A Long Noncoding RNA, GAS5 Can Be a Biomarker for Docetaxel Response in Castration Resistant Prostate Cancer

Integration of Pan-Cancer Cell Line and Single-Cell Transcriptomic Profiles Enables Inference of Therapeutic Vulnerabilities in Heterogeneous Tumors

Single-cell RNA sequencing (scRNA-seq) greatly advanced the understanding of intratumoral heterogeneity by identifying distinct cancer cell subpopulations. However, translating biological differences into treatment strategies is challenging due to a lack of tools to facilitate efficient drug discovery that tackles heterogeneous tumors. Developing such approaches requires accurate prediction of drug response at the single-cell level to offer therapeutic options to specific cell subpopulations. Here, we developed a transparent computational framework (nicknamed scIDUC) to predict therapeutic efficacies on an individual cell basis by integrating single-cell transcriptomic profiles with large, data-rich pan-cancer cell line screening data sets. This method achieved high accuracy in separating cells into their correct cellular drug response statuses. In three distinct prospective tests covering different diseases (rhabdomyosarcoma, pancreatic ductal adenocarcinoma, and castration-resistant prostate cancer), the predicted results using scIDUC were accurate and mirrored biological expectations. In the first two tests, the framework identified drugs for cell subpopulations that were resistant to standard-of-care (SOC) therapies due to intrinsic resistance or tumor microenvironmental effects, and the results showed high consistency with experimental findings from the original studies. In the third test using newly generated SOC therapy-resistant cell lines, scIDUC identified efficacious drugs for the resistant line, and the predictions were validated with in vitro experiments. Together, this study demonstrates the potential of scIDUC to quickly translate scRNA-seq data into drug responses for individual cells, displaying the potential as a tool to improve the treatment of heterogenous tumors.

SIGNIFICANCE

A versatile method that infers cell-level drug response in scRNA-seq data facilitates the development of therapeutic strategies to target heterogeneous subpopulations within a tumor and address issues such as treatment failure and resistance.

Inferring therapeutic vulnerability within tumors through integration of pan-cancer cell line and single-cell transcriptomic profiles

Single-cell RNA sequencing greatly advanced our understanding of intratumoral heterogeneity through identifying tumor subpopulations with distinct biologies. However, translating biological differences into treatment strategies is challenging, as we still lack tools to facilitate efficient drug discovery that tackles heterogeneous tumors. One key component of such approaches tackles accurate prediction of drug response at the single-cell level to offer therapeutic options to specific cell subpopulations. Here, we present a transparent computational framework (nicknamed scIDUC) to predict therapeutic efficacies on an individual-cell basis by integrating single-cell transcriptomic profiles with large, data-rich pan-cancer cell line screening datasets. Our method achieves high accuracy, with predicted sensitivities easily able to separate cells into their true cellular drug resistance status as measured by effect size (Cohen's d > 1.0). More importantly, we examine our method's utility with three distinct prospective tests covering different diseases (rhabdomyosarcoma, pancreatic ductal adenocarcinoma, and castration-resistant prostate cancer), and in each our predicted results are accurate and mirrored biological expectations. In the first two, we identified drugs for cell subpopulations that are resistant to standard-of-care (SOC) therapies due to intrinsic resistance or effects of tumor microenvironments. Our results showed high consistency with experimental findings from the original studies. In the third test, we generated SOC therapy resistant cell lines, used scIDUC to identify efficacious drugs for the resistant line, and validated the predictions with in-vitro experiments. Together, scIDUC quickly translates scRNA-seq data into drug response for individual cells, displaying the potential as a first-line tool for nuanced and heterogeneity-aware drug discovery.

Exploring GAS5's impact on prostate cancer: Recent discoveries and emerging paradigms

Novel treatment targets must be discovered to improve the results for patients with prostate cancer, which continues to be a significant worldwide health problem. Growth Arrest-Specific 5 (GAS5) is a long non-coding RNA (lncRNA) that has emerged as a promising target. GAS5 is a non-coding RNA that is a tumour suppressor in many different cancers by reducing cell proliferation and increasing apoptosis. GAS5 influences cell cycle control and apoptosis via interactions with important signalling pathways and microRNAs, as has been shown by recent studies. Furthermore, GAS5 has attracted interest for its diagnostic and prognostic potential in prostate cancer. GAS5 expression is a promising biomarker for disease classification and individualized treatment approaches because of its association with clinicopathological characteristics such as tumour stage, Gleason score, and metastatic potential. Preclinical models have revealed encouraging anticancer benefits from experimental techniques employing GAS5 overexpression or synthetic analogues, indicating the possibility of translational treatments. Whether GAS5 can be used as a diagnostic biomarker and therapeutic target might lead to more effective and individualized ways to fight prostate cancer, improving patient outcomes and quality of life. To utilize its potential for therapy and establish it as a useful addition to the clinical arsenal against this pervasive malignancy, more investigation into the complex molecular pathways of GAS5 in prostate cancer is essential. This review highlights the recent advancements and insights into the role of GAS5 in prostate cancer pathogenesis and progression.

Computational drug discovery for castration-resistant prostate cancers through in vitro drug response modeling

Prostate cancer (PC) is the most frequently diagnosed malignancy and a leading cause of cancer deaths in US men. Many PC cases metastasize and develop resistance to systemic hormonal therapy, a stage known as castration-resistant prostate cancer (CRPC). Therefore, there is an urgent need to develop effective therapeutic strategies for CRPC. Traditional drug discovery pipelines require significant time and capital input, which highlights a need for novel methods to evaluate the repositioning potential of existing drugs. Here, we present a computational framework to predict drug sensitivities of clinical CRPC tumors to various existing compounds and identify treatment options with high potential for clinical impact. We applied this method to a CRPC patient cohort and nominated drugs to combat resistance to hormonal therapies including abiraterone and enzalutamide. The utility of this method was demonstrated by nomination of multiple drugs that are currently undergoing clinical trials for CRPC. Additionally, this method identified the tetracycline derivative COL-3, for which we validated higher efficacy in an isogenic cell line model of enzalutamide-resistant vs. enzalutamide-sensitive CRPC. In enzalutamide-resistant CRPC cells, COL-3 displayed higher activity for inhibiting cell growth and migration, and for inducing G1-phase cell cycle arrest and apoptosis. Collectively, these findings demonstrate the utility of a computational framework for independent validation of drugs being tested in CRPC clinical trials, and for nominating drugs with enhanced biological activity in models of enzalutamide-resistant CRPC. The efficiency of this method relative to traditional drug development approaches indicates a high potential for accelerating drug development for CRPC.

Epigenetics regulation of prostate cancer: Biomarker and therapeutic potential

Prostate cancer (CaP) is the second leading cause of cancer death and displays a broad range of clinical behavior from relatively indolent to aggressive metastatic disease. The etiology of most cases of CaP is not understood completely, which makes it imperative to search for the molecular basis of CaP and markers for early diagnosis. Epigenetic modifications, including changes in DNA methylation patterns, histone modifications, miRNAs, and lncRNAs are key drivers of prostate tumorigenesis. These epigenetic defects might be due to deregulated expression of the epigenetic machinery, affecting the expression of several important genes like GSTP1, RASSF1, CDKN2, RARRES1, IGFBP3, RARB, TMPRSS2-ERG, ITGB4, AOX1, HHEX, WT1, HSPE, PLAU, FOXA1, ASC, GPX3, EZH2, LSD1, etc. In this review, we highlighted the most important epigenetic gene alterations and their variations as a diagnostic marker and target for therapeutic intervention of CaP in the future. Characterization of epigenetic changes involved in CaP is obscure and adequate validation studies are still required to corroborate the present results that would be the impending future of transforming basic research settings into clinical practice.