Helmholtz Gemeinschaft


Evaluation of colorectal cancer subtypes and cell lines using deep learning

PDF (Original Article) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
[img] Other (Supplemental Information)

Item Type:Article
Title:Evaluation of colorectal cancer subtypes and cell lines using deep learning
Creators Name:Ronen, J. and Hayat, S. and Akalin, A.
Abstract:Colorectal cancer (CRC) is a common cancer with a high mortality rate and a rising incidence rate in the developed world. Molecular profiling techniques have been used to better understand the variability between tumors and disease models such as cell lines. To maximize the translatability and clinical relevance of in vitro studies, the selection of optimal cancer models is imperative. We have developed a deep learning-based method to measure the similarity between CRC tumors and disease models such as cancer cell lines. Our method efficiently leverages multiomics data sets containing copy number alterations, gene expression, and point mutations and learns latent factors that describe data in lower dimensions. These latent factors represent the patterns that are clinically relevant and explain the variability of molecular profiles across tumors and cell lines. Using these, we propose refined CRC subtypes and provide best-matching cell lines to different subtypes. These findings are relevant to patient stratification and selection of cell lines for early-stage drug discovery pipelines, biomarker discovery, and target identification.
Keywords:Biological Models, Colorectal Neoplasms, DNA Copy Number Variations, DNA Sequence Analysis, Deep Learning, Gene Expression Profiling, Machine Learning, Mutation, Neoplasm Proteins, Tumor Biomarkers, Tumor Cell Line
Source:Life Science Alliance
Publisher:Life Science Alliance
Page Range:201900517
Date:December 2019
Official Publication:https://doi.org/10.26508/lsa.201900517
PubMed:View item in PubMed
Related to:
https://edoc.mdc-berlin.de/17910/Preprint version

Repository Staff Only: item control page


Downloads per month over past year

Open Access
MDC Library