Tumor-intrinsic and Cancer-associated Fibroblast Subtypes Independently Predict Outcomes in Pancreatic Cancer.

OBJECTIVE: To assess the utility of tumor-intrinsic and cancer-associated fibroblast (CAF) subtypes of pancreatic ductal adenocarcinoma (PDAC) in predicting response to neoadjuvant therapy (NAT) and overall survival. BACKGROUND: PDAC remains a deadly disease with limited treatment options, and both the tumor as well as the microenvironment play an important role in pathogenesis. Gene expression-based tumor-intrinsic subtypes (classical and basal-like) have been shown to predict outcomes, but tumor microenvironment subtypes are still evolving. METHODS: RNA-sequencing was performed on 114 deidentified resected PDAC tumors. Clinical data were collected by retrospective chart review. Single sample classifiers (SSCs) were used to determine classical and basal-like subtypes as well as tumor-permissive permCAF and tumor-restraining restCAF subtypes. Survival was analyzed using log-rank test. RESULTS: Patients who received NAT had an increase in overall survival (OS), with median survival of 27.9 months compared to 20.1 months for those who did not receive NAT, but the difference did not reach statistical significance (HR 0.64, P=0.076). Either tumor-intrinsic or CAF subtypes alone were associated with OS regardless of NAT or no NAT, and patients with classical or restCAF subtype had the best outcomes. When evaluated together, patients with classical-restCAF subtype had the best OS and basal-permCAF the worst OS (P<0.0001). NAT patients with classical-restCAF subtype demonstrated the longest OS compared to the other groups (P=0.00041). CONCLUSIONS: CAF subtypes have an additive effect over tumor-intrinsic subtypes in predicting survival with or without neoadjuvant FOLFIRINOX in PDAC. Molecular subtyping of both tumor and CAF compartments of PDAC may be important steps in selecting first-line systemic therapy.

Efficient computation of high-dimensional penalized generalized linear mixed models by latent factor modeling of the random effects.

Modern biomedical datasets are increasingly high-dimensional and exhibit complex correlation structures. Generalized linear mixed models (GLMMs) have long been employed to account for such dependencies. However, proper specification of the fixed and random effects in GLMMs is increasingly difficult in high dimensions, and computational complexity grows with increasing dimension of the random effects. We present a novel reformulation of the GLMM using a factor model decomposition of the random effects, enabling scalable computation of GLMMs in high dimensions by reducing the latent space from a large number of random effects to a smaller set of latent factors. We also extend our prior work to estimate model parameters using a modified Monte Carlo Expectation Conditional Minimization algorithm, allowing us to perform variable selection on both the fixed and random effects simultaneously. We show through simulation that through this factor model decomposition, our method can fit high-dimensional penalized GLMMs faster than comparable methods and more easily scale to larger dimensions not previously seen in existing approaches.

Myofibroblastic cancer-associated fibroblast subtype heterogeneity in pancreatic cancer.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) has a fibrotic stroma that has both tumor-promoting and tumor-restraining properties. Different types of cancer-associated fibroblasts (CAFs) have been described. Here, we investigated whether CAFs within the same subtype exhibit heterogeneous functions. METHODS: We evaluated the gene and protein expression differences in two myofibroblastic CAF (myCAF) lines using single-cell and bulk RNA-sequencing. We utilized proliferation and migration assays to determine the effect of different CAF lines on a tumor cell line. RESULTS: We found that myCAF lines express an activated stroma subtype gene signature, which is associated with a shorter survival in patients. Although both myCAF lines expressed α-smooth muscle actin (α-SMA), platelet-derived growth factor-α (PDGFR-α), fibroblast-activated protein (FAP), and vimentin, we observed heterogeneity between the two lines. Similarly, despite being consistent with myCAF gene expression overall, heterogeneity within specific genes was observed. We found that these differences extended to the functional level where the two myCAF lines had different effects on the same tumor cell line. The myCAF216 line, which had slightly increased inflammatory CAF-like gene expression and higher protein expression of α-SMA, PDGFR-α, and FAP was found to restrain migration of tumor cells. CONCLUSIONS: We found that two myCAF lines with globally similar expression characteristics had different effects on the same tumor cell line, one promoting and the other restraining migration. Our study highlights that there may be unappreciated heterogeneity within CAF subtypes. Further investigation and attention to specific genes or proteins that may drive this heterogeneity will be important.

Open-source curation of a pancreatic ductal adenocarcinoma gene expression analysis platform (pdacR) supports a two-subtype model.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease for which potent therapies have limited efficacy. Several studies have described the transcriptomic landscape of PDAC tumors to provide insight into potentially actionable gene expression signatures to improve patient outcomes. Despite centralization efforts from multiple organizations and increased transparency requirements from funding agencies and publishers, analysis of public PDAC data remains difficult. Bioinformatic pitfalls litter public transcriptomic data, such as subtle inclusion of low-purity and non-adenocarcinoma cases. These pitfalls can introduce non-specificity to gene signatures without appropriate data curation, which can negatively impact findings. To reduce barriers to analysis, we have created pdacR ( http://pdacR.bmi.stonybrook.edu , github.com/rmoffitt/pdacR), an open-source software package and web-tool with annotated datasets from landmark studies and an interface for user-friendly analysis in clustering, differential expression, survival, and dimensionality reduction. Using this tool, we present a multi-dataset analysis of PDAC transcriptomics that confirms the basal-like/classical model over alternatives.

CRISPR/Cas9/AAV9-mediated in vivo editing identifies MYC regulation of 3D genome in skeletal muscle stem cell.

Skeletal muscle satellite cells (SCs) are stem cells responsible for muscle development and regeneration. Although CRISPR/Cas9 has been widely used, its application in endogenous SCs remains elusive. Here, we generate mice expressing Cas9 in SCs and achieve robust editing in juvenile SCs at the postnatal stage through AAV9-mediated short guide RNA (sgRNA) delivery. Additionally, we reveal that quiescent SCs are resistant to CRISPR/Cas9-mediated editing. As a proof of concept, we demonstrate efficient editing of master transcription factor (TF) Myod1 locus using the CRISPR/Cas9/AAV9-sgRNA system in juvenile SCs. Application on two key TFs, MYC and BCL6, unveils distinct functions in SC activation and muscle regeneration. Particularly, we reveal that MYC orchestrates SC activation through regulating 3D genome architecture. Its depletion results in strengthening of the topologically associating domain boundaries thus may affect gene expression. Altogether, our study establishes a platform for editing endogenous SCs that can be harnessed to elucidate the functionality of key regulators governing SC activities.

Single-cell transcriptome analysis defines heterogeneity of the murine pancreatic ductal tree.

To study disease development, an inventory of an organ's cell types and understanding of physiologic function is paramount. Here, we performed single-cell RNA-sequencing to examine heterogeneity of murine pancreatic duct cells, pancreatobiliary cells, and intrapancreatic bile duct cells. We describe an epithelial-mesenchymal transitory axis in our three pancreatic duct subpopulations and identify osteopontin as a regulator of this fate decision as well as human duct cell dedifferentiation. Our results further identify functional heterogeneity within pancreatic duct subpopulations by elucidating a role for geminin in accumulation of DNA damage in the setting of chronic pancreatitis. Our findings implicate diverse functional roles for subpopulations of pancreatic duct cells in maintenance of duct cell identity and disease progression and establish a comprehensive road map of murine pancreatic duct cell, pancreatobiliary cell, and intrapancreatic bile duct cell homeostasis.

Irreversible JNK1-JUN inhibition by JNK-IN-8 sensitizes pancreatic cancer to 5-FU/FOLFOX chemotherapy.

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.

Purity Independent Subtyping of Tumors (PurIST), A Clinically Robust, Single-sample Classifier for Tumor Subtyping in Pancreatic Cancer.

PURPOSE: Molecular subtyping for pancreatic cancer has made substantial progress in recent years, facilitating the optimization of existing therapeutic approaches to improve clinical outcomes in pancreatic cancer. With advances in treatment combinations and choices, it is becoming increasingly important to determine ways to place patients on the best therapies upfront. Although various molecular subtyping systems for pancreatic cancer have been proposed, consensus regarding proposed subtypes, as well as their relative clinical utility, remains largely unknown and presents a natural barrier to wider clinical adoption. EXPERIMENTAL DESIGN: We assess three major subtype classification schemas in the context of results from two clinical trials and by meta-analysis of publicly available expression data to assess statistical criteria of subtype robustness and overall clinical relevance. We then developed a single-sample classifier (SSC) using penalized logistic regression based on the most robust and replicable schema. RESULTS: We demonstrate that a tumor-intrinsic two-subtype schema is most robust, replicable, and clinically relevant. We developed Purity Independent Subtyping of Tumors (PurIST), a SSC with robust and highly replicable performance on a wide range of platforms and sample types. We show that PurIST subtypes have meaningful associations with patient prognosis and have significant implications for treatment response to FOLIFIRNOX. CONCLUSIONS: The flexibility and utility of PurIST on low-input samples such as tumor biopsies allows it to be used at the time of diagnosis to facilitate the choice of effective therapies for patients with pancreatic ductal adenocarcinoma and should be considered in the context of future clinical trials.

MyoD- and FoxO3-mediated hotspot interaction orchestrates super-enhancer activity during myogenic differentiation.

Super-enhancers (SEs) are cis-regulatory elements enriching lineage specific key transcription factors (TFs) to form hotspots. A paucity of identification and functional dissection promoted us to investigate SEs during myoblast differentiation. ChIP-seq analysis of histone marks leads to the uncovering of SEs which remodel progressively during the course of differentiation. Further analyses of TF ChIP-seq enable the definition of SE hotspots co-bound by the master TF, MyoD and other TFs, among which we perform in-depth dissection for MyoD/FoxO3 interaction in driving the hotspots formation and SE activation. Furthermore, using Myogenin as a model locus, we elucidate the hierarchical and complex interactions among hotspots during the differentiation, demonstrating SE function is propelled by the physical and functional cooperation among hotspots. Finally, we show MyoD and FoxO3 are key in orchestrating the Myogenin hotspots interaction and activation. Altogether our results identify muscle-specific SEs and provide mechanistic insights into the functionality of SE.