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Biology subjects

Wierdl, M.

Publications and source records attributed to Wierdl, M..

2 recordsLinked to original sources

Chemokine-Dependent Natural Killer Cells Prevent Pulmonary Metastasis in a Newly Established Mouse Osteosarcoma Model

Rampant genomic instability of osteosarcoma (OS) and associated inter- and intratumoral heterogeneity convey a high risk of metastasis despite contemporary multimodal therapy. A mouse primary OS tumor model, originating from Myc-overexpressing Trp53-null mesenchymal progenitor cells, closely mimics cardinal genetic features and gene expression patterns of human OS samples. Subcutaneous injection of OS cells into immunocompromised NSG mice produced extensive metastases, whereas many fewer metastases occurred in T cell-deficient nude mice, suggesting a principal role for innate immunity in controlling OS dissemination. Depletion of natural killer (NK) cells in nude mice facilitated OS metastasis. OS cells released a suite of chemokines, with CCL2 the most prominent. A genome-wide CRISPR/Cas9 screen in OS cells identified 11 genes, including Ccl2, whose loss facilitated pulmonary metastasis in nude mice. Disruption of CCL2 in OS cells partially phenocopied the effects of antibody-dependent NK cell depletion, underscoring a plausible OS-NK signaling pathway that limits OS metastasis. SignificanceOsteosarcoma exhibits complex genomic instability and a propensity for pulmonary metastasis that limit chemotherapeutic response and patient survival. Despite the plethora of heterogeneous genetic alterations that connote poor prognosis, a novel preclinical in vivo model for studying metastasis highlights potentially targetable signaling between osteosarcoma cell-derived chemokines and pulmonary NK cells.

cancer biology↗

Tabular foundation model predicts alternative lengthening of telomeres (ALT) and identifies SMARCAL1 as a target in ALT-driven cancers

Alternative lengthening of telomeres (ALT) is a telomerase-independent pathway used by aggressive cancers to maintain their replicative immortality. Because ALT is absent from normal human cells, it is an appealing target for cancer therapy, but the lack of ability to determine ALT status at scale has hindered discovery. Here, we developed ALTitude, a machine learning method from a tabular foundation model that infers ALT from cell line whole genome sequencing data, without need for paired germline analysis. We deployed ALTitude across the DepMap, doubling the number of known ALT+ cancer models. Systematic integration of ALTitude with CRISPR-Cas9 screens yielded the selective dependency on SMARCAL1 in ALT+ cell lines, where we show it stabilizes the ALT phenotype. Acute depletion of SMARCAL1 leads to G2/M arrest, mitotic catastrophe, and cell death. These data provide a valuable resource for studying ALT-related genomic features and present SMARCAL1 as a therapeutic target for ALT+ malignancies.

cancer biology↗