Collier, O. | Search bioRxiv

bioRxiv2026

Transcriptomic profile of MTUS1-low TNBC reveals candidate therapeutic strategies.

BackgroundTriple-negative breast cancer (TNBC) is a clinically aggressive breast cancer subtype. It is a heterogeneous disease that remains difficult to stratify and that still lacks durable and biomarker-guided therapeutic options. Low expression of the tumour suppressor MTUS1 is associated with aggressive breast cancer features, but the biological properties of MTUS1-low TNBC remain insufficiently defined. Our goal was to determine whether low MTUS1 expression defines shared proliferative and stress-adaptation mechanisms that could guide candidate therapeutic strategies and corresponding target/drug pairs in MTUS1-low TNBC. MethodsWe labelled tumours from seven public TNBC RNA-seq cohorts based on the lowest and highest MTUS1 expression tertiles. Differential gene expression was analysed using gene set enrichment analysis (GSEA) on the Hallmark pathway database to identify deregulated biological pathways between MTUS1-low TNBC tumours and their MTUS1-high counterparts. Reproducibility was examined across independent TNBC cohorts and secondarily in broader breast cancer and selected TCGA tumour cohorts. Gene essentiality scores from CRISPR-Cas9 experiments in TNBC cell-line models were correlated to MTUS1 expression in these cell lines, to propose therapeutic strategies and their corresponding candidate target/drug pairs. ResultsMTUS1-low tumours showed a reproducible pathway-level proliferation mechanism driven by the MYC oncogene and sustained by up-regulated oxidative phosphorylation, combined with stress adaptation mechanisms involving unfolded protein response (UPR), and DNA repair Hallmark gene sets. Based on CRISPR data, we propose 3 therapeutic strategies: (1) targeting MYC to reduce its transcriptional activity, (2) targeting proteins from UPR, (3) targeting DNA-repair. We also propose corresponding candidate target/drug pairs to allow experimental validation of these strategies. ConclusionsProliferation in low MTUS1 TNBC is driven by MYC and stress-adaptation mechanisms. By linking this tumour profile to CRISPR-derived dependency signals, our analysis prioritises experimentally testable target-pathway hypotheses centred on MYC, UPR/proteostasis, and DNA-repair or checkpoint control. Although the proposed therapeutic strategies and candidate targets remain to be experimentally tested, the latter finding is consistent with published work showing that ATIP3-deficient TNBC cell line models are sensitive to inhibition of the WEE1 PKMYT1 G2/M checkpoint kinases.

cancer biology↗

bioRxiv2018

LOTUS: a Single- and Multitask Machine Learning Algorithm for the Prediction of Cancer Driver Genes

Cancer driver genes, i.e., oncogenes and tumor suppressor genes, are involved in the acquisition of important functions in tumors, providing a selective growth advantage, allowing uncontrolled proliferation and avoiding apoptosis. It is therefore important to identify these driver genes, both for the fundamental understanding of cancer and to help finding new therapeutic targets. Although the most frequently mutated driver genes have been identified, it is believed that many more remain to be discovered, particularly for driver genes specific to some cancer types.\n\nIn this paper we propose a new computational method called LOTUS to predict new driver genes. LOTUS is a machine-learning based approach which allows to integrate various types of data in a versatile manner, including informations about gene mutations and protein-protein interactions. In addition, LOTUS can predict cancer driver genes in a pan-cancer setting as well as for specific cancer types, using a multitask learning strategy to share information across cancer types.\n\nWe empirically show that LOTUS outperforms three other state-of-the-art driver gene prediction methods, both in terms of intrinsic consistency and prediction accuracy, and provide predictions of new cancer genes across many cancer types.\n\nAuthor summaryCancer development is driven by mutations and dysfunction of important, so-called cancer driver genes, that could be targeted by targeted therapies. While a number of such cancer genes have already been identified, it is believed that many more remain to be discovered. To help prioritize experimental investigations of candidate genes, several computational methods have been proposed to rank promising candidates based on their mutations in large cohorts of cancer cases, or on their interactions with known driver genes in biological networks. We propose LOTUS, a new computational approach to identify genes with high oncogenic potential. LOTUS implements a machine learning approach to learn an oncogenic potential score from known driver genes, and brings two novelties compared to existing methods. First, it allows to easily combine heterogeneous informations into the scoring function, which we illustrate by learning a scoring function from both known mutations in large cancer cohorts and interactions in biological networks. Second, using a multitask learning strategy, it can predict different driver genes for different cancer types, while sharing information between them to improve the prediction for every type. We provide experimental results showing that LOTUS significantly outperforms several state-of-the-art cancer gene prediction softwares.

bioinformatics↗