Microdissected pancreatic cancer proteomes reveal tumor heterogeneity and therapeutic targets.

Pancreatic ductal adenocarcinoma (PDAC) is characterized by a relative paucity of cancer cells that are surrounded by an abundance of nontumor cells and extracellular matrix, known as stroma. The interaction between stroma and cancer cells contributes to poor outcome, but how proteins from these individual compartments drive aggressive tumor behavior is not known. Here, we report the proteomic analysis of laser-capture microdissected (LCM) PDAC samples. We isolated stroma, tumor, and bulk samples from a cohort with long- and short-term survivors. Compartment-specific proteins were measured by mass spectrometry, yielding what we believe to be the largest PDAC proteome landscape to date. These analyses revealed that, in bulk analysis, tumor-derived proteins were typically masked and that LCM was required to reveal biology and prognostic markers. We validated tumor CALB2 and stromal COL11A1 expression as compartment-specific prognostic markers. We identified and functionally addressed the contributions of the tumor cell receptor EPHA2 to tumor cell viability and motility, underscoring the value of compartment-specific protein analysis in PDAC.

INKA, an integrative data analysis pipeline for phosphoproteomic inference of active kinases.

Identifying hyperactive kinases in cancer is crucial for individualized treatment with specific inhibitors. Kinase activity can be discerned from global protein phosphorylation profiles obtained with mass spectrometry-based phosphoproteomics. A major challenge is to relate such profiles to specific hyperactive kinases fueling growth/progression of individual tumors. Hitherto, the focus has been on phosphorylation of either kinases or their substrates. Here, we combined label-free kinase-centric and substrate-centric information in an Integrative Inferred Kinase Activity (INKA) analysis. This multipronged, stringent analysis enables ranking of kinase activity and visualization of kinase-substrate networks in a single biological sample. To demonstrate utility, we analyzed (i) cancer cell lines with known oncogenes, (ii) cell lines in a differential setting (wild-type versus mutant, +/- drug), (iii) pre- and on-treatment tumor needle biopsies, (iv) cancer cell panel with available drug sensitivity data, and (v) patient-derived tumor xenografts with INKA-guided drug selection and testing. These analyses show superior performance of INKA over its components and substrate-based single-sample tool KARP, and underscore target potential of high-ranking kinases, encouraging further exploration of INKA's functional and clinical value.

miRNA profiling for diagnosis, prognosis and stratification of cancer treatment in cholangiocarcinoma.

Cholangiocarcinoma (CCA) is a lethal malignancy originating from the biliary tract epithelium. Most patients are diagnosed at an advanced stage. Even after resection with curative intent, prognosis remains poor. Previous studies have reported the evolving role of miRNAs as novel biomarkers in cancer diagnosis, prognostication and chemotherapy response. Various miRNAs, such as miR-21, miR-26, miR-122 and miR-150, have been identified as possible blood-based biomarkers for noninvasive diagnosis of CCA. Moreover, epithelial-mesenchymal transition (EMT)- and angiogenesis-associated miRNAs have been implicated in tumor cell dissemination and are able to determine clinical outcome. In fact, miRNAs involved in cell survival might even determine chemotherapy response. This review provides an overview of known miRNAs as CCA-specific biomarkers.