Kinome state is predictive of cell viability in pancreatic cancer tumor and stroma cell lines

Numerous aspects of cellular signaling are regulated by the kinome - the network of over 500 protein kinases that guides and modulates information transfer throughout the cell. The key role played by both individual kinases and assemblies of kinases organized into functional subnetworks leads to kinome dysregulation being a key driver of many diseases, particularly cancer. In the case of pancreatic ductal adenocarcinoma (PDAC), a variety of kinases and associated signaling pathways have been identified for their key role in the establishment of disease as well as its progression. However, the identification of additional relevant therapeutic targets has been slow and is further confounded by interactions between the tumor and the surrounding tissue microenvironment. Fundamentally, it is an open question as to the degree to which knowledge of the state of the kinome at the protein level is able to provide insight into the downstream phenotype of the cell. In this work, we attempt to link the state of the kinome, or kinotype, with cell viability in representative PDAC tumor and stroma cell lines. Through the application of both regression and classification models to independent kinome perturbation and kinase inhibitor cell screen data, we find that the inferred kinotype of a cell has a significant and predictive relationship with cell viability. While regression models perform poorly, we find that classification approaches are able to predict drug viability effects. We further find that models are able to identify a set of kinases whose behavior in response to perturbation drive the majority of viability responses in these cell lines. These results suggest that characterizing the state of the protein kinome provides significant opportunity for better understanding signaling behavior and downstream cell phenotypes, as well as providing insight into the broader design of potential therapy design for PDAC.

MEKK1 Regulates Chemokine Expression in Mammary Fibroblasts: Implications for the Breast Tumor Microenvironment

Breast tumors contain both transformed epithelial cells and non-transformed stroma cells producing secreted factors that can promote metastasis. Previously, we demonstrated that the kinase MEKK1 regulates cell migration and gene expression, and that transgene-induced breast tumor metastasis is markedly inhibited in MEKK1-deficient mice. In this report, we examined the role of MEKK1 in stroma cell gene expression and the consequent effect on breast tumor cell function. Using a heterotypic cell system to quantify the effect of stroma cells on breast tumor cell function, we discovered that MEKK1−/− fibroblasts are significantly less effective at inducing tumor cell invasion than MEKK1+/+ fibroblasts. Expression array analysis revealed that both baseline and tumor cell-induced expression of the chemokines CCL3, CCL4, and CCL5 were markedly reduced in MEKK1−/− mammary fibroblasts. By focusing on the role of MEKK1 in CCL5 regulation, we discovered that MEKK1 kinase activity promotes CCL5 expression, and inactive mutant MEKK1 strongly inhibits CCL5 transcription. CCL5 and the other MEKK1-dependent chemokines are ligands for the GPCR CCR5, and we show that the CCR5 antagonist Maraviroc strongly inhibits fibroblast-induced tumor cell migration. Finally, we report that fibroblast growth factor 5 (FGF-5) is secreted by MDA-MB 231 cells, that FGF-5 activates MEKK1 effectors ERK1/2 and NFκB in fibroblasts, and that chemical inhibition of NFκB inhibits CCL5 expression. Our results suggest that MEKK1 contributes to the formation of a breast tumor microenvironment that supports metastasis by promoting expression of stroma cell chemokine genes in response to tumor cell-induced paracrine signaling.

Potential Roles of Tumor Cell- and Stroma Cell-Derived Small Extracellular Vesicles in Promoting a Pro-Angiogenic Tumor Microenvironment

Extracellular vesicles (EVs) are produced and released by all cells and are present in all body fluids. They exist in a variety of sizes, however, small extracellular vesicles (sEVs), the EV subset with a size range from 30 to 150 nm, are of current interest. They are characterized by a distinct biogenesis and complex cargo composition, which reflects the cytosolic contents and cell-surface molecules of the parent cells. This cargo consists of proteins, nucleic acids, and lipids and is competent in inducing signaling cascades in recipient cells after surface interactions or in initiating the generation of a functional protein by delivering nucleic acids. Based on these characteristics, sEVs are now considered as important mediators of intercellular communication. One hallmark of sEVs is the promotion of angiogenesis. It was shown that sEVs interact with endothelial cells (ECs) and promote an angiogenic phenotype, ultimately leading to increased vascularization of solid tumors and disease progression. It was also shown that sEVs reprogram cells in the tumor microenvironment (TME) and act in a functionally cooperative fashion to promote angiogenesis by a paracrine mechanism involving the differential expression and secretion of angiogenic factors from other cell types. In this review, we will focus on the distinct functions of tumor-cell-derived sEVs (TEX) in promotion of angiogenesis and describe their potential as a therapeutic target for anti-angiogenic therapies. Also, we will focus on non-cancer stroma-cell-derived small extracellular vesicles and their potential role in stimulating a pro-angiogenic TME.

Characterization of the tumor microenvironment landscape and deep learning-guided prediction of prognosis in lung adenocarcinoma with bulk RNA sequencing data.

e21025 Background: The tumor microenvironment (TME) plays an important role in tumor progression and treatment response, therefore profoundly affecting patient outcomes. Efforts to characterize the TME in lung adenocarcinoma are emerging but have been limited by the sample size and lack of treatment timepoints. Methods: We characterized changes in lung TME using the xCell algorithm to distinguish 64 immune and stroma cell types from bulk RNA sequencing data. The correlation between subtype cell population in lung TME and various clinical and biological characteristics was analyzed from over 500 lung adenocarcinoma (LUAD) samples from The Cancer Genome Atlas and an independent cohort of 48 advanced LUAD patients with treatment annotations (treatment-naive, residual disease, and progressive disease). In addition, we used key features in lung TME to predict prognosis using deep learning algorithms. Results: We found significant changes in both immune and stroma cell populations according to various clinical parameters such as smoking history, cancer stage, and treatment status. Specific sub-populations within lung TME correlate with survival outcomes based on Kaplan-Meier survival analyses. CD4- and CD8-positive T-cells are enriched in early stage disease and depleted in late stage disease, suggesting evolution of the TME during cancer progression. Consistent with previous reports, scores of immune cell populations associated with worse survival, such as T helper type 2 cells, are increased in late stage disease. Smoking history also reshapes the lung TME as populations correlated with better survival are decreased in smokers. We also found variations in sub-populations according to the driver oncogenes, with a less abundant lymphoid compartment in EGFR mutant samples compared to KRAS driven samples. Interestingly, we found higher scores of macrophage populations in residual disease following targeted-therapy treatment compared to pre-treatment. Finally, using machine and deep learning methods we identified a panel of 12 key features within the lung TME which could be used to predict prognosis. Conclusions: We comprehensively characterized immune and stroma cell type changes in the lung TME utilizing bulk RNA-seq data, and evaluated the association of sub-type cell populations with different clinical and biological features. Key features in lung TME could be used to predict prognosis.

The Influence of the Bone Marrow Niche on Drug Response Phenotypes of Blood Cancers

Background: Signals provided by the microenvironment can modify and circumvent pathway activities that are therapeutically targeted by drugs. However, a systems-level understanding of how the microenvironment and the genetic and molecular alterations of the tumor interact with each other and contribute to drug resistance is lacking. Methods: To address this unmet need, we established an automated microscopy-based phenotyping platform that uses co-culture conditions mimicking the bone marrow environment. We cultured primary tumor cells from more than 100 leukemia patients (CLL, AML, MCL, T-PLL, HCL) with and without bone marrow stroma cell support in DMEM and 10% human serum and treated each condition with 57 drugs in 3 concentrations. After 72h of incubation, 22 000 images per patient were acquired and processed by our custom made image analysis pipeline. Our set-up allows us to increase sensitivity far beyond simple viability testing, as it reads out additional cell type specific features such as cell morphology, autophagy and cell-cell interactions. Results: Quality assessment revealed that in contrast to mono-culture conditions, assay plate edge effects can be avoided under stable stroma cell co-culture conditions. Correlation of replicated patient samples were comparable between mono- and co-cultures (R2>0.75). In the absence of their native microenvironment, primary leukemia cells undergo spontaneous apoptosis ex-vivo. Viability at culture start was always >90% and dropped to a median of 51% (viability range: 17%-90%) after 72h in mono-cultures. Among CLL samples spontaneous apoptosis was not dependent on either IGHV mutation status or any major cytogenetic risk group. Bone marrow stroma cell co-culture conditions protected tumor cells from spontaneous apoptosis (p=8.2e-6, paired t-test). Patient samples with a high degree of spontaneous apoptosis benefited most from co-culture conditions (p=7.2e-10, Pearson correlation). To model interactions of stroma cell conditions and drug-induced apoptosis we established the following linear model: Viability ~ drug-effect + culture-model + drug-effect:culture-model. While activity of some drugs was significantly altered under co-culture conditions, we could also identify drugs with similar activity in mono- and co-cultures. For instance, the activity of common chemotherapeutics (fludarabine: p=0.002 at 0.6µM, cytarabine: p=0.001 at 1.5µM, ANOVA) or bromodomain inhibitors (I-BET-762: p=5.9e-5 at 4.5µM, JQ1: p=1.5e-8 at 1.5µM, ANOVA) was significantly reduced under co-culture conditions. In contrast, PI3K inhibitors idelalisib and duvelisib had a similar activity in mono-culture and stroma co-culture conditions and might represent a starting point to overcome stroma cell mediated drug resistance. In CLL, we identified IGHV mutation status and trisomy 12 as important determinants of response to kinase inhibitors. We confirmed these findings in stroma cell co-cultures, e.g. a better activity of B-cell receptor inhibitors in trisomy 12 and IGHV unmutated CLL. A systematic comparison of ex-vivo drug response pattern in mono- and co-cultures across 171 drug conditions will be presented. Conclusion: Our results suggest that high throughput co-culture drug testing can be robustly performed and provide an unprecedented understanding of how the stroma cell microenvironment and the genetic make-up of tumor cells contribute to drug resistance and sensitivity. Figure: Over 2 million microscopy images were acquired and analysed to assess drug resistance and sensitivity in a co-culture model of primary leukemia and bone marrow stroma cells. blue= Hoechst33342, green=Calcein AM, red=lysosomal dye NIR Figure 1. Figure 1. Disclosures No relevant conflicts of interest to declare.