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The level of copy number alteration (CNA), termed CNA burden, in the tumor genome is associated with recurrence of primary prostate cancer. Whether CNA burden is associated with prostate cancer survival or outcomes in other cancers is unknown. We analyzed the CNA landscape of conservatively treated prostate cancer in a biopsy and transurethral resection cohort, reflecting an increasingly common treatment approach. We find that CNA burden is prognostic for cancer-specific death, independent of standard clinical prognosticators. More broadly, we find CNA burden is significantly associated with disease-free and overall survival in primary breast, endometrial, renal clear cell, thyroid, and colorectal cancer in TCGA cohorts. To assess clinical applicability, we validated these findings in an independent pan-cancer cohort of patients whose tumors were sequenced using a clinically-certified next generation sequencing assay (MSK-IMPACT), where prognostic value varied based on cancer type. This prognostic association was affected by incorporating tumor purity in some cohorts. Overall, CNA burden of primary and metastatic tumors is a prognostic factor, potentially modulated by sample purity and measurable by current clinical sequencing.
Pubmed ID: 30178746
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Software to estimate purity / ploidy, and from that compute absolute copy-number and mutation multiplicities. When DNA is extracted from an admixed population of cancer and normal cells, the information on absolute copy number per cancer cell is lost in the mixing. The purpose of ABSOLUTE is to re-extract these data from the mixed DNA population. This process begins by generation of segmented copy number data, which is input to the ABSOLUTE algorithm together with pre-computed models of recurrent cancer karyotypes and, optionally, allelic fraction values for somatic point mutations. The output of ABSOLUTE then provides re-extracted information on the absolute cellular copy number of local DNA segments and, for point mutations, the number of mutated alleles.
View all literature mentionsFunctional genomics data repository supporting MIAME-compliant data submissions. Includes microarray-based experiments measuring the abundance of mRNA, genomic DNA, and protein molecules, as well as non-array-based technologies such as serial analysis of gene expression (SAGE) and mass spectrometry proteomic technology. Array- and sequence-based data are accepted. Collection of curated gene expression DataSets, as well as original Series and Platform records. The database can be searched using keywords, organism, DataSet type and authors. DataSet records contain additional resources including cluster tools and differential expression queries.
View all literature mentionsA Software resource for statistical analysis and presentation of graphics.
View all literature mentionsComputational biology research at Memorial Sloan-Kettering Cancer Center (MSKCC) pursues computational biology research projects and the development of bioinformatics resources in the areas of: sequence-structure analysis; gene regulation; molecular pathways and networks, and diagnostic and prognostic indicators. The mission of cBio is to move the theoretical methods and genome-scale data resources of computational biology into everyday laboratory practice and use, and is reflected in the organization of cBio into research and service components ~ the intention being that new computational methods created through the process of scientific inquiry should be generalized and supported as open-source and shared community resources. Faculty from cBio participate in graduate training provided through the following graduate programs: * Gerstner Sloan-Kettering Graduate School of Biomedical Sciences * Graduate Training Program in Computational Biology and Medicine Integral to much of the research and service work performed by cBio is the creation and use of software tools and data resources. The tools that we have created and utilize provide evidence of our involvement in the following areas: * Cancer Genomics * Data Repositories * iPhone & iPod Touch * microRNAs * Pathways * Protein Function * Text Analysis * Transcription Profiling
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