Circuits of cancer drivers revealed by convergent misregulation of transcription factor targets across tumor types

Expanding the coverage of regulons from high-confidence prior knowledge for accurate estimation of transcription factor activities

Gene regulation plays a critical role in the cellular processes that underlie human health and disease. The regulatory relationship between transcription factors (TFs), key regulators of gene expression, and their target genes, the so called TF regulons, can be coupled with computational algorithms to estimate the activity of TFs. However, to interpret these findings accurately, regulons of high reliability and coverage are needed. In this study, we present and evaluate a collection of regulons created using the CollecTRI meta-resource containing signed TF-gene interactions for 1186 TFs. In this context, we introduce a workflow to integrate information from multiple resources and assign the sign of regulation to TF-gene interactions that could be applied to other comprehensive knowledge bases. We find that the signed CollecTRI-derived regulons outperform other public collections of regulatory interactions in accurately inferring changes in TF activities in perturbation experiments. Furthermore, we showcase the value of the regulons by examining TF activity profiles in three different cancer types and exploring TF activities at the level of single-cells. Overall, the CollecTRI-derived TF regulons enable the accurate and comprehensive estimation of TF activities and thereby help to interpret transcriptomics data.

Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning

Early detection of breast cancer and its correct stage determination are important for prognosis and rendering appropriate personalized clinical treatment to breast cancer patients. However, despite considerable efforts and progress, there is a need to identify the specific genomic factors responsible for, or accompanying Invasive Ductal Carcinoma (IDC) progression stages, which can aid the determination of the correct cancer stages. We have developed two-class machine-learning classification models to differentiate the early and late stages of IDC. The prediction models are trained with RNA-seq gene expression profiles representing different IDC stages of 610 patients, obtained from The Cancer Genome Atlas (TCGA). Different supervised learning algorithms were trained and evaluated with an enriched model learning, facilitated by different feature selection methods. We also developed a machine-learning classifier trained on the same datasets with training sets reduced data corresponding to IDC driver genes. Based on these two classifiers, we have developed a web-server Duct-BRCA-CSP to predict early stage from late stages of IDC based on input RNA-seq gene expression profiles. The analysis conducted by us also enables deeper insights into the stage-dependent molecular events accompanying IDC progression. The server is publicly available at http://bioinfo.icgeb.res.in/duct-BRCA-CSP.

Benchmark and integration of resources for the estimation of human transcription factor activities

The prediction of transcription factor (TF) activities from the gene expression of their targets (i.e., TF regulon) is becoming a widely used approach to characterize the functional status of transcriptional regulatory circuits. Several strategies and data sets have been proposed to link the target genes likely regulated by a TF, each one providing a different level of evidence. The most established ones are (1) manually curated repositories, (2) interactions derived from ChIP-seq binding data, (3) in silico prediction of TF binding on gene promoters, and (4) reverse-engineered regulons from large gene expression data sets. However, it is not known how these different sources of regulons affect the TF activity estimations and, thereby, downstream analysis and interpretation. Here we compared the accuracy and biases of these strategies to define human TF regulons by means of their ability to predict changes in TF activities in three reference benchmark data sets. We assembled a collection of TF-target interactions for 1541 human TFs and evaluated how different molecular and regulatory properties of the TFs, such as the DNA-binding domain, specificities, or mode of interaction with the chromatin, affect the predictions of TF activity. We assessed their coverage and found little overlap on the regulons derived from each strategy and better performance by literature-curated information followed by ChIP-seq data. We provide an integrated resource of all TF-target interactions derived through these strategies, with confidence scores, as a resource for enhanced prediction of TF activities.

Gene expression analysis of embryonic pancreas development master regulators and terminal cell fate markers in resected pancreatic cancer: A correlation with clinical outcome

BACKGROUND

Despite the recent introduction of new drugs and the development of innovative multi-target treatments, the prognosis of pancreatic ductal adenocarcinoma (PDAC) remains very poor. Even when PDAC is resectable, the rate of local or widespread disease recurrence remains particularly high. Currently, reliable prognostic biomarkers of recurrence are lacking. We decided to explore the potential usefulness of pancreatic developmental regulators as biomarkers of PDAC relapse.

METHODS

We analyzed by quantitative real-time PCR the mRNA of selected factors involved either in pancreatic organogenesis (ISL1, NEUROD1, NGN3, NKX2.2, NKX6.1, PAX4, PAX6, PDX1 and PTF1α) or associated with terminally committed pancreatic cells (CHGA, CHGB, GAD2, GCG, HNF6α, INS, KRT19, SYP) in 17 PDAC cell lines and in frozen tumor samples from 41 PDAC patients.

RESULTS

High baseline levels of the ISL1, KRT19, PAX6 and PDX1 mRNAs in PDAC cell lines, were risk factors for time-dependent xenograft appearance after subcutaneous injection in CD1-Nude mice. Consistently, in human PDAC samples, high levels of KRT19 mRNA were associated with reduced overall survival and earlier recurrence. Higher levels of PDX1 or PAX6 mRNAs were instead associated with a higher frequency of local recurrence.

CONCLUSIONS

Our findings suggest that selected factors associated with pancreas development or its terminal differentiation might be implicated in mechanisms of PDAC progression and/or metastatic spread and that the measurement of their mRNA in tumors might be potentially used to improve patient prognostic stratification and prediction of the relapse site.