Transcriptional Profiling of Malignant Melanoma Reveals Novel and Potentially Targetable Gene Fusions.

Invasive melanoma is the deadliest type of skin cancer, with 101,110 expected cases to be diagnosed in 2021. Recurrent BRAF and NRAS mutations are well documented in melanoma. Biologic implications of gene fusions and the efficacy of therapeutically targeting them remains unknown. Retrospective review of patient samples that underwent next-generation sequencing of the exons of 592 cancer-relevant genes and whole transcriptome sequencing for the detection of gene fusion events and gene expression profiling. Expression of PDL1 and ERK1/2 was assessed by immunohistochemistry (IHC). There were 33 (2.6%) cases with oncogenic fusions (14 novel), involving BRAF, RAF1, PRKCA, TERT, AXL, and FGFR3. MAPK pathway-associated genes were over-expressed in BRAF and RAF1 fusion-positive tumors in absence of other driver alterations. Increased expression in tumors with PRKCA and TERT fusions was concurrent with MAPK pathway alterations. For a subset of samples with available tissue, increased phosphorylation of ERK1/2 was observed in BRAF, RAF1, and PRKCA fusion-positive tumors. Oncogenic gene fusions are associated with transcriptional activation of the MAPK pathway, suggesting they could be therapeutic targets with available inhibitors. Additional analyses to fully characterize the oncogenic effects of these fusions may support biomarker driven clinical trials.

Integrating Systems Biology and an Ex Vivo Human Tumor Model Elucidates PD-1 Blockade Response Dynamics.

Ex vivo human tumor models have emerged as promising, yet complex tools to study cancer immunotherapy response dynamics. Here, we present a strategy that integrates empirical data from an ex vivo human system with computational models to interpret the response dynamics of a clinically prescribed PD-1 inhibitor, nivolumab, in head and neck squamous cell carcinoma (HNSCC) biopsies (N = 50). Using biological assays, we show that drug-induced variance stratifies samples by T helper type 1 (Th1)-related pathways. We then built a systems biology network and mathematical framework of local and global sensitivity analyses to simulate and estimate antitumor phenotypes, which implicate a dynamic role for the induction of Th1-related cytokines and T cell proliferation patterns. Together, we describe a multi-disciplinary strategy to analyze and interpret the response dynamics of PD-1 blockade using heterogeneous ex vivo data and in silico simulations, which could provide researchers a powerful toolset to interrogate immune checkpoint inhibitors.

Predicting clinical response to anticancer drugs using an ex vivo platform that captures tumour heterogeneity.

Predicting clinical response to anticancer drugs remains a major challenge in cancer treatment. Emerging reports indicate that the tumour microenvironment and heterogeneity can limit the predictive power of current biomarker-guided strategies for chemotherapy. Here we report the engineering of personalized tumour ecosystems that contextually conserve the tumour heterogeneity, and phenocopy the tumour microenvironment using tumour explants maintained in defined tumour grade-matched matrix support and autologous patient serum. The functional response of tumour ecosystems, engineered from 109 patients, to anticancer drugs, together with the corresponding clinical outcomes, is used to train a machine learning algorithm; the learned model is then applied to predict the clinical response in an independent validation group of 55 patients, where we achieve 100% sensitivity in predictions while keeping specificity in a desired high range. The tumour ecosystem and algorithm, together termed the CANScript technology, can emerge as a powerful platform for enabling personalized medicine.