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Development of candidate cancer treatments is a resource-intensive process, with the research community continuing to investigate options beyond static genomic characterization. Toward this goal, we have established the genomic landscapes of 536 patient-derived xenograft (PDX) models across 25 cancer types, together with mutation, copy number, fusion, transcriptomic profiles, and NCI-MATCH arms. Compared with human tumors, PDXs typically have higher purity and fit to investigate dynamic driver events and molecular properties via multiple time points from same case PDXs. Here, we report on dynamic genomic landscapes and pharmacogenomic associations, including associations between activating oncogenic events and drugs, correlations between whole-genome duplications and subclone events, and the potential PDX models for NCI-MATCH trials. Lastly, we provide a web portal having comprehensive pan-cancer PDX genomic profiles and source code to facilitate identification of more druggable events and further insights into PDXs’ recapitulation of human tumors., Patient-derived xenograft models (PDX) have been extensively used to study the molecular and clinical features of cancers. Here the authors present a cohort of 536 PDX models from 25 cancers, as well as their genomic and evolutionary profiles and their suitability for clinical trials.

Current treatment modalities for disseminated cutaneous malignant melanoma (CMM) improve survival, however disease progression commonly ensues. In a previous study we identified afatinib and crizotinib in combination as a novel potential therapy for CMM independent of BRAF/NRAS mutation status. Herein, we elucidate the underlying mechanisms of the combination treatment effect to find biomarkers and novel targets for development of therapy that may provide clinical benefit by proteomic analysis of CMM cell lines and xenografts using mass spectrometry based analysis and reverse phase protein array. Identified candidates were validated using immunoblotting or immunofluorescence. Our analysis revealed that mTOR/Insulin signaling pathways were significantly decreased by the afatinib and crizotinib combination treatment. Both in vitro and in vivo analyses showed that the combination treatment downregulated pRPS6KB1 and pRPS6, downstream of mTOR signaling, and IRS-1 in the insulin signaling pathway, specifically ablating IRS-1 nuclear signal. Silencing of RPS6 and IRS-1 alone had a similar effect on cell death, which was further induced when IRS-1 and RPS6 were concomitantly silenced in the CMM cell lines. Silencing of IRS-1 and RPS6 resulted in reduced sensitivity towards combination treatment. Additionally, we found that IRS-1 and RPS6KB1 expression levels were increased in advanced stages of CMM clinical samples. We could demonstrate that induced resistance towards combination treatment was reversible by a drug holiday. CD171/L1CAM, mTOR and PI3K-p85 were induced in the combination resistant cells whereas AXL and EPHA2, previously identified mediators of resistance to MAPK inhibitor therapy in CMM were downregulated. We also found that CD171/L1CAM and mTOR were increased at progression in tumor biopsies from two matched cases of patients receiving targeted therapy with BRAFi. Overall, these findings provide insights into the molecular mechanisms behind the afatinib and crizotinib combination treatment effect and leverages a platform for discovering novel biomarkers and therapy regimes for CMM treatment.

Metastatic melanoma is challenging to clinically address. Although standard-of-care targeted therapy has high response rates in patients with BRAF-mutant melanoma, therapy relapse occurs in most cases. Intrinsically resistant melanoma cells drive therapy resistance and display molecular and biologic properties akin to neural crest-like stem cells (NCLSC) including high invasiveness, plasticity, and self-renewal capacity. The shared transcriptional programs and vulnerabilities between NCLSCs and cancer cells remains poorly understood. Here, we identify a developmental LPAR1-axis critical for NCLSC viability and melanoma cell survival. LPAR1 activity increased during progression and following acquisition of therapeutic resistance. Notably, genetic inhibition of LPAR1 potentiated BRAFi ± MEKi efficacy and ablated melanoma migration and invasion. Our data define LPAR1 as a new therapeutic target in melanoma and highlights the promise of dissecting stem cell–like pathways hijacked by tumor cells. Significance: This study identifies an LPAR1-axis critical for melanoma invasion and intrinsic/acquired therapy resistance.

Autophagy mediates resistance to various anticancer agents. In melanoma, resistance to targeted therapy has been linked to expression of Wnt5A, an intrinsic inhibitor of β-catenin, which also promotes invasion. In this study, we assessed the interplay between Wnt5A and autophagy by combining expression studies in human clinical biopsies with functional analyses in cell lines and mouse models. Melanoma cells with high Wnt5A and low β-catenin displayed increased basal autophagy. Genetic blockade of autophagy revealed an unexpected feedback loop whereby knocking down the autophagy factor ATG5 in Wnt5Ahigh cells decreased Wnt5A and increased β-catenin. To define the physiologic relevance of this loop, melanoma cells with different Wnt status were treated in vitro and in vivo with the potent lysosomotropic compound Lys05. Wnt5Ahigh cells were less sensitive to Lys05 and could be reverted by inducing β-catenin activity. Our results suggest the efficacy of autophagy inhibitors might be improved by taking the Wnt signature of melanoma cells into account. Cancer Res; 77(21); 5873–85. ©2017 AACR.

Background One of major issues in clinical trials in oncology is their high failure rate, despite the fact that the trials were designed based on the data from successful equivalent preclinical studies. This is in part due to the intrinsic homogeneity of preclinical model systems and the contrasting heterogeneity of actual patient responses. Methods We present a mathematical model-driven framework, phase i (virtual/imaginary) trials, that integrates the heterogeneity of actual patient responses and preclinical studies through a cohort of virtual patients. The framework includes an experimentally calibrated mathematical model, a cohort of heterogeneous virtual patients, an assessment of stratification factors, and treatment optimisation. We show the detailed process through the lens of melanoma combination therapy (chemotherapy and an AKT inhibitor), using both preclinical and clinical data. Results The mathematical model predicts melanoma treatment response and resistance to mono and combination therapies and was calibrated and then validated with in vitro experimental data. The validated model and a genetic algorithm were used to generate virtual patients whose tumour volume responses to the combination therapy matched statistically the actual heterogeneous patient responses in the clinical trial. Analyses on simulated cohorts revealed key model parameters such as a tumour volume doubling rate and a therapy-induced phenotypic switch rate that may have clinical correlates. Finally, our approach predicts optimal AKT inhibitor scheduling suggesting more effective but less toxic treatment strategies. Conclusion Our proposed computational framework to implement phase i trials in cancer can readily capture observed heterogeneous clinical outcomes and predict patient survival. Importantly, phase i trials can be used to optimise future clinical trial design.