Biology subjects

Roussos Torres, E. T.

Publications and source records attributed to Roussos Torres, E. T..

7 recordsLinked to original sources

bioRxiv2026

A class act: HDAC1-Malat1 regulates MDSC apoptosis and cell cycling to decrease suppression of T cells

Myeloid derived suppressor cells (MDSCs) are key players in the immune-suppressed tumor microenvironment (TME) and significantly contribute to immune checkpoint inhibition (ICI) resistance, making them favorable targets for cancer immunotherapy. Epigenetic reprogramming of MDSCs using histone deacetylase (HDAC) inhibitors shows promise to sensitize the TME to ICIs. However, the molecular mechanism of HDAC inhibition in MDSCs has yet to be elucidated. Murine and human MDSC models treated with Entinostat revealed that the long non-coding RNA Malat1 downregulates pSTAT3 and decreases MDSC-mediated suppression of T cell proliferation. Through HDAC inhibitor screens, we identified HDAC1 as preferentially regulating Malat1 expression, STAT3 activation, and MDSC suppression. We also show that HDAC1 inhibition increases MDSC apoptosis by shifting pro-vs. anti-apoptotic signals and increases G0/G1 cell cycle arrest via decreasing G1-S transition cyclin-CDK complexes. Collectively, our findings provide a multi-pronged mechanism of HDAC inhibition in MDSCs that inform the development of future rational combination therapies. One Sentence SummaryHDAC1 inhibition in MDSCs increases Malat1, decreases pSTAT3, induces apoptosis/cell cycle arrest, and decreases suppression of T cells

immunology↗

bioRxiv2025

Beyond RECIST: mathematical modeling and Bayesian inference reveal the importance of immune parameters in metastatic breast cancer

Successful immunotherapies must overcome patient- and organ-specific tumor heterogeneities to mount an effective response. Yet tumor dynamics remain poorly characterized in organ-specific contexts and associated immune environments. To quantify heterogeneous tumor responses, we developed methods to fit mathematical models of the tumor-immune dynamics to patients undergoing combination therapy for metastatic breast cancer: checkpoint inhibition via nivolumab + ipilimumab combined with entinostat, as measured by RECIST criteria. In a subset of patients additional immune dynamics were quantified by spatial proteomics. Bayesian parameter inference revealed that only immune-modulatory parameters controlled response; parameters controlling cytotoxicity were uninformative. Through posterior parameter sampling and simulation, we created virtual tumor cohorts, enabling extrapolation beyond the data to predict probabilities of response in metastatic lesions where no data exist. We validated pre-dictions from our virtual tumor population using held-out data characterizing off-target lesions from the patient cohort. Profile-wise likelihood analysis revealed that scans in the week immediately following treatment hold particularly high value in identifying the tumor dynamics. Overall, we demonstrate how through modeling & inference cohort size limitations can be over-come through the creation of virtual tumor populations, giving insight into the site-specific mechanisms of disease progression and response.

systems biology↗

bioRxiv2025

Cancer systems immunology reveals myeloid - T cell interactions and B cell activation mediate response to checkpoint inhibition in metastatic breast cancer

Sensitization of the immune-suppressed tumor microenvironment (TME) of breast cancer by histone deacetylase inhibition shows promise, but the mechanisms of sensitization are unknown. We investigated the TME of breast-to-lung metastases by combining experimental and clinical data with theory. Knowledge-guided subclustering of single-cell RNA-sequencing data and cell circuits analysis identified 39 cell states and salient interactions, of which myeloid, T cell and B cell subpopulations were most affected by treatment. Using functional immunologic assays, we verified that inhibition of the ICAM pathway partially recapitulated treatment effects. Mathematical modeling of tumor-immune dynamics confirmed that tumor reduction required simultaneous modulation of multiple TME interactions. We found evidence that treatment affected anti-tumor antibody production. Analysis of patient biopsies via spatial proteomics corroborated preclinical findings: in responders we observed increased B cell activation, mature tertiary lymphoid structures, and increased CD8+ T cell--macrophage distances with treatment. Overall, this study provides a framework for the discovery of cell-cell interactions that govern responses in complex TMEs. Statement of significanceThis study provides a framework for the discovery of cell-cell interactions that control responses in complex TMEs. We not only identify impactful tumor immunologic interactions that facilitate sensitization of the metastatic TME but also demonstrate how interdisciplinary data integration fuels cancer systems immunology to accelerate discovery of mechanisms of successful immunotherapeutic response in breast cancers and other previously unresponsive solid tumor types.

cancer biology↗

bioRxiv2025

Inference of marker genes of subtle cell state changes via iterative logistic regression

We present iterative logistic regression (iLR) for the identification of small sets of informative marker genes. Differential expression and marker gene selection methods for single-cell RNA sequencing (scRNAseq) data can struggle to identify small sets of informative genes, especially for subtle differences between cell states, as can be induced by disease or treatment. iLR applied logistic regression iteratively with a Pareto front optimization to balance gene set size with classification performance. We benchmark iLR on in silico datasets demonstrating comparable performance to the state-of-the-art at single-cell classification using only a fraction of the genes. We test iLR on its ability to distinguish neuronal cell subtypes in healthy vs. autism spectrum disorder patients and find it achieves high accuracy with small sets of disease-relevant genes. We apply iLR to investigate immunotherapeutic effects in cell types from different tumor microenvironments and find that iLR infers informative genes that translate across organs and even species (mouse-to-human) comparison. We predicted via iLR that entinostat acts in part through the modulation of myeloid cell differentiation routes in the lung microenvironment. Overall, iLR provides means to infer interpretable transcriptional signatures from complex datasets with prognostic or therapeutic potential.

bioinformatics↗

bioRxiv2024

Modeling the dynamics of EMT reveals genes associated with pan-cancer intermediate states and plasticity

Epithelial-mesenchymal transition (EMT) is a developmental cell state transition co-opted by cancer to drive metastasis and resistance. Stable EMT intermediate states play a particularly important role in cell state plasticity and confer metastatic potential. To explore the dynamics of EMT and identify marker genes of highly metastatic intermediate cells, we analyzed EMT across multiple tumor types and stimuli via mathematical modeling with single-cell RNA-sequencing (scRNA-seq) data. We identified pan-cancer genes consistently expressed or upregulated in EMT intermediate states, most of which were not previously annotated as markers of EMT. Using Bayesian parameter inference, we fit a simple mathematical model to scRNA-seq data, revealing tumor-specific transition rates. This mathematical model offers a framework to quantify EMT progression. A consensus analysis of differential intermediate expression, regulation, and model-derived dynamics identified marker genes associated with persistence of the intermediate EMT state. SFN and NRG1 emerged as genes with the strongest evidence for their role influencing intermediate EMT dynamics. Through analysis of an independent cell line, we verified the role of SFN as a marker intermediate EMT transition. Modeling and inference of genes associated with EMT dynamics offer means to find biomarkers and to identify therapeutic approaches to harness or reverse tumor-promoting cell state transitions driven by EMT.

systems biology↗

bioRxiv2024

Mimicking the breast metastatic microenvironment: characterization of a novel syngeneic model of HER2+ breast cancer

Preclinical murine models in which primary tumors spontaneously metastasize to distant organs are valuable tools to study metastatic progression and novel cancer treatment combinations. Here, we characterize a novel syngeneic murine breast tumor cell line, NT2.5-lung metastasis (- LM), that provides a model of spontaneously metastatic neu-expressing breast cancer with quicker onset of widespread metastases after orthotopic mammary implantation in immune-competent NeuN mice. Within one week of orthotopic implantation of NT2.5-LM in NeuN mice, distant metastases can be observed in the lungs. Within four weeks, metastases are also observed in the bones, spleen, colon, and liver. Metastases are rapidly growing, proliferative, and responsive to HER2-directed therapy. We demonstrate altered expression of markers of epithelial-to-mesenchymal transition (EMT) and enrichment in EMT-regulating pathways, suggestive of their enhanced metastatic potential. The new NT2.5-LM model provides more rapid and spontaneous development of widespread metastases. Besides investigating mechanisms of metastatic progression, this new model may be used for the rationalized development of novel therapeutic interventions and assessment of therapeutic responses targeting distant visceral metastases SUMMARY STATEMENTWe characterize a new syngeneic, immune-competent murine model of breast cancer (NT2.5-LM) that yields rapid and widespread metastases, preserves spontaneous metastasis, and provides a model for studying novel therapeutic interventions.

cancer biology↗

bioRxiv2022

Myeloid-derived suppressor cell dynamics control outcomes in the metastatic niche

Myeloid-derived suppressor cells (MDSCs) play a prominent and rising role in the tumor microenvironment. An understanding of the tumor-MDSC interactions that influence disease progression is critical, and currently lacking. To address this, we developed a mathematical model of metastatic growth and progression in immune-rich tumor microenvironments. We model the tumor-immune dynamics with stochastic delay differential equations, and study the impact of delays in MDSC activation/recruitment on tumor growth outcomes. We find when the circulating level of MDSCs is low, the MDSC delay has a pronounced impact on the probability of new metastatic establishment: blocking MDSC recruitment can reduce the probability of metastasis by as much as 50%. We also quantify the extent to which decreasing the immuno-suppressive capability of the MDSCs impacts the probability that a new metastasis will persist or grow. In order to quantify patient-specific MDSC dynamics under different conditions we fit individual tumors treated with immune checkpoint inhibitors to the tumor-MDSC model via Bayesian parameter inference. We reveal that control of the inhibition rate of natural killer cells by MDSCs has a larger influence on tumor outcomes than controlling the tumor growth rate directly. Posterior classification of tumor outcomes demonstrates that incorporating knowledge of the MDSC responses improves predictive accuracy from 63% to 82%. Our results illustrate the importance of MDSC dynamics in the tumor microenvironment and predict interventions that may shift environments towards a less immune-suppressed state. We argue that there is a pressing need to more often consider MDSCs in analyses of tumor microenvironments.

systems biology↗