Identification of a miRNA multi-targeting therapeutic strategy in glioblastoma.

Glioblastoma (GBM) is a deadly and the most common primary brain tumor in adults. Due to their regulation of a high number of mRNA transcripts, microRNAs (miRNAs) are key molecules in the control of biological processes and are thereby promising therapeutic targets for GBM patients. In this regard, we recently reported miRNAs as strong modulators of GBM aggressiveness. Here, using an integrative and comprehensive analysis of the TCGA database and the transcriptome of GBM biopsies, we identified three critical and clinically relevant miRNAs for GBM, miR-17-3p, miR-222, and miR-340. In addition, we showed that the combinatorial modulation of three of these miRNAs efficiently inhibited several biological processes in patient-derived GBM cells of all these three GBM subtypes (Mesenchymal, Proneural, Classical), induced cell death, and delayed tumor growth in a mouse tumor model. Finally, in a doxycycline-inducible model, we observed a significant inhibition of GBM stem cell viability and a significant delay of orthotopic tumor growth. Collectively, our results reveal, for the first time, the potential of miR-17-3p, miR-222 and miR-340 multi-targeting as a promising therapeutic strategy for GBM patients.

Fusion-negative rhabdomyosarcoma 3D organoids to predict effective drug combinations: A proof-of-concept on cell death inducers.

Rhabdomyosarcoma (RMS) is the main form of pediatric soft-tissue sarcoma. Its cure rate has not notably improved in the last 20 years following relapse, and the lack of reliable preclinical models has hampered the design of new therapies. This is particularly true for highly heterogeneous fusion-negative RMS (FNRMS). Although methods have been proposed to establish FNRMS organoids, their efficiency remains limited to date, both in terms of derivation rate and ability to accurately mimic the original tumor. Here, we present the development of a next-generation 3D organoid model derived from relapsed adult and pediatric FNRMS. This model preserves the molecular features of the patients' tumors and is expandable for several months in 3D, reinforcing its interest to drug combination screening with longitudinal efficacy monitoring. As a proof-of-concept, we demonstrate its preclinical relevance by reevaluating the therapeutic opportunities of targeting apoptosis in FNRMS from a streamlined approach based on transcriptomic data exploitation.

Paclitaxel Response Can Be Predicted With Interpretable Multi-Variate Classifiers Exploiting DNA-Methylation and miRNA Data.

To address the problem of resistance to paclitaxel treatment, we have investigated to which extent is possible to predict Breast Cancer (BC) patient response to this drug. We carried out a large-scale tumor-based prediction analysis using data from the US National Cancer Institute's Genomic Data Commons. These data sets comprise the responses of BC patients to paclitaxel along with six molecular profiles of their tumors. We assessed 10 Machine Learning (ML) algorithms on each of these profiles and evaluated the resulting 60 classifiers on the same BC patients. DNA methylation and miRNA profiles were the most informative overall. In combination with these two profiles, ML algorithms selecting the smallest subset of molecular features generated the most predictive classifiers: a complexity-optimized XGBoost classifier based on CpG island methylation extracted a subset of molecular factors relevant to predict paclitaxel response (AUC = 0.74). A CpG site methylation-based Decision Tree (DT) combining only 2 of the 22,941 considered CpG sites (AUC = 0.89) and a miRNA expression-based DT employing just 4 of the 337 analyzed mature miRNAs (AUC = 0.72) reveal the molecular types associated to paclitaxel-sensitive and resistant BC tumors. A literature review shows that features selected by these three classifiers have been individually linked to the cytotoxic-drug sensitivities and prognosis of BC patients. Our work leads to several molecular signatures, unearthed from methylome and miRNome, able to anticipate to some extent which BC tumors respond or not to paclitaxel. These results may provide insights to optimize paclitaxel-therapies in clinical practice.

IRFinder: assessing the impact of intron retention on mammalian gene expression.

Intron retention (IR) occurs when an intron is transcribed into pre-mRNA and remains in the final mRNA. We have developed a program and database called IRFinder to accurately detect IR from mRNA sequencing data. Analysis of 2573 samples showed that IR occurs in all tissues analyzed, affects over 80% of all coding genes and is associated with cell differentiation and the cell cycle. Frequently retained introns are enriched for specific RNA binding protein sites and are often retained in clusters in the same gene. IR is associated with lower protein levels and intron-retaining transcripts that escape nonsense-mediated decay are not actively translated.