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Bottom-up, integrated -omics analysis identifies broadly dosage-sensitive genes in breast cancer samples from TCGA.

ABSTRACT: The massive genomic data from The Cancer Genome Atlas (TCGA), including proteomics data from Clinical Proteomic Tumor Analysis Consortium (CPTAC), provides a unique opportunity to study cancer systematically. While most observations are made from a single type of genomics data, we apply big data analytics and systems biology approaches by simultaneously analyzing DNA amplification, mRNA and protein abundance. Using multiple genomic profiles, we have discovered widespread dosage compensation for the extensive aneuploidy observed in TCGA breast cancer samples. We do identify 11 genes that show strong correlation across all features (DNA/mRNA/protein) analogous to that of the well-known oncogene HER2 (ERBB2). These genes are generally less well-characterized regarding their role in cancer and we advocate their further study. We also discover that shRNA knockdown of these genes has an impact on cancer cell growth, suggesting a vulnerability that could be used for cancer therapy. Our study shows the advantages of systematic big data methodologies and also provides future research directions.

SUBMITTER: Kechavarzi BD

PROVIDER: S-EPMC6336338 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Bottom-up, integrated -omics analysis identifies broadly dosage-sensitive genes in breast cancer samples from TCGA.

Kechavarzi Bobak D BD Wu Huanmei H Doman Thompson N TN

PloS one 20190117 1

The massive genomic data from The Cancer Genome Atlas (TCGA), including proteomics data from Clinical Proteomic Tumor Analysis Consortium (CPTAC), provides a unique opportunity to study cancer systematically. While most observations are made from a single type of genomics data, we apply big data analytics and systems biology approaches by simultaneously analyzing DNA amplification, mRNA and protein abundance. Using multiple genomic profiles, we have discovered widespread dosage compensation for ...[more]

PMID: 30653567

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Project description:Bottom-up proteomics relies on the use of proteases and is the method of choice for identifying thousands of protein groups in complex samples. Top-down proteomics has been shown to be robust for direct analysis of small proteins and offers a solution to the "peptide-to-protein" inference problem inherent with bottom-up approaches. Here, we describe the first large-scale integration of genomic, bottom-up and top-down proteomic data for the comparative analysis of patient-derived mouse xenograft models of basal and luminal B human breast cancer, WHIM2 and WHIM16, respectively. Using these well-characterized xenograft models established by the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium, we compared and contrasted the performance of bottom-up and top-down proteomics to detect cancer-specific aberrations at the peptide and proteoform levels and to measure differential expression of proteins and proteoforms. Bottom-up proteomic analysis of the tumor xenografts detected almost 10 times as many coding nucleotide polymorphisms and peptides resulting from novel splice junctions than top-down. For proteins in the range of 0-30 kDa, where quantitation was performed using both approaches, bottom-up proteomics quantified 3,519 protein groups from 49,185 peptides, while top-down proteomics quantified 982 proteoforms mapping to 358 proteins. Examples of both concordant and discordant quantitation were found in a ∼60:40 ratio, providing a unique opportunity for top-down to fill in missing information. The two techniques showed complementary performance, with bottom-up yielding eight times more identifications of 0-30 kDa proteins in xenograft proteomes, but failing to detect differences in certain posttranslational modifications (PTMs), such as phosphorylation pattern changes of alpha-endosulfine. This work illustrates the potency of a combined bottom-up and top-down proteomics approach to deepen our knowledge of cancer biology, especially when genomic data are available.

Dataset Information

Bottom-up, integrated -omics analysis identifies broadly dosage-sensitive genes in breast cancer samples from TCGA.

Publications

Bottom-up, integrated -omics analysis identifies broadly dosage-sensitive genes in breast cancer samples from TCGA.

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