A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Identifying differentially methylated sites in samples with varying tumor purity
Tekijät: Antti Häkkinen, Amjad Alkodsi, Chiara Facciotto, Kaiyang Zhang, Katja Kaipio, Sirpa Leppä, Olli Carpén, Seija Grénman, Johanna Hynninen, Sakari Hietanen Rainer Lehtonen, Sampsa Hautaniemi
Kustantaja: OXFORD UNIV PRESS
Julkaisuvuosi: 2018
Journal: Bioinformatics
Tietokannassa oleva lehden nimi: BIOINFORMATICS
Lehden akronyymi: BIOINFORMATICS
Vuosikerta: 34
Numero: 18
Aloitussivu: 3078
Lopetussivu: 3085
Sivujen määrä: 8
ISSN: 1367-4803
DOI: https://doi.org/10.1093/bioinformatics/bty310
Tiivistelmä
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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