Nov 7, 2018

Evaluation of colorectal cancer subtypes and cell lines using deep learning

BioRxiv : the Preprint Server for Biology
Jonathan RonenAltuna Akalin

Abstract

Colorectal cancer (CRC) is a common cancer with a high mortality rate and a rising incidence rate in the developed world. The disease shows variable drug response and outcome. Molecular profiling techniques have been used to better understand the variability between tumours as well as cancer models such as cell lines. Drug discovery programs use cell lines as a proxy for human cancers to characterize their molecular makeup and drug response, identify relevant indications and discover biomarkers. In order to maximize the translatability and the clinical relevance of in vitro studies, selection of optimal cancer models is imperative. We have developed a deep learning based method to measure the similarity between CRC tumors and other tumors or disease models such as cancer cell lines. Our method efficiently leverages multi-omics data sets containing copy number alterations, gene expression and point mutations, and learns latent factors that describe the data in lower dimension. These latent factors represent the patterns across gene expression, copy number, and mutational profiles which are clinically relevant and explain the variability of molecular profiles across tumours and cell lines. Using these, we propose a refined colore...Continue Reading

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Mentioned in this Paper

Biological Markers
Drug Response
Animal Cancer Model
Patterns
Classification
Stratification
Neoplasms
Molecular_function
Molecular Profiling
Profile (Lab Procedure)

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