Abstract
Transcriptome data can provide information on signaling pathways active in cancers, but new computational tools are needed to more accurately quantify pathway activity and identify tissue-specific pathway features. We developed a computational method called “BioTarget” that incorporates ChIP-seq data into cellular pathway analysis. This tool relates the expression of transcription factor TF target genes (based on ChIP-seq data) with the status of upstream signaling components for an accurate quantification of pathway activity. This analysis also reveals TF targets expressed in specific contexts/tissues. We applied BioTarget to assess the activity of TBX21 and GATA3 pathways in cancers. TBX21 and GATA3 are TF regulators that control the differentiation of T cells into Th1 and Th2 helper cells that mediate cell-based and humoral immune responses, respectively. Since tumor immune responses can impact cancer progression, the significance of our pathway scores should be revealed by effective patient stratification. We found that low Th1/Th2 activity ratios were associated with a significantly poorer survival of stomach and breast cancer patients, whereas an unbalanced Th1/Th2 response was correlated with poorer survival of colon cancer patients. Lung adenocarcinoma and lung squamous cell carcinoma patients had the lowest survival rates when both Th1 and Th2 responses were high. Our method also identified context-specific target genes for TBX21 and GATA3. Applying the BioTarget tool to BCL6, a TF associated with germinal center lymphocytes, we observed that patients with an active BCL6 pathway had significantly improved survival for breast, colon, and stomach cancer. Our findings support the effectiveness of the BioTarget tool for transcriptome analysis and point to interesting associations between some immune-response pathways and cancer progression.
Highlights
If one gene Gi is concluded to be a target of the transcription factor (TF) Tj, how to gauge the impact of adding the relationship “Tj regulates Gi” into the pathway? We consider that the Kaplan-Meier (KM) survival analysis[10], the non-parametric statistical method used to study the efficacy of treatments or conditions of cancer patients, can be used for developing such assessment method
What we report here is how the three pathways known for regulating Th1, Th2 and T-fh cell differentiation can be extended and what the implications of these extensions are
We show the outcome of our assessment on how much overlap between Conservative and Optimal Irreproducible Discovery Rate (IDR) gene sets is observed when our Potential Direct Target Genes (PDTG) generation method is applied to six transcription factors: TBX21, GATA3, BCL6, IRF5, PAX5, and STAT1 in Supplementary Information
Summary
We posit that attempting to estimate T cell progression through Th1 differentiation may not be determinable only by examining the upper portion of the pathway; the downstream activity of TBX21 should be examined. As this limitation has been suggested, the literature is scarce in documenting such events. When using TCGA datasets, the question can be answered by testing if our method can meaningfully stratify patients For this evaluation, KM survival analysis can be used, since the relationship between immune responses and cancer progression has been studied extensively[11,12]. Our analysis outcomes show that the scores obtained using extended pathways effectively stratify patients into groups with different survival characteristics, supporting the value of the scoring system
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