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TuBA: Tunable biclustering algorithm reveals clinically relevant tumor transcriptional profiles in breast cancer.

ABSTRACT: BACKGROUND:Traditional clustering approaches for gene expression data are not well adapted to address the complexity and heterogeneity of tumors, where small sets of genes may be aberrantly co-expressed in specific subsets of tumors. Biclustering algorithms that perform local clustering on subsets of genes and conditions help address this problem. We propose a graph-based Tunable Biclustering Algorithm (TuBA) based on a novel pairwise proximity measure, examining the relationship of samples at the extremes of genes' expression profiles to identify similarly altered signatures. RESULTS:TuBA's predictions are consistent in 3,940 breast invasive carcinoma samples from 3 independent sources, using different technologies for measuring gene expression (RNA sequencing and Microarray). More than 60% of biclusters identified independently in each dataset had significant agreement in their gene sets, as well as similar clinical implications. Approximately 50% of biclusters were enriched in the estrogen receptor-negative/HER2-negative (or basal-like) subtype, while >50% were associated with transcriptionally active copy number changes. Biclusters representing gene co-expression patterns in stromal tissue were also identified in tumor specimens. CONCLUSIONS:TuBA offers a simple biclustering method that can identify biologically relevant gene co-expression signatures not captured by traditional unsupervised clustering approaches. It complements biclustering approaches that are designed to identify constant or coherent submatrices in gene expression datasets, and outperforms them in identifying a multitude of altered transcriptional profiles that are associated with observed genomic heterogeneity of diseased states in breast cancer, both within and across tumor subtypes, a promising step in understanding disease heterogeneity, and a necessary first step in individualized therapy.

SUBMITTER: Singh A

PROVIDER: S-EPMC6582332 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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TuBA: Tunable biclustering algorithm reveals clinically relevant tumor transcriptional profiles in breast cancer.

Singh Amartya A Bhanot Gyan G Khiabanian Hossein H

GigaScience 20190601 6

<h4>Background</h4>Traditional clustering approaches for gene expression data are not well adapted to address the complexity and heterogeneity of tumors, where small sets of genes may be aberrantly co-expressed in specific subsets of tumors. Biclustering algorithms that perform local clustering on subsets of genes and conditions help address this problem. We propose a graph-based Tunable Biclustering Algorithm (TuBA) based on a novel pairwise proximity measure, examining the relationship of samp ...[more]

PMID: 31216036

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Project description:BACKGROUND: Pancreatic ductal adenocarcinoma (PDA) is a highly lethal cancer characterized by complex aberrant genomes. A fundamental goal of current studies is to identify those somatic events arising in the variable landscape of PDA genomes that can be exploited for improved clinical outcomes. METHODS: We used DNA content flow sorting to identify and purify tumor nuclei of PDA samples from 50 patients. The genome of each sorted sample was profiled by oligonucleotide comparative genomic hybridization and targeted resequencing of STAG2. Transposon insertions within STAG2 in a KRAS (G12D)-driven genetically engineered mouse model of PDA were screened by RT-PCR. We then used a tissue microarray to survey STAG2 protein expression levels in 344 human PDA tumor samples and adjacent tissues. Univariate Kaplan Meier analysis and multivariate Cox Regression analysis were used to assess the association of STAG2 expression relative to overall survival and response to adjuvant therapy. Finally, RNAi-based assays with PDA cell lines were used to assess the potential therapeutic consequence of STAG2 expression in response to 18 therapeutic agents. RESULTS: STAG2 is targeted by somatic aberrations in a subset (4%) of human PDAs. Transposon-mediated disruption of STAG2 in a KRAS (G12D) genetically engineered mouse model promotes the development of PDA and its progression to metastatic disease. There was a statistically significant loss of STAG2 protein expression in human tumor tissue (Wilcoxon-Rank test) with complete absence of STAG2 staining observed in 15 (4.3%) patients. In univariate Kaplan Meier analysis nearly complete STAG2 positive staining (>95% of nuclei positive) was associated with a median survival benefit of 6.41 months (P?=?0.031). The survival benefit of adjuvant chemotherapy was only seen in patients with a STAG2 staining of less than 95% (median survival benefit 7.65 months; P?=?0.028). Multivariate Cox Regression analysis showed that STAG2 is an independent prognostic factor for survival in pancreatic cancer patients. Finally, we show that RNAi-mediated knockdown of STAG2 selectively sensitizes human PDA cell lines to platinum-based therapy. CONCLUSIONS: Based on these iterative findings we propose that STAG2 is a clinically significant tumor suppressor in PDA.

Dataset Information

TuBA: Tunable biclustering algorithm reveals clinically relevant tumor transcriptional profiles in breast cancer.

Publications

TuBA: Tunable biclustering algorithm reveals clinically relevant tumor transcriptional profiles in breast cancer.

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