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MOSAIC Consortium,

Publications and source records attributed to MOSAIC Consortium,.

2 recordsLinked to original sources

Identification of non-covalent inhibitors for the atypical peroxiredoxin PRDX5 as a therapeutic strategy in malignant pleural mesothelioma

Malignant pleural mesothelioma (MPM) is an aggressive asbestos-linked cancer with limited therapeutic options and a dismal 5-year survival rate of [~]5%. While aberrant production of reactive oxygen and nitrogen species (ROS/RNS) is a hallmark of MPM, targeted approaches to exploit these redox vulnerabilities remain scarce. Here, using the MOSAIC multimodal cancer patient atlas, we identify Peroxiredoxin 5 (PRDX5) as being significantly upregulated in the epithelioid subtype of MPM. We show that MPM cells exhibit enhanced resistance to nitrosative and oxidative stress compared to healthy mesothelial cells, a phenotype correlated with basal PRDX5 expression. Next, utilising a machine learning guided discovery pipeline, we identified three putative allosteric pockets in PRDX5 and conducted a virtual screen of 3.6 million compounds. High-throughput biochemical validation of 452 candidates yielded 36 non-covalent hits, including sub-micromolar inhibitors. These findings establish PRDX5 as a novel, subtype specific therapeutic target in MPM and provide a chemical framework for the development of next-generation redox-modulating oncology treatments.

cancer biology↗

MOSAIC: Intra-tumoral heterogeneity characterization through large-scale spatial and cell-resolved multi-omics profiling.

Precision oncology remains challenging due to gaps in understanding tumor biology and immunology, as well as the scarcity of consistent data across large cohorts. Here, we introduce MOSAIC (Multi-Omics Spatial Atlas in Cancer), a multi-center clinical Omics study designed to systematically profile thousands of cancer samples, with a focus on spatial and single-cell data across multiple tumor types. MOSAIC exploits recent technological advances to integrate spatial and single-cell data with complementary data modalities, including hematoxylin-eosin histology scans, bulk RNA and exome sequences, to generate comprehensive representations of cancer histology, genomics, and transcriptomics. MOSAIC also collects extensive and curated clinical information to ensure that patients meet the precise inclusion criteria for each cohort. The consortium aims to integrate all data modalities using artificial intelligence and other computational approaches in order to identify clinically-relevant biomarkers and cancer subtypes. This paper outlines the core objectives of MOSAIC, its potential impact, early proofs-of-concept, design, and experimental considerations. Additionally, we introduce the MOSAIC Window initiative, featuring the first released dataset from 60 patients, offering a glimpse into the projects groundbreaking potential. Using four selected patients, we demonstrate the power of multi-omic approaches to analyze and interpret intra-tumoral variability within and across patients, providing insights on specific drug sensitivity of cancer subpopulations and revealing potential impacts on therapeutic recommendations.

molecular biology↗