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Abstract
The ability to characterize and predict tumor phenotypes is crucial to precision medicine. In this study, we present an integrative computational approach using a genome-wide association analysis and an Elastic Net prediction method to analyze the relationship between DNA copy number alterations and an archive of gene expression signatures. Across breast cancers, we are able to quantitatively predict many gene signatures levels within individual tumors with high accuracy based upon DNA copy number features alone, including proliferation status and Estrogen-signaling pathway activity. We can also predict many other key phenotypes, including intrinsic molecular subtypes, estrogen receptor status, and TP53 mutation. This approach is also applied to TCGA Pan-Cancer, which identify repeatedly predictable signatures across tumor types including immune features in lung squamous and basal-like breast cancers. These Elastic Net DNA predictors could also be called from DNA-based gene panels, thus facilitating their use as biomarkers to guide therapeutic decision making.
Highlights
The ability to characterize and predict tumor phenotypes is crucial to precision medicine
We first aimed to investigate the possible associations between DNA copy number alterations (CNAs) and multiple gene expression signatures
We applied a panel of 543 published gene expression signatures measuring diverse tumor phenotypes including active signaling pathways, the aforementioned known prognostic clinical models, tumor microenvironment features, and features of DNA amplicons and deletions[13], onto 1038 breast cancers from the The Cancer Genome Atlas (TCGA) breast cancer project[4] (Supplementary Fig. 1, Supplementary Data 1)
Summary
The ability to characterize and predict tumor phenotypes is crucial to precision medicine. We can predict many other key phenotypes, including intrinsic molecular subtypes, estrogen receptor status, and TP53 mutation This approach is applied to TCGA Pan-Cancer, which identify repeatedly predictable signatures across tumor types including immune features in lung squamous and basal-like breast cancers. We use an integrative genomics approach including a genome-wide association analysis[8], as well as an Elastic Net predictive modeling strategy[9], to build models of complex tumor phenotypes using somatic DNA copy number alterations (CNAs). Our results identify associations between many gene expression signatures and CNAs, and between protein expression features and CNAs. Generally, we present an approach that could be applied to many other tumor types for which multiplatform genomic data are available, to evaluate the relationship between CNAs and complex phenotypes, and where predictive models of therapeutic importance could be developed using what are common place DNA-based clinical tools
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