Priming therapy by targeting enhancer-initiated pathways in patient-derived pancreatic cancer cells.

BACKGROUND: Systems biology leveraging multi-OMICs technologies, is rapidly advancing development of precision therapies and matching patients to targeted therapies, leading to improved responses. A new pillar of precision oncology lies in the power of chemogenomics to discover drugs that sensitizes malignant cells to other therapies. Here, we test a chemogenomic approach using epigenomic inhibitors (epidrugs) to reset patterns of gene expression driving the malignant behavior of pancreatic tumors. METHODS: We tested a targeted library of ten epidrugs targeting regulators of enhancers and super-enhancers on reprogramming gene expression networks in seventeen patient-derived primary pancreatic cancer cell cultures (PDPCCs), of both basal and classical subtypes. We subsequently evaluated the ability of these epidrugs to sensitize pancreatic cancer cells to five chemotherapeutic drugs that are clinically used for this malignancy. FINDINGS: To comprehend the impact of epidrug priming at the molecular level, we evaluated the effect of each epidrugs at the transcriptomic level of PDPCCs. The activating epidrugs showed a higher number of upregulated genes than the repressive epidrugs (χ2 test p-value <0.01). Furthermore, we developed a classifier using the baseline transcriptome of epidrug-primed-chemosensitized PDPCCs to predict the best epidrug-priming regime to a given chemotherapy. Six signatures with a significant association with the chemosensitization centroid (R ≤ -0.80; p-value < 0.01) were identified and validated in a subset of PDPCCs. INTERPRETATION: We conclude that targeting enhancer-initiated pathways in patient-derived primary cells, represents a promising approach for developing new therapies for human pancreatic cancer. FUNDING: This work was supported by INCa (Grants number 2018-078 to ND and 2018- 079 to JI), Canceropole PACA (ND), Amidex Foundation (ND), and INSERM (JI).

Subclinical endometritis in dairy cattle is associated with distinct mRNA expression patterns in blood and endometrium.

Cattle with subclinical endometritis (SCE) are sub-fertile and diagnosing subclinical uterine disease remains a challenge. The hypothesis for this study was that endometrial inflammation is reflected in mRNA expression patterns of peripheral blood leucocytes. Transcriptome profiles were evaluated in healthy cows and in cows with SCE using circulating white blood cells (WBC) and endometrial biopsy samples collected from the same animals at 45-55 days postpartum. Bioinformatic analyses of microarray-based transcriptional data identified gene profiles associated with distinct biological functions in circulating WBC and endometrium. In circulating WBC, SCE promotes a pro-inflammatory environment, whereas functions related to tissue remodeling are also affected in the endometrium. Nineteen differentially expressed genes associated with SCE were common to both circulating WBC and the endometrium. Among these genes, transcript abundance of immune factors C3, C2, LTF, PF4 and TRAPPC13 were up-regulated in SCE cows at 45-55 days postpartum. Moreover, mRNA expression of C3, CXCL8, LTF, TLR2 and TRAPPC13 was temporally regulated during the postpartum period in circulating WBC of healthy cows compared with SCE cows. This observation might indicate an advantageous modulation of the immune system in healthy animals. The transcript abundance of these genes represents a potential source of indicators for postpartum uterine health.

SegCorr a statistical procedure for the detection of genomic regions of correlated expression.

BACKGROUND: Detecting local correlations in expression between neighboring genes along the genome has proved to be an effective strategy to identify possible causes of transcriptional deregulation in cancer. It has been successfully used to illustrate the role of mechanisms such as copy number variation (CNV) or epigenetic alterations as factors that may significantly alter expression in large chromosomal regions (gene silencing or gene activation). RESULTS: The identification of correlated regions requires segmenting the gene expression correlation matrix into regions of homogeneously correlated genes and assessing whether the observed local correlation is significantly higher than the background chromosomal correlation. A unified statistical framework is proposed to achieve these two tasks, where optimal segmentation is efficiently performed using dynamic programming algorithm, and detection of highly correlated regions is then achieved using an exact test procedure. We also propose a simple and efficient procedure to correct the expression signal for mechanisms already known to impact expression correlation. The performance and robustness of the proposed procedure, called SegCorr, are evaluated on simulated data. The procedure is illustrated on cancer data, where the signal is corrected for correlations caused by copy number variation. It permitted the detection of regions with high correlations linked to epigenetic marks like DNA methylation. CONCLUSIONS: SegCorr is a novel method that performs correlation matrix segmentation and applies a test procedure in order to detect highly correlated regions in gene expression.

ExactDAS: an exact test procedure for the detection of differential alternative splicing in microarray experiments.

The aim of this paper is to propose a test procedure for the detection of differential alternative splicing across conditions for tiling array or exon chip data. While developed in a mixed model framework, the test procedure is exact (avoiding computational burden) and applicable to a large variety of contrasts, including several previously published ones. A simulation study is presented to evaluate the robustness and performance of the method. It is found to have a good detection power of genes under differential alternative splicing, even for five biological replicates and four probes per exon. The methodology also enables the comparison of various experimental designs through exact power curves. This is illustrated with the comparison of paired and unpaired experiments. The test procedure was applied to two publicly available cancer data sets based on exon arrays, and showed promising results.