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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 contains information on small molecules included in screening library

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

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

File includes drug synergy scores for 4 cell lines screened against CPI-637

File includes drug synergy scores for 6 cell lines screened against JQ1

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.

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Supplementary Figures

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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.

ImportanceBoth tumor-infiltrating lymphocytes (TILs) assessment and immune-related gene expression signatures by RNA profiling predict higher pathologic complete response (pCR) and improved event-free survival (EFS) in patients with early-stage ERBB2/HER2-positive breast cancer. However, whether these 2 measures of immune activation provide similar or additive prognostic value is not known.ObjectiveTo examine the prognostic ability of TILs and immune-related gene expression signatures, alone and in combination, to predict pCR and EFS in patients with early-stage ERBB2/HER2-positive breast cancer treated in 2 clinical trials.Design, Setting, and ParticipantsIn this prognostic study, a correlative analysis was performed on the Cancer and Leukemia Group B (CALGB) 40601 trial and the PAMELA trial. In the CALGB 40601 trial, 305 patients were randomly assigned to weekly paclitaxel with trastuzumab, lapatinib, or both for 16 weeks. The primary end point was pCR, with a secondary end point of EFS. In the PAMELA trial, 151 patients received neoadjuvant treatment with trastuzumab and lapatinib for 18 weeks. The primary end point was the ability of the HER2-enriched subtype to predict pCR. The studies were conducted from October 2013 to November 2015 (PAMELA) and from December 2008 to February 2012 (CALGB 40601). Data analyses were performed from June 1, 2020, to January 1, 2022.Main Outcomes and MeasuresImmune-related gene expression profiling by RNA sequencing and TILs were assessed on 230 CALGB 40601 trial pretreatment tumors and 138 PAMELA trial pretreatment tumors. The association of these biomarkers with pCR (CALGB 40601 and PAMELA) and EFS (CALGB 40601) was studied by logistic regression and Cox analyses.ResultsThe median age of the patients was 50 years (IQR, 42-50 years), and 305 (100%) were women. Of 202 immune signatures tested, 166 (82.2%) were significantly correlated with TILs. In both trials combined, TILs were significantly associated with pCR (odds ratio, 1.01; 95% CI, 1.01-1.02; P = .02). In addition to TILs, 36 immune signatures were significantly associated with higher pCR rates. Seven of these signatures outperformed TILs for predicting pCR, 6 of which were B-cell related. In a multivariable Cox model adjusted for clinicopathologic factors, including PAM50 intrinsic tumor subtype, the immunoglobulin G signature, but not TILs, was independently associated with EFS (immunoglobulin G signature–adjusted hazard ratio, 0.63; 95% CI, 0.42-0.93; P = .02; TIL-adjusted hazard ratio, 1.00; 95% CI, 0.98-1.02; P = .99).Conclusions and RelevanceResults of this study suggest that multiple B-cell–related signatures were more strongly associated with pCR and EFS than TILs, which largely represent T cells. When both TILs and gene expression are available, the prognostic value of immune-related signatures appears to be superior.

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.

Inhibition of the HER2/ERBB2 receptor is a keystone to treating HER2-positive malignancies, particularly breast cancer, but a significant fraction of HER2-positive (HER2+) breast cancers recur or fail to respond. Anti-HER2 monoclonal antibodies, like trastuzumab or pertuzumab, and ATP active site inhibitors like lapatinib, commonly lack durability because of adaptive changes in the tumor leading to resistance. HER2+ cell line responses to inhibition with lapatinib were analyzed by RNAseq and ChIPseq to characterize transcriptional and epigenetic changes. Motif analysis of lapatinib-responsive genomic regions implicated the pioneer transcription factor FOXA1 as a mediator of adaptive responses. Lapatinib in combination with FOXA1 depletion led to dysregulation of enhancers, impaired adaptive upregulation of HER3, and decreased proliferation. HER2-directed therapy using clinically relevant drugs (trastuzumab with or without lapatinib or pertuzumab) in a 7-day clinical trial designed to examine early pharmacodynamic response to antibody-based anti-HER2 therapy showed reduced FOXA1 expression was coincident with decreased HER2 and HER3 levels, decreased proliferation gene signatures, and increased immune gene signatures. This highlights the importance of the immune response to anti-HER2 antibodies and suggests that inhibiting FOXA1-mediated adaptive responses in combination with HER2 targeting is a potential therapeutic strategy.

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.

BACKGROUND: Glioblastoma (GBM) is driven by various genomic alterations. Next-generation sequencing (NGS) could yield targetable alterations that might impact outcomes. The goal of this study was to describe how NGS can inform targeted therapy (TT) in this patient population. METHODS: The medical records of patients with a diagnosis of GBM from 2017 to 2019 were reviewed. Records of patients with recurrent GBM and genomic alterations were evaluated. Objective response rates and disease control rates were determined. RESULTS: A total of 87 patients with GBM underwent NGS. Forty percent (n = 35) were considered to have actionable alterations. Of these 35, 40% (n = 14) had their treatment changed due to the alteration. The objective response rate (ORR) of this population was 43%. The disease control rate (DCR) was 100%. The absolute mean decrease in contrast-enhancing disease was 50.7% (95% CI 34.8–66.6). CONCLUSION: NGS for GBM, particularly in the recurrent setting, yields a high rate of actionable alterations. We observed a high ORR and DCR, reflecting the value of NGS when deciding on therapies to match genomic alterations. In conclusion, patient selection and the availability of NGS might impact outcomes in select patients with recurrent GBM.

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.

Polymerase theta (Pol θ, gene name Polq) is a widely conserved DNA polymerase that mediates a microhomology-mediated, error-prone, double strand break (DSB) repair pathway, referred to as Theta Mediated End Joining (TMEJ). Cells with homologous recombination deficiency are reliant on TMEJ for DSB repair. It is unknown whether deficiencies in other components of the DNA damage response (DDR) also result in Pol θ addiction. Here we use a CRISPR genetic screen to uncover 140 Polq synthetic lethal (PolqSL) genes, the majority of which were previously unknown. Functional analyses indicate that Pol θ/TMEJ addiction is associated with increased levels of replication-associated DSBs, regardless of the initial source of damage. We further demonstrate that approximately 30% of TCGA breast cancers have genetic alterations in PolqSL genes and exhibit genomic scars of Pol θ/TMEJ hyperactivity, thereby substantially expanding the subset of human cancers for which Pol θ inhibition represents a promising therapeutic strategy., Polymerase theta is a widely conserved DNA polymerase that mediates Theta Mediated End Joining. Here authors present a synthetic lethal CRISPR screen to identify DDR gene mutations that induce cellular addiction to Pol theta.

Summary Proliferative and invasive breast tumors evolve heterogeneously in individual patients, posing significant challenges in identifying new druggable targets for precision, effective therapy. Here we present a functional multi-omics method, interaction-Correlated Multi-omic Aberration Patterning (iC-MAP), which dissects intra-tumor heterogeneity and identifies in situ the oncogenic consequences of multi-omics aberrations that drive proliferative and invasive tumors. First, we perform chromatin activity-based chemoproteomics (ChaC) experiments on breast cancer (BC) patient tissues to identify genetic/transcriptomic alterations that manifest as oncogenically active proteins. ChaC employs a biotinylated small molecule probe that specifically binds to the oncogenically active histone methyltransferase G9a, enabling sorting/enrichment of a G9a-interacting protein complex that represents the predominant BC subtype in a tissue. Second, using patient transcriptomic/genomic data, we retrospectively identified some G9a interactor-encoding genes that showed individualized iC-MAP. Our iC-MAP findings represent both new diagnostic/prognostic markers to identify patient subsets with incurable metastatic disease and targets to create individualized therapeutic strategies., Graphical Abstract, Highlights • ChaC dissects tumor heterogeneity for identifying oncogenic-active proteins • An oncogenic-active G9a-interactome represents the invasive tumor in a tissue • iC-MAP identifies multi-omics aberrations that drive invasive tumors • Patient-specific iC-MAP of select interactor genes are of prognostic value, Biological Sciences; Cancer Systems Biology; Cancer

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.

BACKGROUND: High tumor mutation burden (TMB-H) has been proposed as a predictive biomarker for response to immune checkpoint blockade (ICB), largely due to potential for tumor mutations to generate immunogenic neoantigens. Despite recent pan-cancer approval of ICB treatment for any TMB-H tumor, as assessed by the targeted Foundation One CDx assay in 9 tumor types, the utility of this biomarker has not been fully demonstrated across all cancers. PATIENTS AND METHODS: Data from over 10,000 patient tumors included in The Cancer Genome Atlas were used to compare approaches to determine TMB and identify the correlation between predicted neoantigen load and CD8 T cells. Association of TMB with ICB treatment outcomes was analyzed by both objective response rates (ORRs, N=1551) and overall survival (OS, N=1936). RESULTS: In cancer types where CD8 T cell levels positively correlated with neoantigen load, such as melanoma, lung, and bladder cancers, TMB-H tumors exhibited a 39.8% ORR to ICB (95% CI 34.9–44.8), which was significantly higher than that observed in low TMB (TMB-L) tumors (odds ratio (OR) = 4.1, 95% CI 2.9–5.8, P < 2×10(−16)). In cancer types that showed no relationship between CD8 T cell levels and neoantigen load, such as breast cancer, prostate cancer, and glioma, TMB-H tumors failed to achieve a 20% ORR (ORR = 15.3%, 95% CI 9.2–23.4, P = 0.95), and also exhibited a significantly lower ORR relative to TMB-L tumors (OR = 0.46, 95% CI 0.24–0.88, P = 0.02). Bulk ORRs were not significantly different between the two categories of tumors (P = 0.10) for patient cohorts assessed. Equivalent results were obtained by analyzing OS and by treating TMB as a continuous variable. CONCLUSIONS: Our analysis failed to support application of TMB-H as a biomarker for treatment with ICB in all solid cancer types. Further tumor type specific studies are warranted.

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.

The complexity of human cancer often results in significant heterogeneity in response to treatment. Precision medicine offers the potential to improve patient outcomes by leveraging this heterogeneity. Individualized treatment rules (ITRs) formalize precision medicine as maps from the patient covariate space into the space of allowable treatments. The optimal ITR is that which maximizes the mean of a clinical outcome in a population of interest. Patient-derived xenograft (PDX) studies permit the evaluation of multiple treatments within a single tumor, and thus are ideally suited for estimating optimal ITRs. PDX data are characterized by correlated outcomes, a high-dimensional feature space, and a large number of treatments. Here we explore machine learning methods for estimating optimal ITRs from PDX data. We analyze data from a large PDX study to identify biomarkers that are informative for developing personalized treatment recommendations in multiple cancers. We estimate optimal ITRs using regression-based (Q-learning) and direct-search methods (outcome weighted learning). Finally, we implement a superlearner approach to combine multiple estimated ITRs and show that the resulting ITR performs better than any of the input ITRs, mitigating uncertainty regarding user choice. Our results indicate that PDX data are a valuable resource for developing individualized treatment strategies in oncology. Supplementary materials for this article are available online.

BACKGROUND GBM is driven by various genomic alterations. Next generation sequencing (NGS) may reveal targetable alterations. The goal of this study was to describe how NGS can inform targeted therapy (TT) selection. METHODS The medical records of patients (pts) with GBM from 2017–2019 were reviewed. Pts with actionable mutations were included in the analysis. At first progression (PD1), two cohorts of pts were defined: cohort A received TT, while cohort B received physician’s choice chemotherapy (PCC). Regression analyses were used to determine OS and PFS between cohorts. A stratified cox model was utilized to assess the effect of TT, where KPS level (low vs high) was utilized as a stratification factor. A heat map was generated describing the landscape of mutations. Disease response in cohort A was graded per RANO criteria. RESULTS There were 38 GBM pts with actionable alterations. Cohort A had 15 (39%) pts and cohort B had 23 (61%) pts. Of the 26 common alterations, 11 (42%) were deemed actionable. Pts with higher KPS were more likely to receive TT. Pts with a KPS ≥ 70 had a longer PFS while on TT. Although not well powered, pts in cohort A had a longer median OS relative to cohort B (HR 0.37 CI 0.10–1.38). The objective response rate (ORR) was 93%, with afatinib and cabozantinib resulting in complete response, one pt had progressive disease while on TT. CONCLUSION NGS for recurrent GBM yields a high rate of actionable alterations. Pts that go on TT are often younger and with higher KPS. This likely plays into their improved survival; however, it is notable that the high ORR reflects the value of NGS in deciding on TT to match alterations that are likely to respond. In conclusion, patient selection and availability of NGS impacts outcomes in recurrent GBM.

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.

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.

Epigenomics, the study of the human genome and its interactions with proteins and other cellular elements, has become of significant interest in recent years. Such interactions have been shown to regulate essential cellular functions and are associated with multiple complex diseases. Therefore, understanding how these interactions may change across conditions is central in biomedical research. Chromatin immunoprecipi-tation followed by massively-parallel sequencing (ChIP-seq) is one of several techniques to detect local changes in epigenomic activity (peaks). However, existing methods for differential peak calling are not optimized for the diversity in ChIP-seq signal profiles, are limited to the analysis of two conditions, or cannot classify specific patterns of differential change when multiple patterns exist. To address these limitations, we present a flexible and efficient method for the detection of differential epigenomic activity across multiple conditions. We utilize data from the ENCODE Consortium and show that the presented method, mixNBHMM, exhibits superior performance to current tools and it is among the fastest algorithms available. Our method also allows the classification of combinatorial patterns of differential epigenomic activity, and the characterization of chromatin regulatory states. Supplementary materials, including code to replicate the results, are available as an online supplement. The implemented R package is available for download at https://github.com/plbaldoni/mixNBHMM .

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.

In the genomic era, the identification of gene signatures associated with disease is of significant interest. Such signatures are often used to predict clinical outcomes in new patients and aid clinical decision-making. However, recent studies have shown that gene signatures are often not replicable. This occurrence has practical implications regarding the generalizability and clinical applicability of such signatures. To improve replicability, we introduce a novel approach to select gene signatures from multiple datasets whose effects are consistently non-zero and account for between-study heterogeneity. We build our model upon some rank-based quantities, facilitating integration over different genomic datasets. A high dimensional penalized Generalized Linear Mixed Model (pGLMM) is used to select gene signatures and address data heterogeneity. We compare our method to some commonly used strategies that select gene signatures ignoring between-study heterogeneity. We provide asymptotic results justifying the performance of our method and demonstrate its advantage in the presence of heterogeneity through thorough simulation studies. Lastly, we motivate our method through a case study subtyping pancreatic cancer patients from four gene expression studies.

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