Dataset Information

Combining multidimensional genomic measurements for predicting cancer prognosis: observations from TCGA.

ABSTRACT: With accumulating research on the interconnections among different types of genomic regulations, researchers have found that multidimensional genomic studies outperform one-dimensional studies in multiple aspects. Among many sources of multidimensional genomic data, The Cancer Genome Atlas (TCGA) provides the public with comprehensive profiling data on >30 cancer types, making it an ideal test bed for conducting and comparing different analyses. In this article, the analysis goal is to apply several existing methods and associate multidimensional genomic measurements with cancer outcomes in particular prognosis, with special focus on the predictive power of genomic signatures. We exploit clinical data and four types of genomic measurement including mRNA gene expression, DNA methylation, microRNA and copy number alterations for breast invasive carcinoma, glioblastoma multiforme, acute myeloid leukemia and lung squamous cell carcinoma collected by TCGA. To accommodate the high dimensionality, we extract important features using Principal Component Analysis, Partial Least Squares and Least Absolute Shrinkage and Selection Operator (Lasso), which are representative of dimension reduction and variable selection techniques and have been extensively adopted, and fit Cox survival models with combined important features. We calibrate the predictive power of each type of genomic measurement for the prognosis of four cancer types and find that the results vary across cancers. Our analysis also suggests that for most of the cancers in our study and the adopted methods, there is no substantial improvement in prediction when adding other genomic measurement after gene expression and clinical covariates have been included in the model. This is consistent with the findings that molecular features measured at the transcription level affect clinical outcomes more directly than those measured at the DNA/epigenetic level.

SUBMITTER: Zhao Q

PROVIDER: S-EPMC4375393 | biostudies-literature | 2015 Mar

REPOSITORIES: biostudies-literature

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Combining multidimensional genomic measurements for predicting cancer prognosis: observations from TCGA.

Zhao Qing Q Shi Xingjie X Xie Yang Y Huang Jian J Shia BenChang B Ma Shuangge S

Briefings in bioinformatics 20140313 2

With accumulating research on the interconnections among different types of genomic regulations, researchers have found that multidimensional genomic studies outperform one-dimensional studies in multiple aspects. Among many sources of multidimensional genomic data, The Cancer Genome Atlas (TCGA) provides the public with comprehensive profiling data on >30 cancer types, making it an ideal test bed for conducting and comparing different analyses. In this article, the analysis goal is to apply sev ...[more]

PMID: 24632304

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Project description:TP53 mutation is a critical driver mutation that affects the carcinogenesis and prognosis of patients with pancreatic cancer (PC). Currently, there is no driver mutation-derived signature based on TP53 mutational status for prognosis and predicting therapeutic response in PC. In the present study, we characterized the TP53 mutational phenotypes in multiple patient cohorts and developed a prognostic TP53-associated signature based on differentially expressed genes between PC samples with mutated TP53 and wild-type TP53. Comprehensive investigations were carried out in prognostic stratification, genetic variation, immune cell infiltration, and efficacy prediction of chemotherapy and targeted therapy. We found that TP53 mutation commonly occurred as a survival-related driver mutation in PC. In total, 1,154 differentially expressed genes were found between two distinct TP53 mutational phenotypes. A five-gene TP53-associated signature was constructed in The Cancer Genome Atlas (TCGA) cohort by least absolute shrinkage and selection operator (LASSO)-Cox analysis and proven to be a robust prognostic predictor, which performed well in three independent Gene Expression Omnibus (GEO) validating cohorts. Remarkably, patients in the low-risk group were characterized with decreased tumor mutation burden and activity of immunity, with favorable prognosis. Higher fractions of macrophages M0 and impaired CD8 + T cells were observed in patients in the high-risk group, suggesting immunosuppression with poor survival. Patients in the high-risk group also demonstrated enhanced response to specific chemotherapeutic agents, including gemcitabine and paclitaxel. Several targeted inhibitors, like histamine receptor inhibitor, were screened out as promising drugs for PC treatment. Collectively, the TP53-associated signature is a novel prognostic biomarker and predictive indicator of PC. The signature could contribute to optimizing prognostic stratification and guide effective PC treatments.

Dataset Information

Combining multidimensional genomic measurements for predicting cancer prognosis: observations from TCGA.

Publications

Combining multidimensional genomic measurements for predicting cancer prognosis: observations from TCGA.

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