Tumors are mixtures of different compartments. While global gene expression analysis profiles the average expression of all compartments in a sample, identifying the specific contribution of each compartment remains a challenge. With the increasing recognition of the importance of non-neoplastic components, the ability to breakdown the gene expression contribution of each is critical. Here, we develop DECODER, an integrated framework which performs de novo deconvolution and single-sample compartment weight estimation. We use DECODER to deconvolve 33 TCGA tumor RNA-seq data sets and show that it may be applied to other data types including ATAC-seq. We demonstrate that it can be utilized to reproducibly estimate cellular compartment weights in pancreatic cancer that are clinically meaningful. Application of DECODER across cancer types advances the capability of identifying cellular compartments in an unknown sample and may have implications for identifying the tumor of origin for cancers of unknown primary.

Motivation: Single-cell RNA-sequencing (scRNA-seq) has enabled the molecular profiling of thousands to millions of cells simultaneously in biologically heterogenous samples. Currently, the common practice in scRNA-seq is to determine cell type labels through unsupervised clustering and the examination of cluster-specific genes. However, even small differences in analysis and parameter choosing can greatly alter clustering results and thus impose great influence on which cell types are identified. Existing methods largely focus on determining the optimal number of robust clusters, which can be problematic for identifying cells of extremely low abundance due to their subtle contributions toward overall patterns of gene expression. Results: Here, we present a carefully designed framework, SCISSORS, which accurately profiles subclusters within broad cluster(s) for the identification of rare cell types in scRNA-seq data. SCISSORS employs silhouette scoring for the estimation of heterogeneity of clusters and reveals rare cells in heterogenous clusters by a multi-step semi-supervised reclustering process. Additionally, SCISSORS provides a method for the identification of marker genes of high specificity to the cell type. SCISSORS is wrapped around the popular Seurat R package and can be easily integrated into existing Seurat pipelines. Availability and implementation: SCISSORS, including source code and vignettes, are freely available at https://github.com/jr-leary7/SCISSORS.

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.

Elevated levels of Mucin-16 (MUC16) in conjunction with a high expression of truncated O-glycans is implicated in playing crucial roles in the malignancy of pancreatic ductal adenocarcinoma (PDAC). However, the mechanisms by which such aberrant glycoforms present on MUC16 itself promote an increased disease burden in PDAC are yet to be elucidated. This study demonstrates that the CRISPR/Cas9-mediated genetic deletion of MUC16 in PDAC cells decreases tumor cell migration. We found that MUC16 enhances tumor malignancy by activating the integrin-linked kinase and focal adhesion kinase (ILK/FAK)-signaling axis. These findings are especially noteworthy in truncated O-glycan (Tn and STn antigen)-expressing PDAC cells. Activation of these oncogenic-signaling pathways resulted in part from interactions between MUC16 and integrin complexes (α4β1), which showed a stronger association with aberrant glycoforms of MUC16. Using a monoclonal antibody to functionally hinder MUC16 significantly reduced the migratory cascades in our model. Together, these findings suggest that truncated O-glycan containing MUC16 exacerbates malignancy in PDAC by activating FAK signaling through specific interactions with α4 and β1 integrin complexes on cancer cell membranes. Targeting these aberrant glycoforms of MUC16 can aid in the development of a novel platform to study and treat metastatic pancreatic cancer.

Effective therapies for pancreatic ductal adenocarcinoma (PDAC) have been largely elusive. In this study, we perform multiplexed kinase inhibitor bead mass spectrometry on 102 patient-derived xenografts derived from 14 unique primary PDACs to define the tumor-intrinsic kinome landscape. Our findings uncover three kinome subgroups making up two tumor-intrinsic kinome subtypes that we call kinotypes. The kinotypes show enrichment of different kinase classes and recapitulate previously described molecular subtypes, basal-like and classical. The kinotype characterizing basal-like tumors shows enrichment of receptor tyrosine kinases, whereas the kinotype characterizing classical tumors is enriched in understudied kinases involved in Wnt signaling and immune pathways. We validate our findings in two clinical trials and show that only patients with basal-like tumors derive significant benefit from EGFR inhibitors. Our results provide a comprehensive tumor-intrinsic kinome landscape of PDAC that strongly supports actionable kinotype-specific kinase targets and provides a roadmap for kinase inhibitor therapy in PDAC. Significance: We provide a comprehensive tumor-intrinsic kinome landscape that provides a roadmap for the use of kinase inhibitors in PDAC treatment approaches.

Single-cell RNA sequencing (scRNA-seq) studies have uncovered distinct cancer-associated fibroblast (CAF) populations. While useful as a biological framework, no studies have conclusively defined CAF subtypes with clinical significance. We define restraining (rest) and promoting (pro) CAFs in patient samples that are both prognostic and predictive of therapy response in multiple tumor types. We uncover distinct clinical and spatial interactions between pro- and restCAF subtypes and basal-like and classical tumor subtypes that support tumor-stroma crosstalk. Finally, we find striking differences in the immune contexture of pro- and restCAF tumors where restCAF-dominant tumors are more responsive to immune checkpoint inhibition and proCAF-dominant tumors are more responsive to myeloid inhibition in clinical trials. This work defines CAF subtypes that are clinically robust, prognostic, and predictive of immunotherapy response and provides a single-sample classifier, determination of pro- and restCAF subtypes (DeCAF), which is clinically actionable.