Abstract 3355: The Offonome: Methods for characterizing gene expression of rare cell populations from whole tumor profiling data
Authors: Choi Hyo Young , Hayes David N. 
Published: June 2022

Background: Tumors are composed of diverse cell types. While several approaches have been proposed to dissect the cellular heterogeneity using profiling data such as gene expression or methylation, it remains challenging to discover rare cell types from high-throughput sequencing data. To address this need, we apply a novel approach in which we hypothesize that rare cell subpopulations might be detected based on the expression of differential sets of transcripts, generally expressed at low levels proportional to the infiltrating cell percentage. In this case, the challenge is the precise measurement of gene expression at low levels or the detection of genes specifically attributable to those cell populations as being “off” or “on”.

Methods: We propose a novel computational approach for measuring the “offonome”, defined as a gene’s dichotomized state of being “on” or “off”. To accurately determine on/off status of a gene, we compare single nucleotide-level RNA-seq data modeled as vectors in high dimensional data space using the angles between samples. In this framework, sample vectors determined to have low angles can be defined as sharing an “on” gene versus sample vectors with high angle defining a state of “off”. We apply this approach to more than 5,000 human cancer samples drawn from the Cancer Genome Atlas (TCGA) to identify minor cell populations. Additionally, we compare this approach to the alternative gene abundance strategy in which on/off is defined by read-counts-based gene expression estimates from the same dataset.

Results: Using the overall and sub-populations, we identified minor cell populations across the TCGA dataset including in a sub-cohort of 522 TCGA head and neck squamous cell carcinoma (HNSCC). The Offonome approach applied genome-wide in HNSCC identified 5802 genes that were either always or sometimes “off” defined by angle-based statistics. Turning our attention to genes that were sometime, but not always off, we applied unsupervised learning to characterize clusters of genes within HNSCC tumors. At least five gene clusters were clearly observed, each of which was strongly associated with specific signaling pathways. For example, one cluster identified infiltrating myocytes, defined by infiltrating normal muscle cells, found only in tumors of the oral tongue. In addition to clustering analysis, we utilized the approach to identify high expression of genes found in outliers, or even a single sample such as those associated with salivary origin such as olfactory receptors.

Conclusions: We conclude that on/off status of a gene provides new insights into gene expression deconvolution, thereby characterizing interesting gene clusters. These results offer strong evidence that our approach can be useful for picking up rare transcripts from individual samples such as leukocyte transcripts and stromal transcripts.

Citation Format: Hyo Young Choi, David N. Hayes. The Offonome: Methods for characterizing gene expression of rare cell populations from whole tumor profiling data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3355.