Silvia G. Herrera Loeza

Circulating tumor cells (CTCs) are cells shed from solid tumors into circulation and have been shown to be prognostic in the setting of metastatic disease. These cells are obtained through a routine blood draw and may serve as an easily accessible marker for monitoring treatment effectiveness. Because of the rapid progression of pancreatic ductal adenocarcinoma (PDAC), early insight into treatment effectiveness may allow for necessary and timely changes in treatment regimens. The objective of this study was to evaluate CTC burden as a biomarker of response to treatment with a oral phosphatidylinositol-3-kinase inhibitor, BKM120, in patient-derived xenograft (PDX) mouse models of PDAC. PDX mice were randomized to receive vehicle or BKM120 treatment for 28 days and CTCs were enumerated from whole blood before and after treatment using a microfluidic chip that selected for EpCAM (epithelial cell adhesion molecule) positive cells. This microfluidic device allowed for the release of captured CTCs and enumeration of these cells via their electrical impedance signatures. Median CTC counts significantly decreased in the BKM120 group from pre- to post-treatment (26.61 to 2.21 CTCs/250 µL, p = 0.0207) while no significant change was observed in the vehicle group (23.26 to 11.89 CTCs/250 µL, p = 0.8081). This reduction in CTC burden in the treatment group correlated with tumor growth inhibition indicating CTC burden is a promising biomarker of response to treatment in preclinical models. Mutant enriched sequencing of isolated CTCs confirmed that they harbored KRAS G12V mutations, identical to the matched tumors. In the long-term, PDX mice are a useful preclinical model for furthering our understanding of CTCs. Clinically, mutational analysis of CTCs and serial monitoring of CTC burden may be used as a minimally invasive approach to predict and monitor treatment response to guide therapeutic regimens.

Over 55,000 people in the United States are diagnosed with pancreatic ductal adenocarcinoma (PDAC) yearly, and fewer than 20% of these patients survive a year beyond diagnosis. Chemotherapies are considered or used in nearly every PDAC case, but there is limited understanding of the complex signaling responses underlying resistance to these common treatments. Here, we take an unbiased approach to study protein kinase network changes following chemotherapies in patient-derived xenograft (PDX) models of PDAC to facilitate design of rational drug combinations. Proteomics profiling following chemotherapy regimens reveals that activation of JNK-JUN signaling occurs after 5-fluorouracil plus leucovorin (5-FU + LEU) and FOLFOX (5-FU + LEU plus oxaliplatin [OX]), but not after OX alone or gemcitabine. Cell and tumor growth assays with the irreversible inhibitor JNK-IN-8 and genetic manipulations demonstrate that JNK and JUN each contribute to chemoresistance and cancer cell survival after FOLFOX. Active JNK1 and JUN are specifically implicated in these effects, and synergy with JNK-IN-8 is linked to FOLFOX-mediated JUN activation, cell cycle dysregulation, and DNA damage response. This study highlights the potential for JNK-IN-8 as a biological tool and potential combination therapy with FOLFOX in PDAC and reinforces the need to tailor treatment to functional characteristics of individual tumors.

One obstacle in the translation of advances in cancer research into the clinic is a deficiency of adequate preclinical models that recapitulate human disease. Patient-derived xenograft (PDX) models are established by engrafting patient tumor tissue into mice and are advantageous because they capture tumor heterogeneity. One concern with these models is that selective pressure could lead to mutational drift and thus be an inaccurate reflection of patient tumors. Therefore, we evaluated if mutational frequency in PDX models is reflective of patient populations and if crucial mutations are stable across passages. We examined KRAS and PIK3CA gene mutations from pancreatic ductal adenocarcinoma (PDAC) (n = 30) and colorectal cancer (CRC) (n = 37) PDXs for as many as eight passages. DNA was isolated from tumors and target sequences were amplified by polymerase chain reaction. KRAS codons 12/13 and PIK3CA codons 542/545/1047 were examined using pyrosequencing. Twenty-three of 30 (77%) PDAC PDXs had KRAS mutations and one of 30 (3%) had PIK3CA mutations. Fifteen of 37 (41%) CRC PDXs had KRAS mutations and three of 37 (8%) had PIK3CA mutations. Mutations were 100 per cent preserved across passages. We found that the frequency of KRAS (77%) and PIK3CA (3%) mutations in PDAC PDX was similar to frequencies in patient tumors (71 to 100% KRAS, 0 to 11% PIK3CA). Similarly, KRAS (41%) and PIK3CA (8%) mutations in CRC PDX closely paralleled patient tumors (35 to 51% KRAS, 12 to 21% PIK3CA). The accurate mirroring and stability of genetic changes in PDX models compared with patient tumors suggest that these models are good preclinical surrogates for patient tumors.

ABSTRACT 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 tumor 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. Using the models to predict new compounds with cell viability effects and not in the initial data set, we conducted a validation screen that confirmed the accuracy of the models. 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.