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

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.

A Tumor-Activatable Theranostic Nanomedicine Platform for NIR Fluorescence-Guided Surgery and Combinatorial Phototherapy.

Fluorescence image-guided surgery combined with intraoperative therapeutic modalities has great potential for intraoperative detection of oncologic targets and eradication of unresectable cancer residues. Therefore, we have developed an activatable theranostic nanoplatform that can be used concurrently for two purposes: (1) tumor delineation with real-time near infrared (NIR) fluorescence signal during surgery, and (2) intraoperative targeted treatment to further eliminate unresected disease sites by non-toxic phototherapy. Methods: The developed nanoplatform is based on a single agent, silicon naphthalocyanine (SiNc), encapsulated in biodegradable PEG-PCL (poly (ethylene glycol)-b-poly(ɛ-caprolactone)) nanoparticles. It is engineered to be non-fluorescent initially via dense SiNc packing within the nanoparticle's hydrophobic core, with NIR fluorescence activation after accumulation at the tumor site. The activatable nanoplatform was evaluated in vitro and in two different murine cancer models, including an ovarian intraperitoneal metastasis-mimicking model. Furthermore, fluorescence image-guided surgery mediated by this nanoplatform was performed on the employed animal models using a Fluobeam® 800 imaging system. Finally, the phototherapeutic efficacy of the developed nanoplatform was demonstrated in vivo. Results: Our in vitro data suggest that the intracellular environment of cancer cells is capable of compromising the integrity of self-assembled nanoparticles and thus causes disruption of the tight dye packing inside the hydrophobic cores and activation of the NIR fluorescence. Animal studies demonstrated accumulation of activatable nanoparticles at the tumor site following systemic administration, as well as release and fluorescence recovery of SiNc from the polymeric carrier. It was also validated that the developed nanoparticles are compatible with the intraoperative imaging system Fluobeam® 800, and nanoparticle-mediated image-guided surgery provides successful resection of cancer tumors. Finally, in vivo studies revealed that combinatorial phototherapy mediated by the nanoparticles could efficiently eradicate chemoresistant ovarian cancer tumors. Conclusion: The revealed properties of the activatable nanoplatform make it highly promising for further application in clinical image-guided surgery and combined phototherapy, facilitating a potential translation to clinical studies.