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Nones, K.

Publications and source records attributed to Nones, K..

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Genome Scale Epigenetic Profiling Reveals Five Distinct Subtypes of Colorectal Cancer

BACKGROUNDColorectal cancer is an epigenetically heterogeneous disease, however the extent and spectrum of the CpG Island Methylator Phenotype (CIMP) is not clear.\n\nRESULTSAn unselected cohort of 216 colorectal cancers clustered into five clinically and molecularly distinct subgroups using Illumina 450K DNA methylation arrays. CIMP-High cancers were most frequent in the proximal colons of female patients. These dichotomised into CIMP-Hl and CIMP-H2 based on methylation profile which was supported by over representation of BRAF (74%, P<0.0001) or KRAS (55%, P<0.0001) mutation, respectively. Congruent with increasing methylation, there was a stepwise increase in patient age from 62 years in the CI MP-Negative subgroup to 75 years in the CIMP-Hl subgroup (P<0.0001). There was a striking association between PRC2-marked loci and those subjected to significant gene body methylation in CIMP-type cancers (P<1.6xl078). We identified oncogenes susceptible to gene body methylation and Wnt pathway antagonists resistant to gene body methylation. CIMP cluster specific mutations were observed for genes involved in chromatin remodelling, such as in the SWI/SNF and NuRD complexes, suggesting synthetic lethality.\n\nCONCLUSIONThere are five clinically and molecularly distinct subgroups of colorectal cancer based on genome wide epigenetic profiling. These analyses highlighted an unidentified role for gene body methylation in progression of serrated neoplasia. Subgroup-specific mutation of distinct epigenetic regulator genes revealed potentially druggable vulnerabilities for these cancers, which may provide novel precision medicine approaches.

genomics

Profiling copy number alterations in cell-free tumour DNA using a single-reference

BackgroundThe accurate detection of copy number alterations from the analysis of circulating cell free tumour DNA (ctDNA) in blood is essential to realising the potential of liquid biopsies. However, currently available approaches require a large number of plasma samples from healthy individuals, sequenced using the same platform and protocols to act as a reference panel. Obtaining this reference panel can be challenging, prohibitively expensive and limits the ability to migrate to improved sequencing platforms and improved protocols.\n\nMethodsWe developed qCNV and sCNA-seq, two distinct tools that together provide a new approach for profiling somatic copy number alterations (sCNA) through the analysis of cell free DNA (cfDNA) without a reference panel. Our approach was designed to identify sCNA from cfDNA through the analysis of a single plasma sample and a matched normal DNA sample -both of which can be obtained from the same blood draw. qCNV is an efficient method for extracting read-depth from BAM files and sCNA-seq is a method that uses a probabilistic model of read depth to infer the copy number segmentation of the tumour. We compared the results from our pipeline to the established copy number profile of a cell-line, as well as the results from the plasma-Seq analysis of cfDNA-like mixtures and real, clinical data-sets.\n\nResultsWith a single, unmatched, germline reference sample, our pipeline recapitulated the known copy number profile of a cell-line and demonstrated similar results to those obtained from plasma-Seq. With less than 1X genome coverage, our approach identified clinically relevant sCNA in samples with as little as 20 % tumour DNA. When applied to plasma samples from cancer patients, our pipeline identified clinically significant mutations.\n\nConclusionsThese results show it is possible to identify therapeutically-relevant copy number mutations from plasma samples without the need to generate a reference panel from a large number of healthy individuals. Together with the range of sequencing platforms supported by our qCNV+sCNA-Seq pipeline, as well as the Galaxy implementation of this solution, this pipeline makes cfDNA profiling more accessible and makes it easier to identify sCNA from the plasma of cancer patients.

bioinformatics