Biology subjects

Alkodsi, A.

Publications and source records attributed to Alkodsi, A..

4 recordsLinked to original sources

bioRxiv2020

ctDNAtools: An R package to work with sequencing data of circulating tumor DNA

SummarySequencing of cell-free DNA (cfDNA) including circulating tumor DNA (ctDNA) in minimally-invasive liquid biopsies is rapidly maturing towards clinical utility for cancer diagnostics. However, the publicly available bioinformatics tools for the specialized analysis of ctDNA sequencing data are still scarce. Here, we present the ctDNAtools R package, which provides functionalities for testing minimal residual disease (MRD) and analyzing cfDNA fragmentation. MRD detection in ctDNAtools utilizes a Monte Carlo sampling approach to test ctDNA positivity through tracking a set of pre-detected reporter mutations in follow-up samples. Additionally, ctDNAtools includes various functionalities to study cfDNA fragment size histograms, profiles and fragment ends patterns. AvailabilityThe ctDNAtools package is freely available under MIT license at https://github.com/alkodsi/ctDNAtools.

bioinformatics↗

bioRxiv2019

PRISM: Recovering cell type specific expression profilesfrom composite RNA-seq data

A major challenge in analyzing cancer patient transcriptomes is that the tumors are inherently heterogeneous and evolving. We analyzed 214 bulk RNA samples of a longitudinal, prospective ovarian cancer cohort and found that the sample composition changes systematically due to chemotherapy and between the anatomical sites, preventing direct comparison of treatment-naive and treated samples. To overcome this, we developed PRISM, a latent statistical framework to simultaneously extract the sample composition and cell type specific whole-transcriptome profiles adapted to each individual sample. Our results indicate that the PRISM-derived composition-free transcriptomic profiles and signatures derived from them predict the patient response better than the composite raw bulk data. We validated our findings in independent ovarian cancer and melanoma cohorts, and verified that PRISM accurately estimates the composition and cell type specific expression through whole-genome sequencing and RNA in situ hybridization experiments. PRISM is freely available with full source code and documentation.

bioinformatics↗

bioRxiv2018

ESR1, WT1, WNT4, ATM and TERT loci are major contributors to uterine leiomyoma predisposition

Uterine leiomyomas (ULs) are benign tumors that are a major burden to womens health. A genome-wide association study on 5,417 UL cases and 331,791 controls was performed, followed by replication of the genomic risk in two cohorts. Effects of the identified risk alleles were evaluated in view of molecular and clinical features.\n\nFive loci displayed a genome-wide significant association; the previously reported TNRC6B, and four novel loci ESR1 (ER), WT1, WNT4, and ATM. The sixth hit TERT is also a conceivable target. The combined polygenic risk contributed by these loci was associated with MED12 mutation-positive tumors. The findings link genes for uterine development and genetic stability to leiomyomagenesis. While the fundamental role of sex hormones in UL aetiology has been clear, this work reveals a connection to estrogen receptor alpha on genetic level and suggests that determinants of UL growth associated with estrogen exposure have an inherited component.

genomics↗

bioRxiv2018

Identifying differentially methylated sites in samples with varying tumor purity

DNA methylation aberrations are common in many cancer types. A major challenge hindering comparison of patient-derived samples is that they comprise of heterogeneous collection of cancer and microenvironment cells. We present a computational method that allows comparing cancer methylomes in two or more heterogeneous tumor samples featuring differing, unknown fraction of cancer cells. The method is unique in that it allows comparison also in the absence of normal cell control samples and without prior tumor purity estimates, as these are often unavailable or unreliable in clinical samples. We use simulations and next-generation methylome, RNA, and whole-genome sequencing data from two cancer types to demonstrate that the method is accurate and outperforms alternatives. The results show that our method adapts well to various cancer types and to a wide range of tumor content, and works robustly without a control or with controls derived from various sources. The method is freely available at https://bitbucket.org/anthakki/dmml.

bioinformatics↗