58 research outputs found
Identifying Cancer Drivers Using DRIVE: A Feature-Based Machine Learning Model for a Pan-Cancer Assessment of Somatic Missense Mutations.
Sporadic cancer develops from the accrual of somatic mutations. Out of all small-scale somatic aberrations in coding regions, 95% are base substitutions, with 90% being missense mutations. While multiple studies focused on the importance of this mutation type, a machine learning method based on the number of protein-protein interactions (PPIs) has not been fully explored. This study aims to develop an improved computational method for driver identification, validation and evaluation (DRIVE), which is compared to other methods for assessing its performance. DRIVE aims at distinguishing between driver and passenger mutations using a feature-based learning approach comprising two levels of biological classification for a pan-cancer assessment of somatic mutations. Gene-level features include the maximum number of protein-protein interactions, the biological process and the type of post-translational modifications (PTMs) while mutation-level features are based on pathogenicity scores. Multiple supervised classification algorithms were trained on Genomics Evidence Neoplasia Information Exchange (GENIE) project data and then tested on an independent dataset from The Cancer Genome Atlas (TCGA) study. Finally, the most powerful classifier using DRIVE was evaluated on a benchmark dataset, which showed a better overall performance compared to other state-of-the-art methodologies, however, considerable care must be taken due to the reduced size of the dataset. DRIVE outlines the outstanding potential that multiple levels of a feature-based learning model will play in the future of oncology-based precision medicine
Whole-genome sequencing of nine esophageal adenocarcinoma cell lines.
Esophageal adenocarcinoma (EAC) is highly mutated and molecularly heterogeneous. The number of cell lines available for study is limited and their genome has been only partially characterized. The availability of an accurate annotation of their mutational landscape is crucial for accurate experimental design and correct interpretation of genotype-phenotype findings. We performed high coverage, paired end whole genome sequencing on eight EAC cell lines-ESO26, ESO51, FLO-1, JH-EsoAd1, OACM5.1 C, OACP4 C, OE33, SK-GT-4-all verified against original patient material, and one esophageal high grade dysplasia cell line, CP-D. We have made available the aligned sequence data and report single nucleotide variants (SNVs), small insertions and deletions (indels), and copy number alterations, identified by comparison with the human reference genome and known single nucleotide polymorphisms (SNPs). We compare these putative mutations to mutations found in primary tissue EAC samples, to inform the use of these cell lines as a model of EAC.This work was funded by an MRC Programme Grant to R.C.F. and a Cancer Research UK grant to PAWE. The pipeline for
mutation calling is funded by Cancer Research UK as part of the International Cancer Genome Consortium. G.C. is a National Institute for Health
Research Lecturer as part of a NIHR professorship grant to R.C.F. AGL is supported by a Cancer Research UK programme grant
(C14303/A20406) to Simon Tavaré and the European Commission through the Horizon 2020 project SOUND (Grant Agreement no. 633974)
Rearrangement processes and structural variations show evidence of selection in oesophageal adenocarcinomas
Oesophageal adenocarcinoma (OAC) provides an ideal case study to characterize large-scale rearrangements. Using whole genome short-read sequencing of 383 cases, for which 214 had matched whole transcriptomes, we observed structural variations (SV) with a predominance of deletions, tandem duplications and inter-chromosome junctions that could be identified as LINE-1 mobile element (ME) insertions. Complex clusters of rearrangements resembling breakage-fusion-bridge cycles or extrachromosomal circular DNA accounted for 22% of complex SVs affecting known oncogenes. Counting SV events affecting known driver genes substantially increased the recurrence rates of these drivers. After excluding fragile sites, we identified 51 candidate new drivers in genomic regions disrupted by SVs, including ETV5, KAT6B and CLTC. RUNX1 was the most recurrently altered gene (24%), with many deletions inactivating the RUNT domain but preserved the reading frame, suggesting an altered protein product. These findings underscore the importance of identification of SV events in OAC with implications for targeted therapies.</p
Rearrangement processes and structural variations show evidence of selection in oesophageal adenocarcinomas
Oesophageal adenocarcinoma (OAC) provides an ideal case study to characterize large-scale rearrangements. Using whole genome short-read sequencing of 383 cases, for which 214 had matched whole transcriptomes, we observed structural variations (SV) with a predominance of deletions, tandem duplications and inter-chromosome junctions that could be identified as LINE-1 mobile element (ME) insertions. Complex clusters of rearrangements resembling breakage-fusion-bridge cycles or extrachromosomal circular DNA accounted for 22% of complex SVs affecting known oncogenes. Counting SV events affecting known driver genes substantially increased the recurrence rates of these drivers. After excluding fragile sites, we identified 51 candidate new drivers in genomic regions disrupted by SVs, including ETV5, KAT6B and CLTC. RUNX1 was the most recurrently altered gene (24%), with many deletions inactivating the RUNT domain but preserved the reading frame, suggesting an altered protein product. These findings underscore the importance of identification of SV events in OAC with implications for targeted therapies.</p
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RNA Sequencing of Pancreatic Circulating Tumour Cells Implicates WNT Signaling in Metastasis
Circulating tumour cells (CTCs) shed into blood from primary cancers include putative precursors that initiate distal metastases. While these cells are extraordinarily rare, they may identify cellular pathways contributing to the blood-borne dissemination of cancer. Here, we adapted a microfluidic device for efficient capture of CTCs from an endogenous mouse pancreatic cancer model and subjected CTCs to single molecule RNA sequencing, identifying Wnt2 as a candidate gene enriched in CTCs. Expression of Wnt2 in pancreatic cancer cells suppresses anoikis, enhances anchorage-independent sphere formation, and increases metastatic propensity in vivo. This effect is correlated with fibronectin upregulation and suppressed by inhibition of Map3k7 (Tak1) kinase. In humans, formation of non-adherent tumour spheres by pancreatic cancer cells is associated with upregulation of multiple Wnt genes, and pancreatic CTCs revealed enrichment for Wnt signaling in 5 of 11 cases. Thus, molecular analysis of CTCs may identify candidate therapeutic targets to prevent the distal spread of cancer
Pan-cancer analysis of whole genomes identifies driver rearrangements promoted by LINE-1 retrotransposition.
About half of all cancers have somatic integrations of retrotransposons. Here, to characterize their role in oncogenesis, we analyzed the patterns and mechanisms of somatic retrotransposition in 2,954 cancer genomes from 38 histological cancer subtypes within the framework of the Pan-Cancer Analysis of Whole Genomes (PCAWG) project. We identified 19,166 somatically acquired retrotransposition events, which affected 35% of samples and spanned a range of event types. Long interspersed nuclear element (LINE-1; L1 hereafter) insertions emerged as the first most frequent type of somatic structural variation in esophageal adenocarcinoma, and the second most frequent in head-and-neck and colorectal cancers. Aberrant L1 integrations can delete megabase-scale regions of a chromosome, which sometimes leads to the removal of tumor-suppressor genes, and can induce complex translocations and large-scale duplications. Somatic retrotranspositions can also initiate breakage-fusion-bridge cycles, leading to high-level amplification of oncogenes. These observations illuminate a relevant role of L1 retrotransposition in remodeling the cancer genome, with potential implications for the development of human tumors
Mutational signatures in esophageal adenocarcinoma define etiologically distinct subgroups with therapeutic relevance.
Esophageal adenocarcinoma (EAC) has a poor outcome, and targeted therapy trials have thus far been disappointing owing to a lack of robust stratification methods. Whole-genome sequencing (WGS) analysis of 129 cases demonstrated that this is a heterogeneous cancer dominated by copy number alterations with frequent large-scale rearrangements. Co-amplification of receptor tyrosine kinases (RTKs) and/or downstream mitogenic activation is almost ubiquitous; thus tailored combination RTK inhibitor (RTKi) therapy might be required, as we demonstrate in vitro. However, mutational signatures showed three distinct molecular subtypes with potential therapeutic relevance, which we verified in an independent cohort (n = 87): (i) enrichment for BRCA signature with prevalent defects in the homologous recombination pathway; (ii) dominant T>G mutational pattern associated with a high mutational load and neoantigen burden; and (iii) C>A/T mutational pattern with evidence of an aging imprint. These subtypes could be ascertained using a clinically applicable sequencing strategy (low coverage) as a basis for therapy selection.Whole-genome sequencing of esophageal adenocarcinoma samples was performed as part of the International Cancer Genome Consortium (ICGC) through the oEsophageal Cancer Clinical and Molecular Stratification (OCCAMS) Consortium and was funded by Cancer Research UK. We thank the ICGC members for their input on verification standards as part of the benchmarking exercise. We thank the Human Research Tissue Bank, which is supported by the National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre, from Addenbrooke’s Hospital and UCL. Also the University Hospital of Southampton Trust and the Southampton, Birmingham, Edinburgh and UCL Experimental Cancer Medicine Centres and the QEHB charities. This study was partly funded by a project grant from Cancer Research UK. R.C.F. is funded by an NIHR Professorship and receives core funding from the Medical Research Council and infrastructure support from the Biomedical Research Centre and the Experimental Cancer Medicine Centre. We acknowledge the support of The University of Cambridge, Cancer Research UK (C14303/A17197) and Hutchison Whampoa Limited. We would like to thank Dr. Peter Van Loo for providing the NGS version of ASCAT for copy number calling. We are grateful to all the patients who provided written consent for participation in this study and the staff at all participating centres.
Some of the work was undertaken at UCLH/UCL who received a proportion of funding from the Department of Health’s NIHR Biomedical Research Centres funding scheme. The work at UCLH/UCL was also supported by the CRUK UCL Early Cancer Medicine Centre.This is the author accepted manuscript. The final version is available from Nature Publishing Group via http://dx.doi.org/10.1038/ng.365
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Patient-specific cancer genes contribute to recurrently perturbed pathways and establish therapeutic vulnerabilities in esophageal adenocarcinoma
Abstract: The identification of cancer-promoting genetic alterations is challenging particularly in highly unstable and heterogeneous cancers, such as esophageal adenocarcinoma (EAC). Here we describe a machine learning algorithm to identify cancer genes in individual patients considering all types of damaging alterations simultaneously. Analysing 261 EACs from the OCCAMS Consortium, we discover helper genes that, alongside well-known drivers, promote cancer. We confirm the robustness of our approach in 107 additional EACs. Unlike recurrent alterations of known drivers, these cancer helper genes are rare or patient-specific. However, they converge towards perturbations of well-known cancer processes. Recurrence of the same process perturbations, rather than individual genes, divides EACs into six clusters differing in their molecular and clinical features. Experimentally mimicking the alterations of predicted helper genes in cancer and pre-cancer cells validates their contribution to disease progression, while reverting their alterations reveals EAC acquired dependencies that can be exploited in therapy
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