Genomic characterization of biliary tract cancers identifies driver genes and predisposing mutations

Background & Aims Biliary tract cancers (BTCs) are clinically and pathologically heterogeneous and respond poorly to treatment. Genomic profiling can offer a clearer understanding of their carcinogenesis, classification and treatment strategy. We performed large-scale genome sequencing analyses on BTCs to investigate their somatic and germline driver events and characterize their genomic landscape. Methods We analyzed 412 BTC samples from Japanese and Italian populations, 107 by whole-exome sequencing (WES), 39 by whole-genome sequencing (WGS), and a further 266 samples by targeted sequencing. The subtypes were 136 intrahepatic cholangiocarcinomas (ICCs), 101 distal cholangiocarcinomas (DCCs), 109 peri-hilar type cholangiocarcinomas (PHCs), and 66 gallbladder or cystic duct cancers (GBCs/CDCs). We identified somatic alterations and searched for driver genes in BTCs, finding pathogenic germline variants of cancer-predisposing genes. We predicted cell-of-origin for BTCs by combining somatic mutation patterns and epigenetic features. Results We identified 32 significantly and commonly mutated genes including TP53 , KRAS , SMAD4 , NF1 , ARID1A , PBRM1 , and ATR , some of which negatively affected patient prognosis. A novel deletion of MUC17 at 7q22.1 affected patient prognosis. Cell-of-origin predictions using WGS and epigenetic features suggest hepatocyte-origin of hepatitis-related ICCs. Deleterious germline mutations of cancer-predisposing genes such as BRCA1 , BRCA2 , RAD51D , MLH1 , or MSH2 were detected in 11% (16/146) of BTC patients. Conclusions BTCs have distinct genetic features including somatic events and germline predisposition. These findings could be useful to establish treatment and diagnostic strategies for BTCs based on genetic information. Lay summary We here analyzed genomic features of 412 BTC samples from Japanese and Italian populations. A total of 32 significantly and commonly mutated genes were identified, some of which negatively affected patient prognosis, including a novel deletion of MUC17 at 7q22.1 . Cell-of-origin predictions using WGS and epigenetic features suggest hepatocyte-origin of hepatitis-related ICCs. Deleterious germline mutations of cancer-predisposing genes were detected in 11% of patients with BTC. BTCs have distinct genetic features including somatic events and germline predisposition

A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns

In cancer, the primary tumour's organ of origin and histopathology are the strongest determinants of its clinical behaviour, but in 3% of cases a patient presents with a metastatic tumour and no obvious primary. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we train a deep learning classifier to predict cancer type based on patterns of somatic passenger mutations detected in whole genome sequencing (WGS) of 2606 tumours representing 24 common cancer types produced by the PCAWG Consortium. Our classifier achieves an accuracy of 91% on held-out tumor samples and 88% and 83% respectively on independent primary and metastatic samples, roughly double the accuracy of trained pathologists when presented with a metastatic tumour without knowledge of the primary. Surprisingly, adding information on driver mutations reduced accuracy. Our results have clinical applicability, underscore how patterns of somatic passenger mutations encode the state of the cell of origin, and can inform future strategies to detect the source of circulating tumour DNA

A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns.

In cancer, the primary tumour's organ of origin and histopathology are the strongest determinants of its clinical behaviour, but in 3% of cases a patient presents with a metastatic tumour and no obvious primary. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we train a deep learning classifier to predict cancer type based on patterns of somatic passenger mutations detected in whole genome sequencing (WGS) of 2606 tumours representing 24 common cancer types produced by the PCAWG Consortium. Our classifier achieves an accuracy of 91% on held-out tumor samples and 88% and 83% respectively on independent primary and metastatic samples, roughly double the accuracy of trained pathologists when presented with a metastatic tumour without knowledge of the primary. Surprisingly, adding information on driver mutations reduced accuracy. Our results have clinical applicability, underscore how patterns of somatic passenger mutations encode the state of the cell of origin, and can inform future strategies to detect the source of circulating tumour DNA

A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns

Abstract: In cancer, the primary tumour’s organ of origin and histopathology are the strongest determinants of its clinical behaviour, but in 3% of cases a patient presents with a metastatic tumour and no obvious primary. Here,as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we train a deep learning classifier to predict cancer type based on patterns of somatic passenger mutations detected in whole genome sequencing (WGS) of 2606 tumours representing 24 common cancer types produced by the PCAWG Consortium. Our classifier achieves an accuracy of 91% on held-out tumor samples and 88% and 83% respectively on independent primary and metastatic samples, roughly double the accuracy of trained pathologists when presented with a metastatic tumour without knowledge of the primary. Surprisingly, adding information on driver mutations reduced accuracy. Our results have clinical applicability, underscore how patterns of somatic passenger mutations encode the state of the cell of origin, and can inform future strategies to detect the source of circulating tumour DNA

Einfluss von Histonmodifikationen auf mRNA-Menge und -Struktur

Histones are frequently decorated with covalent modifications. These histone modifications are thought to be involved in various chromatin-dependent processes including transcription and splicing. To elucidate the relationship between histone modifications and these two processes, we derived models to predict the expression level of genes and the structure of transcribed mRNA from histone modification levels. We found that histone modification levels and gene expression are very well correlated. Moreover, we show that only a small number of histone modifications are necessary to accurately predict gene expression. We show that different sets of histone modifications are necessary to predict gene expression driven by high CpG content promoters (HCPs) or low CpG content promoters (LCPs). Quantitative models involving H3K4me3 and H3K79me1 are the most predictive of the expression levels in LCPs, whereas HCPs require H3K27ac and H4K20me1. We propose a preliminary “Histone Code of Transcription”, where H3K4me3 is involved in RNA polymerase II (PolII) recruitment and/or initiation, the combinatorial action of H3K27ac and H4K20me1 leads to the transition to elongation, and finally H3K79me1 and H4K20me1 signal the transition to an elongating PolII. The preliminary “Histone Code of Transcription” awaits confirmation by further experimental studies. We furthermore show that the connections between histone modifications and gene expression seem to be general, as we were able to predict gene expression levels of one cell type using a model trained on another one. We propose that our model could be further improved by including information about different mechanisms of regulation of mRNA stability and degradation, giving rise to more accurate predictions of gene expression levels. Using logistic models, we showed that levels of histone modifications, nucleosomes and PolII are predictive of the splicing outcomes of alternative exons, and that this result is not a consequence of experimental artifacts. Furthermore, we identified four histone modifications, namely H3K27me1, H3K36ac, H3K36me3 and H3K79me2, which consistently have a significant contribution to prediction accuracy of our models. This finding implies that they could be directly related to the splicing process, in agreement with recent analyses of the relationship of chromatin structure and the splicing process. We also established that histone modifications convey information about alternative splicing different from the one encoded in the DNA sequence of exons and surrounding regions, suggesting a possible interplay between these two mechanisms of splicing regulation. Finally, we confirmed the existence of functional coupling of transcription and splicing, by studying the dependence of structure of transcripts on their expression levels. The exact mechanisms behind these observations will have to be studied further.Histonproteinen liegen häufig chemisch modifiziert vor. Diese Modifikationen sind an vielen chromatinabhängigen Prozessen beteiligt. Diese Arbeit untersucht den Zusammenhang zwischen Histonmodifikationen und Transkription bzw. Splicing anhand von Modellen, die den Expressionslevel von Genen bzw. die Struktur von mRNAs mit Hilfe der Histonmodifikationen vorhersagen. Unsere Ergebnisse zeigen, dass die Häufigkeit von Histonmodifikationen am Promotor und der Genexpressionslevel stark miteinander korreliert sind. Die Vorhersage der Genexpression hängt dabei nur von wenigen Histonmodifikationen ab. Dabei haben wir unterschiedliche Gruppen von Modifikationen identifiziert, die für eine gute Vorhersagequalität in Promotoren mit hohem bzw. niedrigen CpG Gehalt notwendig sind. Quantitative Modelle, die die Information von H4K4me3 und H3K79me1 beinhalten, haben die beste Qualität in Promotoren mit niedrigem CpG Gehalt, während Modelle, die H3K27ac und H4K20me1 verwenden, am Besten sind für Promotoren mit hohem CpG Gehalt. Basierend auf diesen Ergebnissen schlagen wir einen vorläufigen „Histoncode für die Transkription“ vor, in dem H3K4me3 an der Rekrutierung und/oder Initiation von RNA Polymerase II (Pol II) beteiligt ist, H3K27ac und H4K20me1 den Übergang zur Elongation ermöglicht und H3K79me1 und H4K20me1 den erfolgreichen Übergang anzeigt. Dieser vorläufige „Histoncode für die Transkription“ muss in zukünftigen experimentellen Studien kritisch überprüft werden. Die gefundenen Zusammenhänge zwischen Histonmodifikationen und Genexpression sind von allgemeiner Natur, da es möglich war, die Genexpression von Zellen mit Modellen vorherzusagen, die in einem anderen Zelltyp erstellt worden sind. Unsere Ergebnisse zeigen weiterhin, dass unsere Modelle durch die Berücksichtigung von Prozessen, die die mRNA Stabilität beeinflussen, weiter verbessert werden können. Unsere Untersuchungen zeigen, dass die Häufigkeit von Histonmodifikationen, Nukleosomen und Pol II verwendet werden können, um alternatives Splicing vorherzusagen. Wir haben vier Histonmodifikationen (H3K27me1, H3K36ac, H3K36me3 und H3K79me2) identifiziert, die signifikant zur Vorhersagequalität unserer Modelle beitragen. Dieses Ergebnis legt nahe, dass diese Modifikationen im direkten Zusammenhang mit dem Splicingprozess stehen könnten. Histonmodifikationen tragen Information über alternatives Splicing, die z.T. komplementär zu den Sequenzinformationen in Exons und den umgebenden Regionen sind, was auf ein Zusammenspiel von Histonmodifikations- und Sequenzabhängigen Prozessen in der Regulation von Splicing hindeutet. Unsere Ergbnisse zeigen eine Abhängigkeit zwischen der Struktur von Transkripten und deren Expressionslevel, was eine funkti onelle Kopplung zwischen Transkription und Splicing bestätigt. Die Mechanismen, die diesen Beobachtungen zu Grunde liegen, müssen in Zukunft weiter untersucht werden