Probabilistic modeling of personalized drug combinations from integrated chemical screen and molecular data in sarcoma.

ABSTRACT:

Background

Cancer patients with advanced disease routinely exhaust available clinical regimens and lack actionable genomic medicine results, leaving a large patient population without effective treatments options when their disease inevitably progresses. To address the unmet clinical need for evidence-based therapy assignment when standard clinical approaches have failed, we have developed a probabilistic computational modeling approach which integrates molecular sequencing data with functional assay data to develop patient-specific combination cancer treatments.

Methods

Tissue taken from a murine model of alveolar rhabdomyosarcoma was used to perform single agent drug screening and DNA/RNA sequencing experiments; results integrated via our computational modeling approach id

SUBMITTER: Berlow NE 

PROVIDER: S-EPMC6580486 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature