Ovarian Cells Have Increased Proliferation in Response to Heparin-Binding Epidermal Growth Factor as Collagen Density Increases.

It is well known that during ovarian cancer progression, the omentum transforms from a thin lacy organ to a thick tougher tissue. However, the mechanisms regulating this transformation and the implications of the altered microenvironment on ovarian cancer progression remain unclear. To address these questions, the global and local concentrations of collagen I were determined for normal and metastatic human omentum. Collagen I was increased 5.3-fold in omenta from ovarian cancer patients and localized to areas of activated fibroblasts rather than regions with a high density of cancer cells. Transforming growth factor beta 1 (TGFß1) was detected in ascites from ovarian cancer patients (4 ng/mL), suggesting a potential role for TGFß1 in the observed increase in collagen. Treatment with TGFß1 induced fibroblast activation, proliferation, and collagen deposition in mouse omental explants and an in vitro model with human omental fibroblasts. Finally, the impact of increased collagen I on ovarian cancer cells was determined by examining proliferation on collagen I gels formulated to mimic normal and cancerous omenta. While collagen density alone had no impact on proliferation, a synergistic effect was observed with collagen density and heparin-binding epidermal growth factor treatment. These results suggest that TGFß1 induces collagen deposition from the resident fibroblasts in the omentum and that this altered microenvironment impacts cancer cell response to growth factors found in ascites. Impact statement Using quantitative analysis of patient samples, in vitro models of the metastatic ovarian cancer microenvironment were designed with pathologically relevant collagen densities and growth factor concentrations. Studies in these models support a mechanism where transforming growth factor ß1 in the ascites fluid induces omental fibroblast proliferation, activation, and deposition of collagen I, which then impacts tumor cell proliferation in response to additional ascites growth factors such as heparin-binding epidermal growth factor. This approach can be used to dissect mechanisms involved in microenvironmental modeling in multiple disease applications.

Tumor cell sensitivity to vemurafenib can be predicted from protein expression in a BRAF-V600E basket trial setting.

BACKGROUND: Genetics-based basket trials have emerged to test targeted therapeutics across multiple cancer types. However, while vemurafenib is FDA-approved for BRAF-V600E melanomas, the non-melanoma basket trial was unsuccessful, suggesting mutation status is insufficient to predict response. We hypothesized that proteomic data would complement mutation status to identify vemurafenib-sensitive tumors and effective co-treatments for BRAF-V600E tumors with inherent resistance. METHODS: Reverse Phase Proteomic Array (RPPA, MD Anderson Cell Lines Project), RNAseq (Cancer Cell Line Encyclopedia) and vemurafenib sensitivity (Cancer Therapeutic Response Portal) data for BRAF-V600E cancer cell lines were curated. Linear and nonlinear regression models using RPPA protein or RNAseq were evaluated and compared based on their ability to predict BRAF-V600E cell line sensitivity (area under the dose response curve). Accuracies of all models were evaluated using hold-out testing. CausalPath software was used to identify protein-protein interaction networks that could explain differential protein expression in resistant cells. Human examination of features employed by the model, the identified protein interaction networks, and model simulation suggested anti-ErbB co-therapy would counter intrinsic resistance to vemurafenib. To validate this potential co-therapy, cell lines were treated with vemurafenib and dacomitinib (a pan-ErbB inhibitor) and the number of viable cells was measured. RESULTS: Orthogonal partial least squares (O-PLS) predicted vemurafenib sensitivity with greater accuracy in both melanoma and non-melanoma BRAF-V600E cell lines than other leading machine learning methods, specifically Random Forests, Support Vector Regression (linear and quadratic kernels) and LASSO-penalized regression. Additionally, use of transcriptomic in place of proteomic data weakened model performance. Model analysis revealed that resistant lines had elevated expression and activation of ErbB receptors, suggesting ErbB inhibition could improve vemurafenib response. As predicted, experimental evaluation of vemurafenib plus dacomitinb demonstrated improved efficacy relative to monotherapies. CONCLUSIONS: Combined, our results support that inclusion of proteomics can predict drug response and identify co-therapies in a basket setting.