Journal
BIOINFORMATICS
Volume 29, Issue 15, Pages 1865-1871Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btt301
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Funding
- IASLC Young Investigator Award
- [1R01 CA174206-01]
- [R01 CA154591]
- [5P30 CA006516-46]
- [NSF 3501501]
- [W81XWH-07-1-0306]
- [5U24 CA143883-04]
- [P30 CA016672]
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Motivation: Tissue samples of tumor cells mixed with stromal cells cause underdetection of gene expression signatures associated with cancer prognosis or response to treatment. In silico dissection of mixed cell samples is essential for analyzing expression data generated in cancer studies. Currently, a systematic approach is lacking to address three challenges in computational deconvolution: (i) violation of linear addition of expression levels from multiple tissues when logtransformed microarray data are used; (ii) estimation of both tumor proportion and tumor-specific expression, when neither is known a priori; and (iii) estimation of expression profiles for individual patients. Results: We have developed a statistical method for deconvolving mixed cancer transcriptomes, DeMix, which addresses the aforementioned issues in array-based expression data. We demonstrate the performance of our model in synthetic and real, publicly available, datasets. DeMix can be applied to ongoing biomarker-based clinical studies and to the vast expression datasets previously generated from mixed tumor and stromal cell samples.
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