Biology subjects

Vavoulis, D. V.

Publications and source records attributed to Vavoulis, D. V..

3 recordsLinked to original sources

bioRxiv2020

A statistical approach for tracking clonal dynamics in cancer using longitudinal next-generation sequencing data

Tumours are composed of genotypically and phenotypically distinct cancer cell populations (clones), which are subject to a process of Darwinian evolution in response to changes in their local micro-environment, such as drug treatment. In a cancer patient, this process of continuous adaptation can be studied through next-generation sequencing of multiple tumour samples combined with appropriate bioinformatics and statistical methodologies. One family of statistical methods for clonal deconvolution seeks to identify groups of mutations and estimate the prevalence of each group in the tumour, while taking into account its purity and copy number profile. These methods have been used in the analysis of cross-sectional data, as well as for longitudinal data by discarding information on the timing of sample collection. Two key questions are how (in the case of longitudinal data) can we incorporate such information in our analyses and if there is any benefit in doing so. Regarding the first question, we incorporated information on the temporal spacing of longitudinally collected samples into standard non-parametric approaches for clonal deconvolution by modelling the time dependence of the prevalence of each clone as a Gaussian process. This permitted reconstruction of the temporal profile of the abundance of each clone continuously from several sparsely collected samples and without any strong prior assumptions on the functional form of this profile. Regarding the second question, we tested various model configurations on a range of whole genome, whole exome and targeted sequencing data from patients with chronic lymphocytic leukaemia, on liquid biopsy data from a patient with melanoma and on synthetic data. We demonstrate that incorporating temporal information in our analysis improves model performance, as long as data of sufficient volume and complexity are available for estimating free model parameters. We expect that our approach will be useful in cases where collecting a relatively long sequence of tumour samples is feasible, as in the case of liquid cancers (e.g. leukaemia) and liquid biopsies. The statistical methodology presented in this paper is freely available at github.com/dvav/clonosGP.

bioinformatics↗

bioRxiv2019

Differential genomic and transcriptomic events associated with high-grade transformation of Chronic Lymphocytic Leukemia

The transformation of chronic lymphocytic leukemia (CLL) to high-grade diffuse large B-cell lymphoma (DLBCL), also called Richters Syndrome (RS), is a rare cancer with dismal prognosis. Drug discovery for RS is hampered by the lack of suitable experimental models, and effective therapies remain elusive rendering RS an area of high unmet clinical need. We performed whole genome sequencing (WGS) to interrogate paired CLL and RS samples from 17 patients enrolled in a prospective multicenter Phase 2 clinical trial (CHOP-OR) and we found that subclones affected by mutations in MAPK and PI3K pathways show a high expansion probability during transformation. We also demonstrate for the first time that non-coding mutation clusters in a PAX5 enhancer, situated 330kb upstream from the transcription initiation site, correlate with transformation. Finally, we confirm our findings by employing targeted DNA sequencing (TGS) and RNA expression profiling on an extended cohort of 38 patients.\n\nStatement of significanceThrough integrated analysis of WGS, TGS and RNA expression data, we identified drivers of transformation not previously implicated in RS, which can be targeted therapeutically and tested in the clinic. Our results have informed the design of a new clinical platform study, which is now open to recruitment in the UK.

genomics↗

bioRxiv2019

A method for non-invasive prenatal diagnosis of monogenic autosomal recessive disorders

Non-invasive prenatal testing (NIPT) to date is used in the clinic primarily to detect foetal aneuploidy. Few studies so far have focused on the detection of monogenic autosomal recessive disorders where mother and foetus carry the same mutation. In particular, NIPT is currently not available for the detection of Sickle Cell Anaemia (SCA), the most common monogenic disorder world-wide and the most common indication for invasive prenatal testing in high-income countries. Here, we report the clinical validation of a novel diagnostic approach that combines ultra-sensitive amplicon-based sequencing of cell-free DNA (cfDNA) with internal controls and bias factor correction to calculate the probability for the presence of allelic imbalance from maternal plasma without prior knowledge of the paternal genotype. Identification of the foetal genotype was determined using a hierarchical probabilistic model based on the relative number of reads from the sequencing, along with the foetal fraction. NIPT was performed on a cohort of 57 patients, all of whom had previously undergone invasive prenatal testing so that the foetal genotype was known. Overall, NIPT demonstrated 100% sensitivity and negative predictive value for foetal fractions higher than 0.5%, and 100% specificity and positive predictive value for foetal fractions higher than or equal to 4%. Our methodology can be used as a safe, non-invasive screening tool in any clinical scenarios where early prenatal diagnosis of SCA or other recessive disorders is important.

genomics↗