Modeling precision treatment of breast cancer

Abstract

BackgroundFirst-generation molecular profiles for human breast cancers have enabled the identification of features that can predict therapeutic response; however, little is known about how the various data types can best be combined to yield optimal predictors. Collections of breast cancer cell lines mirror many aspects of breast cancer molecular pathobiology, and measurements of their omic and biological therapeutic responses are well-suited for development of strategies to identify the most predictive molecular feature sets.ResultsWe used least squares-support vector machines and random forest algorithms to identify molecular features associated with responses of a collection of 70 breast cancer cell lines to 90 experimental or approved therapeutic agents. The datasets analyzed included measurements of copy number aberrations, mutations, gene and isoform expression, promoter methylation and protein expression. Transcriptional subtype contributed strongly to response predictors for 25% of compounds, and adding other molecular data types improved prediction for 65%. No single molecular dataset consistently out-performed the others, suggesting that therapeutic response is mediated at multiple levels in the genome. Response predictors were developed and applied to TCGA data, and were found to be present in subsets of those patient samples.ConclusionsThese results suggest that matching patients to treatments based on transcriptional subtype will improve response rates, and inclusion of additional features from other profiling data types may provide additional benefit. Further, we suggest a systems biology strategy for guiding clinical trials so that patient cohorts most likely to respond to new therapies may be more efficiently identified.

Endocytic trafficking of laminin is controlled by dystroglycan and disrupted in cancers

“…These cells were subsequently assayed for laminin internalization by flow cytometry. Control cells showed almost no measurable internalization of laminin over background, despite the expression of multiple laminin-binding integrin receptor subunits; MDA-MB-231 cells express the a1, a2, a3, a6, b1 and b4 integrin subunits, but not a7, a8 or a9 (Daemen et al, 2013). By contrast, LARGE-expressing cells showed robust laminin internalization (Fig.…”

Forkhead Box Q1 Is a Novel Target of Breast Cancer Stem Cell Inhibition by Diallyl Trisulfide

“…Additional breast cancer cell line data were obtained from GEO (GSE48213) (31). Log2 transformed transcript per million values were generated from FASTQ files using lightweight alignment -salmon v0.5.1 (21-kmer size, quasi-mapping, Ensembl v82) (32).…”

Single-cell analyses of transcriptional heterogeneity during drug tolerance transition in cancer cells by RNA sequencing

“…We first identified the DNA variants that were specific to the cell line by comparing the population DNA to the human reference genome (UCSC hg19). The population DNA was from two sources: one is from our whole genome DNA sequencing (20 times), and the other is from ultra-deep exome sequencing (200 times) (52). Next, we compared untreated RNA to population DNA and the human reference and declared all differences as DNA-RNA variants and those with sufficient evidence as RNA variants.…”