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

Study ID Alternative Stable ID Type
EGAS00001003564 Other

Study Description

Background: Cancer patients with advanced disease 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 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 identified a synergistic personalized two-drug combination. Cells derived from the tumor were allografted into mouse models and used to validate the personalized two-drug combination. Computational modeling of single agent drug screening and RNA sequencing of multiple heterogenous sites from a single patient’s epithelioid sarcoma ... (Show More)

Study Datasets 1 dataset.

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Dataset ID Description Technology Samples
Whole exome sequencing of human and mouse sarcoma samples for creation of personalized therapy options. Tissues were sequenced directly; no interventions or alterations were made to the tissue samples
Illumina HiSeq 4000 4

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