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Summary Differential transcript usage (DTU) occurs when the relative expression of multiple transcripts arising from the same gene changes between different conditions. Existing approaches to detect DTU often rely on computational procedures that can have speed and scalability issues as the number of samples increases. Here we propose a new method, CompDTU, that uses compositional regression to model the relative abundance proportions of each transcript that are of interest in DTU analyses. This procedure leverages fast matrix-based computations that make it ideally suited for DTU analysis with larger sample sizes. This method also allows for the testing of and adjustment for multiple categorical or continuous covariates. Additionally, many existing approaches for DTU ignore quantification uncertainty in the expression estimates for each transcript in RNA-seq data. We extend our CompDTU method to incorporate quantification uncertainty leveraging common output from RNA-seq expression quantification tool in a novel method CompDTUme. Through several power analyses, we show that CompDTU has excellent sensitivity and reduces false positive results relative to existing methods. Additionally, CompDTUme results in further improvements in performance over CompDTU with sufficient sample size for genes with high levels of quantification uncertainty, while also maintaining favorable speed and scalability. We motivate our methods using data from the Cancer Genome Atlas Breast Invasive Carcinoma data set, specifically using RNA-seq data from primary tumors for 740 patients with breast cancer. We show greatly reduced computation time from our new methods as well as the ability to detect several novel genes with significant DTU across different breast cancer subtypes.

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Pancreatic ductal adenocarcinoma (PDA) is an aggressive malignancy characterized by a paucity of tumor-proximal CD8+ T cells and resistance to immunotherapeutic interventions. Cancer-associated mechanisms that elicit CD8+ T-cell exclusion and resistance to immunotherapy are not well-known. Here, using a Kras- and p53-driven model of PDA, we describe a mechanism of action for the protumorigenic cytokine IL35 through STAT3 activation in CD8+ T cells. Distinct from its action on CD4+ T cells, IL35 signaling in gp130+CD8+ T cells activated the transcription factor STAT3, which antagonized intratumoral infiltration and effector function of CD8+ T cells via suppression of CXCR3, CCR5, and IFNγ expression. Inhibition of STAT3 signaling in tumor-educated CD8+ T cells improved PDA growth control upon adoptive transfer to tumor-bearing mice. We showed that activation of STAT3 in CD8+ T cells was driven by B cell– but not regulatory T cell–specific production of IL35. We also demonstrated that B cell–specific deletion of IL35 facilitated CD8+ T-cell activation independently of effector or regulatory CD4+ T cells and was sufficient to phenocopy therapeutic anti-IL35 blockade in overcoming resistance to anti–PD-1 immunotherapy. Finally, we identified a circulating IL35+ B-cell subset in patients with PDA and demonstrated that the presence of IL35+ cells predicted increased occurrence of phosphorylated (p)Stat3+CXCR3−CD8+ T cells in tumors and inversely correlated with a cytotoxic T-cell signature in patients. Together, these data identified B cell–mediated IL35/gp130/STAT3 signaling as an important direct link to CD8+ T-cell exclusion and immunotherapy resistance in PDA.

File includes drug synergy scores for 1 cell line screened against OTX015

File includes AlphaScreening values for activity of the screening library against the BET or p300 bromodomains

File contains information on small molecules included in screening library

Supplemental figures, figure legends, table legends, methods, and references

File contains enrichment scores and p-values for gene sets evaluated using GSEA for RNA sequencing data sets

Table S4

Supplementary Methods

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.

Table S5

Supplementary Figures

Table S2

Table S1

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Table S3

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.

Although oncogenic driver mutations in RAS occur in 20% of cancers, heterogeneity in the biologic outputs of different RAS mutants has hampered efforts to develop effective treatments for RAS-mutated cancers. In this issue of Science Signaling, Huynh et al. show that even among KRAS(Q61) mutants, the specific amino acid that is substituted substantially affects mutant KRAS biologic activity and oncogenicity.

Missense mutations at the three hotspots in the guanosine triphosphatase (GTPase) RAS—Gly 12 , Gly 13 , and Gln 61 (commonly known as G12, G13, and Q61, respectively)—occur differentially among the three RAS isoforms. Q61 mutations in KRAS are infrequent and differ markedly in occurrence. Q61H is the predominant mutant (at 57%), followed by Q61R/L/K (collectively 40%), and Q61P and Q61E are the rarest (2 and 1%, respectively). Probability analysis suggested that mutational susceptibility to different DNA base changes cannot account for this distribution. Therefore, we investigated whether these frequencies might be explained by differences in the biochemical, structural, and biological properties of KRAS Q61 mutants. Expression of KRAS Q61 mutants in NIH 3T3 fibroblasts and RIE-1 epithelial cells caused various alterations in morphology, growth transformation, effector signaling, and metabolism. The relatively rare KRAS Q61E mutant stimulated actin stress fiber formation, a phenotype distinct from that of KRAS Q61H/R/L/P , which disrupted actin cytoskeletal organization. The crystal structure of KRAS Q61E was unexpectedly similar to that of wild-type KRAS, a potential basis for its weak oncogenicity. KRAS Q61H/L/R -mutant pancreatic ductal adenocarcinoma (PDAC) cell lines exhibited KRAS-dependent growth and, as observed with KRAS G12 -mutant PDAC, were susceptible to concurrent inhibition of ERK-MAPK signaling and of autophagy. Our results uncover phenotypic heterogeneity among KRAS Q61 mutants and support the potential utility of therapeutic strategies that target KRAS Q61 mutant–specific signaling and cellular output.

Background PIK3CA mutations have been described in 20-25% of early-stage HER2-positive breast tumors [1], and are associated with reduced pathologic complete response (pCR) rate after chemotherapy and anti-HER2 agents [2]. However, the independence of this finding and association with long-term outcomes within HER2+ patients is still largely unknown. Here, we studied the prognostic implications of PIK3CA mutations by hormone receptor (HR) status and intrinsic subtype in patients with early stage HER2+ breast cancer enrolled in CALGB 40601. Method In CALGB 40601, gene expression profiling by RNA sequencing (RNAseq) with PAM50-determined intrinsic subtype and PIK3CA mutations from whole exome sequencing (WES) were obtained from 184/305 (60%) pretreatment core biopsies. We examined the association of PIK3CA mutations with pCR and event free survival (EFS) by HR status and intrinsic subtype using logistic and Cox regression analyses. Results PIK3CA mutations were found in 32 patients (32/184, 17%). The most frequent mutation was H1047R (12/32,38%), followed by E545K (7/32,22%) and E542K (5/32,16%). PIK3CA mutations were present in 20% and 15% of HR-positive and HR-negative BC subpopulations, respectively. Within Luminal-B, Luminal-A and HER2-Enriched breast tumors, PIK3CA mutations occurred in 36%, 10% and 19% respectively. In the overall population there was lower rate of pCR in mutated-PIK3CA patients than wild-type (34% vs 49%). Using only the subset of patients treated with neoadjuvant trastuzumab-based therapy as standard of care (excluding the lapatinib plus paclitaxel arm), we found a statistically significant lower pCR rate among PIK3CA-mutated tumors using logistic regression model (30% vs 54%, OR=0.30, p=0.045). At a median follow-up of 9.1 years, the presence of PIK3CA mutation was significantly associated with worse EFS in the overall study population (HR 2.58, 95% CI 1.24- 5.35, p=0.011). In a multivariable model including pCR status, HR status and intrinsic subtype (HER2-E vs. not), PIK3CA mutation was independently and significantly associated with worse EFS (HR 2.18, 95% CI 1.04- 4.56, p=0.039). The negative impact of PIK3CA mutation on EFS was statistically significant only in patients with HR-positive (HR 3.6, 95% CI 1.45-8.96, p=0.06) and luminal breast tumors (HR 4.84, 95% CI 1.08-21.7, p=0.039), but not in HR-negative and non-luminal subtypes. Conclusion In our study, the presence of PIK3CA mutation was significantly associated with lower pCR rates in patients treated with chemotherapy plus trastuzumab. Moreover, in uni- and multivariable Cox models, PIK3CA mutations were associated with worse long-term survival, which appeared to be driven by HR-positive and luminal HER2-positive breast tumors. References 1. Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumors. Nature 2012;490:61–70. 2. Loibl S, Majewski I, Guarneri V, Nekljudova V, Holmes E, Bria E, et al PIK3CA mutations are associated with reduced pathological complete response rates in primary HER2-positive breast cancer: pooled analysis of 967 patients from five prospective trials investigating lapatinib and trastuzumab. Ann Oncol 2016;27:1519–25. Citation Format: Paola Zagami, Aranzazu Fernandez-Martinez, Naim U. Rashid, Katherine A Hoadley, Patty Spears, Charles M. Perou, Lisa Carey. Prognostic implications of PIK3CA mutation by hormone receptor status and intrinsic subtype in early stage HER2-positive breast cancer: a correlative analysis from CALGB 40601. [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD18-04.

Pancreatic ductal adenocarcinoma (PDA) is an aggressive malignancy characterized by a paucity of tumor-proximal CD8+ T cells and resistance to immunotherapeutic interventions. Cancer-associated mechanisms that elicit CD8+ T-cell exclusion and resistance to immunotherapy are not well-known. Here, using a Kras- and p53-driven model of PDA, we describe a mechanism of action for the protumorigenic cytokine IL35 through STAT3 activation in CD8+ T cells. Distinct from its action on CD4+ T cells, IL35 signaling in gp130+CD8+ T cells activated the transcription factor STAT3, which antagonized intratumoral infiltration and effector function of CD8+ T cells via suppression of CXCR3, CCR5, and IFNγ expression. Inhibition of STAT3 signaling in tumor-educated CD8+ T cells improved PDA growth control upon adoptive transfer to tumor-bearing mice. We showed that activation of STAT3 in CD8+ T cells was driven by B cell– but not regulatory T cell–specific production of IL35. We also demonstrated that B cell–specific deletion of IL35 facilitated CD8+ T-cell activation independently of effector or regulatory CD4+ T cells and was sufficient to phenocopy therapeutic anti-IL35 blockade in overcoming resistance to anti–PD-1 immunotherapy. Finally, we identified a circulating IL35+ B-cell subset in patients with PDA and demonstrated that the presence of IL35+ cells predicted increased occurrence of phosphorylated (p)Stat3+CXCR3−CD8+ T cells in tumors and inversely correlated with a cytotoxic T-cell signature in patients. Together, these data identified B cell–mediated IL35/gp130/STAT3 signaling as an important direct link to CD8+ T-cell exclusion and immunotherapy resistance in PDA.

Human tissue samples are often mixtures of heterogeneous cell types, which can confound the analyses of gene expression data derived from such tissues. The cell type composition of a tissue sample may itself be of interest and is needed for proper analysis of differential gene expression. A variety of computational methods have been developed to estimate cell type proportions using gene-level expression data. However, RNA isoforms can also be differentially expressed across cell types, and isoform-level expression could be equally or more informative for determining cell type origin than gene-level expression. We propose a new computational method, IsoDeconvMM, which estimates cell type fractions using isoform-level gene expression data. A novel and useful feature of IsoDeconvMM is that it can estimate cell type proportions using only a single gene, though in practice we recommend aggregating estimates of a few dozen genes to obtain more accurate results. We demonstrate the performance of IsoDeconvMM using a unique data set with cell type-specific RNA-seq data across more than 135 individuals. This data set allows us to evaluate different methods given the biological variation of cell type-specific gene expression data across individuals. We further complement this analysis with additional simulations.

Single-cell RNA-sequencing (scRNA-seq) has enabled the molecular profiling of thousands to millions of cells simultaneously in biologically heterogenous samples. Currently, 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 choice can greatly alter clustering solutions and thus impose great influence on which cell types are identified. Existing methods largely focus on determining the optimal number of robust clusters, which is not favorable for identifying cells of extremely low abundance due to their subtle contributions towards overall patterns of gene expression. Here we present a carefully designed framework, SCISSORS, which accurately profiles subclusters within major 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 implementing a multi-step, semi-supervised reclustering process. Additionally, SCISSORS provides a method for the identification of marker genes of rare cells, which may be used for further study. SCISSORS is wrapped around the popular Seurat R package and can be easily integrated into existing Seurat pipelines. SCISSORS, including source code and vignettes for two example datasets, is freely available at https://github.com/jrleary/SCISSORS.

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.

Oncogenic KRAS drives cancer growth by activating diverse signaling networks, not all of which have been fully delineated. We set out to establish a system-wide profile of the KRAS-regulated kinase signaling network (kinome) in KRAS-mutant pancreatic ductal adenocarcinoma (PDAC). We knocked down KRAS expression in a panel of six cell lines, and then applied Multiplexed Inhibitor Bead/Mass Spectrometry (MIB/MS) chemical proteomics to monitor changes in kinase activity and/or expression. We hypothesized that depletion of KRAS would result in downregulation of kinases required for KRAS-mediated transforming activities, and in upregulation of other kinases that could potentially compensate for the deleterious consequences of the loss of KRAS. We identified 15 upregulated and 13 downregulated kinases in common across the panel. In agreement with our hypothesis, all 15 of the upregulated kinases have established roles as cancer drivers (e.g., SRC, TGFBR1, ILK), and pharmacologic inhibition of the upregulated kinase, DDR1, suppressed PDAC growth. Interestingly, 11 of the 13 downregulated kinases have established driver roles in cell cycle progression, particularly in mitosis (e.g., WEE1, Aurora A, PLK1). Consistent with a crucial role for the downregulated kinases in promoting KRAS-driven proliferation, we found that pharmacologic inhibition of WEE1 also suppressed PDAC growth. The unexpected paradoxical activation of ERK upon WEE1 inhibition led us to inhibit both WEE1 and ERK concurrently, which caused further potent growth suppression and enhanced apoptotic death than WEE1 inhibition alone. We conclude that system-wide delineation of the KRAS-regulated kinome can identify potential therapeutic targets for KRAS-mutant pancreatic cancer.

Clustering is a form of unsupervised learning that aims to uncover latent groups within data based on similarity across a set of features. A common application of this in biomedical research is in delineating novel cancer subtypes from patient gene expression data, given a set of informative genes. However, it is typically unknown a priori what genes may be informative in discriminating between clusters, and what the optimal number of clusters are. Few methods exist for performing unsupervised clustering of RNA-seq samples, and none currently adjust for between-sample global normalization factors, select cluster-discriminatory genes, or account for potential confounding variables during clustering. To address these issues, we propose the Feature Selection and Clustering of RNA-seq (FSCseq): a model-based clustering algorithm that utilizes a finite mixture of regression (FMR) model and the quadratic penalty method with a Smoothly-Clipped Absolute Deviation (SCAD) penalty. The maximization is done by a penalized Classification EM algorithm, allowing us to include normalization factors and confounders in our modeling framework. Given the fitted model, our framework allows for subtype prediction in new patients via posterior probabilities of cluster membership, even in the presence of batch effects. Based on simulations and real data analysis, we show the advantages of our method relative to competing approaches.

MotivationQuantification estimates of gene expression from single-cell RNA-seq (scRNA-seq) data have inherent uncertainty due to reads that map to multiple genes. Many existing scRNA-seq quantification pipelines ignore multi-mapping reads and therefore underestimate expected read counts for many genes. alevin accounts for multi-mapping reads and allows for the generation of “inferential replicates”, which reflect quantification uncertainty. Previous methods have shown improved performance when incorporating these replicates into statistical analyses, but storage and use of these replicates increases computation time and memory requirements.ResultsWe demonstrate that storing only the mean and variance from a set of inferential replicates (“compression”) is sufficient to capture gene-level quantification uncertainty. Using these values, we generate “pseudo-inferential” replicates from a negative binomial distribution and propose a general procedure for incorporating these replicates into a proposed statistical testing framework. We show reduced false positives when applying this procedure to trajectory-based differential expression analyses. We additionally extend the Swish method to incorporate pseudo-inferential replicates and demonstrate improvements in computation time and memory consumption without any loss in performance. Lastly, we show that the removal of multi-mapping reads can result in significant underestimation of counts for functionally important genes in a real dataset.Availability and implementationmakeInfReps and splitSwish are implemented in the development branch of the R/Bioconductor fishpond package available at http://bioconductor.org/packages/devel/bioc/html/fishpond.html. Sample code to calculate the uncertainty-aware p-values can be found on GitHub at https://github.com/skvanburen/scUncertaintyPaperCode.Contactmichaelisaiahlove@gmail.com

Differential transcript usage (DTU) occurs when the relative transcript abundance of a gene changes between different conditions. Existing approaches to analyze DTU often rely on computational procedures that can have speed and scalability issues as the number of samples increases. In this paper, we propose a new method, termed CompDTU, that utilizes compositional regression to model transcript-level relative abundance proportions that are of interest in DTU analyses. This procedure does not suffer from speed and scalability issues due to the relative computational simplicity, making it ideally suited for DTU analysis with large sample sizes. The method also allows for the testing of and controlling for multiple categorical or continuous covariates. Additionally, many existing approaches for DTU ignore quantification uncertainty present in RNA-Seq data, where prior work has shown that accounting for such uncertainty may improve testing performance. We extend our CompDTU method to incorporate quantification uncertainty using bootstrap replicates of abundance estimates from Salmon and term this method CompDTUme. Through several power analyses, we show that CompDTU improves sensitivity and reduces false positive results relative to existing methods. Additionally, CompDTUme results in further improvements in performance over CompDTU with sufficient sample size for genes with high levels of quantification uncertainty while maintaining favorable speed and scalability.

Oncogenic KRAS drives cancer growth by activating diverse signaling networks, not all of which have been fully delineated. We set out to establish a system-wide profile of the KRAS-regulated kinase signaling network (kinome) in KRAS-mutant pancreatic ductal adenocarcinoma (PDAC). We knocked down KRAS expression in a panel of six cell lines and then applied multiplexed inhibitor bead/MS to monitor changes in kinase activity and/or expression. We hypothesized that depletion of KRAS would result in downregulation of kinases required for KRAS-mediated transformation and in upregulation of other kinases that could potentially compensate for the deleterious consequences of the loss of KRAS. We identified 15 upregulated and 13 downregulated kinases in common across the panel of cell lines. In agreement with our hypothesis, all 15 of the upregulated kinases have established roles as cancer drivers (e.g., SRC, TGF-β1, ILK), and pharmacological inhibition of one of these upregulated kinases, DDR1, suppressed PDAC growth. Interestingly, 11 of the 13 downregulated kinases have established driver roles in cell cycle progression, particularly in mitosis (e.g., WEE1, Aurora A, PLK1). Consistent with a crucial role for the downregulated kinases in promoting KRAS-driven proliferation, we found that pharmacological inhibition of WEE1 also suppressed PDAC growth. The unexpected paradoxical activation of ERK upon WEE1 inhibition led us to inhibit both WEE1 and ERK concurrently, which caused further potent growth suppression and enhanced apoptotic death compared with WEE1 inhibition alone. We conclude that system-wide delineation of the KRAS-regulated kinome can identify potential therapeutic targets for KRAS-mutant pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDA) is an aggressive malignancy characterized by paucity of tumor-proximal CD8(+) T cells and resistance to immunotherapeutic interventions. Cancer-associated mechanisms that elicit CD8(+) T-cell exclusion and resistance to immunotherapy are not well known. Here, using a Kras- and p53-driven model of PDA, we describe a mechanism of action for the pro-tumorigenic cytokine IL35 through STAT3 activation in CD8(+) T cells. Distinct from its action on CD4(+) T cells, IL35 signaling in gp130(+)CD8(+) T cells activated the transcription factor STAT3, which antagonized intratumoral infiltration and effector function of CD8(+) T cells via suppression of CXCR3, CCR5, and IFNγ expression. Inhibition of STAT3 signaling in tumor-educated CD8(+) T cells improved PDA growth control upon adoptive transfer to tumor-bearing mice. We showed that activation of STAT3 in CD8(+) T cells was driven by B cell– but not Treg-specific production of IL35. We also demonstrated that B cell–specific deletion of IL35 facilitated CD8(+) T-cell activation independently of effector or regulatory CD4(+) T cells and was sufficient to phenocopy therapeutic anti-IL35 blockade in overcoming resistance to anti–PD-1 immunotherapy. Finally, we identified a circulating IL35(+) B-cell subset in patients with PDA and demonstrated that presence of IL35(+) cells predicted increased occurrence of phosphorylated (p)Stat3(+)CXCR3(–)CD8(+) T cells in tumors and inversely correlated with a cytotoxic T-cell signature in patients. Together, these data identified B cell–mediated IL35/gp130/STAT3 signaling as an important direct link to CD8(+) T-cell exclusion and immunotherapy resistance in PDA.

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.

Summary: The Mre11-Rad50-Nbs1 complex is a DNA double-strand break sensor that mediates a tumor-suppressive DNA damage response (DDR) in cells undergoing oncogenic stress, yet the mechanisms underlying this effect are poorly understood. Using a genetically inducible primary mammary epithelial cell model, we demonstrate that Mre11 suppresses proliferation and DNA damage induced by diverse oncogenic drivers through a p53-independent mechanism. Breast tumorigenesis models engineered to express a hypomorphic Mre11 allele exhibit increased levels of oncogene-induced DNA damage, R-loop accumulation, and chromosomal instability with a characteristic copy number loss phenotype. Mre11 complex dysfunction is identified in a subset of human triple-negative breast cancers and is associated with increased sensitivity to DNA-damaging therapy and inhibitors of ataxia telangiectasia and Rad3 related (ATR) and poly (ADP-ribose) polymerase (PARP). Thus, deficiencies in the Mre11-dependent DDR drive proliferation and genome instability patterns in p53-deficient breast cancers and represent an opportunity for therapeutic exploitation. : The origins of genome instability in cancer remain poorly understood. Fagan-Solis et al. reveal a p53-independent genome integrity checkpoint pathway mediated by Mre11 that protects against genome instability in breast cancer. Mre11 dysfunction in breast cancer models induces a genomic loss signature and vulnerability to PARP and ATR inhibitors. Keywords: breast cancer, genome instability, chromosomal instability, DNA damage response, oncogenic stress, Mre11, R loops, genomic scar, replication stress

Objective A novel approach to regulate obesity-associated adipose inflammation may be through metabolic reprogramming of macrophages (MΦs). Broadly speaking, MΦs dependent on glucose are pro-inflammatory, classically activated MΦs (CAM), which contribute to adipose inflammation and insulin resistance. In contrast, MΦs that primarily metabolize fatty acids are alternatively activated MΦs (AAM) and maintain tissue insulin sensitivity. In actuality, there is much flexibility and overlap in the CAM-AAM spectrum in vivo dependent upon various stimuli in the microenvironment. We hypothesized that specific lipid trafficking proteins, e.g. fatty acid transport protein 1 (FATP1), would direct MΦ fatty acid transport and metabolism to limit inflammation and contribute to the maintenance of adipose tissue homeostasis. Methods Bone marrow derived MΦs (BMDMs) from Fatp1−/− and Fatp1+/+ mice were used to investigate FATP1-dependent substrate metabolism, bioenergetics, metabolomics, and inflammatory responses. We also generated C57BL/6J chimeric mice by bone marrow transplant specifically lacking hematopoetic FATP1 (Fatp1B−/−) and controls Fatp1B+/+. Mice were challenged by high fat diet (HFD) or low fat diet (LFD) and analyses including MRI, glucose and insulin tolerance tests, flow cytometric, histologic, and protein quantification assays were conducted. Finally, an FATP1-overexpressing RAW 264.7 MΦ cell line (FATP1-OE) and empty vector control (FATP1-EV) were developed as a gain of function model to test effects on substrate metabolism, bioenergetics, metabolomics, and inflammatory responses. Results Fatp1 is downregulated with pro-inflammatory stimulation of MΦs. Fatp1−/− BMDMs and FATP1-OE RAW 264.7 MΦs demonstrated that FATP1 reciprocally controled metabolic flexibility, i.e. lipid and glucose metabolism, which was associated with inflammatory response. Supporting our previous work demonstrating the positive relationship between glucose metabolism and inflammation, loss of FATP1 enhanced glucose metabolism and exaggerated the pro-inflammatory CAM phenotype. Fatp1B−/− chimeras fed a HFD gained more epididymal white adipose mass, which was inflamed and oxidatively stressed, compared to HFD-fed Fatp1B+/+ controls. Adipose tissue macrophages displayed a CAM-like phenotype in the absence of Fatp1. Conversely, functional overexpression of FATP1 decreased many aspects of glucose metabolism and diminished CAM-stimulated inflammation in vitro. FATP1 displayed acyl-CoA synthetase activity for long chain fatty acids in MΦs and modulated lipid mediator metabolism in MΦs. Conclusion Our findings provide evidence that FATP1 is a novel regulator of MΦ activation through control of substrate metabolism. Absence of FATP1 exacerbated pro-inflammatory activation in vitro and increased local and systemic components of the metabolic syndrome in HFD-fed Fatp1B−/− mice. In contrast, gain of FATP1 activity in MΦs suggested that Fatp1-mediated activation of fatty acids, substrate switch to glucose, oxidative stress, and lipid mediator synthesis are potential mechanisms. We demonstrate for the first time that FATP1 provides a unique mechanism by which the inflammatory tone of adipose and systemic metabolism may be regulated., Graphical abstract, Highlights • FATP1-mediated activation of fatty acids is a novel approach to limit inflammation. • Fatp1 deficiency primed macrophages for pro-inflammatory activation. • Lack of Fatp1 led to greater HFD-induced adipose inflammation. • Fatp1−/− adipose tissue macrophages were classically activated.

Chromatin immunoprecipitation followed by next generation sequencing (ChIP-seq) is a technique to detect genomic regions containing protein-DNA interaction, such as transcription factor binding sites or regions containing histone modifications. One goal of the analysis of ChIP-seq experiments is to identify genomic loci enriched for sequencing reads pertaining to DNA bound to the factor of interest. The accurate identification of such regions aids in the understanding of epigenomic marks and gene regulatory mechanisms. Given the reduction of massively parallel sequencing costs, methods to detect consensus regions of enrichment across multiple samples are of interest. Here, we present a statistical model to detect broad consensus regions of enrichment from ChIP-seq technical or biological replicates through a class of Zero-Inflated Mixed Effects Hidden Markov Models. We show that the proposed model outperforms existing methods for consensus peak calling in common epigenomic marks by accounting for the excess zeros and sample-specific biases. We apply our method to data from the Encyclopedia of DNA Elements (ENCODE) and Roadmap Epigenomics projects and also from an extensive simulation study.

Cisplatin is a major cancer chemotherapeutic drug. It kills cancer cells by damaging their DNA, mainly in the form of Pt-d(GpG) diadducts. However, it also has serious side effects, including nephrotoxicity and hepatotoxicity that limit its usefulness. Chronotherapy is taking circadian time into account during therapy to improve the therapeutic index, by improving efficacy and/or limiting toxicity. To this end, we tested the impact of clock time on excision repair of cisplatin-induced DNA damage at single-nucleotide resolution across the genome in mouse kidney and liver. We found that genome repair is controlled by two circadian programs. Repair of the transcribed strand (TS) of active, circadian-controlled genes is dictated by each gene's phase of transcription, which falls across the circadian cycle with prominent peaks at dawn and dusk. In contrast, repair of the nontranscribed strand (NTS) of all genes, repair of intergenic DNA, and global repair overall peaks at Zeitgeber time ZT08, as basal repair capacity, which is controlled by the circadian clock, peaks at this circadian time. Consequently, the TS and NTS of many genes are repaired out of phase. As most cancers are thought to have defective circadian rhythms, these results suggest that future research on timed dosage of cisplatin could potentially reduce damage to healthy tissue and improve its therapeutic index.

Pancreatic ductal adenocarcinoma (PDAC) remains a lethal disease with a 5-year survival rate of 4%. A key hallmark of PDAC is extensive stromal involvement, which makes capturing precise tumor-specific molecular information difficult. Here we have overcome this problem by applying blind source separation to a diverse collection of PDAC gene expression microarray data, including data from primary tumor, metastatic and normal samples. By digitally separating tumor, stromal and normal gene expression, we have identified and validated two tumor subtypes, including a 'basal-like' subtype that has worse outcome and is molecularly similar to basal tumors in bladder and breast cancers. Furthermore, we define 'normal' and 'activated' stromal subtypes, which are independently prognostic. Our results provide new insights into the molecular composition of PDAC, which may be used to tailor therapies or provide decision support in a clinical setting where the choice and timing of therapies are critical.

Background and Objectives Over 130,000 patients are diagnosed with colorectal cancer annually, with approximately 20% presenting with unresectable metastatic disease. Recent consensus guidelines recommend against primary tumor resection for asymptomatic patients with unresectable metastases. Our goal was to examine the trends and predictors of surgical resection. Methods Cases of colorectal cancer with synchronous metastases diagnosed between 1988–2010 were identified using the Surveillance, Epidemiology and End Results (SEER) Database. Associations between resection and clinicopathologic variables were sought using univariate and multivariate logistic regression. Results Overall, 68% of patients with synchronous metastatic colorectal cancer underwent primary tumor resection. Resection rates were as high as 76% in the earliest time period (1988–1992) and steadily dropped to 60% in the most recent period (2008–2010). Socioeconomic factors associated with resection on univariate analysis included age, race, gender, marital status, insurance status, and geographic region. Clinicopathologic characteristics associated with resection included tumor location, grade, size, and CEA level. In the multivariate model, gender, geographic region, insurance status, tumor location, grade and CEA level were independent predictors of primary tumor resection. Conclusions Surgical resection of the primary site remains common practice for patients with synchronous metastatic colorectal cancer. Treatment disparities are associated with socioeconomic as well as clinicopathologic factors. J. Surg. Oncol. 2015 111:911–916. © 2014 Wiley Periodicals, Inc.

Background Triple-negative breast cancer (TNBC), lacking expression of hormone and human epidermal growth factor receptor 2 receptors, is an aggressive subtype that frequently metastasizes to the brain and has no FDA-approved systemic therapies. Previous literature demonstrates mitogen-activated protein kinase kinase (MEK) pathway activation in TNBC brain metastases. Thus, we aimed to discover rational combinatorial therapies with MEK inhibition, hypothesizing that co-inhibition using clinically available brain-penetrant inhibitors would improve survival in preclinical models of TNBC brain metastases. Methods Using human-derived TNBC cell lines, synthetic lethal small interfering RNA kinase screens were evaluated with brain-penetrant inhibitors against MEK1/2 (selumetinib, AZD6244) or phosphatidylinositol-3 kinase (PI3K; buparlisib, BKM120). Mice bearing intracranial TNBC tumors (SUM149, MDA-MB-231Br, MDA-MB-468, or MDA-MB-436) were treated with MEK, PI3K, or platelet derived growth factor receptor (PDGFR; pazopanib) inhibitors alone or in combination. Tumors were analyzed by western blot and multiplexed kinase inhibitor beads/mass spectrometry to assess treatment effects. Results Screens identified MEK+PI3K and MEK+PDGFR inhibitors as tractable, rational combinations. Dual treatment of selumetinib with buparlisib or pazopanib was synergistic in TNBC cells in vitro. Both combinations improved survival in intracranial SUM149 and MDA-MB-231Br, but not MDA-MB-468 or MDA-MB-436. Treatments decreased mitogen-activated protein kinase (MAPK) and PI3K (Akt) signaling in sensitive (SUM149 and 231Br) but not resistant models (MDA-MB-468). Exploratory analysis of kinome reprogramming in SUM149 intracranial tumors after MEK ± PI3K inhibition demonstrates extensive kinome changes with treatment, especially in MAPK pathway members. Conclusions Results demonstrate that rational combinations of the clinically available inhibitors selumetinib with buparlisib or pazopanib may prove to be promising therapeutic strategies for the treatment of some TNBC brain metastases. Additionally, effective combination treatments cause widespread alterations in kinase pathways, including targetable potential resistance drivers.

The enzyme catechol-O-methyltransferase (COMT) metabolizes catecholamine neurotransmitters involved in a number of physiological functions including pain perception. Both human and mouse COMT genes possess functional polymorphisms contributing to inter-individual variability in pain phenotypes such as sensitivity to noxious stimuli, severity of clinical pain and response to pain treatment. In this study, we found that the effects of Comt functional variation in mice are modality-specific. Spontaneous inflammatory nociception and thermal nociception behaviors were correlated the most with the presence of the B2 SINE transposon insertion residing in the 3’UTR mRNA region. Similarly, in humans, COMT functional haplotypes were associated with thermal pain perception and with capsaicin-induced pain. Furthermore, COMT genetic variations contributed to pain behaviors in mice and pain ratings in humans in a sex-specific manner. The ancestral Comt variant, without a B2 SINE insertion, was more strongly associated with sensitivity to capsaicin in female versus male mice. In humans, the haplotype coding for low COMT activity increased capsaicin-induced pain perception in women, but not men. These findings reemphasize the fundamental contribution of COMT to pain processes, and provide a fine-grained resolution of this contribution at the genetic level that can be used to guide future studies in the area of pain genetics.

Our recent ERK inhibitor analyses in pancreatic ductal adenocarcinoma (PDAC) demonstrated that MYC protein loss is a marker of inhibitor sensitivity, and indicated that resistant cell lines possess KRASdependent, ERK-independent mechanisms maintaining MYC protein levels. In seeking to identify these mechanisms, we first determined the signaling networks by which mutant KRAS regulates MYC. Acute KRAS suppression caused rapid proteasome-dependent loss of MYC protein, through both ERK1/2-dependent and -independent mechanisms. Surprisingly, this MYC degradation was independent of both PI3K-AKTGSK3β signaling and the E3 ligase FBWX7. We then established and applied a high-throughput screen for MYC protein degradation, and performed a kinome-wide proteomics screen. We identified an ERK1/2- inhibition-induced feed-forward mechanism dependent on EGFR and SRC, leading to ERK5 activation and phosphorylation of MYC at S62, preventing MYC degradation. We further determined that concurrent inhibition of ERK1/2 and ERK5 disrupted this feed-forward compensatory mechanism, synergistically causing loss of MYC and suppressing PDAC growth.

Adaptive resistance to targeted cancer therapies is a universal problem in cancer treatment where tumor cells circumvent targeted pathway inhibition to reactivate growth signaling. In our previous work, we observed genome-wide enhancer remodeling following MEK inhibition (MEKi) capable of driving adaptive gene transcription in triple-negative breast cancer (TNBC). Adaptive enhancers were enriched for the BET bromodomain protein BRD4 and cotreatment with MEKi and BET inhibitor (JQ1) could durably suppress TNBC growth in multiple cell lines and preclinical mouse models. There are currently 10 BET inhibitors in clinical trials being tested as single agents across multiple tumor types including TNBC. There is also a growing body of preclinical literature using epigenetic inhibitors to block the adaptive ability of tumor cells in combination with multiple targeted therapies. This led us to screen for inhibitors that synergize with JQ1 to suppress growth of TNBC using a 176-compound library enriched for epigenetic and kinase inhibitors. We performed synergy screens in 6 TNBC cell lines across 6 doses of JQ1 and each library compound. Using the Bliss Independence model to assess synergy, we found that inhibition of MEK, CDK9, Aurora Kinase, CREBBP/P300, and BAZ2A/B was strongly synergistic with JQ1. BRD4, CDK9, and the acetyltransferase CREBBP/P300 are all members of the P-TEFb transcriptional elongation complex. When we performed additional synergy screens against the P300 bromodomain inhibitor CPI-637, we found a significant overlap in synergistic targets with the JQ1 screen including MEK, BET bromodomain proteins, ERK, Aurora Kinase, and CDK9. BAZ2A/B inhibition using the small-molecule inhibitor GSK2801, which targets the bromodomain of BAZ2A/B, synergized significantly stronger with JQ1 across all cell lines compared to CPI637. BAZ2A/B proteins are members of nucleosome remodeling complexes that mediate DNA silencing by aiding in recruitment of histone modifying enzymes. Ongoing studies seek to understand the role of BAZ2A/B and the mechanism of GSK2801 synergy with BET bromodomain inhibition using RNA sequencing and ChIP sequencing experiments. These results define novel targets that synergize with JQ1 to suppress tumor cell growth and illuminate additional mechanisms of transcriptional regulation driven by BET bromodomain proteins in TNBC. Citation Format: Samantha M. Bevill, Noah Sciaky, Brian T. Golitz, Naim U. Rashid, Jon S. Zawistowski, Gary L. Johnson. Novel synergistic combination therapies with BET bromodomain inhibitors in triple-negative breast cancer [abstract]. In: Proceedings of the AACR Special Conference: Advances in Breast Cancer Research; 2017 Oct 7-10; Hollywood, CA. Philadelphia (PA): AACR; Mol Cancer Res 2018;16(8_Suppl):Abstract nr B34.

Background: We examined the association of platinum resistance with genetic mutations in high grade serous ovarian cancer (HSOC) patients undergoing high-throughput genomic tumor sequencing. Methods: Snap-frozen and fresh frozen paraffin embedded tissue samples were collected from HSOC patients enrolled on UNCseq (NCT01457196). UNCSeq is an institutional protocol which uses next generation sequencing to detect genetic mutations in a wide array of malignancies. Illumina libraries were prepared separately from tumor and a matched normal sample from each patient. Relevant targets were enriched by a custom designed Agilent SureSelect hybrid capture enrichment library using standard protocols. Samples were sequenced on Illumina HiSeq machines in a variety of formats. Mutations with a quality score or < 6 months of disease free interval following completion of induction therapy, respectively. Results: Overall 39 HSOC cases met inclusion criteria; 32 tumors met criteria for platinum sensitive and 7 platinum resistant. 308 mutations were noted in at least one individual across all patients. The top observed mutations in platinum sensitive HSOC were TP53 (41%, n= 13), GucylA2 (19%, n=6), MLL2 (19%, n=6) and MTOR (16%, n=5). The top observed mutations in platinum resistant tumors were TP53 (71%, n=5), TET1 (43%, n=3), NF1 (43%, n=3) and MLL3 (43%, n=3). There was a trend toward more p53 mutations in resistant tumors (71% versus 41%, p=0.21) There was no difference in the total number of mutations per tumor in platinum sensitive and resistant patients, (3 vs. 4, p=0.516). Conclusions: We did not detect a difference in the number of genetic mutations in HSOC according to platinum sensitivity. Platinum resistant tumors had a trend toward higher frequency of TP53 mutations than platinum sensitive HSOCs. Furthermore, we identified 3 frequently mutated genes in platinum resistant HSOC: TET1, an epigenetic regulator, NF1, a tumor suppressor gene and MLL3, a histone modifier gene. Ongoing tumor sequencing of HSOCs on UNCseq will help to confirm these results. Citation Format: Tara Castellano, Leslie H. Clark, Naim Rashid, Victoria Bae-Jump. Genetic variation in platinum-sensitive and platinum-resistant high-grade serous epithelial ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 412. doi:10.1158/1538-7445.AM2017-412

Multiple myeloma is an incurable plasma cell malignancy with a complex and incompletely understood molecular pathogenesis. Here we use whole-exome sequencing, copy-number profiling and cytogenetics to analyse 84 myeloma samples. Most cases have a complex subclonal structure and show clusters of subclonal variants, including subclonal driver mutations. Serial sampling reveals diverse patterns of clonal evolution, including linear evolution, differential clonal response and branching evolution. Diverse processes contribute to the mutational repertoire, including kataegis and somatic hypermutation, and their relative contribution changes over time. We find heterogeneity of mutational spectrum across samples, with few recurrent genes. We identify new candidate genes, including truncations of SP140, LTB, ROBO1 and clustered missense mutations in EGR1. The myeloma genome is heterogeneous across the cohort, and exhibits diversity in clonal admixture and in dynamics of evolution, which may impact prognostic stratification, therapeutic approaches and assessment of disease response to treatment., Multiple myeloma is a malignant plasma cell disorder with a complex molecular pathogenesis. Here, the authors perform whole-exome sequencing, copy-number profiling and cytogenetic analysis in 84 myeloma samples and highlight the diversity and evolution of the mutational profile underlying the disease.