Open AccessAll-FIT: allele-frequency-based imputation of tumor purity from high-depth sequencing data
Author(s) -
Jui Wan Loh,
Caitlin Guccione,
Frances Di Clemente,
Gregory Riedlinger,
Shridar Ganesan,
Hossein Khiabanian
Publication year - 2019
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btz865
Subject(s) - imputation (statistics) , allele frequency , computational biology , dna sequencing , deep sequencing , biology , germline , germline mutation , somatic cell , computer science , allele , minor allele frequency , genetics , genome , mutation , gene , missing data , machine learning
Clinical sequencing aims to identify somatic mutations in cancer cells for accurate diagnosis and treatment. However, most widely used clinical assays lack patient-matched control DNA and additional analysis is needed to distinguish somatic and unfiltered germline variants. Such computational analyses require accurate assessment of tumor cell content in individual specimens. Histological estimates often do not corroborate with results from computational methods that are primarily designed for normal-tumor matched data and can be confounded by genomic heterogeneity and presence of sub-clonal mutations. Allele-frequency-based imputation of tumor (All-FIT) is an iterative weighted least square method to estimate specimen tumor purity based on the allele frequencies of variants detected in high-depth, targeted, clinical sequencing data. Using simulated and clinical data, we demonstrate All-FIT's accuracy and improved performance against leading computational approaches, highlighting the importance of interpreting purity estimates based on expected biology of tumors.