BAP1 loss in hepatocellular carcinoma is associated to PKA activation and fibrolamellar features

Hirsch, Theo; Negulescu, Ana; Gupta, Barkha; Caruso, S.; Noblet, Bénédicte; Couchy, Gabrielle; Bayard, Quentin; Meunier, Lea; Morcrette, Guillaume; Scoazec, Jean Yves; Blanc, Jean‐Frédéric; Amaddeo, Giuliana; Nault, Jean–Charles; Bioulac‐Sage, Paulette; Ziol, Marianne; Beaufrère, Aurélie; Paradis, Valérie; Julien, Claude; Imbeaud, Sandrine; Zucman‐Rossi, Jessica

doi:10.1016/s0168-8278(20)31765-7

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“…We used an in-house series of 401 liver cancers with matched whole exome (WES, n=275) / genome (WGS, n=126) sequencing and RNA-seq data to benchmark the performance of SpliceAI. Initial bioinformatic analyses (alignment and variant calling) were already performed in previous studies (19)(20)(21)(22)(23)(24)(25) and we directly used processed data in this study. We then analyzed a large pan-cancer series comprising 17,714 tumor samples across 25 cancer types from the ICGC-PCAWG (n=2,699 WGS) (26), Hartwig Medical Foundation (n=4,818 WGS) (27) and TCGA (n=10,197 WES) (28) projects.…”

Section: Datasetsmentioning

confidence: 99%

“…We used an in-house series of 401 liver cancers (19)(20)(21)(22)(23)(24)(25) with whole exome (n=275) or whole genome sequencing (n=126) and matched RNA-seq data to predict and validate cryptic splice mutations using SpliceAI, MaxEntScan and GeneSplicer (Figure 1A). A total of 1,704,461 somatic mutations were analyzed with the three tools.…”

Section: Spliceai Outperforms Traditional Tools For Predicting Crypti...mentioning

confidence: 99%

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Discovering cryptic splice mutations in cancers via a deep neural network framework

Teboul

Grabias

Zucman‐Rossi

et al. 2022

Preprint

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Somatic mutations can disrupt splicing regulatory elements and have dramatic effects on cancer genes, yet the functional consequences of mutations located in extended splice regions is difficult to predict. Here, we use a deep neural network (SpliceAI) to characterize the landscape of splice-altering mutations in cancer. In our in-house liver cancer series, SpliceAI uncovers many cryptic splice mutations, located outside essential splice sites, that validate at a high rate in matched RNA-seq data. We then extend the analysis to a large pan-cancer cohort of 18,115 tumors, revealing >100,000 cryptic splice mutations. Taking into account these mutations increases the power of driver gene discovery, revealing >100 new candidate driver genes. It also reveals new driver mutations in known cancer genes, doubling the frequency of splice alterations in tumor suppressor genes. Mutational signature analysis reveals the mutational processes that give rise to splice mutations in each cancer type, with an enrichment of signatures related to clock-like processes and DNA repair deficiency. Altogether, this work sheds light on the causes and impact of cryptic splice mutations in cancer, and highlights the power of deep learning approaches to better annotate the functional consequences of mutations in oncology.

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Section: Datasetsmentioning

confidence: 99%

Section: Spliceai Outperforms Traditional Tools For Predicting Crypti...mentioning

confidence: 99%

Discovering cryptic splice mutations in cancers via a deep neural network framework

Teboul

Grabias

Zucman‐Rossi

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

BAP1 loss in hepatocellular carcinoma is associated to PKA activation and fibrolamellar features

Cited by 1 publication

References 0 publications

Discovering cryptic splice mutations in cancers via a deep neural network framework

Discovering cryptic splice mutations in cancers via a deep neural network framework

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