Biology subjects

Perduca, V.

Publications and source records attributed to Perduca, V..

3 recordsLinked to original sources

bioRxiv2018

Computational tools to detect signatures of mutational processes in DNA from tumours: a review and empirical comparison of performance

Mutational signatures refer to patterns in the occurrence of somatic mutations that reflect underlying mutational processes. To date, after the analysis of tens of thousands of genomes and exomes from about 40 different cancers types, 30 mutational signatures characterized by a unique probability profile across the 96 mutation types have been identified, validated and listed on the COSMIC (Catalogue of Somatic Mutations in Cancer) website. Simultaneously with this development, the last few years saw the publication of several concurrent methods (mathematical algorithms implemented in publicly available software packages) for either the quantification of the contribution of prespecified signatures (e.g. COSMIC signatures) in a given cancer genome or the identification of new signatures from a sample of cancer genomes. A review about existing computational tools has been recently published to guide researchers and practitioners in conducting their mutational signatures analysis, however, other tools have been introduced since its publication and, to date, there has not been a systematic evaluation and comparison of the performance of such tools. In order to fill this gap, we carry on an empirical evaluation study of all available packages to date, using both real and simulated data.

bioinformatics↗

bioRxiv2018

Appraising the causal relevance of DNA methylation for risk of lung cancer

DNA methylation changes in peripheral blood have been identified in relation to lung cancer risk. However, the causal nature of these associations remains to be fully elucidated. Meta-analysis of four epigenome-wide association studies (918 cases, 918 controls) revealed differential methylation at 16 CpG sites (FDR < 0.05) in relation to lung cancer risk. A two-sample Mendelian randomization analysis, using genetic instruments for methylation at 14 of the 16 CpG sites, and 29,863 cases and 55,586 controls from the TRICL-ILCCO lung cancer consortium, was performed to appraise the causal role of methylation at these sites on lung cancer. This approach provided little evidence that DNA methylation in peripheral blood at the 14 CpG sites play a causal role in lung cancer development, including for cg05575921 AHRR, where methylation is strongly associated with lung cancer risk. Further studies are needed to investigate the causal role played by DNA methylation in lung tissue.

epidemiology↗

bioRxiv2017

Curve Selection For Predicting Breast Cancer Metastasis From Prospective Gene Expression In Blood

We investigate whether there is information in gene expression levels in blood that predicts breast cancer metastasis. Our data comes from the NOWAC epidemiological cohort study where blood samples were provided at enrollment. This could be anywhere from years to weeks before any cancer diagnosis. When and if a cancer is diagnosed, it could be so in different ways: at a screening, between screenings, or in the clinic, outside of the screening program. To build predictive models we propose that variable selection should include followup time and stratify by detection method. We show by simulations that this improves the probability of selecting relevant predictor genes. We also demonstrate that it leads to improved predictions and more stable gene signatures in our data. There is some indication that blood gene expression levels hold predictive information about metastasis. With further development such information could be used for early detection of metastatic potential and as such aid in cancer treatment.

bioinformatics↗