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Matthews, N.

Publications and source records attributed to Matthews, N..

2 recordsLinked to original sources

Ultra-sensitive mutation detection and genome-wide DNA copy number reconstruction by error corrected circulating tumour DNA sequencing

Minimally invasive circulating free DNA (cfDNA) analysis can portray cancer genome landscapes but highly sensitive and specific genetic approaches are necessary to accurately detect mutations with often low variant frequencies. We developed a targeted cfDNA sequencing technology using novel off-the-shelf molecular barcodes for error correction, in combination with custom solution hybrid capture enrichment. Modelling based on cfDNA yields from 58 patients shows that our assay, which requires 25ng of cfDNA input, should be applicable to >95% of patients with metastatic colorectal cancer. Sequencing of a 163.3 kb target region including 32 genes detected 100% of single nucleotide variants with 0.15% variant frequency in cfDNA spike-in experiments. Molecular barcode error correction reduced false positive mutation calls by 98.6%. In a series of 28 patients with metastatic colorectal cancers, 80 out of 91 (88%) mutations previously detected by tumour tissue sequencing were called in the cfDNA. Call rates were similar for single nucleotide variants and small insertions/deletions. Mutations only called in cfDNA but not detectable in matched tumour tissue included, among others, a subclonal resistance driver mutation to anti-EGFR antibodies in the KRAS gene, multiple activating PIK3CA mutations in each of two patients (indicative of parallel evolution), and TP53 mutations originating from clonal haematopoiesis. Furthermore, we demonstrate that cfDNA off-target read analysis allows the reconstruction of genome wide copy number aberration profiles from 71% of these 28 cases. This error-corrected ultra-deep cfDNA sequencing assay with a target region that can be readily customized enables broad insights into cancer genomes and evolution.

genomics

Auditory prediction errors as individual biomarkers of schizophrenia

Schizophrenia is a complex psychiatric disorder, typically diagnosed through symptomatic evidence collected through patient interview. We aim to develop an objective biologically-based computational tool which aids diagnosis and relies on accessible imaging technologies such as electroencephalography (EEG). To achieve this, we used machine learning techniques and a combination of paradigms designed to elicit prediction errors or Mismatch Negativity (MMN) responses. MMN, an EEG component elicited by unpredictable changes in sequences of auditory stimuli, has previously been shown to be reduced in people with schizophrenia and this is arguably one of the most reproducible neurophysiological markers of schizophrenia.\n\nEEG data were acquired from 21 patients with schizophrenia and 22 healthy controls whilst they listened to three auditory oddball paradigms comprising sequences of tones which deviated in 10% of trials from regularly occurring standard tones. Deviant tones shared the same properties as standard tones, except for one physical aspect: 1) duration-the deviant stimulus was twice the duration of the standard; 2) monaural gap-deviants had a silent interval omitted from the standard, or 3) inter-aural timing difference, which caused the deviant location to be perceived as 90{degrees} away from the standards.\n\nWe used multivariate pattern analysis, a machine learning technique implemented in the Pattern Recognition for Neuroimaging Toolbox (PRoNTo) to classify images generated through statistical parametric mapping (SPM) of spatiotemporal EEG data, i.e. event-related potentials measured on the two-dimensional surface of the scalp over time. Using support vector machine (SVM) and Gaussian processes classifiers (GPC), we were able classify individual patients and controls with balanced accuracies of up to 80.48% (p-values = 0.0326, FDR corrected) and an ROC analysis yielding an AUC of 0.87. Crucially, a GPC regression revealed that MMN predicted global assessment of functioning (GAF) scores (correlation = 0.73, R2 = 0.53, p = 0.0006)

neuroscience