Marín-Goñi, Irene

Abstract Human genomic studies link reduced CUB domain-containing protein 1 (CDCP1) expression with myocardial recovery in heart failure. While CDCP1 regulates cardiac fibroblast proliferation in vitro, it’s in vivo role in cardiac fibrosis remains unclear. Using a Cdcp1 -knockout (KO) angiotensin II/phenylephrine mouse model, we show that Cdcp1 deletion reduces echocardiographic left ventricular mass, histologic cardiac fibrosis, and pro-fibrotic gene expression, along with decreased fibroblast activation and inflammatory markers. Spatial transcriptomics identified a pressure overload–expanded fibroblast subpopulation enriched for growth factor and TGF-β signaling (FB5), which was markedly attenuated in Cdcp1 -KO hearts, alongside reduction of a pro-inflammatory cardiomyocyte subtype (CM4). Complementary studies in human ventricular fibroblasts demonstrate that CDCP1 knockdown reduced extracellular matrix gene expression and collagen I deposition. These findings establish CDCP1 as a regulator of cardiac fibrotic remodeling in vivo and open avenues for its further investigation as a potential therapeutic target.

Background: Cardiac fibrosis is a central pathological process that leads to worse outcomes in various cardiomyopathies. Recent evidence shows that CDCP1 (CUB domain-containing protein 1) modulates myocardial recovery by attenuating human cardiac fibroblast (HCF) proliferation. This study investigates the hypothesis that selective targeting of the CDCP1 cleavage domain can mitigate fibroblast proliferation, thereby offering a novel therapeutic approach to reducing cardiac fibrosis. Methods: A high-throughput in silico screening was performed using the Enamine compound library, focusing on structural regions that include the cleavage and adjacent sites of CDCP1 . A total of 147 candidate compounds were prioritized based on docking scores and binding stability profiles. Selected compounds were subsequently evaluated in vitro using HCF cultures seeded at 3,000 cells/well in 96-well plates. Each compound was administered at 10 μM concentration, and cell proliferation and viability were assessed by time-lapse imaging in the Incucyte® system at 24 and 96 hours. All experiments were done in triplicate with appropriate DMSO vehicle controls. Results: Compound C3 significantly reduced HCF proliferation (Fig B) to 27.3±2.4% relative to DMSO controls (99.9±0.5%, P <0.001) at 96 hours. Detailed structural modeling indicated that C3 binds to the CDCP1 cleavage site (Fig C) with a favorable Glide XP docking score of -7.66 kcal/mol. The interaction is stabilized by hydrogen bonds with Lys365 and Cys374 (Fig D), and supplementary hydrophobic interactions between aromatic rings and aliphatic groups, which contribute to a strong inhibitory effect. Conclusion: Targeting the CDCP1 cleavage domain with compound C3 effectively diminishes HCF proliferation in vitro. Therefore, these results substantiate the therapeutic potential of CDCP1 inhibition in cardiac fibrosis. Ongoing in vivo studies will explore the translational relevance of these findings.

Abstract Deconvolution models are a powerful tool for extracting cell-type-specific information from bulk gene expression profiles. Current methods leverage advanced machine learning models and high-resolution sequencing, like single-cell RNA-sequencing, showing promising results across diverse tissues and conditions. However, they still present important limitations: First, many depend on selecting a robust reference, which can strongly affect the deconvolution. Second, pseudobulk data used for training and real bulk RNA-seq samples often exhibit strong distribution shifts, which are currently unaccounted for. Finally, most deconvolution approaches behave as black boxes, which can compromise the reliability of the results. Here, we present Sweetwater, an adaptive and interpretable autoencoder that efficiently deconvolves bulk samples leveraging multiple classes of reference data. Moreover, we propose an improved way of generating training data from a mixture of FACS-sorted FASTQ files, reducing platform-specific biases and outperforming current single-cell-based references. Furthermore, we introduce a gold standard dataset to facilitate fair and accurate evaluation of deconvolution approaches. Finally, we demonstrate that Sweetwater adapts effectively to deconvolved samples during training, uncovering biologically meaningful patterns and enhancing result’s reliability. Sweetwater is available at https://doi.org/10.6084/m9.figshare.29609180, and we anticipate it will expedite the accurate examination of high-throughput clinical data across diverse applications.

Abstract Background: Second generation androgen receptor signaling inhibitors (ARSIs), such as abiraterone, have demonstrated improved survival in metastatic castration-resistant prostate cancer (mCRPC) patients, however, acquired resistance to these agents often arises. Novel and alternative therapies are needed to combat the ARSI resistant tumor. In the last two decades patient-derived xenograft (PDX) and PDX organoids have become pivotal models in anticancer drug development and biomarker discovery due to their ability to faithfully recapitulate the complex and heterogeneous tumor biology. Methods: We analyzed RNAseq data from mCRPC patients and corresponding PDX models (PROMOTE study, NCT #01953640) and employed L1000 Chem Perturbation down/up signatures database to find candidate drugs to overcome abiraterone resistance by reverse signature search. We then performed proliferation assay using 49 compounds in three PROMOTE-PDX organoid models (2 abiraterone-resistant and 1 abiraterone-sensitive) using our semi high-throughput drug screening platform. Results: Overall, we identified a total of 32 compounds with IC50&lt;10µM in at least one of the models, among which, 5 agents are active in all three models and 5 in at least two of the models. (Table 1). Within active compounds we find selective kinase inhibitors (BRAF, JAK2, AKT, FLT3) and histone deacetylase inhibitors (belinostat and pracinostat). Particularly interesting are two drugs that are only active in the abiraterone resistant setting but not the sensitive PDX. Conclusions: In summary, we leveraged RNAseq data from mCRPC patients and PDX models and performed a high-throughput drug screen in PDX-derived organoid models to identify potential drugs that could overcome abiraterone-resistance in lethal advanced prostate cancer. These data highlight potential drug combinations or subsequent therapeutic strategies for these patients. Citation Format: Irene Marín-Goñi, Huanyao Gao, Joachim L. Petit, Shreya Indulkar, Adam M. Kase, Cassandra N. Moore, Krishna R. Kalari, Michael T. Barrett, Richard Weinshilboum, Winston Tan, John A. Copland, Liewei Wang. Overcoming resistance in mCRPC: drug discovery using PDX models and high-throughput drug screening [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5658.

Abstract Grouping gene expression into gene set activity scores (GSAS) provides better biological insights than studying individual genes. However, existing gene set projection methods cannot return representative, robust, and interpretable GSAS. We developed NetActivity, a machine learning framework that generates GSAS based on a sparsely-connected autoencoder, where each neuron in the inner layer represents a gene set. We proposed a three-tier training that yielded representative, robust, and interpretable GSAS. NetActivity model was trained with 1518 GO biological processes terms and KEGG pathways and all GTEx samples. NetActivity generates GSAS robust to the initialization parameters and representative of the original transcriptome, and assigned higher importance to more biologically relevant genes. Moreover, NetActivity returns GSAS with a more consistent definition and higher interpretability than GSVA and hipathia, state-of-the-art gene set projection methods. Finally, NetActivity enables combining bulk RNA-seq and microarray datasets in a meta-analysis of prostate cancer progression, highlighting gene sets related to cell division, key for disease progression. When applied to metastatic prostate cancer, gene sets associated with cancer progression were also altered due to drug resistance, while a classical enrichment analysis identified gene sets irrelevant to the phenotype. NetActivity is publicly available in Bioconductor and GitHub.

Abstract Survival rates of patients with metastatic castration-resistant prostate cancer (mCRPC) are low due to lack of response or acquired resistance to available therapies, such as abiraterone (Abi). A better understanding of the underlying molecular mechanisms is needed to identify effective targets to overcome resistance. Given the complexity of the transcriptional dynamics in cells, differential gene expression analysis of bulk transcriptomics data cannot provide sufficient detailed insights into resistance mechanisms. Incorporating network structures could overcome this limitation to provide a global and functional perspective of Abi resistance in mCRPC. Here, we developed TraRe, a computational method using sparse Bayesian models to examine phenotypically driven transcriptional mechanistic differences at three distinct levels: transcriptional networks, specific regulons, and individual transcription factors (TF). TraRe was applied to transcriptomic data from 46 patients with mCRPC with Abi-response clinical data and uncovered abrogated immune response transcriptional modules that showed strong differential regulation in Abi-responsive compared with Abi-resistant patients. These modules were replicated in an independent mCRPC study. Furthermore, key rewiring predictions and their associated TFs were experimentally validated in two prostate cancer cell lines with different Abi-resistance features. Among them, ELK3, MXD1, and MYB played a differential role in cell survival in Abi-sensitive and Abi-resistant cells. Moreover, ELK3 regulated cell migration capacity, which could have a direct impact on mCRPC. Collectively, these findings shed light on the underlying transcriptional mechanisms driving Abi response, demonstrating that TraRe is a promising tool for generating novel hypotheses based on identified transcriptional network disruptions. Significance: The computational method TraRe built on Bayesian machine learning models for investigating transcriptional network structures shows that disruption of ELK3, MXD1, and MYB signaling cascades impacts abiraterone resistance in prostate cancer.