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Molecular Correlates of Venous Thromboembolism (VTE) in Ovarian Cancer. Cancers (Basel) 2022; 14:cancers14061496. [PMID: 35326647 PMCID: PMC8946269 DOI: 10.3390/cancers14061496] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 03/08/2022] [Accepted: 03/11/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND The incidence of venous thromboembolism (VTE) in patients with ovarian cancer is higher than most solid tumors, ranging between 10-30%, and a diagnosis of VTE in this patient population is associated with worse oncologic outcomes. The tumor-specific molecular factors that may lead to the development of VTE are not well understood. OBJECTIVES The aim of this study was to identify molecular features present in ovarian tumors of patients with VTE compared to those without. METHODS We performed a multiplatform omics analysis incorporating RNA and DNA sequencing, quantitative proteomics, as well as immune cell profiling of high-grade serous ovarian carcinoma (HGSC) samples from a cohort of 32 patients with or without VTE. RESULTS Pathway analyses revealed upregulation of both inflammatory and coagulation pathways in the VTE group. While DNA whole-exome sequencing failed to identify significant coding alterations between the groups, the results of an integrated proteomic and RNA sequencing analysis indicated that there is a relationship between VTE and the expression of platelet-derived growth factor subunit B (PDGFB) and extracellular proteins in tumor cells, namely collagens, that are correlated with the formation of thrombosis. CONCLUSIONS In this comprehensive analysis of HGSC tumor tissues from patients with and without VTE, we identified markers unique to the VTE group that could contribute to development of thrombosis. Our findings provide additional insights into the molecular alterations underlying the development of VTE in ovarian cancer patients and invite further investigation into potential predictive biomarkers of VTE in ovarian cancer.
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Hao D, He S, Harada K, Pizzi MP, Lu Y, Guan P, Chen L, Wang R, Zhang S, Sewastjanow-Silva M, Abdelhakeem A, Shanbhag N, Bhutani M, Han G, Lee JH, Zhao S, Weston B, Blum Murphy M, Waters R, Estrella JS, Roy-Chowdhuri S, Gan Q, Lee JS, Peng G, Hanash SM, Calin GA, Song X, Zhang J, Song S, Wang L, Ajani JA. Integrated genomic profiling and modelling for risk stratification in patients with advanced oesophagogastric adenocarcinoma. Gut 2021; 70:2055-2065. [PMID: 33334899 PMCID: PMC10643023 DOI: 10.1136/gutjnl-2020-322707] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 11/25/2020] [Accepted: 11/28/2020] [Indexed: 12/18/2022]
Abstract
OBJECTIVE Prognosis of patients with advanced oesophagogastric adenocarcinoma (mEGAC) is poor and molecular determinants of shorter or longer overall survivors are lacking. Our objective was to identify molecular features and develop a prognostic model by profiling the genomic features of patients with mEGAC with widely varying outcomes. DESIGN We profiled 40 untreated mEGACs (20 shorter survivors <13 months and 20 longer survivors >36 months) with whole-exome sequencing (WES) and RNA sequencing and performed an integrated analysis of exome, transcriptome, immune profile and pathological phenotypes to identify the molecular determinants, developing an integrated model for prognosis and comparison with The Cancer Genome Atlas (TCGA) cohorts. RESULTS KMT2C alterations were exclusively observed in shorter survivors together with high level of intratumour heterogeneity and complex clonal architectures, whereas the APOBEC mutational signatures were significantly enriched in longer survivors. Notably, the loss of heterozygosity in chromosome 4 (Chr4) was associated with shorter survival and 'cold' immune phenotype characterised by decreased B, CD8, natural killer cells and interferon-gamma responses. Unsupervised transcriptomic clustering revealed a shorter survivor subtype with distinct expression features (eg, upregulated druggable targets JAK2, MAP3K13 and MECOM). An integrated model was then built based on clinical variables and the identified molecular determinants, which significantly segregated shorter and longer survivors. All the above features and the integrated model have been validated independently in multiple TCGA cohorts. CONCLUSION This study discovered novel molecular features prognosticating overall survival in patients with mEGAC and identified potential novel targets in shorter survivors.
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Affiliation(s)
- Dapeng Hao
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Siyuan He
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Kazuto Harada
- Gastroenterological Surgery, Kumamoto University, Kumamoto, Kumamoto, Japan
| | - Melissa Pool Pizzi
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Yang Lu
- Department of Nuclear Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Pujun Guan
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Lu Chen
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Ruiping Wang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Shaojun Zhang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Matheus Sewastjanow-Silva
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Ahmed Abdelhakeem
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Namita Shanbhag
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Manoop Bhutani
- Gastroenterology Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Guangchun Han
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jeffrey H Lee
- Gastroenterology Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Shuangtao Zhao
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Brian Weston
- Gastroenterology Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Mariela Blum Murphy
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Rebecca Waters
- Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | | | | | - Qiong Gan
- Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Ju-Seog Lee
- Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Guang Peng
- Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Samir M Hanash
- Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - George Adrian Calin
- Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Xingzhi Song
- Computational Genomics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jianhua Zhang
- Computational Genomics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Shumei Song
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Linghua Wang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jaffer A Ajani
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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Mohanty V, Wang F, Mills GB, Chen K. Uncoupling of gene expression from copy number presents therapeutic opportunities in aneuploid cancers. Cell Rep Med 2021; 2:100349. [PMID: 34337565 PMCID: PMC8324495 DOI: 10.1016/j.xcrm.2021.100349] [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: 09/28/2020] [Revised: 02/11/2021] [Accepted: 06/22/2021] [Indexed: 12/12/2022]
Abstract
Uncoupling of mRNA expression from copy number (UECN) might be a strategy for cancer cells to a tolerate high degree of aneuploidy. To test the extent and role of UECN across cancers, we perform integrative multiomic analysis of The Cancer Genome Atlas (TCGA) dataset, encompassing ∼5,000 individual tumors. We find UECN is common in cancers and is associated with increased oncogenic signaling, proliferation, and immune suppression. UECN appears to be orchestrated by complex regulatory changes, with transcription factors (TFs) playing a prominent role. To further dissect the regulatory mechanisms, we develop a systems-biology approach to identify candidate TFs, which could serve as targets to disrupt UECN and reduce tumor fitness. Applying our approach to TCGA data, we identify 21 putative targets, 42.8% of which are validated by independent sources. Together, our study indicates that UECN is likely an important mechanism in development of aneuploid tumors and might be therapeutically targetable.
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Affiliation(s)
- Vakul Mohanty
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Fang Wang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Gordon B. Mills
- Cell, Developmental and Cancer Biology, Knight Cancer Institute, Oregon Health University, Portland, OR 97201, USA
| | - CTD2 Research Network
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Cell, Developmental and Cancer Biology, Knight Cancer Institute, Oregon Health University, Portland, OR 97201, USA
| | - Ken Chen
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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Abstract
The observation and analysis of intra-tumour heterogeneity (ITH), particularly in genomic studies, has advanced our understanding of the evolutionary forces that shape cancer growth and development. However, only a subset of the variation observed in a single tumour will have an impact on cancer evolution, highlighting the need to distinguish between functional and non-functional ITH. Emerging studies highlight a role for the cancer epigenome, transcriptome and immune microenvironment in functional ITH. Here, we consider the importance of both genetic and non-genetic ITH and their role in tumour evolution, and present the rationale for a broad research focus beyond the cancer genome. Systems-biology analytical approaches will be necessary to outline the scale and importance of functional ITH. By allowing a deeper understanding of tumour evolution this will, in time, encourage development of novel therapies and improve outcomes for patients.
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Affiliation(s)
- James R M Black
- Cancer Genome Evolution Research Group, University College London Cancer Institute, London, UK
- Cancer Research UK Lung Cancer Center of Excellence, University College London Cancer Institute, London, UK
| | - Nicholas McGranahan
- Cancer Genome Evolution Research Group, University College London Cancer Institute, London, UK.
- Cancer Research UK Lung Cancer Center of Excellence, University College London Cancer Institute, London, UK.
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Przytycki PF, Singh M. Differential Allele-Specific Expression Uncovers Breast Cancer Genes Dysregulated by Cis Noncoding Mutations. Cell Syst 2020; 10:193-203.e4. [PMID: 32078798 PMCID: PMC7457951 DOI: 10.1016/j.cels.2020.01.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Revised: 12/04/2019] [Accepted: 01/22/2020] [Indexed: 01/23/2023]
Abstract
Identifying cancer-relevant mutations in noncoding regions is challenging due to the large numbers of such mutations, their low levels of recurrence, and difficulties in interpreting their functional impact. To uncover genes that are dysregulated due to somatic mutations in cis, we build upon the concept of differential allele-specific expression (ASE) and introduce methods to identify genes within an individual's cancer whose ASE differs from what is found in matched normal tissue. When applied to breast cancer tumor samples, our methods detect the known allele-specific effects of copy number variation and nonsense-mediated decay. Further, genes that are found to recurrently exhibit differential ASE across samples are cancer relevant. Genes with cis mutations are enriched for differential ASE, and we find 147 potentially functional noncoding mutations cis to genes that exhibit significant differential ASE. We conclude that differential ASE is a promising means for discovering gene dysregulation due to cis noncoding mutations.
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Affiliation(s)
- Pawel F Przytycki
- Department of Computer Science, Princeton University, Princeton, NJ 08544, USA; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
| | - Mona Singh
- Department of Computer Science, Princeton University, Princeton, NJ 08544, USA; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.
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