<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Iorio F</submitter><funding>Cancer Research UK</funding><pagination>6713</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC5928049</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>8(1)</volume><pubmed_abstract>Cancer hallmarks are evolutionary traits required by a tumour to develop. While extensively characterised, the way these traits are achieved through the accumulation of somatic mutations in key biological pathways is not fully understood. To shed light on this subject, we characterised the landscape of pathway alterations associated with somatic mutations observed in 4,415 patients across ten cancer types, using 374 orthogonal pathway gene-sets mapped onto canonical cancer hallmarks. Towards this end, we developed SLAPenrich: a computational method based on population-level statistics, freely available as an open source R package. Assembling the identified pathway alterations into sets of hallmark signatures allowed us to connect somatic mutations to clinically interpretable cancer mechani</pubmed_abstract><journal>Scientific reports</journal><pubmed_title>Pathway-based dissection of the genomic heterogeneity of cancer hallmarks' acquisition with SLAPenrich.</pubmed_title><pmcid>PMC5928049</pmcid><funding_grant_id>21777</funding_grant_id><pubmed_authors>Iorio F</pubmed_authors><pubmed_authors>Brammeld JS</pubmed_authors><pubmed_authors>Martincorena I</pubmed_authors><pubmed_authors>Saez-Rodriguez J</pubmed_authors><pubmed_authors>Wille DR</pubmed_authors><pubmed_authors>Garcia-Alonso L</pubmed_authors><pubmed_authors>McDermott U</pubmed_authors></additional><is_claimable>false</is_claimable><name>Pathway-based dissection of the genomic heterogeneity of cancer hallmarks' acquisition with SLAPenrich.</name><description>Cancer hallmarks are evolutionary traits required by a tumour to develop. While extensively characterised, the way these traits are achieved through the accumulation of somatic mutations in key biological pathways is not fully understood. To shed light on this subject, we characterised the landscape of pathway alterations associated with somatic mutations observed in 4,415 patients across ten cancer types, using 374 orthogonal pathway gene-sets mapped onto canonical cancer hallmarks. Towards this end, we developed SLAPenrich: a computational method based on population-level statistics, freely available as an open source R package. Assembling the identified pathway alterations into sets of hallmark signatures allowed us to connect somatic mutations to clinically interpretable cancer mechani</description><dates><release>2018-01-01T00:00:00Z</release><publication>2018 Apr</publication><modification>2025-04-04T09:24:40.222Z</modification><creation>2019-03-27T00:13:02Z</creation></dates><accession>S-EPMC5928049</accession><cross_references><pubmed>29713020</pubmed><doi>10.1038/s41598-018-25076-6</doi></cross_references></HashMap>