Computational Molecular Pathology Research for Cancer Diagnostics and Biomarkers

Division Of Basic Sciences - Nci

Investigators

Linked publications & trials

Abstract

Research is conducted to characterize and develop new animal models of human disease and to develop the means to better characterize a model's disease relevance, addressing critical barriers to research progress. Additional aims include the development of new research technologies for the evaluation and application of disease biomarkers and to translate approaches to cancer treatment intended for clinical application. Progress was made in developing research resources useful in developing and characterizing new models of human cancer and cancer diagnostics. This research project included developing capabilities in molecular diagnostics for cancer models, developing methods for automated morphometric image analysis of cancer specimens for quantitative pathology through collaboration for enterprise scale of image analysis platforms and database. In characterizing new animal models of cancer disease, advancement was made in preclinical development of targeted therapy for mucosal melanoma. Distinct from genetically engineered models, sporadic naturally occurring canine melanocytic neoplasms share several characteristics with human disease that could make investigation in dogs a more relevant pre-clinical model. Canine melanomas rarely arise in sun-exposed site and most occur spontaneously in the oral cavity. The spectrum of naturally occurring canine melanocytic neoplasia, as is true in people, includes benign lesions with some analogy to nevi, as well as invasive primary melanoma, and widespread metastasis. As in humans, distinct melanoma subtypes differing in somatic and predisposing germ-line genetic alterations, cell of origin, epidemiology, relationship to ultraviolet radiation and progression from benign to malignant tumors, also exist naturally pet dogs. Melanomas arising spontaneously in the mucous membranes are a rare and aggressive subtype. New treatment approaches are needed, yet accumulating sufficient evidence to improve patient outcomes is difficult. Clinical and pathological correlates between human and canine mucosal melanomas are substantial, and the relatively greater incidence of spontaneous naturally occurring mucosal melanoma in dogs represents a promising opportunity for predictive modeling using a comparative oncology approach. Both canine and human mucosal melanomas appear to harbor BRAF, NRAS and c-kit mutations uncommonly, compared to human cutaneous melanomas. The genomic landscapes of human and canine mucosal melanomas appear highly diverse. Although much remains to be determined, evidence indicates that Ras/MAPK and/or PI3K/AKT/mTOR signaling pathway activations are common in both species and may represent targets for therapeutic intervention. Through this research, the mTORC1/2 inhibitor sapanisertib was selected from a PI3K/mTOR inhibitor library to collaborate with MEK inhibition; the latter preclinical efficacy was demonstrated previously for canine mucosal melanoma. Combined inhibition of MEK and mTORC1/2, using trametinib and sapanisertib, produced apoptosis and cell-cycle alteration, synergistically reducing cell survival in canine mucosal melanoma cell lines with varying basal signaling activation levels. Compared with individual inhibitors, a staggered sapanisertib dose, coupled with daily trametinib, was optimal for limiting primary mucosal melanoma xenograft growth in mice, and tumor dissemination in a metastasis model, while minimizing hematologic and renal side effects. Inhibitors down-modulated respective signaling targets and the combination additionally suppressed pathway reciprocal crosstalk. The combination did not significantly change plasma sapanisertib pharmacokinetics; however, trametinib area under the curve following a single dose was increased in the presence of sapanisertib. Targeting Ras/MAPK and PI3K/ AKT/mTOR signal transduction pathways appear rational therapies for canine and human mucosal melanoma.