RT Journal Article
SR Electronic
T1 Evaluation of colorectal cancer subtypes and cell lines using deep learning
JF Life Science Alliance
JO Life Sci. Alliance
FD Life Science Alliance LLC
SP e201900517
DO 10.26508/lsa.201900517
VO 2
IS 6
A1 Ronen, Jonathan
A1 Hayat, Sikander
A1 Akalin, Altuna
YR 2019
UL http://www.life-science-alliance.org/content/2/6/e201900517.abstract
AB 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.