Genomic analyses identify molecular subtypes of pancreatic cancer
University of Queensland1, University of Glasgow2, QIMR Berghofer Medical Research Institute3, Garvan Institute of Medical Research4, Baylor College of Medicine5, University of Utah6, University of Adelaide7, South Australia Pathology8, Harvard University9, Campbelltown Hospital10, St. Vincent's Health System11, University of New South Wales12, University of Newcastle13, Royal North Shore Hospital14, University of Sydney15, Royal Prince Alfred Hospital16, Fiona Stanley Hospital17, Royal Adelaide Hospital18, Princess Alexandra Hospital19, University of Western Australia20, Beatson West of Scotland Cancer Centre21, Southern General Hospital22, Dresden University of Technology23, University of Texas MD Anderson Cancer Center24, Memorial Sloan Kettering Cancer Center25, Johns Hopkins University School of Medicine26, University of Verona27, Mayo Clinic28, University of Melbourne29
TL;DR: Detailed genomic analysis of 456 pancreatic ductal adenocarcinomas identified 32 recurrently mutated genes that aggregate into 10 pathways: KRAS, TGF-β, WNT, NOTCH, ROBO/SLIT signalling, G1/S transition, SWI-SNF, chromatin modification, DNA repair and RNA processing.
Abstract: Integrated genomic analysis of 456 pancreatic ductal adenocarcinomas identified 32 recurrently mutated genes that aggregate into 10 pathways: KRAS, TGF-β, WNT, NOTCH, ROBO/SLIT signalling, G1/S transition, SWI-SNF, chromatin modification, DNA repair and RNA processing. Expression analysis defined 4 subtypes: (1) squamous; (2) pancreatic progenitor; (3) immunogenic; and (4) aberrantly differentiated endocrine exocrine (ADEX) that correlate with histopathological characteristics. Squamous tumours are enriched for TP53 and KDM6A mutations, upregulation of the TP63∆N transcriptional network, hypermethylation of pancreatic endodermal cell-fate determining genes and have a poor prognosis. Pancreatic progenitor tumours preferentially express genes involved in early pancreatic development (FOXA2/3, PDX1 and MNX1). ADEX tumours displayed upregulation of genes that regulate networks involved in KRAS activation, exocrine (NR5A2 and RBPJL), and endocrine differentiation (NEUROD1 and NKX2-2). Immunogenic tumours contained upregulated immune networks including pathways involved in acquired immune suppression. These data infer differences in the molecular evolution of pancreatic cancer subtypes and identify opportunities for therapeutic development.
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Institute for Systems Biology1, BC Cancer Agency2, University of California, San Francisco3, University of North Carolina at Chapel Hill4, Columbia University5, Discovery Institute6, Massachusetts Institute of Technology7, Arizona State University8, Sage Bionetworks9, Harvard University10, Johns Hopkins University11, Stanford University12, University of Calgary13, Université libre de Bruxelles14, University of Texas MD Anderson Cancer Center15, Medical College of Wisconsin16, Qatar Airways17, Cold Spring Harbor Laboratory18, University of São Paulo19, Henry Ford Hospital20, University of Alabama at Birmingham21, Van Andel Institute22, Stony Brook University23
TL;DR: An extensive immunogenomic analysis of more than 10,000 tumors comprising 33 diverse cancer types by utilizing data compiled by TCGA identifies six immune subtypes that encompass multiple cancer types and are hypothesized to define immune response patterns impacting prognosis.
3,246 citations
Benjamin J. Raphael, Ralph H. Hruban, Andrew J. Aguirre, Richard A. Moffitt +262 more•Institutions (1)
TL;DR: An integrated multi-platform analysis of 150 pancreatic ductal adenocarcinoma specimens reveals a complex molecular landscape of PDAC and provides a roadmap for precision medicine.
1,259 citations
Cites background or methods or result from "Genomic analyses identify molecular..."
...The percentage of PDAC samples with a mutation detected by automated calling is noted at the left....
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...Previous analyses of gene expression have identified mRNA subtypes of pancreatic cancer (Bailey et al., 2016; Collisson et al., 2011; Moffitt et al., 2015)....
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...Gene expression studies have identified subtypes of PDACwith prognostic and biological relevance (Bailey et al., 2016; Collisson et al., 2011; Moffitt et al., 2015)....
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...Along the right side of the heatmap are red and blue indicators of whether or not a gene was significantly over or under expressed in one of the Bailey et al. subtypes (supplemental table, Bailey et al.(Bailey et al., 2016))....
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...Our results suggest a potentially important relationship between non-coding RNAs and differentiation genes, including GATA6, that have previously been associated with classical/progenitor subtype tumors (Bailey et al., 2016; Collisson et al., 2011; Moffitt et al., 2015), as well as potentially new relationships between non-coding RNA and the more aggressive basal-like/squamous subtype tumors (Bailey et al....
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TL;DR: This review aims to outline the most up-to-date knowledge of pancreatic adenocarcinoma risk, diagnostics, treatment and outcomes, while identifying gaps that aim to stimulate further research in this understudied malignancy.
Abstract: This review aims to outline the most up-to-date knowledge of pancreatic adenocarcinoma risk, diagnostics, treatment and outcomes, while identifying gaps that aim to stimulate further research in this understudied malignancy. Pancreatic adenocarcinoma is a lethal condition with a rising incidence, predicted to become the second leading cause of cancer death in some regions. It often presents at an advanced stage, which contributes to poor five-year survival rates of 2%-9%, ranking firmly last amongst all cancer sites in terms of prognostic outcomes for patients. Better understanding of the risk factors and symptoms associated with this disease is essential to inform both health professionals and the general population of potential preventive and/or early detection measures. The identification of high-risk patients who could benefit from screening to detect pre-malignant conditions such as pancreatic intraepithelial neoplasia, intraductal papillary mucinous neoplasms and mucinous cystic neoplasms is urgently required, however an acceptable screening test has yet to be identified. The management of pancreatic adenocarcinoma is evolving, with the introduction of new surgical techniques and medical therapies such as laparoscopic techniques and neo-adjuvant chemoradiotherapy, however this has only led to modest improvements in outcomes. The identification of novel biomarkers is desirable to move towards a precision medicine era, where pancreatic cancer therapy can be tailored to the individual patient, while unnecessary treatments that have negative consequences on quality of life could be prevented for others. Research efforts must also focus on the development of new agents and delivery systems. Overall, considerable progress is required to reduce the burden associated with pancreatic cancer. Recent, renewed efforts to fund large consortia and research into pancreatic adenocarcinoma are welcomed, but further streams will be necessary to facilitate the momentum needed to bring breakthroughs seen for other cancer sites.
951 citations
TL;DR: A new population of CAFs that express MHC class II and CD74, but do not express classical co-stimulatory molecules are described, and it is found that they activate CD4+ T cells in an antigen-specific fashion in a model system, confirming their putative immune-modulatory capacity.
Abstract: Cancer-associated fibroblasts (CAF) are major players in the progression and drug resistance of pancreatic ductal adenocarcinoma (PDAC). CAFs constitute a diverse cell population consisting of several recently described subtypes, although the extent of CAF heterogeneity has remained undefined. Here we use single-cell RNA sequencing to thoroughly characterize the neoplastic and tumor microenvironment content of human and mouse PDAC tumors. We corroborate the presence of myofibroblastic CAFs and inflammatory CAFs and define their unique gene signatures in vivo. Moreover, we describe a new population of CAFs that express MHC class II and CD74, but do not express classic costimulatory molecules. We term this cell population "antigen-presenting CAFs" and find that they activate CD4+ T cells in an antigen-specific fashion in a model system, confirming their putative immune-modulatory capacity. Our cross-species analysis paves the way for investigating distinct functions of CAF subtypes in PDAC immunity and progression. SIGNIFICANCE: Appreciating the full spectrum of fibroblast heterogeneity in pancreatic ductal adenocarcinoma is crucial to developing therapies that specifically target tumor-promoting CAFs. This work identifies MHC class II-expressing CAFs with a capacity to present antigens to CD4+ T cells, and potentially to modulate the immune response in pancreatic tumors.See related commentary by Belle and DeNardo, p. 1001.This article is highlighted in the In This Issue feature, p. 983.
900 citations
TL;DR: In this paper, the authors used genetic, immunohistochemical and transcriptional immunoprofiling, computational biophysics, and functional assays to identify T-cell antigens in long-term survivors of pancreatic cancer.
Abstract: Pancreatic ductal adenocarcinoma is a lethal cancer with fewer than 7% of patients surviving past 5 years. T-cell immunity has been linked to the exceptional outcome of the few long-term survivors, yet the relevant antigens remain unknown. Here we use genetic, immunohistochemical and transcriptional immunoprofiling, computational biophysics, and functional assays to identify T-cell antigens in long-term survivors of pancreatic cancer. Using whole-exome sequencing and in silico neoantigen prediction, we found that tumours with both the highest neoantigen number and the most abundant CD8+ T-cell infiltrates, but neither alone, stratified patients with the longest survival. Investigating the specific neoantigen qualities promoting T-cell activation in long-term survivors, we discovered that these individuals were enriched in neoantigen qualities defined by a fitness model, and neoantigens in the tumour antigen MUC16 (also known as CA125). A neoantigen quality fitness model conferring greater immunogenicity to neoantigens with differential presentation and homology to infectious disease-derived peptides identified long-term survivors in two independent datasets, whereas a neoantigen quantity model ascribing greater immunogenicity to increasing neoantigen number alone did not. We detected intratumoural and lasting circulating T-cell reactivity to both high-quality and MUC16 neoantigens in long-term survivors of pancreatic cancer, including clones with specificity to both high-quality neoantigens and predicted cross-reactive microbial epitopes, consistent with neoantigen molecular mimicry. Notably, we observed selective loss of high-quality and MUC16 neoantigenic clones on metastatic progression, suggesting neoantigen immunoediting. Our results identify neoantigens with unique qualities as T-cell targets in pancreatic ductal adenocarcinoma. More broadly, we identify neoantigen quality as a biomarker for immunogenic tumours that may guide the application of immunotherapies.
774 citations
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TL;DR: The Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure outperforms other aligners by a factor of >50 in mapping speed.
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TL;DR: EdgeR as mentioned in this paper is a Bioconductor software package for examining differential expression of replicated count data, which uses an overdispersed Poisson model to account for both biological and technical variability and empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference.
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TL;DR: It is shown that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads, and estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired- end reads, depending on the number of possible splice forms for each gene.
Abstract: RNA-Seq is revolutionizing the way transcript abundances are measured. A key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. This issue is particularly important for quantification with de novo transcriptome assemblies in the absence of sequenced genomes, as it is difficult to determine which transcripts are isoforms of the same gene. A second significant issue is the design of RNA-Seq experiments, in terms of the number of reads, read length, and whether reads come from one or both ends of cDNA fragments. We present RSEM, an user-friendly software package for quantifying gene and isoform abundances from single-end or paired-end RNA-Seq data. RSEM outputs abundance estimates, 95% credibility intervals, and visualization files and can also simulate RNA-Seq data. In contrast to other existing tools, the software does not require a reference genome. Thus, in combination with a de novo transcriptome assembler, RSEM enables accurate transcript quantification for species without sequenced genomes. On simulated and real data sets, RSEM has superior or comparable performance to quantification methods that rely on a reference genome. Taking advantage of RSEM's ability to effectively use ambiguously-mapping reads, we show that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads. On the other hand, estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired-end reads, depending on the number of possible splice forms for each gene. RSEM is an accurate and user-friendly software tool for quantifying transcript abundances from RNA-Seq data. As it does not rely on the existence of a reference genome, it is particularly useful for quantification with de novo transcriptome assemblies. In addition, RSEM has enabled valuable guidance for cost-efficient design of quantification experiments with RNA-Seq, which is currently relatively expensive.
14,524 citations
TL;DR: The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis that includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software.
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14,243 citations