A machine learning-based gene signature of response to the novel alkylating agent LP-184 distinguishes its potential tumor indications.

ABSTRACT:

Background

Non-targeted cytotoxics with anticancer activity are often developed through preclinical stages using response criteria observed in cell lines and xenografts. A panel of the NCI-60 cell lines is frequently the first line to define tumor types that are optimally responsive. Open data on the gene expression of the NCI-60 cell lines, provides a unique opportunity to add another dimension to the preclinical development of such drugs by interrogating correlations with gene expression patterns. Machine learning can be used to reduce the complexity of whole genome gene expression patterns to derive manageable signatures of response. Application of machine learning in early phases of preclinical development is likely to allow a better positioning and ultimate clinical success of

SUBMITTER: Kathad U 

PROVIDER: S-EPMC7923321 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature