Abstract
We present SLAPenrich, a statistical approach implemented in an R package to search for pathways enriched for genomic alterations in large datasets. SLAPenrich mines for sample-population level enrichments, accounting for mutation rates, gene exonic lengths, and mutational mutual exclusivity. We show that SLAPenrich detects in lung adenocarcinoma pathways known to be typically altered, therapeutic targets, and associations with clinicopathological features. Finally, we explore with SLAPenrich the landscape of pathways contributing to the acquisition of the cancer hallmarks in large cohorts of patients across 10 cancer types, highlighting potential novel cancer driver genes and networks.
Copyright
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