Victor, B. | bioRxiv Science

bioRxiv2026

Large oncosomes reprogram bone marrow mesenchymal stem cells to establish a pre-metastatic niche

Metastatic progression depends on the systemic remodeling of distant tissues before tumor cell arrival, yet the cancer-derived signals that orchestrate this process remain poorly understood. Large oncosomes (LOs) are atypically large (>1 um), tumor-derived extracellular vesicles shed by invasive cancer cells. Here, we demonstrate that LOs function as systemic mediators of pre-metastatic niche formation by activating innate immune sensing in bone marrow mesenchymal stem cells (BM-MSCs). Systemic administration of prostate- and breast cancer-derived LOs to immunocompetent tumor-bearing mice did not affect primary tumor growth but increased metastatic burden. LOs induced a robust, dose-dependent interferon-driven inflammatory program, characterized by interferon-stimulated genes and neutrophil chemokines. Mechanistically, this response was driven by LO-associated nucleic acids activating convergent cytosolic DNA- and RNA-sensing pathways in recipient stromal cells. LO-conditioned BM-MSCs promoted the accumulation and polarization of neutrophils toward an immunosuppressive, polymorphonuclear myeloid-derived suppressor cell (PMN-MDSC) phenotype. In vivo, genetic suppression of LO shedding in tumor cells reduced PMN-MDSC accumulation in the bone marrow, which was reverted by systemic LO administration. Together, these findings establish LOs as specialized carriers of immunomodulatory signals that reprogram the bone marrow microenvironment to support metastatic colonization, identifying a previously unrecognized mechanism linking tumor vesiculation to immune remodeling at distant sites.

cancer biology↗

bioRxiv2019

DeepSSV: detecting somatic small variants in paired tumor and normal sequencing data with convolutional neural network

MotivationIt is of considerable interest to detect somatic mutations in paired tumor and normal sequencing data. A number of callers that are based on statistical or machine learning approaches have been developed to detect somatic small variants. However, they take into consideration only limited information about the reference and potential variant allele in both samples at a candidate somatic site. Also, they differ in how biological and technological noises are addressed. Hence, they are expected to produce divergent outputs.\n\nResultsTo overcome the drawbacks of existing somatic callers, we develop a deep learning-based tool called DeepSSV, which employs a convolutional neural network (CNN) model to learn increasingly abstract feature representations from the raw data in higher feature layers. DeepSSV creates a spatially-oriented representation of read alignments around the candidate somatic sites adapted for the convolutional architecture, which enables it to expand to effectively gather scattered evidences. Moreover, DeepSSV incorporates the mapping information of both reference-allele-supporting and variant-allele-supporting reads in the tumor and normal samples at a genomic site that are readily available in the pileup format file. Together, the CNN model can process the whole alignment information. Such representational richness allows the model to capture the dependencies in the sequence and identify context-based sequencing artifacts, and alleviates the need of post-call filters that heavily depend on prior knowledge. We fitted the model on ground truth somatic mutations, and did benchmarking experiments on simulated and real tumors. The benchmarking results demonstrate that DeepSSV outperforms its state-of-the-art competitors in overall F1 score.\n\nAvailability and Implementationhttps://github.com/jingmeng-bioinformatics/DeepSSV\n\nContacta.salim@latrobe.edu.au\n\nSupplementary informationSupplementary data are available at online.

bioinformatics↗