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.

CD203c is expressed by human fetal hepatoblasts and distinguishes subsets of hepatoblastoma.

Background & Aims: Hepatocytic cells found during prenatal development have unique features compared to their adult counterparts, and are believed to be the precursors of pediatric hepatoblastoma. The cell-surface phenotype of hepatoblasts and hepatoblastoma cell lines was evaluated to discover new markers of these cells and gain insight into the development of hepatocytic cells and the phenotypes and origins of hepatoblastoma. Methods: Human midgestation livers and four pediatric hepatoblastoma cell lines were screened using flow cytometry. Expression of over 300 antigens was evaluated on hepatoblasts defined by their expression of CD326 (EpCAM) and CD14. Also analyzed were hematopoietic cells, expressing CD45, and liver sinusoidal-endothelial cells (LSECs), expressing CD14 but lacking CD45 expression. Select antigens were further examined by fluorescence immunomicroscopy of fetal liver sections. Antigen expression was also confirmed on cultured cells by both methods. Gene expression analysis by liver cells, 6 hepatoblastoma cell lines, and hepatoblastoma cells was performed. Immunohistochemistry was used to evaluate CD203c, CD326, and cytokeratin-19 expression on three hepatoblastoma tumors. Results: Antibody screening identified many cell surface markers commonly or divergently expressed by hematopoietic cells, LSECs, and hepatoblasts. Thirteen novel markers expressed on fetal hepatoblasts were identified including ectonucleotide pyrophosphatase/phosphodiesterase family member 3 (ENPP-3/CD203c), which was found to be expressed by hepatoblasts with widespread expression in the parenchyma of the fetal liver. In culture CD203c+CD326++ cells resembled hepatocytic cells with coexpression of albumin and cytokeratin-19 confirming a hepatoblast phenotype. CD203c expression declined rapidly in culture whereas the loss of CD326 was not as pronounced. CD203c and CD326 were co-expressed on a subset of hepatoblastoma cell lines and hepatoblastomas with an embryonal pattern. Conclusions: CD203c is expressed on hepatoblasts and may play a role in purinergic signaling in the developing liver. Hepatoblastoma cell lines were found to consist of two broad phenotypes consisting of a cholangiocyte-like phenotype that expressed CD203c and CD326 and a hepatocyte-like phenotype with diminished expression of these markers. CD203c was expressed by some hepatoblastoma tumors and may represent a marker of a less differentiated embryonal component.