In vitro cell line models provide a valuable resource to investigate compounds useful
in the systemic chemotherapy of cancer. However, the due to the dispersal of the data
into several different databases, the utilization of these resources is limited. Here,
our aim was to establish a platform enabling the validation of chemoresistance-associated
genes and the ranking of available cell line models.We processed four independent
databases, DepMap, GDSC1, GDSC2, and CTRP. The gene expression data was quantile normalized
and HUGO gene names were assigned to have unambiguous identification of the genes.
Resistance values were exported for all agents. The correlation between gene expression
and therapy resistance is computed using ROC test.We combined four datasets with chemosensitivity
data of 1562 agents and transcriptome-level gene expression of 1250 cancer cell lines.
We have set up an online tool utilizing this database to correlate available cell
line sensitivity data and treatment response in a uniform analysis pipeline (www.rocplot.com/cells).
We employed the established pipeline to by rank genes related to resistance against
afatinib and lapatinib, two inhibitors of the tyrosine-kinase domain of ERBB2.The
computational tool is useful 1) to correlate gene expression with resistance, 2) to
identify and rank resistant and sensitive cell lines, and 3) to rank resistance associated
genes, cancer hallmarks, and gene ontology pathways. The platform will be an invaluable
support to speed up cancer research by validating gene-resistance correlations and
by selecting the best cell line models for new experiments.
