Mutant SF3B1 promotes malignancy in PDAC

The splicing factor SF3B1 is recurrently mutated in various tumors, including pancreatic ductal adenocarcinoma (PDAC). The impact of the hotspot mutation SF3B1K700E on the PDAC pathogenesis, however, remains elusive. Here, we demonstrate that Sf3b1K700E alone is insufficient to induce malignant transformation of the murine pancreas, but that it increases aggressiveness of PDAC if it co-occurs with mutated KRAS and p53. We further show that Sf3b1K700E already plays a role during early stages of pancreatic tumor progression and reduces the expression of TGF-β1-responsive epithelial-mesenchymal transition (EMT) genes. Moreover, we found that SF3B1K700E confers resistance to TGF-β1-induced cell death in pancreatic organoids and cell lines, partly mediated through aberrant splicing of Map3k7. Overall, our findings demonstrate that SF3B1K700E acts as an oncogenic driver in PDAC, and suggest that it promotes the progression of early stage tumors by impeding the cellular response to tumor suppressive effects of TGF-β.

A history of the MetaSUB consortium: Tracking urban microbes around the globe

The MetaSUB Consortium, founded in 2015, is a global consortium with an interdisciplinary team of clinicians, scientists, bioinformaticians, engineers, and designers, with members from more than 100 countries across the globe. This network has continually collected samples from urban and rural sites including subways and transit systems, sewage systems, hospitals, and other environmental sampling. These collections have been ongoing since 2015 and have continued when possible, even throughout the COVID-19 pandemic. The consortium has optimized their workflow for the collection, isolation, and sequencing of DNA and RNA collected from these various sites and processing them for metagenomics analysis, including the identification of SARS-CoV-2 and its variants. Here, the Consortium describes its foundations, and its ongoing work to expand on this network and to focus its scope on the mapping, annotation, and prediction of emerging pathogens, mapping microbial evolution and antibiotic resistance, and the discovery of novel organisms and biosynthetic gene clusters.

RNA Instant Quality Check: Alignment-Free RNA-Degradation Detection

With the constant increase of large-scale genomic data projects, automated and high-throughput quality assessment becomes a crucial component of any analysis. Whereas small projects often have a more homogeneous design and a manageable structure allowing for a manual per-sample analysis of quality, large-scale studies tend to be much more heterogeneous and complex. Many quality metrics have been developed to assess the quality of an individual sample on the raw read level. Degradation effects are typically assessed based on the RNA integrity (RIN) score, or on postalignment data. In this study, we show that single commonly used quality criteria such as the RIN score alone are not sufficient to ensure RNA sample quality. We developed a new approach and provide an efficient tool that estimates RNA sample degradation by computing the 5'/3' bias based on all genes in an alignment-free manner. That enables degradation assessment right after data generation and not during the analysis procedure allowing for early intervention in the sample handling process. Our analysis shows that this strategy is fast, robust to annotation and differences in library size, and provides complementary quality information to RIN scores enabling the accurate identification of degraded samples.

A global metagenomic map of urban microbiomes and antimicrobial resistance

We present a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities over 3 years, representing the first systematic, worldwide catalog of the urban microbial ecosystem. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance (AMR) markers, and genetic elements, including 10,928 viruses, 1,302 bacteria, 2 archaea, and 838,532 CRISPR arrays not found in reference databases. We identified 4,246 known species of urban microorganisms and a consistent set of 31 species found in 97% of samples that were distinct from human commensal organisms. Profiles of AMR genes varied widely in type and density across cities. Cities showed distinct microbial taxonomic signatures that were driven by climate and geographic differences. These results constitute a high-resolution global metagenomic atlas that enables discovery of organisms and genes, highlights potential public health and forensic applications, and provides a culture-independent view of AMR burden in cities.

NGS-Based S. aureus Typing and Outbreak Analysis in Clinical Microbiology Laboratories: Lessons Learned From a Swiss-Wide Proficiency Test

Whole genome sequencing (WGS) enables high resolution typing of bacteria up to the single nucleotide polymorphism (SNP) level. WGS is used in clinical microbiology laboratories for infection control, molecular surveillance and outbreak analyses. Given the large palette of WGS reagents and bioinformatics tools, the Swiss clinical bacteriology community decided to conduct a ring trial (RT) to foster harmonization of NGS-based bacterial typing. The RT aimed at assessing methicillin-susceptible Staphylococcus aureus strain relatedness from WGS and epidemiological data. The RT was designed to disentangle the variability arising from differences in sample preparation, SNP calling and phylogenetic methods. Nine laboratories participated. The resulting phylogenetic tree and cluster identification were highly reproducible across the laboratories. Cluster interpretation was, however, more laboratory dependent, suggesting that an increased sharing of expertise across laboratories would contribute to further harmonization of practices. More detailed bioinformatic analyses unveiled that while similar clusters were found across laboratories, these were actually based on different sets of SNPs, differentially retained after sample preparation and SNP calling procedures. Despite this, the observed number of SNP differences between pairs of strains, an important criterion to determine strain relatedness given epidemiological information, was similar across pipelines for closely related strains when restricting SNP calls to a common core genome defined by S. aureus cgMLST schema. The lessons learned from this pilot study will serve the implementation of larger-scale RT, as a mean to have regular external quality assessments for laboratories performing WGS analyses in a clinical setting.

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale1-3. Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter4; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation5,6; analyses timings and patterns of tumour evolution7; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity8,9; and evaluates a range of more-specialized features of cancer genomes8,10-18.

Analyses of non-coding somatic drivers in 2,658 cancer whole genomes

The discovery of drivers of cancer has traditionally focused on protein-coding genes1-4. Here we present analyses of driver point mutations and structural variants in non-coding regions across 2,658 genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium5 of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). For point mutations, we developed a statistically rigorous strategy for combining significance levels from multiple methods of driver discovery that overcomes the limitations of individual methods. For structural variants, we present two methods of driver discovery, and identify regions that are significantly affected by recurrent breakpoints and recurrent somatic juxtapositions. Our analyses confirm previously reported drivers6,7, raise doubts about others and identify novel candidates, including point mutations in the 5' region of TP53, in the 3' untranslated regions of NFKBIZ and TOB1, focal deletions in BRD4 and rearrangements in the loci of AKR1C genes. We show that although point mutations and structural variants that drive cancer are less frequent in non-coding genes and regulatory sequences than in protein-coding genes, additional examples of these drivers will be found as more cancer genomes become available.

Abstract SY10-02: Pan-cancer study of recurrent and heterogeneous RNA aberrations and association with whole-genome variants

Whole-exome sequencing studies have transformed our understanding of recurrent somatic mutations that contribute to cancer pathogenesis; however, these studies limit our ability to identify cancer-associated mutations to those that cause protein-coding changes. To more comprehensively catalogue cancer-associated gene alterations, we have extensively characterized tumor transcriptomes from 1,220 donors with matched whole-genome sequence data to identify recurrent RNA-level aberrations. Specifically, we created a unified RNA-Seq analysis pipeline including sequence alignment and quality control and subsequently identified gene alterations through outlier detection from estimated gene expression levels, alternative splicing, alternative transcription starts, and allele-specific expression and through identified RNA-edited sites and gene fusions. Our data represent an extensive catalog of RNA aberrations for each gene across 27 cancer types. We have also tested for genetic associations with these RNA phenotypes. Using an integrative analysis approach, we have mapped genome-wide cis and trans effects on individual RNA phenotypes, considering both common germline variants as well as somatic SNVs in gene promoters, enhancers, and intronic and other regions. Many of the regulatory associations we identify are not accessible by exome sequencing, underlining the importance of whole-genome sequence data. Utilizing this RNA-centric view, we have identified genes that are recurrently altered, yet have not been previously characterized as cancer genes or identified through DNA-level driver gene analysis. To identify further supporting evidence that these recurrent alterations are potential drivers, we identified genes with mutually exclusive RNA-level alterations. Our findings reveal new insights into selective advantages of somatic changes and molecular mechanisms of cancer. This work is by the Transcriptome Working Group of the Pan-Cancer Analysis of Whole Genomes (PCAWG) consortium and authors are listed in alphabetical order.

Citation Format: Samirkumar Amin, Philip Awadalla, Andrew Biankin, Paul Boutros, Alvis Brazma, Angela Norie Brooks, Claudia Calabrese, David Chang, Aurélien Chateigner, Ken Chen, Zechen Chong, Brian Craft, Chad Creighton, Deniz Demircioğlu, Nuno Fonseca, Milana Frenkel-Morgenstern, Gad Getz, Jonathan Göke, Mary Goldman, Liliana Greger, Syed Haider, Yao He, Katherine Hoadley, Yuan Ji, Andre Kahles, Ekta Khurana, Jan Korbel, Kjong Lehmann, Han Liang, Fenglin Liu, Maximillian Marin, Matthew Meyerson, Akinyemi Ojesina, Francis Ouellette, Chandra Pedamallu, Marc Perry, Gunnar Rätsch, Roland Schwarz, Yuichi Shiraishi, Cameron Soulette, Oliver Stegle, Patrick Tan, Alfonso Valencia, Linda Xiang, Christina Yung, Junjun Zhang, Fan Zhang, Zemin Zhang, Jingchun Zhu. Pan-cancer study of recurrent and heterogeneous RNA aberrations and association with whole-genome variants [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 SY10-02. doi:10.1158/1538-7445.AM2017-SY10-02

Abstract 389: Integrating diverse transcriptomic alterations to identify cancer-relevant genes

Introduction:

We present a novel method to identify cancer driver genes that jointly examines any number of diverse transcriptomic alterations with the goal to uncover highly recurrent and heterogeneous patterns in 1190 samples across 26 cancer types as part of the PanCancer Analysis of Whole Genomes (PCAWG) of the International Cancer Genome Consortium (ICGC).

Motivation:

Previous pan-cancer genomic studies have focused on the analysis of somatic mutations as the driver of phenotypic changes. Here, we propose a method to integrate a wide variety of RNA and DNA changes to redefine the concept of driver events and account for the transcriptome’s role in tumorigenesis. PTK2 provides a motivating example, since it has many RNA alterations that correlate with patient survival, such as overexpression, exon-skips, and alternative promoter usage.

In our analysis, we integrate an unprecedented amount of various alterations including gene fusions, RNA editing, alternative splicing, expression outliers, alternative promoters, allele specific expression, and somatic mutations. This enables us to also identify mutually exclusive (MutE) and co-occurring (CoO) patterns between different types of alterations within a gene.

Methods:

Our method has 3 main strengths: flexibility to handle any number or type of alteration, sensitivity to different frequencies of alterations so rare events are not lost in the recurrence analysis, and diversity of ranking such that genes with multiple alterations are prioritized. Our method is summarized in two steps:

1) Identify genes that are both recurrently and heterogeneously altered across many samples by calculating a rank-based score for each gene.

2) Identify MutE and CoO patterns between alteration types for the genes identified in the previous step.

To ensure that alterations were comparable, we applied a thresholding model to binarize all alterations for gene-sample pairs, allowing us to account for the properties of the different modalities involved.

Step 1 of our method calculates a score for each gene that takes into account: 1) the number of alterations to a gene across all samples, 2) the rarity of each alteration, and 3) how many types of alterations are observed per gene. The score is then used to rank the genes and top genes are considered for MutE and CoO analyses.

Results:

Our top 100 ranked genes were highly enriched for cancer census genes (adjusted p-value: 2.06e-9), indicating that we identify cancer relevant genes. Our top five ranked cancer census genes were IGF2, ERBB2, RARA, CREBBP, and ARID1A; all of which had at least 4 of 7 possible alterations, showing our scoring method prioritizes genes with diverse alterations. We also found that alternative promoter usage and alternative splicing were highly co-occurring alterations, with PTK2 having the highest co-occurrence between them. In summary, we propose a new method to analyze various RNA disruptions and show it can yield new insights beyond genomic variation.

Citation Format: Natalie R. Davidson, PanCancer Analysis of Whole Genomes 3 (PCAWG-3) for ICGC, Alvis Brazma, Angela N. Brooks, Claudia Calabrese, Nuno A. Fonseca, Jonathan Goke, Yao He, Xueda Hu, Andre Kahles, Kjong-Van Lehmann, Fenglin Liu, Gunnar Rätsch, Siliang Li, Roland F. Schwarz, Mingyu Yang, Zemin Zhang, Fan Zhang, Liangtao Zheng. Integrating diverse transcriptomic alterations to identify cancer-relevant genes [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 389. doi:10.1158/1538-7445.AM2017-389