A1 Refereed original research article in a scientific journal
Identifying differentially methylated sites in samples with varying tumor purity
Authors: Antti Häkkinen, Amjad Alkodsi, Chiara Facciotto, Kaiyang Zhang, Katja Kaipio, Sirpa Leppä, Olli Carpén, Seija Grénman, Johanna Hynninen, Sakari Hietanen Rainer Lehtonen, Sampsa Hautaniemi
Publisher: OXFORD UNIV PRESS
Publication year: 2018
Journal: Bioinformatics
Journal name in source: BIOINFORMATICS
Journal acronym: BIOINFORMATICS
Volume: 34
Issue: 18
First page : 3078
Last page: 3085
Number of pages: 8
ISSN: 1367-4803
DOI: https://doi.org/10.1093/bioinformatics/bty310
Abstract
Motivation: 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.Results: 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.
Motivation: 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.Results: 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.
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