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Sagan, A.

Publications and source records attributed to Sagan, A..

3 recordsLinked to original sources

Role of the NuRD complex and altered proteostasis in cancer cell quiescence

Cytotoxic chemotherapy remains the primary treatment for ovarian cancer (OvCa). Development of chemoresistance typically results in patient death within two years. As such, understanding chemoresistance is critical. One underexplored mechanism of chemotherapy resistance is quiescence. Quiescent cells, which have reversibly exited the cell cycle, are refractory to most chemotherapies which primarily target rapidly proliferating cells. Here, we report that CHD4 and MBD3, components of the nucleosome remodeling and deacetylase (NuRD) complex, are downregulated in quiescent OvCa cells (qOvCa). Indicating a direct role for NuRD complex downregulation in the induction of quiescence, either CHD4 or MBD3 knockdown or histone deacetylase inhibitors (HDACi), such as vorinostat, induce quiescence in OvCa cells. RNA-Seq analysis of HDACi-treated cells confirmed expression changes consistent with induction of quiescence. We also find that both primary qOvCa and vorinostat-induced qOvCa demonstrate altered proteostasis, including increased proteasome activity and autophagy, and combination therapy of HDACi and proteasome inhibitors or autophagy inhibitors demonstrated profound synergistic death of OvCa cells. Finally, we overlapped RNA-Seq signatures from quiescent ovarian cancer cells with genes essential for quiescence in yeast to identify a "quiescent cell core signature." This core quiescent cell signature appeared to be conserved across multiple cancer types, suggesting new therapeutic targets.

cancer biology↗

STAN, a computational framework for inferring spatially informed transcription factor activity across cellular contexts

Transcription factors (TFs) drive significant cellular changes in response to environmental cues and intercellular signaling. Neighboring cells influence TF activity and, consequently, cellular fate and function. Spatial transcriptomics (ST) captures mRNA expression patterns across tissue samples, enabling characterization of the local microenvironment. However, these datasets have not been fully leveraged to systematically estimate TF activity governing cell identity. Here, we present STAN (Spatially informed Transcription factor Activity Network), a linear mixed-effects computational method that predicts spot-specific, spatially informed TF activities by integrating curated TF-target gene priors, mRNA expression, spatial coordinates, and morphological features from corresponding imaging data. We tested STAN using lymph node, breast cancer, and glioblastoma ST datasets to demonstrate its applicability by identifying TFs associated with specific cell types, spatial domains, pathological regions, and ligand-receptor pairs. STAN augments the utility of STs to reveal the intricate interplay between TFs and spatial organization across a spectrum of cellular contexts.

bioinformatics↗

Isolated BAP1 loss in malignant pleural mesothelioma predicts immunogenicity with implications for immunotherapeutic response

Malignant pleural mesothelioma (MPM), an aggressive cancer of the mesothelial cells lining the pleural cavity, lacks effective treatments. Multiple somatic mutations and copy number losses in tumor suppressor genes (TSGs) BAP1, CDKN2A/B, and NF2 are associated with MPM. The impact of single versus multiple losses of TSG on MPM biology, the immune tumor microenvironment, clinical outcomes, and treatment responses are unknown. Tumors with alterations in BAP1 alone were associated with a longer overall patient survival rate compared to tumors with CDKN2A/B and/or NF2 alterations with or without BAP1 and formed a distinct immunogenic subtype with altered transcription factor and pathway activity patterns. CDKN2A/B loss consistently contributed to an adverse clinical outcome. Since the loss of only BAP1 was associated with the PD-1 therapy response signature and higher LAG3 and VISTA gene expression, it is a candidate for immune checkpoint blockade therapy. Our results on the impact of TSG genotypes on MPM and the correlations between TSG alterations and molecular pathways provide a foundation for developing MPM therapies.

bioinformatics↗