Detection and benchmarking of somatic mutations in cancer genomes using RNA-seq data

Coudray, Alexandre; Battenhouse, Anna M.; Bücher, Philipp; Iyer, Vishwanath R.

doi:10.1101/249219

Cited by 18 publications

(24 citation statements)

References 60 publications

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“…MuTect behaviour is due to the clustered events filter which removes variants found on haplotypes where other variants are already detected ( Figure S2). This behaviour was also observed in a previous study comparing variants called from matched RNA-Seq and WES samples where the same flag was responsible for filtering the largest number of RNA variants [14]. Using knowledge from external databases allows retention of two NRAS SNVs present in COS-MIC [10], which are discarded by default-filters as they are also present in the PON samples at very low frequency.…”

Section: Sensitivity In the Leucegene Cohortsupporting

confidence: 62%

Finding a suitable library size to call variants in RNA-seq

Quaglieri

Flensburg

Speed

et al. 2019

Preprint

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Background RNA-Seq allows the study of both gene expression changes and transcribed mutations, providing a highly effective way to gain insight into cancer biology. When planning the sequencing of a large cohort of samples, library size is a fundamental factor affecting both the overall cost and the quality of the results. While several studies analyse the effect that library size has on differential expression analyses, sensitivity analysis for variant detection has received far less attention. Results We simulated shallower sequencing depths by downsampling 45 AML samples that are part of the Leucegene project, which were originally sequenced at high depth. We compared the sensitivity of six methods of recovering validated mutations on the same samples. The methods compared are a combination of three popular callers (MuTect, VarScan, and VarDict) and two filtering strategies. We observed an incremental loss in sensitivity when simulating libraries of 80M, 50M, 40M, 30M and 20M fragments, with the largest loss detected with less than 30M fragments (below 90%). The sensitivity in recovering indels varied markedly between callers, with VarDict showing the highest sensitivity (60%). Single nucleotide variant sensitivity is relatively consistent across methods, apart from MuTect, whose default filters need adjustment when using RNA-Seq. We also analysed 136 RNA-Seq samples from the TCGA-LAML cohort, assessing the change in sensitivity between the initial libraries (average 59M fragments) and after downsampling to 40M fragments. When considering single nucleotide variants in recurrently mutated myeloid genes we found a comparable performance, with a 3% average loss in sensitivity using 40M fragments. Conclusions Between 30M and 40M fragments are needed to recover 90%-95% of the initial variants on recurrently mutated myeloid genes. To extend this result to another cancer type, an exploration of the characteristics of its mutations and gene expression patterns is suggested.

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Section: Sensitivity In the Leucegene Cohortsupporting

confidence: 62%

Finding a suitable library size to call variants in RNA-seq

Quaglieri

Flensburg

Speed

et al. 2019

Preprint

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“…5b). Limited overlap between point mutations identified in both DNA and RNA sequencing has been observed in non-small cell lung cancer as well as glioblastoma suggesting that this finding is not unique to OS 23,24 . In addition, few predicted rearrangements involving coding regions were expressed ( Fig.…”

Section: Resultsmentioning

confidence: 95%

Immuno-genomic landscape of osteosarcoma

et al. 2020

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Limited clinical activity has been seen in osteosarcoma (OS) patients treated with immune checkpoint inhibitors (ICI). To gain insights into the immunogenic potential of these tumors, we conducted whole genome, RNA, and T-cell receptor sequencing, immunohistochemistry and reverse phase protein array profiling (RPPA) on OS specimens from 48 pediatric and adult patients with primary, relapsed, and metastatic OS. Median immune infiltrate level was lower than in other tumor types where ICI are effective, with concomitant low T-cell receptor clonalities. Neoantigen expression in OS was lacking and significantly associated with high levels of nonsense-mediated decay (NMD). Samples with low immune infiltrate had higher number of deleted genes while those with high immune infiltrate expressed higher levels of adaptive resistance pathways. PARP2 expression levels were significantly negatively associated with the immune infiltrate. Together, these data reveal multiple immunosuppressive features of OS and suggest immunotherapeutic opportunities in OS patients.

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“…Furthermore, some of the discrepancies can be also due to low coverage in the genome sequence, which generated a falsenegative in the calling. Although calling variants from RNA-Seq data has been shown to be more challenging, it is an interesting alternative for genome sequencing and a large amount of tumor RNA-seq samples do not have normal matched data [39,40].…”

Section: Variant Identification On Rna-seqmentioning

confidence: 99%

neoANT-HILL: an integrated tool for identification of potential neoantigens

et al. 2020

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Background: Cancer neoantigens have attracted great interest in immunotherapy due to their capacity to elicit antitumoral responses. These molecules arise from somatic mutations in cancer cells, resulting in alterations on the original protein. Neoantigens identification remains a challenging task due largely to a high rate of false-positives. Results: We have developed an efficient and automated pipeline for the identification of potential neoantigens. neoANT-HILL integrates several immunogenomic analyses to improve neoantigen detection from Next Generation Sequence (NGS) data. The pipeline has been compiled in a pre-built Docker image such that minimal computational background is required for download and setup. NeoANT-HILL was applied in The Cancer Genome Atlas (TCGA) melanoma dataset and found several putative neoantigens including ones derived from the recurrent RAC1:P29S and SERPINB3:E250K mutations. neoANT-HILL was also used to identify potential neoantigens in RNA-Seq data with a high sensitivity and specificity. Conclusion: neoANT-HILL is a user-friendly tool with a graphical interface that performs neoantigens prediction efficiently. neoANT-HILL is able to process multiple samples, provides several binding predictors, enables quantification of tumor-infiltrating immune cells and considers RNA-Seq data for identifying potential neoantigens.

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Detection and benchmarking of somatic mutations in cancer genomes using RNA-seq data

Cited by 18 publications

References 60 publications

Finding a suitable library size to call variants in RNA-seq

Finding a suitable library size to call variants in RNA-seq

Immuno-genomic landscape of osteosarcoma

neoANT-HILL: an integrated tool for identification of potential neoantigens

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