Biology subjects

Alshalalfa, M.

Publications and source records attributed to Alshalalfa, M..

5 recordsLinked to original sources

bioRxiv2025

Genome-wide CRISPR screens identify PTGES3 as a novel AR modulator

The androgen receptor (AR) is a critical driver of prostate cancer (PCa). To study regulators of AR protein levels and oncogenic activity, we created the first live cell quantitative endogenous AR fluorescent reporters. Leveraging this novel AR reporter, we performed genome-scale CRISPRi flow cytometry sorting screens to systematically identify genes that modulate AR protein levels. We identified and validated known AR protein regulators including HOXB13 and GATA2 and also unexpected top hits including PTGES3, a poorly characterized gene in PCa. PTGES3 repression resulted in loss of AR protein, cell cycle arrest, and cell death in AR-driven PCa models. PTGES3 is not a commonly essential gene, and our data nominate it as a prime PCa therapeutic target. Clinically, analysis of PCa data demonstrate that PTGES3 expression is associated with AR-directed therapy resistance. Mechanistically, we show PTGES3 binds directly to AR, forms a protein complex with AR in the nucleus, regulates AR protein stability in vitro and in vivo and modulates AR function in the nucleus at AR target genes. PTGES3 represents a novel therapeutic target for overcoming known mechanisms of resistance to existing AR-directed therapies in PCa.

cancer biology↗

bioRxiv2020

Human Prostate Cancer-Associated Macrophage Subtypes with Prognostic Potential Revealed by Single-cell Transcriptomics

Macrophages in the tumor microenvironment are causally linked with prostate cancer development and progression, yet little is known about their composition in neoplastic human tissue. By performing single cell transcriptomic analysis of human prostate cancer resident macrophages, three distinct populations were identified in the diseased prostate. Unexpectedly, macrophages isolated from the tumor-adjacent site of the prostatectomy specimen were identical to those from the tumorous site. Markers associated with canonical M1 and M2 macrophage phenotypes were identifiable, however these were not the main factors defining unique subtypes. The genes selectively associated with each macrophage cluster were used to develop a gene signature which was highly associated with both recurrence-free and metastasis-free survival. These results highlight the relevance of tissue-specific macrophage subtypes in the tumour microenvironment for prostate cancer progression and demonstrates the utility of profiling single-cell transcriptomics in human tumor samples as a strategy to design gene classifiers for patient prognostication.

immunology↗

bioRxiv2020

Role of Specialized mSWI/SNF Complexes in Prostate Cancer Lineage Plasticity

Advanced prostate cancer initially responds to hormonal treatment, but ultimately becomes resistant and requires more potent therapies. One mechanism of resistance observed in [~]10% of these patients is through lineage plasticity, which manifests in a partial or complete small cell or neuroendocrine prostate cancer (NEPC) phenotype. Here, we investigate the role of the mammalian SWI/SNF (mSWI/SNF) chromatin remodeling complex in NEPC. Using large patient datasets, patient-derived organoids and cancer cell lines, we identify mSWI/SNF subunits that are deregulated in NEPC and demonstrate that SMARCA4 (BRG1) overexpression is associated with aggressive disease. We also show that SWI/SNF complexes interact with different lineage-specific factors in NEPC compared to prostate adenocarcinoma. These data suggest a role for mSWI/SNF complexes in therapy-related lineage plasticity, which may be relevant for other solid tumors.

cancer biology↗

bioRxiv2018

Epigenetic analysis identifies factors driving racial disparity in prostate cancer

Prostate cancer (PCa) is the second most leading cause of death in men worldwide. African American men (AA) represent more aggressive form of PCa as compared to Caucasian (CA) counterparts. Evidence suggests that genetic and other biological factors could account for the observed racial disparity. We analyzed the cancer genome atlas (TCGA) dataset (2015) for existing epigenetic variation in AA and CA prostate cancer patients, and carried out Reduced Representation Bisulphite Sequencing (RRBS) analysis to identify global methylation changes in AA and CA prostate cancer patients. The TCGA dataset analysis revealed that the epigenetic heterogeneity could be categorized into 4 classes, where AA associated primarily to methylation cluster 1 (p value 0.048), and CA associated to methylation cluster 3 (p value 0.000146). We identified enrichment of Wnt signaling genes in both AA and CA, however they were differentially activated in terms of canonical and non-canonical Wnt signaling pathway activation. This was further validated using the GenomeDx expression data. Our RRBS data also suggested distinct methylation patterns in AA compared to CA, and in part validated our TCGA findings. Survival analysis using the RRBS data suggested hypomethylated genes to be significantly associated with recurrence of prostate cancer in CA (p=6.07x10-6) as well as in AA (p=0.0077). Overall, the observed racial disparity in the molecular mechanism involved in the pathogenesis of prostate cancer suggests diverse heterogeneity that potentially could affect survival and should be considered during prognosis and treatment.

cancer biology↗

bioRxiv2018

Deep Genomic Signature for early metastasis prediction in prostate cancer

For prostate cancer patients, timing and intensity of therapy are adjusted based on their prognosis. Clinical and pathological factors, and recently, gene expression-based signatures have been shown to predict metastatic prostate cancer. Previous studies used labelled datasets, i.e. those with information on the metastasis outcome, to discover gene signatures to predict metastasis. Due to steady progression of prostate cancer, datasets for this cancer have a limited number of labelled samples but more unlabelled samples. In addition to this issue, the high dimensionality of the gene expression data also poses a significant challenge to train a classifier and predict metastasis accurately. In this study, we aim to boost the prediction accuracy by utilizing both labelled and unlabelled datasets together. We propose Deep Genomic Signature (DGS), a method based on Denoising Auto-Encoders (DAEs) and transfer learning. DGS has the following steps: first, we train a DAE on a large unlabelled gene expression dataset to extract the most salient features of its samples. Then, we train another DAE on a small labelled dataset for a similar purpose. Since the labelled dataset is small, we employ a transfer learning approach and use the parameters learned from the first DAE in the second one. This approach enables us to train a large DAE on a small dataset. After training the second DAE, we obtain the list of genes with high weights by applying a standard deviation filter on the transferred and learned weights. Finally, we train an elastic net logistic regression model on the expression of the selected genes to predict metastasis. Because of the elastic net regularization, some of the selected genes have non-zero coefficients in the classifier which we consider as the DGS gene signature for metastasis. We apply DGS to six labelled and one large unlabelled prostate cancer datasets. Results on five validation datasets indicate that DGS outperforms state-of-the-art gene signatures (obtained from only labelled datasets) in terms of prediction accuracy. Survival analyses demonstrate the potential clinical utility of our gene signature that adds novel prognostic information to the well-established clinical factors and the state-of-the-art gene signatures. Finally, pathway analysis reveals that the DGS gene signature captures the hallmarks of prostate cancer metastasis. These results suggest that our method helps to identify a robust gene signature that may improve patient management.

bioinformatics↗