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 identified a synergistic personalized two-drug combination. Cells derived from the primary murine 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 identified a personalized two-drug combination effective across all tumor regions. The heterogeneity-consensus combination was validated in a xenograft model derived from the patient’s primary tumor. Cell cultures derived from human and canine undifferentiated pleomorphic sarcoma were assayed by drug screen; computational modeling identified a resistance-abrogating two-drug combination common to both cell cultures. This combination was validated in vitro via a cell regrowth assay. Results Our computational modeling approach addresses three major challenges in personalized cancer therapy: synergistic drug combination predictions (validated in vitro and in vivo in a genetically engineered murine cancer model), identification of unifying therapeutic targets to overcome intra-tumor heterogeneity (validated in vivo in a human cancer xenograft), and mitigation of cancer cell resistance and rewiring mechanisms (validated in vitro in a human and canine cancer model). Conclusions These proof-of-concept studies support the use of an integrative functional approach to personalized combination therapy prediction for the population of high-risk cancer patients lacking viable clinical options and without actionable DNA sequencing-based therapy. Electronic supplementary material The online version of this article (10.1186/s12885-019-5681-6) contains supplementary material, which is available to authorized users.

Sarcoma treatment in the era of molecular medicine

“…Likewise, genomic analyses in combination with screening cancer cell lines against libraries of drugs have the potential to improve the correlation between genomic biomarkers and response to therapy. Such an approach has been used to identify biomarkers for response to therapy of several sarcomas using cell lines, patient‐derived samples, and canine sarcoma as proof of principle (Berlow et al , 2019). This approach is challenging for studying sarcoma, due to the limited number of cell lines available, although isolation of new cell lines (Salawu et al , 2016) and sarcoma PDX models is improving (Stebbing et al , 2014).…”

Is the era of sorafenib over? A review of the literature

“…This system could help more patients benefit from precision therapy. 154 Surprisingly, another group developed an in silico prediction system that identifies new combinations, which solved another problem of combination therapy, finding new combinations. 155 With the help of machine learning and artificial intelligence, drug combinations containing sorafenib could benefit more HCC patients.…”

Identification of low-dose multidrug combinations for sunitinib-naive and pre-treated renal cell carcinoma

“…Combinations of drugs that have a specific targeting profile in different signalling pathways at lower doses will demonstrate a better safety profile, and are easier to adapt in a personalised fashion. 60 Previous studies have shown the importance of combining drugs that interfere with molecules up- [61][62][63] and downstream [64][65][66] of a certain signalling cascade. However, the choice of targets and drugs is crucial and determines treatment success.…”

“…Combinations of drugs that have a specific targeting profile in different signalling pathways at lower doses will demonstrate a better safety profile, and are easier to adapt in a personalised fashion. 60 …”