Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines

Ellrott, Kyle; Bailey, Matthew H.; Saksena, Gordon; Covington, Kyle R.; Kandoth, Cyriac; Stewart, Chip; Hess, Julian M.; Ma, Xiaotu; Chiotti, Kami; McLellan, Michael D.; Sofia, Heidi J.; Hu, Hai; Getz, Gad; Wheeler, David A.; Li, Ding

doi:10.1016/j.cels.2018.03.002

Cited by 651 publications

(609 citation statements)

References 37 publications

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“…Large cancer genome programs such as TCGA has generated an enormous amount of data that is publicly available to the cancer research community. Mining those datasets have enabled novel insights into cancer biology [15][16][17][18][19]. Our analysis suggests that predictive models can be derived from those massive data and used to assign patients to distinctive subtypes.…”

Section: Discussionmentioning

confidence: 97%

Patient stratification of clear cell renal cell carcinoma using the global transcription factor activity landscape derived from RNA-seq data

Zhu

Cang

Chen³

et al. 2019

Preprint

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Clear cell renal cell carcinoma represents the most common type of kidney cancer. Precision medicine approach to ccRCC requires an accurate stratification of patients that can predict prognosis and guide therapeutic decision. Transcription factors are implicated in the initiation and progression of human carcinogenesis. However, no comprehensive analysis of transcription factor activity has been proposed so far to realize patient stratification. Here we propose a novel approach to determine the subtypes of ccRCC patients based on global transcription factor activity landscape. Using the TCGA cohort dataset, we identified different subtypes that have distinct upregulated biomarkers and altered biological pathways. More important, this subtype information can be used to predict the overall survival of ccRCC patients. Our results suggest that transcription factor activity can be harnessed to perform patient stratification.

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Section: Discussionmentioning

confidence: 97%

Patient stratification of clear cell renal cell carcinoma using the global transcription factor activity landscape derived from RNA-seq data

Zhu

Cang

Chen³

et al. 2019

Preprint

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“…To assess candidate immunogenic somatic mutations in patients from the TCGA cohort, we developed and implemented a basic pipeline based on whole-exome and RNA sequencing data. Our analysis builds upon work from the TCGA PanCancer Analysis teams for drivers (43), mutation calling (44) and cancer immune landscapes (45). We obtained somatic mutation calls for all cancer types from Multi-Center Mutation Calling in Multiple Cancers (MC3) (v0.2.8) (7775 patients).…”

Section: Tcga Analysis Pipelinementioning

confidence: 99%

High-throughput prediction of MHC Class I and Class II neoantigens with MHCnuggets

Shao

Bhattacharya

Huang

et al. 2019

Preprint

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Running title: High-throughput prediction of neoantigens with MHCnuggets Potential Conflicts of Interest: V.A receives research funding from Bristol-Myers Abstract Computational prediction of binding between neoantigen peptides and major histocompatibility complex (MHC) proteins is an emerging biomarker for predicting patient response to cancer immunotherapy. Current neoantigen predictors focus on in silico estimation of MHC binding affinity and are limited by low positive predictive value for actual peptide presentation, inadequate support for rare MHC alleles and poor scalability to high-throughput data sets. To address these limitations, we developed MHCnuggets, a deep neural network method to predict peptide-MHC binding. MHCnuggets is the only method to handle binding prediction for common or rare alleles of MHC Class I or II, with a single neural network architecture. Using a long shortterm memory network (LSTM), MHCnuggets accepts peptides of variable length and is capable of faster performance than other methods. When compared to methods that integrate binding affinity and HLAp data from mass spectrometry, MHCnuggets yields a fourfold increase in positive predictive value on independent MHC-bound peptide (HLAp) data. We applied MHCnuggets to 26 cancer types in TCGA, processing 52.6 million allele-peptide comparisons in under 2.3 hours, yielding 103,587 candidate immunogenic missense mutations (IMMs). IMM hotspots occurred in 36 genes, including 22 driver genes. Predicted IMM load was significantly associated with increased immune cell infiltration (p<2e-16) including CD8+ T cells. Notably, only 0.15% of predicted immunogenic missense mutations were observed in >2 patients, with 65% of these derived from driver mutations. Our results provide a new method for neoantigen prediction with high performance characteristics and demonstrate its utility in large data sets across human cancers. SynopsisWe developed a new in silico predictor of Major Histocompatibility Complex (MHC) ligand binding and demonstrated its utility to assess potential neoantigens and immunogenic missense mutations (IMMs) in 6613 TCGA patients.

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“…Clinical information on these tumours is included in Supplemental Table S1. These tumours had significantly fewer somatic single base substitutions (SBSs) than the OSCCs and HNSCCs analyzed by the TCGA consortium (median 1.02 versus 1.66 and 2.44 mutations per megabase, p = 4.11×10 -5 and 4.85×10 -10 respectively, Wilcoxon rank sum tests) (Ellrott et al 2018;Alexandrov et al 2019).…”

Section: Bacterial Infection Associated Osccs Show Novel Mutational Smentioning

confidence: 99%

“…Previously compiled whole-exome (N=19,184) and whole-genome (N=4,645) sequencing data was screened for presence of the signature in 62074759 (Alexandrov et al 2019). This included 2,780 whole-genomes from the Pan Cancer of Whole Genomes consortium (Campbell et al 2017), and 9,493 whole-exomes from the TCGA consortium (Ellrott et al 2018). We examined tumours with ≥50 (exomes) or ≥500 (genomes) thymine mutations, to identify enrichment for mutations with the 5' sequence context characteristic of the signature in 62074759 (Supplemental Data S1).…”

Section: Identification Of Additional Tumours With the Signature Inmentioning

confidence: 99%

Identification of novel mutational signatures in Asian oral squamous cell carcinomas associated with bacterial infections

Boot

et al. 2018

Preprint

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Mutational signatures can reveal the history of mutagenic processes that cells were exposed to prior to and during tumourigenesis. We expect that as-yet-undiscovered mutational processes will shed further light on mutagenesis leading to carcinogenesis. With this in mind, we analyzed the mutational spectra of 36 Asian oral squamous cell carcinomas. The mutational spectra of two samples from patients who presented with oral bacterial infections, showed novel mutational signatures. One of these novel signatures, SBS_AnT, is characterized by a preponderance of thymine mutations, strong transcriptional strand bias, and striking enrichment for adenines in the 4 base pairs 5’ of mutation sites. Examination of publicly available sequencing data revealed SBS_AnT in 25 tumours from several mucosal tissue types, all of which harbour human symbionts or are adjacent to tissues that harbour symbionts. Data in a preprint released while this manuscript was in revision strongly suggest that the bacterial compound colibactin causes SBS_AnT.

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Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines

Cited by 651 publications

References 37 publications

Patient stratification of clear cell renal cell carcinoma using the global transcription factor activity landscape derived from RNA-seq data

Patient stratification of clear cell renal cell carcinoma using the global transcription factor activity landscape derived from RNA-seq data

High-throughput prediction of MHC Class I and Class II neoantigens with MHCnuggets

Identification of novel mutational signatures in Asian oral squamous cell carcinomas associated with bacterial infections

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