DeMix: deconvolution for mixed cancer transcriptomes using raw measured data

Abstract

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. Availability: All codes are written in C and integrated into an R function, which is available at http://odin.mdacc.tmc.edu/$wwang7/ DeMix.html.

The Molecular Taxonomy of Primary Prostate Cancer

“…In total, 78% of Gleason scores were concordant within one grade of the secondary pattern (Supplementary Methods). Moreover, due to the challenge of acquiring primary prostate cancer specimens of high tumor cellularity, we also performed a multi-platform analysis of tumor content, estimating tumor purity with analytical approaches utilizing both DNA (Carter et al, 2012, Prandi et al, 2014) and RNA (Quon et al, 2013, Ahn et al, 2013) sequencing data. The molecular and pathologic estimates are presented in Table S1A and Figure S1.…”

Computational deconvolution: extracting cell type-specific information from heterogeneous samples

“…We note that the input cell-subset proportions in these two method classes, may come either from actual measurements or computationally estimated. In fact, a fifth category (E) consists of complete deconvolution methods which estimate both proportions and cell type-specific expression profiles, often using a combination of deconvolution methods (B and D), and require some limited prior knowledge on proportions [31] or expression profiles [30, 32, 19, 33, 34, 35, 36]. …”

One-Class Detection of Cell States in Tumor Subtypes

“…1 The most popular approach is to use unsupervised methods such as those based on non-negative matrix factorization 2 or other matrix decomposition techniques (e.g. independent component analysis).…”