRCA1/BRCA2/CHEK2/PALB2/TP53). BCDG mutation carriers, especially those with two-hit inactivation, demonstrated younger onset, higher tumor mutation burden, and greater clinical benefits from platinum drugs, PARP inhibitors, and immune checkpoint inhibitors. Furthermore, we leveraged a multiomics cohort to reveal that clinical benefits derived from two-hit events are associated with increased genome instability and an immune-activated tumor microenvironment. We also established an ethnicity-specific tool to predict BCDG mutation and two-hit status for genetic evaluation and therapeutic decisions. Overall, this study leveraged the large sequencing cohort of Chinese breast cancers, optimizing genomics-guided selection of DNA damaging-targeted therapy and immunotherapy within a broader population.
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Affiliation(s)
- Chao Chen
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Cai-Jin Lin
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yu-Chen Pei
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Precision Cancer Medical Center Affiliated to Fudan University Shanghai Cancer Center, Shanghai, China
| | - Ding Ma
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Li Liao
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Si-Yuan Li
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Lei Fan
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Gen-Hong Di
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Song-Yang Wu
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Xi-Yu Liu
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yun-Jin Wang
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Precision Cancer Medical Center Affiliated to Fudan University Shanghai Cancer Center, Shanghai, China
| | - Qi Hong
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Precision Cancer Medical Center Affiliated to Fudan University Shanghai Cancer Center, Shanghai, China
| | - Guo-Liang Zhang
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Precision Cancer Medical Center Affiliated to Fudan University Shanghai Cancer Center, Shanghai, China
| | - Lin-Lin Xu
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Precision Cancer Medical Center Affiliated to Fudan University Shanghai Cancer Center, Shanghai, China
| | - Bei-Bei Li
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Precision Cancer Medical Center Affiliated to Fudan University Shanghai Cancer Center, Shanghai, China
| | - Wei Huang
- Precision Cancer Medical Center Affiliated to Fudan University Shanghai Cancer Center, Shanghai, China
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China
| | - Jin-Xiu Shi
- Precision Cancer Medical Center Affiliated to Fudan University Shanghai Cancer Center, Shanghai, China
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China
| | - Yi-Zhou Jiang
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
| | - Xin Hu
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.
- Precision Cancer Medical Center Affiliated to Fudan University Shanghai Cancer Center, Shanghai, China.
| | - Zhi-Ming Shao
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
- Precision Cancer Medical Center Affiliated to Fudan University Shanghai Cancer Center, Shanghai, China.
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12
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Li A, Wang Y, Yu Z, Tan Z, He L, Fu S, Shi M, Du W, Luo L, Li Z, Liu J, Zhou Y, Fang W, Yang Y, Zhang L, Hong S. STK11/LKB1-Deficient Phenotype Rather Than Mutation Diminishes Immunotherapy Efficacy and Represents STING/Type I Interferon/CD8 + T-Cell Dysfunction in NSCLC. J Thorac Oncol 2023; 18:1714-1730. [PMID: 37495171 DOI: 10.1016/j.jtho.2023.07.020] [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: 05/25/2023] [Revised: 07/13/2023] [Accepted: 07/17/2023] [Indexed: 07/28/2023]
Abstract
INTRODUCTION Conflicting findings have been reported regarding the association between STK11/LKB1 mutations and immune checkpoint inhibitor (ICB) efficacy in NSCLC. It has been reported that tumors could exhibit impaired STK11/LKB1 function even without STK11 mutations. We hypothesized that STK11 phenotype rather than mutation may better stratify ICB outcomes. METHODS Selected functional STK11 events and LKB1 protein data were leveraged to establish a transcriptomics-based classifier of STK11 phenotype (STK11-deficient [-def] or -proficient [-prof]). We analyzed in-house and Genentech/Roche's data of three randomized trials of programmed cell death protein-1 or programmed death-ligand 1 (PD-L1) inhibition in NSCLC (ORIENT-11, n = 171; OAK, n = 699; POPLAR, n = 192) and The Cancer Genome Atlas-NSCLC cohort. RESULTS Tissue STK11 mutation did not affect ICB outcomes. However, the survival benefit of ICB versus chemotherapy were lost or reversed in STK11-def tumors (hazard ratios for death, 95% confidence interval: OAK [0.97, 0.69-1.35]; POPLAR [1.61, 0.88-2.97]; ORIENT-11 [1.07, 0.50-2.29]), while remaining in STK11-prof tumors (hazard ratios for death, 95% confidence interval: OAK [0.81, 0.66-0.99]; POPLAR [0.66, 0.46-0.95]; ORIENT-11 [0.59, 0.37-0.92]). In tumors differentially classified by phenotype and mutation status, STK11-wild-type/def tumors had significantly worse ICB outcomes than STK11-mutated (STK11-MUT)/prof tumors (p < 0.05). The deleterious impact of STK11 deficiency was independent of STK11/KRAS/KEAP1 status or PD-L1 expression. The STING/interferon-I signaling, which was previously shown to be suppressed in STK11-MUT models, was perturbed in patients with STK11-def tumors rather than those with STK11-MUT tumors. Surprisingly, whereas high CD8+ T-cell infiltration was significantly associated with prolonged survival with ICB in STK11-prof tumors (p < 0.05 for 3 trials), it predicted an opposite trend toward worse ICB outcomes in STK11-def tumors across three trials. This suggested an association between STK11 deficiency and CD8+ T-cell dysfunction, which might not be reversed by programmed cell death protein 1 or PD-L1 blockade. CONCLUSIONS STK11 phenotype rather than mutation status can accurately identify patients with ICB-refractory NSCLC and reflect immune suppression. It can help refine stratification algorithms for future clinical research and also provide a reliable resource aiding basic and translational studies in identifying therapeutic targets.
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Affiliation(s)
- Anlin Li
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China; State Key Laboratory of Oncology in South People's Republic of China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China
| | - Yuanyuan Wang
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China; State Key Laboratory of Oncology in South People's Republic of China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China
| | - Zhixin Yu
- State Key Laboratory of Oncology in South People's Republic of China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China; Department of VIP Region, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China
| | - Zihui Tan
- State Key Laboratory of Oncology in South People's Republic of China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China; Department of Thoracic Surgery, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China
| | - Lina He
- State Key Laboratory of Oncology in South People's Republic of China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China
| | - Sha Fu
- Department of Cellular and Molecular Diagnostics Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation of Sun Yat-Sen University, Guangzhou, People's Republic of China
| | - Mengting Shi
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China; State Key Laboratory of Oncology in South People's Republic of China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China; Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Wei Du
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China; State Key Laboratory of Oncology in South People's Republic of China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China
| | - Linfeng Luo
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China; State Key Laboratory of Oncology in South People's Republic of China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China
| | - Zhichao Li
- State Key Laboratory of Oncology in South People's Republic of China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China; Department of Thoracic Surgery, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China
| | - Jiaqing Liu
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China; State Key Laboratory of Oncology in South People's Republic of China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China
| | - Yixin Zhou
- State Key Laboratory of Oncology in South People's Republic of China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China; Department of VIP Region, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China
| | - Wenfeng Fang
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China; State Key Laboratory of Oncology in South People's Republic of China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China
| | - Yunpeng Yang
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China; State Key Laboratory of Oncology in South People's Republic of China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China
| | - Li Zhang
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China; State Key Laboratory of Oncology in South People's Republic of China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China
| | - Shaodong Hong
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China; State Key Laboratory of Oncology in South People's Republic of China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China.
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13
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Zhang Y, Liu Z, Li J, Wu B, Li X, Duo M, Xu H, Liu L, Su X, Duan X, Luo P, Zhang J, Li Z. Oncogenic pathways refine a new perspective on the classification of hepatocellular carcinoma. Cell Signal 2023; 111:110890. [PMID: 37714446 DOI: 10.1016/j.cellsig.2023.110890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 09/03/2023] [Accepted: 09/12/2023] [Indexed: 09/17/2023]
Abstract
BACKGROUND Genetic alterations in oncogenic pathways are critical for cancer initiation, development, and treatment resistance. However, studies are limited regarding pathways correlated with prognosis, sorafenib, and transcatheter arterial chemoembolization (TACE) in hepatocellular carcinoma (HCC). METHODS In this study, 1928 patients from 11 independent datasets and a clinical in-house cohort were screened to explore the relationships among canonical pathway alterations in HCC patients. The molecular mechanisms, biological functions, immune landscape, and clinical outcomes among three heterogeneous phenotypes were further explored. RESULTS We charted the detailed landscape of pathway alterations in the TCGA-LIHC cohort, screened three pivotal pathways (p53, PI3K, and WNT), identified co-occurrence patterns and mutual exclusively, and stratified patients into three altered-pathway dominant phenotypes (ADPs). P53|PI3K ADP characterized by genomic instability (e.g., highest TMB, FGA, FGG, and FGL) indicated an unfavorable prognosis. While, patients in WNT ADP suggested a median prognosis, enhanced immune activation, and sensitivity to PD-L1 therapy. Remarkably, sorafenib and TACE exhibited efficacy for patients in WNT ADP and low frequent alteration phenotype (LFP). Additionally, ADP could work independently of common clinical traits (e.g., AJCC stage) and previous molecular classifications (e.g., iCluster, serum biomarkers). CONCLUSIONS ADP provides a new perspective for identifying patients at high risk of recurrence and could optimize precision treatment to improve the clinical outcomes in HCC.
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Affiliation(s)
- Yuyuan Zhang
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China; Interventional Institute of Zhengzhou University, Zhengzhou, Henan 450052, China; Interventional Treatment and Clinical Research Center of Henan Province, Zhengzhou, Henan 450052, China
| | - Zaoqu Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China; State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, Department of Pathophysiology, Peking Union Medical College, Beijing 100730, China
| | - Jie Li
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China; Interventional Institute of Zhengzhou University, Zhengzhou, Henan 450052, China; Interventional Treatment and Clinical Research Center of Henan Province, Zhengzhou, Henan 450052, China
| | - Bailu Wu
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China; Interventional Institute of Zhengzhou University, Zhengzhou, Henan 450052, China; Interventional Treatment and Clinical Research Center of Henan Province, Zhengzhou, Henan 450052, China
| | - Xin Li
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China; Interventional Institute of Zhengzhou University, Zhengzhou, Henan 450052, China; Interventional Treatment and Clinical Research Center of Henan Province, Zhengzhou, Henan 450052, China
| | - Mengjie Duo
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Hui Xu
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China; Interventional Institute of Zhengzhou University, Zhengzhou, Henan 450052, China; Interventional Treatment and Clinical Research Center of Henan Province, Zhengzhou, Henan 450052, China
| | - Long Liu
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | | | - Xuhua Duan
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China; Interventional Institute of Zhengzhou University, Zhengzhou, Henan 450052, China; Interventional Treatment and Clinical Research Center of Henan Province, Zhengzhou, Henan 450052, China
| | - Peng Luo
- Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Jian Zhang
- Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Zhen Li
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China; Interventional Institute of Zhengzhou University, Zhengzhou, Henan 450052, China; Interventional Treatment and Clinical Research Center of Henan Province, Zhengzhou, Henan 450052, China.
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14
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Chang L, Jung NY, Atari A, Rodriguez DJ, Kesar D, Song TY, Rees MG, Ronan M, Li R, Ruiz P, Chaturantabut S, Ito T, van Tienen LM, Tseng YY, Roth JA, Sellers WR. Systematic profiling of conditional pathway activation identifies context-dependent synthetic lethalities. Nat Genet 2023; 55:1709-1720. [PMID: 37749246 DOI: 10.1038/s41588-023-01515-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 08/22/2023] [Indexed: 09/27/2023]
Abstract
The paradigm of cancer-targeted therapies has focused largely on inhibition of critical pathways in cancer. Conversely, conditional activation of signaling pathways as a new source of selective cancer vulnerabilities has not been deeply characterized. In this study, we sought to systematically identify context-specific gene-activation-induced lethalities in cancer. To this end, we developed a method for gain-of-function genetic perturbations simultaneously across ~500 barcoded cancer cell lines. Using this approach, we queried the pan-cancer vulnerability landscape upon activating ten key pathway nodes, revealing selective activation dependencies of MAPK and PI3K pathways associated with specific biomarkers. Notably, we discovered new pathway hyperactivation dependencies in subsets of APC-mutant colorectal cancers where further activation of the WNT pathway by APC knockdown or direct β-catenin overexpression led to robust antitumor effects in xenograft and patient-derived organoid models. Together, this study reveals a new class of conditional gene-activation dependencies in cancer.
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Affiliation(s)
- Liang Chang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Nancy Y Jung
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Adel Atari
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Devishi Kesar
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Tian-Yu Song
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | | | - Melissa Ronan
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ruitong Li
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Paloma Ruiz
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Saireudee Chaturantabut
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Department of Biopharmacy, Faculty of Pharmacy, Silpakorn University, Nakhon Pathom, Thailand
| | - Takahiro Ito
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Laurens M van Tienen
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Yuen-Yi Tseng
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - William R Sellers
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
- Harvard Medical School, Boston, MA, USA.
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15
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Cucchiara F, Crucitta S, Petrini I, de Miguel Perez D, Ruglioni M, Pardini E, Rolfo C, Danesi R, Del Re M. Gene-network analysis predicts clinical response to immunotherapy in patients affected by NSCLC. Lung Cancer 2023; 183:107308. [PMID: 37473500 DOI: 10.1016/j.lungcan.2023.107308] [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: 05/13/2023] [Revised: 06/23/2023] [Accepted: 07/14/2023] [Indexed: 07/22/2023]
Abstract
OBJECTIVES Predictive biomarkers of response to immune checkpoint inhibitors (ICIs) have been extensively studied in non-small cell lung cancer (NSCLC) with controversial results. Recently, gene-network analysis emerged as a new tool to address tumor biology and behavior, representing a potential tool to evaluate response to therapies. METHODS Clinical data and genetic profiles of 644 advanced NSCLCs were retrieved from cBioPortal and the Cancer Genome Atlas (TCGA); 243 ICI-treated NSCLCs were used to identify an immunotherapy response signatures via mutated gene network analysis and K-means unsupervised clustering. Signatures predictive values were tested in an external dataset of 242 cases and assessed versus a control group of 159 NSCLCs treated with standard chemotherapy. RESULTS At least two mutations in the coding sequence of genes belonging to the chromatin remodelling pathway (A signature), and/or at least two mutations of genes involved in cell-to-cell signalling pathways (B signature), showed positive prediction in ICI-treated advanced NSCLC. Signatures performed best when combined for patients undergoing first-line immunotherapy, and for those receiving combined ICIs. CONCLUSIONS Alterations in genes related to chromatin remodelling complexes and cell-to-cell crosstalk may force dysfunctional immune evasion, explaining susceptibility to immunotherapy. Therefore, exploring mutated gene networks could be valuable for determining essential biological interactions, contributing to treatment personalization.
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Affiliation(s)
- Federico Cucchiara
- Unit of Clinical Pharmacology and Pharmacogenetics, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Stefania Crucitta
- Unit of Clinical Pharmacology and Pharmacogenetics, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Iacopo Petrini
- Cardiothoracic and Vascular Department, University of Pisa, Pisa, Italy; Unit of General Pathology, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Diego de Miguel Perez
- Center for Thoracic Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Martina Ruglioni
- Unit of Clinical Pharmacology and Pharmacogenetics, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Eleonora Pardini
- Unit of General Pathology, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Christian Rolfo
- Center for Thoracic Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
| | - Romano Danesi
- Unit of Clinical Pharmacology and Pharmacogenetics, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy.
| | - Marzia Del Re
- Unit of Clinical Pharmacology and Pharmacogenetics, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy; Center for Thoracic Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
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16
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Frankell AM, Dietzen M, Al Bakir M, Lim EL, Karasaki T, Ward S, Veeriah S, Colliver E, Huebner A, Bunkum A, Hill MS, Grigoriadis K, Moore DA, Black JRM, Liu WK, Thol K, Pich O, Watkins TBK, Naceur-Lombardelli C, Cook DE, Salgado R, Wilson GA, Bailey C, Angelova M, Bentham R, Martínez-Ruiz C, Abbosh C, Nicholson AG, Le Quesne J, Biswas D, Rosenthal R, Puttick C, Hessey S, Lee C, Prymas P, Toncheva A, Smith J, Xing W, Nicod J, Price G, Kerr KM, Naidu B, Middleton G, Blyth KG, Fennell DA, Forster MD, Lee SM, Falzon M, Hewish M, Shackcloth MJ, Lim E, Benafif S, Russell P, Boleti E, Krebs MG, Lester JF, Papadatos-Pastos D, Ahmad T, Thakrar RM, Lawrence D, Navani N, Janes SM, Dive C, Blackhall FH, Summers Y, Cave J, Marafioti T, Herrero J, Quezada SA, Peggs KS, Schwarz RF, Van Loo P, Miedema DM, Birkbak NJ, Hiley CT, Hackshaw A, Zaccaria S, Jamal-Hanjani M, McGranahan N, Swanton C. The evolution of lung cancer and impact of subclonal selection in TRACERx. Nature 2023; 616:525-533. [PMID: 37046096 PMCID: PMC10115649 DOI: 10.1038/s41586-023-05783-5] [Citation(s) in RCA: 71] [Impact Index Per Article: 71.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 02/02/2023] [Indexed: 04/14/2023]
Abstract
Lung cancer is the leading cause of cancer-associated mortality worldwide1. Here we analysed 1,644 tumour regions sampled at surgery or during follow-up from the first 421 patients with non-small cell lung cancer prospectively enrolled into the TRACERx study. This project aims to decipher lung cancer evolution and address the primary study endpoint: determining the relationship between intratumour heterogeneity and clinical outcome. In lung adenocarcinoma, mutations in 22 out of 40 common cancer genes were under significant subclonal selection, including classical tumour initiators such as TP53 and KRAS. We defined evolutionary dependencies between drivers, mutational processes and whole genome doubling (WGD) events. Despite patients having a history of smoking, 8% of lung adenocarcinomas lacked evidence of tobacco-induced mutagenesis. These tumours also had similar detection rates for EGFR mutations and for RET, ROS1, ALK and MET oncogenic isoforms compared with tumours in never-smokers, which suggests that they have a similar aetiology and pathogenesis. Large subclonal expansions were associated with positive subclonal selection. Patients with tumours harbouring recent subclonal expansions, on the terminus of a phylogenetic branch, had significantly shorter disease-free survival. Subclonal WGD was detected in 19% of tumours, and 10% of tumours harboured multiple subclonal WGDs in parallel. Subclonal, but not truncal, WGD was associated with shorter disease-free survival. Copy number heterogeneity was associated with extrathoracic relapse within 1 year after surgery. These data demonstrate the importance of clonal expansion, WGD and copy number instability in determining the timing and patterns of relapse in non-small cell lung cancer and provide a comprehensive clinical cancer evolutionary data resource.
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Affiliation(s)
- Alexander M Frankell
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Michelle Dietzen
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Maise Al Bakir
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Emilia L Lim
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Takahiro Karasaki
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK
| | - Sophia Ward
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Advanced Sequencing Facility, The Francis Crick Institute, London, UK
| | - Selvaraju Veeriah
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Emma Colliver
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Ariana Huebner
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Abigail Bunkum
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK
- Computational Cancer Genomics Research Group, University College London Cancer Institute, London, UK
| | - Mark S Hill
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Kristiana Grigoriadis
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - David A Moore
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Department of Cellular Pathology, University College London Hospitals, London, UK
| | - James R M Black
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Wing Kin Liu
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK
| | - Kerstin Thol
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Oriol Pich
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Thomas B K Watkins
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | | | - Daniel E Cook
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Roberto Salgado
- Department of Pathology, ZAS Hospitals, Antwerp, Belgium
- Division of Research, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Gareth A Wilson
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Chris Bailey
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Mihaela Angelova
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Robert Bentham
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Carlos Martínez-Ruiz
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Christopher Abbosh
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Andrew G Nicholson
- Department of Histopathology, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - John Le Quesne
- Cancer Research UK Beatson Institute, Glasgow, UK
- School of Cancer Sciences, University of Glasgow, Glasgow, UK
- Pathology Department, Queen Elizabeth University Hospital, NHS Greater Glasgow and Clyde, Glasgow, UK
| | - Dhruva Biswas
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Bill Lyons Informatics Centre, University College London Cancer Institute, London, UK
| | - Rachel Rosenthal
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Clare Puttick
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Sonya Hessey
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK
- Computational Cancer Genomics Research Group, University College London Cancer Institute, London, UK
| | - Claudia Lee
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Division of Medicine, University College London, London, UK
| | - Paulina Prymas
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Antonia Toncheva
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Jon Smith
- Scientific Computing, The Francis Crick Institute, London, UK
| | - Wei Xing
- Scientific Computing, The Francis Crick Institute, London, UK
| | - Jerome Nicod
- Advanced Sequencing Facility, The Francis Crick Institute, London, UK
| | - Gillian Price
- Department of Medical Oncology, Aberdeen Royal Infirmary NHS Grampian, Aberdeen, UK
- University of Aberdeen, Aberdeen, UK
| | - Keith M Kerr
- University of Aberdeen, Aberdeen, UK
- Department of Pathology, Aberdeen Royal Infirmary NHS Grampian, Aberdeen, UK
| | - Babu Naidu
- Birmingham Acute Care Research Group, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK
- University Hospital Birmingham NHS Foundation Trust, Birmingham, UK
| | - Gary Middleton
- University Hospital Birmingham NHS Foundation Trust, Birmingham, UK
- Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham, UK
| | - Kevin G Blyth
- Cancer Research UK Beatson Institute, Glasgow, UK
- School of Cancer Sciences, University of Glasgow, Glasgow, UK
- Queen Elizabeth University Hospital, Glasgow, UK
| | - Dean A Fennell
- University of Leicester, Leicester, UK
- University Hospitals of Leicester NHS Trust, Leicester, UK
| | - Martin D Forster
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Department of Oncology, University College London Hospitals, London, UK
| | - Siow Ming Lee
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Department of Oncology, University College London Hospitals, London, UK
| | - Mary Falzon
- Department of Cellular Pathology, University College London Hospitals, London, UK
| | - Madeleine Hewish
- Royal Surrey Hospital, Royal Surrey Hospitals NHS Foundation Trust, Guilford, UK
- University of Surrey, Guilford, UK
| | | | - Eric Lim
- Academic Division of Thoracic Surgery, Imperial College London, London, UK
- Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Sarah Benafif
- Department of Oncology, University College London Hospitals, London, UK
| | - Peter Russell
- Princess Alexandra Hospital, The Princess Alexandra Hospital NHS Trust, Harlow, UK
| | - Ekaterini Boleti
- Royal Free Hospital, Royal Free London NHS Foundation Trust, London, UK
| | - Matthew G Krebs
- Division of Cancer Sciences, The University of Manchester and The Christie NHS Foundation Trust, Manchester, UK
| | - Jason F Lester
- Singleton Hospital, Swansea Bay University Health Board, Swansea, UK
| | | | - Tanya Ahmad
- Department of Oncology, University College London Hospitals, London, UK
| | - Ricky M Thakrar
- Department of Thoracic Medicine, University College London Hospitals, London, UK
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
| | - David Lawrence
- Department of Thoracic Surgery, University College London Hospital NHS Trust, London, UK
| | - Neal Navani
- Department of Thoracic Medicine, University College London Hospitals, London, UK
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
| | - Sam M Janes
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
| | - Caroline Dive
- Cancer Research UK Manchester Institute Cancer Biomarker Centre, University of Manchester, Manchester, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University of Manchester, Manchester, UK
| | - Fiona H Blackhall
- Division of Cancer Sciences, The University of Manchester and The Christie NHS Foundation Trust, Manchester, UK
| | - Yvonne Summers
- Division of Cancer Sciences, The University of Manchester and The Christie NHS Foundation Trust, Manchester, UK
| | - Judith Cave
- Department of Oncology, University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Teresa Marafioti
- Department of Cellular Pathology, University College London Hospitals, London, UK
| | - Javier Herrero
- Bill Lyons Informatics Centre, University College London Cancer Institute, London, UK
| | - Sergio A Quezada
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Immune Regulation and Tumour Immunotherapy Group, Cancer Immunology Unit, Research Department of Haematology, University College London Cancer Institute, London, UK
| | - Karl S Peggs
- Department of Haematology, University College London Hospitals, London, UK
- Cancer Immunology Unit, Research Department of Haematology, University College London Cancer Institute, London, UK
| | - Roland F Schwarz
- Institute for Computational Cancer Biology, Center for Integrated Oncology (CIO), Cancer Research Center Cologne Essen (CCCE), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Berlin Institute for the Foundations of Learning and Data (BIFOLD), Berlin, Germany
| | - Peter Van Loo
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Cancer Genomics Laboratory, The Francis Crick Institute, London, UK
| | - Daniël M Miedema
- LEXOR, Center for Experimental and Molecular Medicine, Cancer Center Amsterdam and Amsterdam Gastroenterology and Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Oncode Institute, Amsterdam, The Netherlands
| | - Nicolai J Birkbak
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
| | - Crispin T Hiley
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Allan Hackshaw
- Cancer Research UK and UCL Cancer Trials Centre, London, UK
| | - Simone Zaccaria
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Computational Cancer Genomics Research Group, University College London Cancer Institute, London, UK
| | - Mariam Jamal-Hanjani
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
- Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK.
- Department of Oncology, University College London Hospitals, London, UK.
| | - Nicholas McGranahan
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
| | - Charles Swanton
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK.
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
- Department of Oncology, University College London Hospitals, London, UK.
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17
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Kuśnierczyk P. Genetic differences between smokers and never-smokers with lung cancer. Front Immunol 2023; 14:1063716. [PMID: 36817482 PMCID: PMC9932279 DOI: 10.3389/fimmu.2023.1063716] [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: 10/07/2022] [Accepted: 01/12/2023] [Indexed: 02/05/2023] Open
Abstract
Smoking is a major risk factor for lung cancer, therefore lung cancer epidemiological trends reflect the past trends of cigarette smoking to a great extent. The geographic patterns in mortality closely follow those in incidence. Although lung cancer is strongly associated with cigarette smoking, only about 15% of smokers get lung cancer, and also some never-smokers develop this malignancy. Although less frequent, lung cancer in never smokers is the seventh leading cause of cancer deaths in both sexes worldwide. Lung cancer in smokers and never-smokers differs in many aspects: in histological types, environmental factors representing a risk, and in genes associated with this disease. In this review, we will focus on the genetic differences between lung cancer in smokers versus never-smokers: gene expression, germ-line polymorphisms, gene mutations, as well as ethnic and gender differences. Finally, treatment options for smokers and never-smokers will be briefly reviewed.
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Affiliation(s)
- Piotr Kuśnierczyk
- Laboratory of Immunogenetics and Tissue Immunology, Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Wrocław, Poland
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18
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Mina M, Iyer A, Ciriello G. Epistasis and evolutionary dependencies in human cancers. Curr Opin Genet Dev 2022; 77:101989. [PMID: 36182742 DOI: 10.1016/j.gde.2022.101989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 08/29/2022] [Accepted: 08/31/2022] [Indexed: 01/27/2023]
Abstract
Cancer evolution is driven by the concerted action of multiple molecular alterations, which emerge and are selected during tumor progression. An alteration is selected when it provides an advantage to the tumor cell. However, the advantage provided by a specific alteration depends on the tumor lineage, cell epigenetic state, and presence of additional alterations. In this case, we say that an evolutionary dependency exists between an alteration and what influences its selection. Epistatic interactions between altered genes lead to evolutionary dependencies (EDs), by favoring or vetoing specific combinations of events. Large-scale cancer genomics studies have discovered examples of such dependencies, and showed that they influence tumor progression, disease phenotypes, and therapeutic response. In the past decade, several algorithmic approaches have been proposed to infer EDs from large-scale genomics datasets. These methods adopt diverse strategies to address common challenges and shed new light on cancer evolutionary trajectories. Here, we review these efforts starting from a simple conceptualization of the problem, presenting the tackled and still unmet needs in the field, and discussing the implications of EDs in cancer biology and precision oncology.
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Affiliation(s)
- Marco Mina
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland; Swiss Cancer Center Leman, Lausanne, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Arvind Iyer
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland; Swiss Cancer Center Leman, Lausanne, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Giovanni Ciriello
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland; Swiss Cancer Center Leman, Lausanne, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland.
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19
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Gadelha RB, Machado CB, Pessoa FMCDP, Pantoja LDC, Barreto IV, Ribeiro RM, de Moraes Filho MO, de Moraes MEA, Khayat AS, Moreira-Nunes CA. The Role of WRAP53 in Cell Homeostasis and Carcinogenesis Onset. Curr Issues Mol Biol 2022; 44:5498-5515. [PMID: 36354684 PMCID: PMC9688736 DOI: 10.3390/cimb44110372] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 08/30/2022] [Accepted: 09/29/2022] [Indexed: 11/07/2023] Open
Abstract
The WD repeat containing antisense to TP53 (WRAP53) gene codifies an antisense transcript for tumor protein p53 (TP53), stabilization (WRAP53α), and a functional protein (WRAP53β, WDR79, or TCAB1). The WRAP53β protein functions as a scaffolding protein that is important for telomerase localization, telomere assembly, Cajal body integrity, and DNA double-strand break repair. WRAP53β is one of many proteins known for containing WD40 domains, which are responsible for mediating a variety of cell interactions. Currently, WRAP53 overexpression is considered a biomarker for a diverse subset of cancer types, and in this study, we describe what is known about WRAP53β's multiple interactions in cell protein trafficking, Cajal body formation, and DNA double-strand break repair and its current perspectives as a biomarker for cancer.
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Affiliation(s)
- Renan Brito Gadelha
- Pharmacogenetics Laboratory, Department of Medicine, Drug Research and Development Center (NPDM), Federal University of Ceará, Fortaleza 60430-275, CE, Brazil
| | - Caio Bezerra Machado
- Pharmacogenetics Laboratory, Department of Medicine, Drug Research and Development Center (NPDM), Federal University of Ceará, Fortaleza 60430-275, CE, Brazil
| | - Flávia Melo Cunha de Pinho Pessoa
- Pharmacogenetics Laboratory, Department of Medicine, Drug Research and Development Center (NPDM), Federal University of Ceará, Fortaleza 60430-275, CE, Brazil
| | - Laudreísa da Costa Pantoja
- Department of Pediatrics, Octávio Lobo Children’s Hospital, Belém 60430-275, PA, Brazil
- Department of Biological Sciences, Oncology Research Center, Federal University of Pará, Belém 66073-005, PA, Brazil
| | - Igor Valentim Barreto
- Pharmacogenetics Laboratory, Department of Medicine, Drug Research and Development Center (NPDM), Federal University of Ceará, Fortaleza 60430-275, CE, Brazil
| | | | - Manoel Odorico de Moraes Filho
- Pharmacogenetics Laboratory, Department of Medicine, Drug Research and Development Center (NPDM), Federal University of Ceará, Fortaleza 60430-275, CE, Brazil
| | - Maria Elisabete Amaral de Moraes
- Pharmacogenetics Laboratory, Department of Medicine, Drug Research and Development Center (NPDM), Federal University of Ceará, Fortaleza 60430-275, CE, Brazil
| | - André Salim Khayat
- Department of Biological Sciences, Oncology Research Center, Federal University of Pará, Belém 66073-005, PA, Brazil
| | - Caroline Aquino Moreira-Nunes
- Pharmacogenetics Laboratory, Department of Medicine, Drug Research and Development Center (NPDM), Federal University of Ceará, Fortaleza 60430-275, CE, Brazil
- Department of Biological Sciences, Oncology Research Center, Federal University of Pará, Belém 66073-005, PA, Brazil
- Northeast Biotechnology Network (RENORBIO), Itaperi Campus, Ceará State University, Fortaleza 60740-903, CE, Brazil
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20
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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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21
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Johnson RM, Qu X, Lin CF, Huw LY, Venkatanarayan A, Sokol E, Ou FS, Ihuegbu N, Zill OA, Kabbarah O, Wang L, Bourgon R, de Sousa E Melo F, Bolen C, Daemen A, Venook AP, Innocenti F, Lenz HJ, Bais C. ARID1A mutations confer intrinsic and acquired resistance to cetuximab treatment in colorectal cancer. Nat Commun 2022; 13:5478. [PMID: 36117191 PMCID: PMC9482920 DOI: 10.1038/s41467-022-33172-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 09/05/2022] [Indexed: 11/25/2022] Open
Abstract
Most colorectal (CRC) tumors are dependent on EGFR/KRAS/BRAF/MAPK signaling activation. ARID1A is an epigenetic regulator mutated in approximately 5% of non-hypermutated CRC tumors. Here we show that anti-EGFR but not anti-VEGF treatment enriches for emerging ARID1A mutations in CRC patients. In addition, we find that patients with ARID1A mutations, at baseline, are associated with worse outcome when treated with cetuximab- but not bevacizumab-containing therapies; thus, this suggests that ARID1A mutations may provide both an acquired and intrinsic mechanism of resistance to anti-EGFR therapies. We find that, ARID1A and EGFR-pathway genetic alterations are mutually exclusive across lung and colorectal cancers, further supporting a functional connection between these pathways. Our results not only suggest that ARID1A could be potentially used as a predictive biomarker for cetuximab treatment decisions but also provide a rationale for exploring therapeutic MAPK inhibition in an unexpected but genetically defined segment of CRC patients. ARID1A is an epigenetic regulator mutated in approximately 5% of non-hypermutated colorectal cancer tumors, however, its relationship with treatment response remains to be explored. Here, the authors suggest that ARID1A mutations may confer intrinsic and acquired resistance to cetuximab treatment.
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Affiliation(s)
- Radia M Johnson
- Bioinformatics & Computational Biology, Genentech, Inc., South San Francisco, CA, USA.
| | - Xueping Qu
- Oncology Biomarker Development, Genentech, Inc., South San Francisco, CA, USA.
| | - Chu-Fang Lin
- Real World Data Science Analytics, Genentech, Inc., South San Francisco, CA, USA
| | - Ling-Yuh Huw
- Oncology Biomarker Development, Genentech, Inc., South San Francisco, CA, USA
| | | | - Ethan Sokol
- Cancer Genomics Research, Foundation Medicine, Inc., Cambridge, MA, USA
| | - Fang-Shu Ou
- Alliance Statistics and Data Management Center, Mayo Clinic, Rochester, MN, USA
| | | | - Oliver A Zill
- Bioinformatics & Computational Biology, Genentech, Inc., South San Francisco, CA, USA
| | - Omar Kabbarah
- Oncology Biomarker Development, Genentech, Inc., South San Francisco, CA, USA
| | - Lisa Wang
- Real World Data Science Analytics, Genentech, Inc., South San Francisco, CA, USA
| | - Richard Bourgon
- Bioinformatics & Computational Biology, Genentech, Inc., South San Francisco, CA, USA
| | | | - Chris Bolen
- Bioinformatics & Computational Biology, Genentech, Inc., South San Francisco, CA, USA
| | - Anneleen Daemen
- Bioinformatics & Computational Biology, Genentech, Inc., South San Francisco, CA, USA
| | - Alan P Venook
- University of California, San Francisco, San Francisco, CA, USA
| | | | | | - Carlos Bais
- Oncology Biomarker Development, Genentech, Inc., South San Francisco, CA, USA.
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22
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Iranzo J, Gruenhagen G, Calle-Espinosa J, Koonin EV. Pervasive conditional selection of driver mutations and modular epistasis networks in cancer. Cell Rep 2022; 40:111272. [PMID: 36001960 DOI: 10.1016/j.celrep.2022.111272] [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: 01/04/2022] [Revised: 04/18/2022] [Accepted: 08/05/2022] [Indexed: 11/19/2022] Open
Abstract
Cancer driver mutations often display mutual exclusion or co-occurrence, underscoring the key role of epistasis in carcinogenesis. However, estimating the magnitude of epistasis and quantifying its effect on tumor evolution remains a challenge. We develop a method (Coselens) to quantify conditional selection on the excess of nonsynonymous substitutions in cancer genes. Coselens infers the number of drivers per gene in different partitions of a cancer genomics dataset using covariance-based mutation models and determines whether coding mutations in a gene affect selection for drivers in any other gene. Using Coselens, we identify 296 conditionally selected gene pairs across 16 cancer types in the TCGA dataset. Conditional selection affects 25%-50% of driver substitutions in tumors with >2 drivers. Conditionally co-selected genes form modular networks, whose structures challenge the traditional interpretation of within-pathway mutual exclusivity and across-pathway synergy, suggesting a more complex scenario where gene-specific across-pathway epistasis shapes differentiated cancer subtypes.
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Affiliation(s)
- Jaime Iranzo
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Madrid, Spain; Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, Zaragoza, Spain.
| | - George Gruenhagen
- Institute of Bioengineering and Biosciences, School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA
| | - Jorge Calle-Espinosa
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Madrid, Spain
| | - Eugene V Koonin
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
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23
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Davis AA, Gerratana L, Mina M. Editorial: Cancer evolution: From biological insights to therapeutic opportunities. Front Genet 2022; 13:984032. [PMID: 36017499 PMCID: PMC9397365 DOI: 10.3389/fgene.2022.984032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 07/12/2022] [Indexed: 11/27/2022] Open
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24
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The Therapeutic Potential of the Restoration of the p53 Protein Family Members in the EGFR-Mutated Lung Cancer. Int J Mol Sci 2022; 23:ijms23137213. [PMID: 35806218 PMCID: PMC9267050 DOI: 10.3390/ijms23137213] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 06/24/2022] [Accepted: 06/27/2022] [Indexed: 02/06/2023] Open
Abstract
Despite the recent development of precision medicine and targeted therapies, lung cancer remains the top cause of cancer-related mortality worldwide. The patients diagnosed with metastatic disease have a five-year survival rate lower than 6%. In metastatic disease, EGFR is the most common driver of mutation, with the most common co-driver hitting TP53. EGFR-positive patients are offered the frontline treatment with tyrosine kinase inhibitors, yet the development of resistance and the lack of alternative therapies make this group of patients only fit for clinical trial participation. Since mutant p53 is the most common co-driver in the metastatic setting, therapies reactivating the p53 pathway might serve as a promising alternative therapeutic approach in patients who have developed a resistance to tyrosine kinase inhibitors. This review focuses on the molecular background of EGFR-mutated lung cancer and discusses novel therapeutic options converging on the reactivation of p53 tumor suppressor pathways.
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25
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Luthra A, Mastrogiacomo B, Smith SA, Chakravarty D, Schultz N, Sanchez-Vega F. Computational methods and translational applications for targeted next-generation sequencing platforms. Genes Chromosomes Cancer 2022; 61:322-331. [PMID: 35066956 PMCID: PMC10129038 DOI: 10.1002/gcc.23023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 01/10/2022] [Indexed: 11/09/2022] Open
Abstract
During the past decade, next-generation sequencing (NGS) technologies have become widely adopted in cancer research and clinical care. Common applications within the clinical setting include patient stratification into relevant molecular subtypes, identification of biomarkers of response and resistance to targeted and systemic therapies, assessment of heritable cancer risk based on known pathogenic variants, and longitudinal monitoring of treatment response. The need for efficient downstream processing and reliable interpretation of sequencing data has led to the development of novel algorithms and computational pipelines, as well as structured knowledge bases that link genomic alterations to currently available drugs and ongoing clinical trials. Cancer centers around the world use different types of targeted solid-tissue and blood based NGS assays to analyze the genomic and transcriptomic profile of patients as part of their routine clinical care. Recently, cross-institutional collaborations have led to the creation of large pooled datasets that can offer valuable insights into the genomics of rare cancers.
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Affiliation(s)
- Anisha Luthra
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Brooke Mastrogiacomo
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Shaleigh A Smith
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Debyani Chakravarty
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Nikolaus Schultz
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Francisco Sanchez-Vega
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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26
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Cucchiara F, Petrini I, Passaro A, Attili I, Crucitta S, Pardini E, de Marinis F, Danesi R, Re MD. Gene-Networks analyses define a subgroup of Small Cell Lung Cancers with short survival. Clin Lung Cancer 2022; 23:510-521. [DOI: 10.1016/j.cllc.2022.05.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 05/06/2022] [Accepted: 05/13/2022] [Indexed: 11/03/2022]
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27
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Cancer mutation profiles predict ICIs efficacy in patients with non-small cell lung cancer. Expert Rev Mol Med 2022; 24:e16. [PMID: 35373730 DOI: 10.1017/erm.2022.9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Although immune checkpoint inhibitors (ICIs) have produced remarkable responses in non-small cell lung cancer (NSCLC) patients, receivers still have a relatively low response rate. Initial response assessment by conventional imaging and evaluation criteria is often unable to identify whether patients can achieve durable clinical benefit from ICIs. Overall, there are sparse effective biomarkers identified to screen NSCLC patients responding to this therapy. A lot of studies have reported that patients with specific gene mutations may benefit from or resist to immunotherapy. However, the single gene mutation may be not effective enough to predict the benefit from immunotherapy for patients. With the advancement in sequencing technology, further studies indicate that many mutations often co-occur and suggest a drastic transformation of tumour microenvironment phenotype. Moreover, co-mutation events have been reported to synergise to activate or suppress signalling pathways of anti-tumour immune response, which also indicates a potential target for combining intervention. Thus, the different mutation profile (especially co-mutation) of patients may be an important concern for predicting or promoting the efficacy of ICIs. However, there is a lack of comprehensive knowledge of this field until now. Therefore, in this study, we reviewed and elaborated the value of cancer mutation profile in predicting the efficacy of immunotherapy and analysed the underlying mechanisms, to provide an alternative way for screening dominant groups, and thereby, optimising individualised therapy for NSCLC patients.
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28
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Angelopoulos N, Chatzipli A, Nangalia J, Maura F, Campbell PJ. Bayesian networks elucidate complex genomic landscapes in cancer. Commun Biol 2022; 5:306. [PMID: 35379892 PMCID: PMC8980036 DOI: 10.1038/s42003-022-03243-w] [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: 10/08/2021] [Accepted: 03/09/2022] [Indexed: 11/27/2022] Open
Abstract
Bayesian networks (BNs) are disciplined, explainable Artificial Intelligence models that can describe structured joint probability spaces. In the context of understanding complex relations between a number of variables in biological settings, they can be constructed from observed data and can provide a guiding, graphical tool in exploring such relations. Here we propose BNs for elucidating the relations between driver events in large cancer genomic datasets. We present a methodology that is specifically tailored to biologists and clinicians as they are the main producers of such datasets. We achieve this by using an optimal BN learning algorithm based on well established likelihood functions and by utilising just two tuning parameters, both of which are easy to set and have intuitive readings. To enhance value to clinicians, we introduce (a) the use of heatmaps for families in each network, and (b) visualising pairwise co-occurrence statistics on the network. For binary data, an optional step of fitting logic gates can be employed. We show how our methodology enhances pairwise testing and how biologists and clinicians can use BNs for discussing the main relations among driver events in large genomic cohorts. We demonstrate the utility of our methodology by applying it to 5 cancer datasets revealing complex genomic landscapes. Our networks identify central patterns in all datasets including a central 4-way mutual exclusivity between HDR, t(4,14), t(11,14) and t(14,16) in myeloma, and a 3-way mutual exclusivity of three major players: CALR, JAK2 and MPL, in myeloproliferative neoplasms. These analyses demonstrate that our methodology can play a central role in the study of large genomic cancer datasets. Bayesian network inference on several blood and solid cancer genomic datasets provides more accessible ways to explore driver events in cancer.
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Affiliation(s)
- Nicos Angelopoulos
- The Cancer, Ageing and Somatic Mutation Programme, Wellcome Sanger Institute, Hinxton, Cambridgeshire, CB10 1SA, UK. .,Systems Immunity Research Institute, Medical School, Cardiff University, Cardiff, CF14 4XN, UK.
| | - Aikaterini Chatzipli
- The Cancer, Ageing and Somatic Mutation Programme, Wellcome Sanger Institute, Hinxton, Cambridgeshire, CB10 1SA, UK
| | - Jyoti Nangalia
- The Cancer, Ageing and Somatic Mutation Programme, Wellcome Sanger Institute, Hinxton, Cambridgeshire, CB10 1SA, UK
| | - Francesco Maura
- Myeloma Program, Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL, USA
| | - Peter J Campbell
- The Cancer, Ageing and Somatic Mutation Programme, Wellcome Sanger Institute, Hinxton, Cambridgeshire, CB10 1SA, UK
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29
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Park TY, Leiserson MD, Klau GW, Raphael BJ. SuperDendrix algorithm integrates genetic dependencies and genomic alterations across pathways and cancer types. CELL GENOMICS 2022; 2. [PMID: 35382456 PMCID: PMC8979493 DOI: 10.1016/j.xgen.2022.100099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Recent genome-wide CRISPR-Cas9 loss-of-function screens have identified genetic dependencies across many cancer cell lines. Associations between these dependencies and genomic alterations in the same cell lines reveal phenomena such as oncogene addiction and synthetic lethality. However, comprehensive identification of such associations is complicated by complex interactions between genes across genetically heterogeneous cancer types. We introduce and apply the algorithm SuperDendrix to CRISPR-Cas9 loss-of-function screens from 769 cancer cell lines, to identify differential dependencies across cell lines and to find associations between differential dependencies and combinations of genomic alterations and cell-type-specific markers. These associations respect the position and type of interactions within pathways: for example, we observe increased dependencies on downstream activators of pathways, such as NFE2L2, and decreased dependencies on upstream activators of pathways, such as CDK6. SuperDendrix also reveals dozens of dependencies on lineage-specific transcription factors, identifies cancer-type-specific correlations between dependencies, and enables annotation of individual mutated residues. Using SuperDendrix, Park et al. examine associations between genetic dependencies in 769 cancer cell lines. They report 127 genetic dependencies explained by combinations of mutually exclusive somatic mutations congregating into a few oncogenic pathways across cancer subtypes. These present a small number of prominent and highly specific genetic vulnerabilities in cancer. Graphical abstract
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30
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Wang XY, Zhu WW, Wang Z, Huang JB, Wang SH, Bai FM, Li TE, Zhu Y, Zhao J, Yang X, Lu L, Zhang JB, Jia HL, Dong QZ, Chen JH, Andersen JB, Ye D, Qin LX. Driver mutations of intrahepatic cholangiocarcinoma shape clinically relevant genomic clusters with distinct molecular features and therapeutic vulnerabilities. Am J Cancer Res 2022; 12:260-276. [PMID: 34987644 PMCID: PMC8690927 DOI: 10.7150/thno.63417] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 09/22/2021] [Indexed: 12/15/2022] Open
Abstract
Purpose: To establish a clinically applicable genomic clustering system, we investigated the interactive landscape of driver mutations in intrahepatic cholangiocarcinoma (ICC). Methods: The genomic data of 1481 ICCs from diverse populations was analyzed to investigate the pair-wise co-occurrences or mutual exclusivities among recurrent driver mutations. Clinicopathological features and outcomes were compared among different clusters. Gene expression and DNA methylation profiling datasets were analyzed to investigate the molecular distinctions among mutational clusters. ICC cell lines with different gene mutation backgrounds were used to evaluate the cluster specific biological behaviors and drug sensitivities. Results: Statistically significant mutation-pairs were identified across 21 combinations of genes. Seven most recurrent driver mutations (TP53, KRAS, SMAD4, IDH1/2, FGFR2-fus and BAP1) showed pair-wise co-occurrences or mutual exclusivities and could aggregate into three genetic clusters: Cluster1: represented by tripartite interaction of KRAS, TP53 and SMAD4 mutations, exhibited large bile duct histological phenotype with high CA19-9 level and dismal prognosis; Cluster2: co-association of IDH/BAP1 or FGFR2-fus/BAP1 mutation, was characterized by small bile duct phenotype, low CA19-9 level and optimal prognosis; Cluster3: mutation-free ICC cases with intermediate clinicopathological features. These clusters showed distinct molecular traits, biological behaviors and responses to therapeutic drugs. Finally, we identified S100P and KRT17 as “cluster-specific”, “lineage-dictating” and “prognosis-related” biomarkers, which in combination with CA19-9 could well stratify Cluster3 ICCs into two biologically and clinically distinct subtypes. Conclusions: This clinically applicable clustering system can be instructive to ICC prognostic stratification, molecular classification, and therapeutic optimization.
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31
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Ahmed R, Erten C, Houdjedj A, Kazan H, Yalcin C. A Network-Centric Framework for the Evaluation of Mutual Exclusivity Tests on Cancer Drivers. Front Genet 2021; 12:746495. [PMID: 34899838 PMCID: PMC8664367 DOI: 10.3389/fgene.2021.746495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 10/27/2021] [Indexed: 12/03/2022] Open
Abstract
One of the key concepts employed in cancer driver gene identification is that of mutual exclusivity (ME); a driver mutation is less likely to occur in case of an earlier mutation that has common functionality in the same molecular pathway. Several ME tests have been proposed recently, however the current protocols to evaluate ME tests have two main limitations. Firstly the evaluations are mostly with respect to simulated data and secondly the evaluation metrics lack a network-centric view. The latter is especially crucial as the notion of common functionality can be achieved through searching for interaction patterns in relevant networks. We propose a network-centric framework to evaluate the pairwise significances found by statistical ME tests. It has three main components. The first component consists of metrics employed in the network-centric ME evaluations. Such metrics are designed so that network knowledge and the reference set of known cancer genes are incorporated in ME evaluations under a careful definition of proper control groups. The other two components are designed as further mechanisms to avoid confounders inherent in ME detection on top of the network-centric view. To this end, our second objective is to dissect the side effects caused by mutation load artifacts where mutations driving tumor subtypes with low mutation load might be incorrectly diagnosed as mutually exclusive. Finally, as part of the third main component, the confounding issue stemming from the use of nonspecific interaction networks generated as combinations of interactions from different tissues is resolved through the creation and use of tissue-specific networks in the proposed framework. The data, the source code and useful scripts are available at: https://github.com/abu-compbio/NetCentric.
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Affiliation(s)
- Rafsan Ahmed
- Electrical and Computer Engineering Graduate Program, Antalya Bilim University, Antalya, Turkey
| | - Cesim Erten
- Department of Computer Engineering, Antalya Bilim University, Antalya, Turkey
| | - Aissa Houdjedj
- Department of Computer Engineering, Antalya Bilim University, Antalya, Turkey
| | - Hilal Kazan
- Department of Computer Engineering, Antalya Bilim University, Antalya, Turkey
| | - Cansu Yalcin
- Department of Computer Engineering, Antalya Bilim University, Antalya, Turkey
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32
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Inam H, Sokirniy I, Rao Y, Shah A, Naeemikia F, O'Brien E, Dong C, McCandlish DM, Pritchard JR. Genomic and experimental evidence that ALK ATI does not predict single agent sensitivity to ALK inhibitors. iScience 2021; 24:103343. [PMID: 34825133 PMCID: PMC8603052 DOI: 10.1016/j.isci.2021.103343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 06/17/2021] [Accepted: 10/22/2021] [Indexed: 12/01/2022] Open
Abstract
Genomic data can facilitate personalized treatment decisions by enabling therapeutic hypotheses in individual patients. Mutual exclusivity has been an empirically useful signal for identifying activating mutations that respond to single agent targeted therapies. However, a low mutation frequency can underpower this signal for rare variants. We develop a resampling based method for the direct pairwise comparison of conditional selection between sets of gene pairs. We apply this method to a transcript variant of anaplastic lymphoma kinase (ALK) in melanoma, termed ALKATI that was suggested to predict sensitivity to ALK inhibitors and we find that it is not mutually exclusive with key melanoma oncogenes. Furthermore, we find that ALKATI is not likely to be sufficient for cellular transformation or growth, and it does not predict single agent therapeutic dependency. Our work strongly disfavors the role of ALKATI as a targetable oncogenic driver that might be sensitive to single agent ALK treatment.
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Affiliation(s)
- Haider Inam
- Department of Biomedical Engineering, 211 Wartik Lab, The Pennsylvania State University, University Park, PA 16802, USA
| | - Ivan Sokirniy
- The Huck Institute for the Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA
| | - Yiyun Rao
- The Huck Institute for the Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA
| | - Anushka Shah
- Department of Biology, The Pennsylvania State University, University Park, PA 16802, USA
| | - Farnaz Naeemikia
- Department of Biomedical Engineering, 211 Wartik Lab, The Pennsylvania State University, University Park, PA 16802, USA
| | - Edward O'Brien
- Department of Chemistry, The Pennsylvania State University, University Park, PA 16802, USA
| | - Cheng Dong
- Department of Biomedical Engineering, 211 Wartik Lab, The Pennsylvania State University, University Park, PA 16802, USA
| | - David M. McCandlish
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Justin R. Pritchard
- Department of Biomedical Engineering, 211 Wartik Lab, The Pennsylvania State University, University Park, PA 16802, USA
- The Huck Institute for the Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA
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33
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Molecular characterization of metabolic subtypes of gastric cancer based on metabolism-related lncRNA. Sci Rep 2021; 11:21491. [PMID: 34728653 PMCID: PMC8563741 DOI: 10.1038/s41598-021-00410-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 09/23/2021] [Indexed: 11/17/2022] Open
Abstract
Increasing evidence has demonstrated that lncRNAs are critical regulators in diverse biological processes, but the function of lncRNA in metabolic regulation remains largely unexplored. In this study, we evaluated the association between lncRNA and metabolic pathways and identified metabolism-related lncRNAs. Gastric cancer can be mainly subdivided into 2 clusters based on these metabolism-related lncRNA regulators. Comparative analysis shows that these subtypes are found to be highly consistent with previously identified subtypes based on other omics data. Functional enrichment analysis shows that they are enriched in distinct biological processes. Mutation analysis shows that ABCA13 is a protective factor in subtype C1 but a risk factor in C2. Analysis of chemotherapeutic and immunotherapeutic sensitivity shows that these subtypes tend to display distinct sensitivity to the same chemical drugs. In conclusion, these findings demonstrated the significance of lncRNA in metabolic regulation. These metabolism-related lncRNA regulators can improve our understanding of the underlying mechanism of lncRNAs and advance the research of immunotherapies in the clinical management of gastric cancer.
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34
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Murakami K, Kanto A, Sakai K, Miyagawa C, Takaya H, Nakai H, Kotani Y, Nishio K, Matsumura N. Frequent PIK3CA mutations in eutopic endometrium of patients with ovarian clear cell carcinoma. Mod Pathol 2021; 34:2071-2079. [PMID: 34172890 PMCID: PMC8514336 DOI: 10.1038/s41379-021-00861-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 06/11/2021] [Accepted: 06/14/2021] [Indexed: 12/23/2022]
Abstract
Recent studies have reported cancer-associated mutations in normal endometrium. Mutations in eutopic endometrium may lead to endometriosis and endometriosis-associated ovarian cancer. We investigated PIK3CA mutations (PIK3CAm) for three hotspots (E542K, E545K, H1047R) in eutopic endometrium in patients with ovarian cancer and endometriosis from formalin-fixed paraffin-embedded specimens by laser-capture microdissection and droplet digital PCR. The presence of PIK3CAm in eutopic endometrial glands with mutant allele frequency ≥ 15% were as follows: ovarian clear cell carcinoma (OCCC) with PIK3CAm in tumors, 20/300 hotspots in 11/14 cases; OCCC without PIK3CAm, 42/78 hotspots in 11/12 cases; high-grade serous ovarian carcinoma, 8/45 hotspots in 3/5 cases; and endometriotic cysts, 5/63 hotspots in 5/6 cases. These rates were more frequent than in noncancer nonendometriosis controls (7/309 hotspots in 5/17 cases). In OCCC without PIK3CAm, 7/12 (58%) cases showed multiple hotspot mutations in the same eutopic endometrial glands. In 3/54 (5.6%) cases, PIK3CAm was found in eutopic endometrial stroma. Multisampling of the OCCC tumors with PIK3CAm showed intratumor heterogeneity in three of eight cases. In two cases, PIK3CAm was detected in the stromal component of the tumor. Homogenous PIK3CAm in the epithelial component of the tumor matched the mutation in eutopic endometrial glands in only one case. Eutopic endometrial glands in ovarian cancer and endometriosis show high frequency of PIK3CAm that is not consistent with tumors, and multiple hotspot mutations are often found in the same glands. While the mutations identified in eutopic endometrium may not be driver mutations in the patient's cancer, these are still driver mutations but this specific clone has not undergone the requisite steps for the development of cancer.
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Affiliation(s)
- Kosuke Murakami
- Department of Obstetrics and Gynecology, Kindai University Faculty of Medicine, Osaka, Japan
| | - Akiko Kanto
- Department of Obstetrics and Gynecology, Kindai University Faculty of Medicine, Osaka, Japan
| | - Kazuko Sakai
- Department of Genome Biology, Kindai University Faculty of Medicine, Osaka, Japan
| | - Chiho Miyagawa
- Department of Obstetrics and Gynecology, Kindai University Faculty of Medicine, Osaka, Japan
| | - Hisamitsu Takaya
- Department of Obstetrics and Gynecology, Kindai University Faculty of Medicine, Osaka, Japan
| | - Hidekatsu Nakai
- Department of Obstetrics and Gynecology, Kindai University Faculty of Medicine, Osaka, Japan
| | - Yasushi Kotani
- Department of Obstetrics and Gynecology, Kindai University Faculty of Medicine, Osaka, Japan
| | - Kazuto Nishio
- Department of Genome Biology, Kindai University Faculty of Medicine, Osaka, Japan
| | - Noriomi Matsumura
- Department of Obstetrics and Gynecology, Kindai University Faculty of Medicine, Osaka, Japan.
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35
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Liu Z, Liu Y, Qian L, Jiang S, Gai X, Ye S, Chen Y, Wang X, Zhai L, Xu J, Pu C, Li J, He F, Huang M, Tan M. A proteomic and phosphoproteomic landscape of KRAS mutant cancers identifies combination therapies. Mol Cell 2021; 81:4076-4090.e8. [PMID: 34375582 DOI: 10.1016/j.molcel.2021.07.021] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 05/20/2021] [Accepted: 07/15/2021] [Indexed: 12/17/2022]
Abstract
KRAS mutant cancer, characterized by the activation of a plethora of phosphorylation signaling pathways, remains a major challenge for cancer therapy. Despite recent advancements, a comprehensive profile of the proteome and phosphoproteome is lacking. This study provides a proteomic and phosphoproteomic landscape of 43 KRAS mutant cancer cell lines across different tissue origins. By integrating transcriptomics, proteomics, and phosphoproteomics, we identify three subsets with distinct biological, clinical, and therapeutic characteristics. The integrative analysis of phosphoproteome and drug sensitivity information facilitates the identification of a set of drug combinations with therapeutic potentials. Among them, we demonstrate that the combination of DOT1L and SHP2 inhibitors is an effective treatment specific for subset 2 of KRAS mutant cancers, corresponding to a set of TCGA clinical tumors with the poorest prognosis. Together, this study provides a resource to better understand KRAS mutant cancer heterogeneity and identify new therapeutic possibilities.
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Affiliation(s)
- Zhiwei Liu
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China; University of Chinese Academy of Sciences, Beijing, China
| | - Yingluo Liu
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China; University of Chinese Academy of Sciences, Beijing, China
| | - Lili Qian
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Shangwen Jiang
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Xiameng Gai
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing, China
| | - Shu Ye
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China; Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Yuehong Chen
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Xiaomin Wang
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Linhui Zhai
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China; School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing, China; Jiangsu Key Laboratory of Marine Pharmaceutical Compound Screening, College of Pharmacy, Jiangsu Ocean University, Lianyungang, China
| | - Jun Xu
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Congying Pu
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China; University of Chinese Academy of Sciences, Beijing, China
| | - Jing Li
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Fuchu He
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China
| | - Min Huang
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China; University of Chinese Academy of Sciences, Beijing, China; School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing, China.
| | - Minjia Tan
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China; University of Chinese Academy of Sciences, Beijing, China; School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing, China.
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36
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A systematic analysis of genetic interactions and their underlying biology in childhood cancer. Commun Biol 2021; 4:1139. [PMID: 34615983 PMCID: PMC8494736 DOI: 10.1038/s42003-021-02647-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 09/08/2021] [Indexed: 02/08/2023] Open
Abstract
Childhood cancer is a major cause of child death in developed countries. Genetic interactions between mutated genes play an important role in cancer development. They can be detected by searching for pairs of mutated genes that co-occur more (or less) often than expected. Co-occurrence suggests a cooperative role in cancer development, while mutual exclusivity points to synthetic lethality, a phenomenon of interest in cancer treatment research. Little is known about genetic interactions in childhood cancer. We apply a statistical pipeline to detect genetic interactions in a combined dataset comprising over 2,500 tumors from 23 cancer types. The resulting genetic interaction map of childhood cancers comprises 15 co-occurring and 27 mutually exclusive candidates. The biological explanation of most candidates points to either tumor subtype, pathway epistasis or cooperation while synthetic lethality plays a much smaller role. Thus, other explanations beyond synthetic lethality should be considered when interpreting genetic interaction test results.
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37
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Fedrizzi T, Ciani Y, Lorenzin F, Cantore T, Gasperini P, Demichelis F. Fast mutual exclusivity algorithm nominates potential synthetic lethal gene pairs through brute force matrix product computations. Comput Struct Biotechnol J 2021; 19:4394-4403. [PMID: 34429855 PMCID: PMC8369001 DOI: 10.1016/j.csbj.2021.08.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 08/02/2021] [Accepted: 08/03/2021] [Indexed: 12/12/2022] Open
Abstract
Mutual Exclusivity analysis of genomic aberrations contributes to the exploration of potential synthetic lethal (SL) relationships thus guiding the nomination of specific cancer cells vulnerabilities. When multiple classes of genomic aberrations and large cohorts of patients are interrogated, exhaustive genome-wide analyses are not computationally feasible with commonly used approaches. Here we present Fast Mutual Exclusivity (FaME), an algorithm based on matrix multiplication that employs a logarithm-based implementation of the Fisher's exact test to achieve fast computation of genome-wide mutual exclusivity tests; we show that brute force testing for mutual exclusivity of hundreds of millions of aberrations combinations can be performed in few minutes. We applied FaME to allele-specific data from whole exome experiments of 27 TCGA studies cohorts, detecting both mutual exclusivity of point mutations, as well as allele-specific copy number signals that span sets of contiguous cytobands. We next focused on a case study involving the loss of tumor suppressors and druggable genes while exploiting an integrated analysis of both public cell lines loss of function screens data and patients' transcriptomic profiles. FaME algorithm implementation as well as allele-specific analysis output are publicly available at https://github.com/demichelislab/FaME.
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Affiliation(s)
- Tarcisio Fedrizzi
- Department of Cellular, Computational and Integrative Biology, University of Trento, 38123 Trento, Italy
| | - Yari Ciani
- Department of Cellular, Computational and Integrative Biology, University of Trento, 38123 Trento, Italy
| | - Francesca Lorenzin
- Department of Cellular, Computational and Integrative Biology, University of Trento, 38123 Trento, Italy
| | - Thomas Cantore
- Department of Cellular, Computational and Integrative Biology, University of Trento, 38123 Trento, Italy
| | - Paola Gasperini
- Department of Cellular, Computational and Integrative Biology, University of Trento, 38123 Trento, Italy
| | - Francesca Demichelis
- Department of Cellular, Computational and Integrative Biology, University of Trento, 38123 Trento, Italy
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Al-Saud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10021, USA
- The Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA
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38
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Setton J, Zinda M, Riaz N, Durocher D, Zimmermann M, Koehler M, Reis-Filho JS, Powell SN. Synthetic Lethality in Cancer Therapeutics: The Next Generation. Cancer Discov 2021; 11:1626-1635. [PMID: 33795234 PMCID: PMC8295179 DOI: 10.1158/2159-8290.cd-20-1503] [Citation(s) in RCA: 86] [Impact Index Per Article: 28.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 01/01/2021] [Accepted: 02/23/2021] [Indexed: 12/25/2022]
Abstract
Synthetic lethality (SL) provides a conceptual framework for tackling targets that are not classically "druggable," including loss-of-function mutations in tumor suppressor genes required for carcinogenesis. Recent technological advances have led to an inflection point in our understanding of genetic interaction networks and ability to identify a wide array of novel SL drug targets. Here, we review concepts and lessons emerging from first-generation trials aimed at testing SL drugs, discuss how the nature of the targeted lesion can influence therapeutic outcomes, and highlight the need to develop clinical biomarkers distinct from those based on the paradigms developed to target activated oncogenes. SIGNIFICANCE: SL offers an approach for the targeting of loss of function of tumor suppressor and DNA repair genes, as well as of amplification and/or overexpression of genes that cannot be targeted directly. A next generation of tumor-specific alterations targetable through SL has emerged from high-throughput CRISPR technology, heralding not only new opportunities for drug development, but also important challenges in the development of optimal predictive biomarkers.
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Affiliation(s)
- Jeremy Setton
- Memorial Sloan Kettering Cancer Center, New York, New York
| | | | - Nadeem Riaz
- Memorial Sloan Kettering Cancer Center, New York, New York
| | - Daniel Durocher
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | | | | | | | - Simon N Powell
- Memorial Sloan Kettering Cancer Center, New York, New York.
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39
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Liu S, Liu J, Xie Y, Zhai T, Hinderer EW, Stromberg AJ, Vanderford NL, Kolesar JM, Moseley HNB, Chen L, Liu C, Wang C. MEScan: a powerful statistical framework for genome-scale mutual exclusivity analysis of cancer mutations. Bioinformatics 2021; 37:1189-1197. [PMID: 33165532 PMCID: PMC8189684 DOI: 10.1093/bioinformatics/btaa957] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2019] [Revised: 03/20/2020] [Accepted: 10/31/2020] [Indexed: 12/17/2022] Open
Abstract
MOTIVATION Cancer somatic driver mutations associated with genes within a pathway often show a mutually exclusive pattern across a cohort of patients. This mutually exclusive mutational signal has been frequently used to distinguish driver from passenger mutations and to investigate relationships among driver mutations. Current methods for de novo discovery of mutually exclusive mutational patterns are limited because the heterogeneity in background mutation rate can confound mutational patterns, and the presence of highly mutated genes can lead to spurious patterns. In addition, most methods only focus on a limited number of pre-selected genes and are unable to perform genome-wide analysis due to computational inefficiency. RESULTS We introduce a statistical framework, MEScan, for accurate and efficient mutual exclusivity analysis at the genomic scale. Our framework contains a fast and powerful statistical test for mutual exclusivity with adjustment of the background mutation rate and impact of highly mutated genes, and a multi-step procedure for genome-wide screening with the control of false discovery rate. We demonstrate that MEScan more accurately identifies mutually exclusive gene sets than existing methods and is at least two orders of magnitude faster than most methods. By applying MEScan to data from four different cancer types and pan-cancer, we have identified several biologically meaningful mutually exclusive gene sets. AVAILABILITY AND IMPLEMENTATION MEScan is available as an R package at https://github.com/MarkeyBBSRF/MEScan. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Jinpeng Liu
- Markey Cancer Center, University of Kentucky, Lexington, KY 40536, USA
| | - Yanqi Xie
- Molecular and Cellular Biochemistry, University of Kentucky, Lexington, KY 40536, USA
| | - Tingting Zhai
- Department of Statistics, University of Kentucky, Lexington, KY 40536, USA
| | - Eugene W Hinderer
- Molecular and Cellular Biochemistry, University of Kentucky, Lexington, KY 40536, USA
| | - Arnold J Stromberg
- Department of Statistics, University of Kentucky, Lexington, KY 40536, USA
| | - Nathan L Vanderford
- Markey Cancer Center, University of Kentucky, Lexington, KY 40536, USA.,Department of Toxicology and Cancer Biology, University of Kentucky, Lexington, KY 40536, USA
| | - Jill M Kolesar
- Markey Cancer Center, University of Kentucky, Lexington, KY 40536, USA.,Department of Pharmacy Practice and Science, University of Kentucky, Lexington, KY 40536, USA
| | - Hunter N B Moseley
- Markey Cancer Center, University of Kentucky, Lexington, KY 40536, USA.,Molecular and Cellular Biochemistry, University of Kentucky, Lexington, KY 40536, USA
| | - Li Chen
- Markey Cancer Center, University of Kentucky, Lexington, KY 40536, USA.,Department of Internal Medicine, University of Kentucky, Lexington, KY 40536, USA
| | - Chunming Liu
- Markey Cancer Center, University of Kentucky, Lexington, KY 40536, USA.,Molecular and Cellular Biochemistry, University of Kentucky, Lexington, KY 40536, USA
| | - Chi Wang
- Markey Cancer Center, University of Kentucky, Lexington, KY 40536, USA.,Department of Internal Medicine, University of Kentucky, Lexington, KY 40536, USA
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40
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Cucchiara F, Petrini I, Romei C, Crucitta S, Lucchesi M, Valleggi S, Scavone C, Capuano A, De Liperi A, Chella A, Danesi R, Del Re M. Combining liquid biopsy and radiomics for personalized treatment of lung cancer patients. State of the art and new perspectives. Pharmacol Res 2021; 169:105643. [PMID: 33940185 DOI: 10.1016/j.phrs.2021.105643] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 04/22/2021] [Accepted: 04/22/2021] [Indexed: 12/11/2022]
Abstract
Lung cancer has become a paradigm for precision medicine in oncology, and liquid biopsy (LB) together with radiomics may have a great potential in this scenario. They are both minimally invasive, easy to perform, and can be repeated during patient's follow-up. Also, increasing evidence suggest that LB and radiomics may provide an efficient way to screen and diagnose tumors at an early stage, including the monitoring of any change in the tumor molecular profile. This could allow treatment optimization, improvement of patients' quality of life, and healthcare-related costs reduction. Latest reports on lung cancer patients suggest a combination of these two strategies, along with cutting-edge data analysis, to decode valuable information regarding tumor type, aggressiveness, progression, and response to treatment. The approach seems more compatible with clinical practice than the current standard, and provides new diagnostic companions being able to suggest the best treatment strategy compared to conventional methods. To implement radiomics and liquid biopsy directly into clinical practice, an artificial intelligence (AI)-based system could help to link patients' clinical data together with tumor molecular profiles and imaging characteristics. AI could also solve problems and limitations related to LB and radiomics methodologies. Further work is needed, including new health policies and the access to large amounts of high-quality and well-organized data, allowing a complementary and synergistic combination of LB and imaging, to provide an attractive choice e in the personalized treatment of lung cancer.
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Affiliation(s)
- Federico Cucchiara
- Unit of Clinical Pharmacology and Pharmacogenetics, Department of Clinical and Experimental Medicine, University Hospital of Pisa, Pisa, Italy
| | - Iacopo Petrini
- Unit of Pneumology, Department of Translational Research and New Technologies in Medicine, University Hospital of Pisa, Pisa, Italy
| | - Chiara Romei
- Unit II of Radio-diagnostics, Department of Diagnostic and Imaging, University Hospital of Pisa, Pisa, Italy
| | - Stefania Crucitta
- Unit of Clinical Pharmacology and Pharmacogenetics, Department of Clinical and Experimental Medicine, University Hospital of Pisa, Pisa, Italy
| | - Maurizio Lucchesi
- Unit of Pneumology, Department of Translational Research and New Technologies in Medicine, University Hospital of Pisa, Pisa, Italy
| | - Simona Valleggi
- Unit of Pneumology, Department of Translational Research and New Technologies in Medicine, University Hospital of Pisa, Pisa, Italy
| | - Cristina Scavone
- Department of Experimental Medicine, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Annalisa Capuano
- Department of Experimental Medicine, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Annalisa De Liperi
- Unit II of Radio-diagnostics, Department of Diagnostic and Imaging, University Hospital of Pisa, Pisa, Italy
| | - Antonio Chella
- Unit of Pneumology, Department of Translational Research and New Technologies in Medicine, University Hospital of Pisa, Pisa, Italy
| | - Romano Danesi
- Unit of Clinical Pharmacology and Pharmacogenetics, Department of Clinical and Experimental Medicine, University Hospital of Pisa, Pisa, Italy.
| | - Marzia Del Re
- Unit of Clinical Pharmacology and Pharmacogenetics, Department of Clinical and Experimental Medicine, University Hospital of Pisa, Pisa, Italy
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41
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Klein MI, Cannataro VL, Townsend JP, Newman S, Stern DF, Zhao H. Identifying modules of cooperating cancer drivers. Mol Syst Biol 2021; 17:e9810. [PMID: 33769711 PMCID: PMC7995435 DOI: 10.15252/msb.20209810] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 01/20/2021] [Accepted: 01/26/2021] [Indexed: 12/22/2022] Open
Abstract
Identifying cooperating modules of driver alterations can provide insights into cancer etiology and advance the development of effective personalized treatments. We present Cancer Rule Set Optimization (CRSO) for inferring the combinations of alterations that cooperate to drive tumor formation in individual patients. Application to 19 TCGA cancer types revealed a mean of 11 core driver combinations per cancer, comprising 2-6 alterations per combination and accounting for a mean of 70% of samples per cancer type. CRSO is distinct from methods based on statistical co-occurrence, which we demonstrate is a suboptimal criterion for investigating driver cooperation. CRSO identified well-studied driver combinations that were not detected by other approaches and nominated novel combinations that correlate with clinical outcomes in multiple cancer types. Novel synergies were identified in NRAS-mutant melanomas that may be therapeutically relevant. Core driver combinations involving NFE2L2 mutations were identified in four cancer types, supporting the therapeutic potential of NRF2 pathway inhibition. CRSO is available at https://github.com/mikekleinsgit/CRSO/.
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Affiliation(s)
- Michael I Klein
- Program in Computational Biology and BioinformaticsYale UniversityNew HavenCTUSA
- Bioinformatics R&DSema4StamfordCTUSA
| | - Vincent L Cannataro
- Department of BiologyEmmanuel CollegeBostonMAUSA
- Department of BiostatisticsYale School of Public HealthNew HavenCTUSA
| | - Jeffrey P Townsend
- Program in Computational Biology and BioinformaticsYale UniversityNew HavenCTUSA
- Department of BiostatisticsYale School of Public HealthNew HavenCTUSA
- Yale Cancer CenterYale UniversityNew HavenCTUSA
| | | | - David F Stern
- Yale Cancer CenterYale UniversityNew HavenCTUSA
- Department of PathologyYale School of MedicineNew HavenCTUSA
| | - Hongyu Zhao
- Program in Computational Biology and BioinformaticsYale UniversityNew HavenCTUSA
- Department of BiostatisticsYale School of Public HealthNew HavenCTUSA
- Yale Cancer CenterYale UniversityNew HavenCTUSA
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42
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Tavernari D, Battistello E, Dheilly E, Petruzzella AS, Mina M, Sordet-Dessimoz J, Peters S, Krueger T, Gfeller D, Riggi N, Oricchio E, Letovanec I, Ciriello G. Nongenetic Evolution Drives Lung Adenocarcinoma Spatial Heterogeneity and Progression. Cancer Discov 2021; 11:1490-1507. [PMID: 33563664 DOI: 10.1158/2159-8290.cd-20-1274] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 12/21/2020] [Accepted: 01/22/2021] [Indexed: 11/16/2022]
Abstract
Cancer evolution determines molecular and morphologic intratumor heterogeneity and challenges the design of effective treatments. In lung adenocarcinoma, disease progression and prognosis are associated with the appearance of morphologically diverse tumor regions, termed histologic patterns. However, the link between molecular and histologic features remains elusive. Here, we generated multiomics and spatially resolved molecular profiles of histologic patterns from primary lung adenocarcinoma, which we integrated with molecular data from >2,000 patients. The transition from indolent to aggressive patterns was not driven by genetic alterations but by epigenetic and transcriptional reprogramming reshaping cancer cell identity. A signature quantifying this transition was an independent predictor of patient prognosis in multiple human cohorts. Within individual tumors, highly multiplexed protein spatial profiling revealed coexistence of immune desert, inflamed, and excluded regions, which matched histologic pattern composition. Our results provide a detailed molecular map of lung adenocarcinoma intratumor spatial heterogeneity, tracing nongenetic routes of cancer evolution. SIGNIFICANCE: Lung adenocarcinomas are classified based on histologic pattern prevalence. However, individual tumors exhibit multiple patterns with unknown molecular features. We characterized nongenetic mechanisms underlying intratumor patterns and molecular markers predicting patient prognosis. Intratumor patterns determined diverse immune microenvironments, warranting their study in the context of current immunotherapies.This article is highlighted in the In This Issue feature, p. 1307.
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Affiliation(s)
- Daniele Tavernari
- Swiss Cancer Center Leman, Lausanne, Switzerland.,Department of Computational Biology, University of Lausanne (UNIL), Lausanne, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Elena Battistello
- Swiss Cancer Center Leman, Lausanne, Switzerland.,Department of Computational Biology, University of Lausanne (UNIL), Lausanne, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland.,Swiss Institute for Experimental Cancer Research (ISREC), EPFL, Lausanne, Switzerland
| | - Elie Dheilly
- Swiss Cancer Center Leman, Lausanne, Switzerland.,Swiss Institute for Experimental Cancer Research (ISREC), EPFL, Lausanne, Switzerland
| | - Aaron S Petruzzella
- Swiss Cancer Center Leman, Lausanne, Switzerland.,Swiss Institute for Experimental Cancer Research (ISREC), EPFL, Lausanne, Switzerland
| | - Marco Mina
- Swiss Cancer Center Leman, Lausanne, Switzerland.,Department of Computational Biology, University of Lausanne (UNIL), Lausanne, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | | | - Solange Peters
- Swiss Cancer Center Leman, Lausanne, Switzerland.,Department of Oncology, University Hospital of Lausanne (CHUV), Lausanne, Switzerland
| | - Thorsten Krueger
- Division of Thoracic Surgery, University Hospital of Lausanne (CHUV), Lausanne, Switzerland
| | - David Gfeller
- Swiss Cancer Center Leman, Lausanne, Switzerland.,Department of Oncology, Ludwig Institute for Cancer Research, University of Lausanne (UNIL), Lausanne, Switzerland
| | - Nicolo Riggi
- Swiss Cancer Center Leman, Lausanne, Switzerland.,Institute of Pathology, University Hospital of Lausanne (CHUV), Lausanne, Switzerland
| | - Elisa Oricchio
- Swiss Cancer Center Leman, Lausanne, Switzerland.,Swiss Institute for Experimental Cancer Research (ISREC), EPFL, Lausanne, Switzerland
| | - Igor Letovanec
- Swiss Cancer Center Leman, Lausanne, Switzerland. .,Institute of Pathology, University Hospital of Lausanne (CHUV), Lausanne, Switzerland.,Department of Pathology, Central Institute, Hôpital du Valais, Sion, Switzerland
| | - Giovanni Ciriello
- Swiss Cancer Center Leman, Lausanne, Switzerland. .,Department of Computational Biology, University of Lausanne (UNIL), Lausanne, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland
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43
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Dual functions of SPOP and ERG dictate androgen therapy responses in prostate cancer. Nat Commun 2021; 12:734. [PMID: 33531470 PMCID: PMC7854732 DOI: 10.1038/s41467-020-20820-x] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 12/22/2020] [Indexed: 12/19/2022] Open
Abstract
Driver genes with a mutually exclusive mutation pattern across tumor genomes are thought to have overlapping roles in tumorigenesis. In contrast, we show here that mutually exclusive prostate cancer driver alterations involving the ERG transcription factor and the ubiquitin ligase adaptor SPOP are synthetic sick. At the molecular level, the incompatible cancer pathways are driven by opposing functions in SPOP. ERG upregulates wild type SPOP to dampen androgen receptor (AR) signaling and sustain ERG activity through degradation of the bromodomain histone reader ZMYND11. Conversely, SPOP-mutant tumors stabilize ZMYND11 to repress ERG-function and enable oncogenic androgen receptor signaling. This dichotomy regulates the response to therapeutic interventions in the AR pathway. While mutant SPOP renders tumor cells susceptible to androgen deprivation therapies, ERG promotes sensitivity to high-dose androgen therapy and pharmacological inhibition of wild type SPOP. More generally, these results define a distinct class of antagonistic cancer drivers and a blueprint toward their therapeutic exploitation. Gene fusions involving the ERG transcription factor and point mutations in the ubiquitin ligase adaptor SPOP are two truncal mutations that are mutually exclusively present in prostate cancer. Here, the authors show that mutations in SPOP render prostate tumor cells sensitive to antiandrogen therapy and that the presence of ERG promotes sensitivity to high dose of androgen and SPOP inhibition.
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44
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Genotyping data of routinely processed matched primary/metastatic tumor samples. Data Brief 2020; 34:106646. [PMID: 33365374 PMCID: PMC7749371 DOI: 10.1016/j.dib.2020.106646] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 12/07/2020] [Accepted: 12/08/2020] [Indexed: 02/05/2023] Open
Abstract
Genotypic and phenotypic comparisons of tumors in multiple tissue samples from the same patient are important for understanding disease evolution and treatment possibilities. Panel NGS genotyping is currently widely used in this context, whereby NGS variant filtering and final evaluation constitute the basis for meaningful comparisons. Here, we present the genotype data used for genotype / phenotype comparisons between matched primary / metastatic colorectal tumors in the work by Chatzopoulos et al (doi: 10.1016/j.humpath.2020.10.009), as well as the process followed for obtaining these data. We describe key issues while processing routinely formalin-fixed paraffin-embedded (FFPE) tumors for genotyping, NGS application (Ion Torrent), a stringent variant filtering algorithm for genotype analyses in FFPE tissues and particularly in matched tumor samples, and provide the respective datasets. Apart from research, tumor NGS genotyping is currently applied for clinical diagnostic purposes in Oncology. The datasets and method description provided herein (a) are important for comprehending the peculiarities of FFPE tumor genotyping, which is still mostly based on principles of germline DNA genotyping; (b) can be used in pooled analyses, e.g., of primary / metastatic tumors for the investigation of tumor evolution.
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45
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Taylor MS. Evolutionary dependencies show paths to cancer development. Nat Genet 2020; 52:1135-1136. [PMID: 33128046 DOI: 10.1038/s41588-020-00728-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Martin S Taylor
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.
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46
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Lu J, Wilfred P, Korbie D, Trau M. Regulation of Canonical Oncogenic Signaling Pathways in Cancer via DNA Methylation. Cancers (Basel) 2020; 12:E3199. [PMID: 33143142 PMCID: PMC7692324 DOI: 10.3390/cancers12113199] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 10/24/2020] [Accepted: 10/28/2020] [Indexed: 02/07/2023] Open
Abstract
Disruption of signaling pathways that plays a role in the normal development and cellular homeostasis may lead to the dysregulation of cellular signaling and bring about the onset of different diseases, including cancer. In addition to genetic aberrations, DNA methylation also acts as an epigenetic modifier to drive the onset and progression of cancer by mediating the reversible transcription of related genes. Although the role of DNA methylation as an alternative driver of carcinogenesis has been well-established, the global effects of DNA methylation on oncogenic signaling pathways and the presentation of cancer is only emerging. In this article, we introduced a differential methylation parsing pipeline (MethylMine) which mined for epigenetic biomarkers based on feature selection. This pipeline was used to mine for biomarkers, which presented a substantial difference in methylation between the tumor and the matching normal tissue samples. Combined with the Data Integration Analysis for Biomarker discovery (DIABLO) framework for machine learning and multi-omic analysis, we revisited the TCGA DNA methylation and RNA-Seq datasets for breast, colorectal, lung, and prostate cancer, and identified differentially methylated genes within the NRF2-KEAP1/PI3K oncogenic pathway, which regulates the expression of cytoprotective genes, that serve as potential therapeutic targets to treat different cancers.
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Affiliation(s)
- Jennifer Lu
- Centre for Personalised Nanomedicine, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, St Lucia, QLD 4072, Australia; (J.L.); (P.W.)
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, St Lucia, QLD 4072, Australia
| | - Premila Wilfred
- Centre for Personalised Nanomedicine, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, St Lucia, QLD 4072, Australia; (J.L.); (P.W.)
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, St Lucia, QLD 4072, Australia
| | - Darren Korbie
- Centre for Personalised Nanomedicine, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, St Lucia, QLD 4072, Australia; (J.L.); (P.W.)
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, St Lucia, QLD 4072, Australia
| | - Matt Trau
- Centre for Personalised Nanomedicine, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, St Lucia, QLD 4072, Australia; (J.L.); (P.W.)
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, St Lucia, QLD 4072, Australia
- School of Chemistry and Molecular Biosciences, The University of Queensland, St Lucia, QLD 4072, Australia
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47
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Mina M, Iyer A, Tavernari D, Raynaud F, Ciriello G. Discovering functional evolutionary dependencies in human cancers. Nat Genet 2020; 52:1198-1207. [DOI: 10.1038/s41588-020-0703-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 08/26/2020] [Indexed: 02/07/2023]
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48
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Mateo L, Duran-Frigola M, Gris-Oliver A, Palafox M, Scaltriti M, Razavi P, Chandarlapaty S, Arribas J, Bellet M, Serra V, Aloy P. Personalized cancer therapy prioritization based on driver alteration co-occurrence patterns. Genome Med 2020; 12:78. [PMID: 32907621 PMCID: PMC7488324 DOI: 10.1186/s13073-020-00774-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 08/11/2020] [Indexed: 12/19/2022] Open
Abstract
Identification of actionable genomic vulnerabilities is key to precision oncology. Utilizing a large-scale drug screening in patient-derived xenografts, we uncover driver gene alteration connections, derive driver co-occurrence (DCO) networks, and relate these to drug sensitivity. Our collection of 53 drug-response predictors attains an average balanced accuracy of 58% in a cross-validation setting, rising to 66% for a subset of high-confidence predictions. We experimentally validated 12 out of 14 predictions in mice and adapted our strategy to obtain drug-response models from patients’ progression-free survival data. Our strategy reveals links between oncogenic alterations, increasing the clinical impact of genomic profiling.
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Affiliation(s)
- Lidia Mateo
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Miquel Duran-Frigola
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Albert Gris-Oliver
- Experimental Therapeutics Group, Vall d'Hebron Institute of Oncology, Barcelona, Catalonia, Spain
| | - Marta Palafox
- Experimental Therapeutics Group, Vall d'Hebron Institute of Oncology, Barcelona, Catalonia, Spain
| | - Maurizio Scaltriti
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center (MSKCC), New York, NY, 10065, USA.,Department of Pathology, MSKCC, New York, NY, 10065, USA
| | - Pedram Razavi
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center (MSKCC), New York, NY, 10065, USA.,Breast Medicine Service, Department of Medicine, MSKCC and Weill-Cornell Medical College, New York, NY, 10065, USA
| | - Sarat Chandarlapaty
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center (MSKCC), New York, NY, 10065, USA.,Breast Medicine Service, Department of Medicine, MSKCC and Weill-Cornell Medical College, New York, NY, 10065, USA
| | - Joaquin Arribas
- Growth Factors Laboratory, Vall d'Hebron Institute of Oncology, Barcelona, Catalonia, Spain.,Department of Biochemistry and Molecular Biology, Universitat Autònoma de Barcelona, Bellaterra, Catalonia, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain.,CIBERONC, Barcelona, Spain
| | - Meritxell Bellet
- Breast Cancer Group, Vall d'Hebron Institute of Oncology, Barcelona, Catalonia, Spain.,Department of Medical Oncology, Hospital Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Catalonia, Spain
| | - Violeta Serra
- Experimental Therapeutics Group, Vall d'Hebron Institute of Oncology, Barcelona, Catalonia, Spain.,CIBERONC, Barcelona, Spain
| | - Patrick Aloy
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain. .,Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain.
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49
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Przanowski P, Lou S, Tihagam RD, Mondal T, Conlan C, Shivange G, Saltani I, Singh C, Xing K, Morris BB, Mayo MW, Teixeira L, Lehmann-Che J, Tushir-Singh J, Bhatnagar S. Oncogenic TRIM37 Links Chemoresistance and Metastatic Fate in Triple-Negative Breast Cancer. Cancer Res 2020; 80:4791-4804. [PMID: 32855208 DOI: 10.1158/0008-5472.can-20-1459] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 07/21/2020] [Accepted: 08/19/2020] [Indexed: 12/19/2022]
Abstract
The majority of clinical deaths in patients with triple-negative breast cancer (TNBC) are due to chemoresistance and aggressive metastases, with high prevalence in younger women of African ethnicity. Although tumorigenic drivers are numerous and varied, the drivers of metastatic transition remain largely unknown. Here, we uncovered a molecular dependence of TNBC tumors on the TRIM37 network, which enables tumor cells to resist chemotherapeutic as well as metastatic stress. TRIM37-directed histone H2A monoubiquitination enforces changes in DNA repair that rendered TP53-mutant TNBC cells resistant to chemotherapy. Chemotherapeutic drugs triggered a positive feedback loop via ATM/E2F1/STAT signaling, amplifying the TRIM37 network in chemoresistant cancer cells. High expression of TRIM37 induced transcriptomic changes characteristic of a metastatic phenotype, and inhibition of TRIM37 substantially reduced the in vivo propensity of TNBC cells. Selective delivery of TRIM37-specific antisense oligonucleotides using antifolate receptor 1-conjugated nanoparticles in combination with chemotherapy suppressed lung metastasis in spontaneous metastatic murine models. Collectively, these findings establish TRIM37 as a clinically relevant target with opportunities for therapeutic intervention. SIGNIFICANCE: TRIM37 drives aggressive TNBC biology by promoting resistance to chemotherapy and inducing a prometastatic transcriptional program; inhibition of TRIM37 increases chemotherapy efficacy and reduces metastasis risk in patients with TNBC.
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Affiliation(s)
- Piotr Przanowski
- Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, Virginia
| | - Song Lou
- Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, Virginia
| | - Rachisan Djiake Tihagam
- Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, Virginia
| | - Tanmoy Mondal
- Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, Virginia
| | - Caroline Conlan
- Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, Virginia
| | - Gururaj Shivange
- Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, Virginia
| | - Ilyas Saltani
- Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, Virginia
| | - Chandrajeet Singh
- Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, Virginia
| | - Kun Xing
- Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, Virginia
| | - Benjamin B Morris
- Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, Virginia
| | - Marty W Mayo
- Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, Virginia.,UVA Cancer Center, University of Virginia School of Medicine, Charlottesville, Virginia
| | - Luis Teixeira
- Breast Disease Unit, AP-HP, Hospital Saint Louis, Paris, France.,University of Paris, INSERM U976, HIPI, IRSL-Saint Louis, Paris, France
| | - Jacqueline Lehmann-Che
- University of Paris, INSERM U976, HIPI, IRSL-Saint Louis, Paris, France.,Molecular Oncology Unit, AP-HP Hospital Saint Louis, Paris, France
| | - Jogender Tushir-Singh
- Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, Virginia. .,UVA Cancer Center, University of Virginia School of Medicine, Charlottesville, Virginia.,Laboratory of Novel Biologics, University of Virginia School of Medicine, Charlottesville, Virginia
| | - Sanchita Bhatnagar
- Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, Virginia. .,UVA Cancer Center, University of Virginia School of Medicine, Charlottesville, Virginia.,Department of Neuroscience, University of Virginia School of Medicine, Charlottesville, Virginia
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50
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Liu SH, Shen PC, Chen CY, Hsu AN, Cho YC, Lai YL, Chen FH, Li CY, Wang SC, Chen M, Chung IF, Cheng WC. DriverDBv3: a multi-omics database for cancer driver gene research. Nucleic Acids Res 2020; 48:D863-D870. [PMID: 31701128 PMCID: PMC7145679 DOI: 10.1093/nar/gkz964] [Citation(s) in RCA: 87] [Impact Index Per Article: 21.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2019] [Revised: 10/09/2019] [Accepted: 11/06/2019] [Indexed: 12/13/2022] Open
Abstract
An integrative multi-omics database is needed urgently, because focusing only on analysis of one-dimensional data falls far short of providing an understanding of cancer. Previously, we presented DriverDB, a cancer driver gene database that applies published bioinformatics algorithms to identify driver genes/mutations. The updated DriverDBv3 database (http://ngs.ym.edu.tw/driverdb) is designed to interpret cancer omics’ sophisticated information with concise data visualization. To offer diverse insights into molecular dysregulation/dysfunction events, we incorporated computational tools to define CNV and methylation drivers. Further, four new features, CNV, Methylation, Survival, and miRNA, allow users to explore the relations from two perspectives in the ‘Cancer’ and ‘Gene’ sections. The ‘Survival’ panel offers not only significant survival genes, but gene pairs synergistic effects determine. A fresh function, ‘Survival Analysis’ in ‘Customized-analysis,’ allows users to investigate the co-occurring events in user-defined gene(s) by mutation status or by expression in a specific patient group. Moreover, we redesigned the web interface and provided interactive figures to interpret cancer omics’ sophisticated information, and also constructed a Summary panel in the ‘Cancer’ and ‘Gene’ sections to visualize the features on multi-omics levels concisely. DriverDBv3 seeks to improve the study of integrative cancer omics data by identifying driver genes and contributes to cancer biology.
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Affiliation(s)
- Shu-Hsuan Liu
- Graduate Institute of Biomedical Science, China Medical University, Taichung 40403, Taiwan
| | - Pei-Chun Shen
- Graduate Institute of Biomedical Science, China Medical University, Taichung 40403, Taiwan
| | - Chen-Yang Chen
- Cytoaurora Biotechnologies, Inc. Hsinchu Science Park, Hsinchu 30261, Taiwan
| | - An-Ni Hsu
- Graduate Institute of Biomedical Science, China Medical University, Taichung 40403, Taiwan
| | - Yi-Chun Cho
- Graduate Institute of Biomedical Science, China Medical University, Taichung 40403, Taiwan
| | - Yo-Liang Lai
- Graduate Institute of Biomedical Science, China Medical University, Taichung 40403, Taiwan.,Department of Radiation Oncology, China Medical University Hospital, Taichung 40403, Taiwan
| | - Fang-Hsin Chen
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan 33302, Taiwan.,Department of Radiation Oncology, Chang Gung Memorial Hospital at Linkou, Taoyuan 33302, Taiwan.,Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital, Taoyuan 33302, Taiwan
| | - Chia-Yang Li
- Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
| | - Shu-Chi Wang
- Department of Medical Laboratory Science and Biotechnology, College of Health Sciences, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
| | - Ming Chen
- Center for Medical Genetics, Changhua Christian Hospital, Changhua 50006, Taiwan
| | - I-Fang Chung
- Institute of BioMedical Informatics, National Yang-Ming University, Taipei 11221, Taiwan
| | - Wei-Chung Cheng
- Graduate Institute of Biomedical Science, China Medical University, Taichung 40403, Taiwan.,Research Center for Tumor Medical Science, China Medical University, Taichung 40403, Taiwan
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