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Lee, C. A.

Publications and source records attributed to Lee, C. A..

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Functional and genetic determinants of mutation rate variability in regulatory elements of cancer genomes

BackgroundCancer genomes are shaped by mutational processes with complex spatial variation at multiple scales. Entire classes of regulatory elements are affected by local variations in mutation frequency. However, the underlying mutational mechanisms with functional and genetic determinants remain poorly understood. ResultsWe characterised the mutational landscape of 1.3 million gene regulatory and chromatin architectural elements in 2,419 whole cancer genomes with transcriptional and pathway activity, functional conservation and recurrent driver events. We developed RM2, a statistical model that quantifies mutational enrichment or depletion in classes of genomic elements through genetic, trinucleotide and megabase-scale effects. We report a map of localised mutational processes affecting CTCF binding sites, transcription start sites (TSS) and tissue-specific open-chromatin regions. We show that increased mutational frequency in TSSs correlates with mRNA abundance in most cancer types, while open-chromatin regions are generally enriched in mutations. We identified [~]10,000 CTCF binding sites with core DNA motifs and constitutive binding in 66 cell types that represent focal points of local mutagenesis. We detected site-specific mutational signatures, such as SBS40 in open-chromatin regions in prostate cancer and SBS17b in CTCF binding sites in gastrointestinal cancers. We also proposed candidate drivers of localised mutagenesis: BRAF mutations associate with mutational enrichments at CTCF binding sites in melanoma, and ARID1A mutations with TSS-specific mutations in pancreatic cancer. ConclusionsOur method and catalogue of localised mutational processes provide novel perspectives to cancer genome evolution, mutagenesis, DNA repair and driver discovery. Functional and genetic correlates of localised mutagenesis provide mechanistic hypotheses for future studies.

genomics

Comprehensive analysis of lncRNAs reveals candidate prognostic biomarkers in multiple cancer types

Long non-coding RNAs (lncRNAs) are increasingly recognized as functional units in cancer pathways and powerful molecular biomarkers, however most lncRNAs remain uncharacterized. Here we performed a systematic discovery of prognostic lncRNAs in 9,326 patient tumors of 29 types using a proportional-hazards elastic net machine-learning framework. lncRNAs showed highly tissue-specific transcript abundance patterns. We identified 179 prognostic lncRNAs whose abundance correlated with patient risk and improved the performance of common clinical variables and molecular tumor subtypes. Pathway analysis revealed a large diversity of the high-risk tumors stratified by lncRNAs and suggested their functional associations. In lower-grade gliomas, discrete activation of HOXA10-AS indicated poor patient prognosis, neurodevelopmental pathway activation and a transcriptomic similarity to glioblastomas. HOXA10-AS knockdown in patient-derived glioblastoma cells caused decreased cell proliferation and deregulation of glioma driver genes and proliferation pathways. Our study underlines the pan-cancer potential of the non-coding transcriptome for developing molecular biomarkers and innovative therapeutic strategies.

cancer biology