Pancreatic ductal adenocarcinoma (PDA) is characterized by aggressive local invasion and metastatic spread, leading to high lethality. Although driver gene mutations during PDA progression are conserved, no specific mutation is correlated with the dissemination of metastases1-3. Here we analysed RNA splicing data of a large cohort of primary and metastatic PDA tumours to identify differentially spliced events that correlate with PDA progression. De novo motif analysis of these events detected enrichment of motifs with high similarity to the RBFOX2 motif. Overexpression of RBFOX2 in a patient-derived xenograft (PDX) metastatic PDA cell line drastically reduced the metastatic potential of these cells in vitro and in vivo, whereas depletion of RBFOX2 in primary pancreatic tumour cell lines increased the metastatic potential of these cells. These findings support the role of RBFOX2 as a potent metastatic suppressor in PDA. RNA-sequencing and splicing analysis of RBFOX2 target genes revealed enrichment of genes in the RHO GTPase pathways, suggesting a role of RBFOX2 splicing activity in cytoskeletal organization and focal adhesion formation. Modulation of RBFOX2-regulated splicing events, such as via myosin phosphatase RHO-interacting protein (MPRIP), is associated with PDA metastases, altered cytoskeletal organization and the induction of focal adhesion formation. Our results implicate the splicing-regulatory function of RBFOX2 as a tumour suppressor in PDA and suggest a therapeutic approach for metastatic PDA.

Omics analyses often result in dozens to hundreds of potential targets, requiring validation for their biological relevance. Current high-throughput functional investigation methods are frequently labor-intensive, expensive, and display low reproducibility. The Immune Co-Culture Cell Microarray (ICCM) is a formalin-fixed paraffin-embedded cell block microarray based on co-cultures of patient-derived tumor-infiltrating lymphocytes and their autologous melanoma cells. Each ICCM slide represents the same experiment and can be stained using standard immunohistochemistry and immunofluorescence techniques. Functional dynamics assessment of both proteins and microRNAs using ICCM stained slides demonstrated similar findings to flow cytometry assays and to previously published patient-derived biopsy reports.

Multiple studies have identified metabolic changes within the tumor and its microenvironment during carcinogenesis. Yet, the mechanisms by which tumors affect the host metabolism are unclear. We find that systemic inflammation induced by cancer leads to liver infiltration of myeloid cells at early extrahepatic carcinogenesis. The infiltrating immune cells via IL6-pSTAT3 immune-hepatocyte cross-talk cause the depletion of a master metabolic regulator, HNF4α, consequently leading to systemic metabolic changes that promote breast and pancreatic cancer proliferation and a worse outcome. Preserving HNF4α levels maintains liver metabolism and restricts carcinogenesis. Standard liver biochemical tests can identify early metabolic changes and predict patients' outcomes and weight loss. Thus, the tumor induces early metabolic changes in its macroenvironment with diagnostic and potentially therapeutic implications for the host.

While synthetic lethality (SL) holds promise in developing effective cancer therapies, SL candidates found via experimental screens often have limited translational value. Here we present a data-driven approach, ISLE (identification of clinically relevant synthetic lethality), that mines TCGA cohort to identify the most likely clinically relevant SL interactions (cSLi) from a given candidate set of lab-screened SLi. We first validate ISLE via a benchmark of large-scale drug response screens and by predicting drug efficacy in mouse xenograft models. We then experimentally test a select set of predicted cSLi via new screening experiments, validating their predicted context-specific sensitivity in hypoxic vs normoxic conditions and demonstrating cSLi's utility in predicting synergistic drug combinations. We show that cSLi can successfully predict patients' drug treatment response and provide patient stratification signatures. ISLE thus complements existing actionable mutation-based methods for precision cancer therapy, offering an opportunity to expand its scope to the whole genome.

Yeast libraries revolutionized the systematic study of cell biology. To extensively increase the number of such libraries, we used our previously devised SWAp-Tag (SWAT) approach to construct a genome-wide library of ~5,500 strains carrying the SWAT NOP1promoter-GFP module at the N terminus of proteins. In addition, we created six diverse libraries that restored the native regulation, created an overexpression library with a Cherry tag, or enabled protein complementation assays from two fragments of an enzyme or fluorophore. We developed methods utilizing these SWAT collections to systematically characterize the yeast proteome for protein abundance, localization, topology, and interactions.

The yeast Saccharomyces cerevisiae is ideal for systematic studies relying on collections of modified strains (libraries). Despite the significance of yeast libraries and the immense variety of available tags and regulatory elements, only a few such libraries exist, as their construction is extremely expensive and laborious. To overcome these limitations, we developed a SWAp-Tag (SWAT) method that enables one parental library to be modified easily and efficiently to give rise to an endless variety of libraries of choice. To showcase the versatility of the SWAT approach, we constructed and investigated a library of ∼1,800 strains carrying SWAT-GFP modules at the amino termini of endomembrane proteins and then used it to create two new libraries (mCherry and seamless GFP). Our work demonstrates how the SWAT method allows fast and effortless creation of yeast libraries, opening the door to new ways of systematically studying cell biology.

Pancreatic ductal adenocarcinoma has limited treatment options. There is an urgent need for developing appropriate pre-clinical models recapitulating metastatic disease, the most common clinical scenario at presentation. Ascites accumulation occurs in up to 20-30% of patients with pancreatic cancer; this milieu represents a highly cellular research resource of metastatic peritoneal spread. In this study, we utilized pancreatic ascites/pleural effusion cancer cells to establish patient derived xenografts.Ascites/pleural effusion-patient derived xenografts were established from twelve independent cases. Xenografts were serially passed in nude mice and tissue bio-specimen banking has been established. Histopathology of emergent tumors demonstrates poorly to moderately differentiated, glandular and mucin producing tumors, mirroring morphology of primary pancreatic cancer tumors. Whole genome sequencing of six patient derived xenografts samples demonstrates common mutations and structural variations similar to those reported in primary pancreatic cancer. Xenograft tumors were dissociated to single-cells and in-vitro drug sensitivity screen assays demonstrated chemo-resistance, correlating with patient clinical scenarios, thus serving as a platform for clinically relevant translational research.Therefore, establishment of this novel ascites/pleural effusion patient derived xenograft model, with extensive histopathology and genomic characterization, opens an opportunity for the study of advanced aggressive pancreatic cancer. Characterization of metastatic disease and mechanisms of resistance to therapeutics may lead to the development of novel drug combinations.

Advances in artificial intelligence have paved the way for leveraging hematoxylin and eosin (H&E)-stained tumor slides for precision oncology. We present ENLIGHT-DeepPT, an approach for predicting response to multiple targeted and immunotherapies from H&E-slides. In difference from existing approaches that aim to predict treatment response directly from the slides, ENLIGHT-DeepPT is an indirect two-step approach consisting of (1) DeepPT, a new deep-learning framework that predicts genome-wide tumor mRNA expression from slides, and (2) ENLIGHT, which predicts response based on the DeepPT inferred expression values. DeepPT successfully predicts transcriptomics in all 16 TCGA cohorts tested and generalizes well to two independent datasets. Our key contribution is showing that ENLIGHT-DeepPT successfully predicts true responders in five independent patients' cohorts involving four different treatments spanning six cancer types with an overall odds ratio of 2.44, increasing the baseline response rate by 43.47% among predicted responders, without the need for any treatment data for training. Furthermore, its prediction accuracy on these datasets is comparable to a supervised approach predicting the response directly from the images, which needs to be trained and tested on the same cohort. ENLIGHT-DeepPT future application could provide clinicians with rapid treatment recommendations to an array of different therapies and importantly, may contribute to advancing precision oncology in developing countries.

Germline BRCA-associated pancreatic ductal adenocarcinoma (glBRCA PDAC) tumors are susceptible to platinum and PARP inhibition. The clinical outcomes of 125 patients with glBRCA PDAC were stratified based on the spectrum of response to platinum/PARP inhibition: (i) refractory [overall survival (OS) <6 months], (ii) durable response followed by acquired resistance (OS <36 months), and (iii) long-term responders (OS >36 months). Patient-derived xenografts (PDX) were generated from 25 patients with glBRCA PDAC at different clinical time points. Response to platinum/PARP inhibition in vivo and ex vivo culture (EVOC) correlated with clinical response. We deciphered the mechanisms of resistance in glBRCA PDAC and identified homologous recombination (HR) proficiency and secondary mutations restoring partial functionality as the most dominant resistant mechanism. Yet, a subset of HR-deficient (HRD) patients demonstrated clinical resistance. Their tumors displayed basal-like molecular subtype and were more aneuploid. Tumor mutational burden was high in HRD PDAC and significantly higher in tumors with secondary mutations. Anti-PD-1 attenuated tumor growth in a novel humanized glBRCA PDAC PDX model. This work demonstrates the utility of preclinical models, including EVOC, to predict the response of glBRCA PDAC to treatment, which has the potential to inform time-sensitive medical decisions.