Distinct mesenchymal cell states mediate prostate cancer progression.

In the complex tumor microenvironment (TME), mesenchymal cells are key players, yet their specific roles in prostate cancer (PCa) progression remain to be fully deciphered. This study employs single-cell RNA sequencing to delineate molecular changes in tumor stroma that influence PCa progression and metastasis. Analyzing mesenchymal cells from four genetically engineered mouse models (GEMMs) and correlating these findings with human tumors, we identify eight stromal cell populations with distinct transcriptional identities consistent across both species. Notably, stromal signatures in advanced mouse disease reflect those in human bone metastases, highlighting periostin's role in invasion and differentiation. From these insights, we derive a gene signature that predicts metastatic progression in localized disease beyond traditional Gleason scores. Our results illuminate the critical influence of stromal dynamics on PCa progression, suggesting new prognostic tools and therapeutic targets.

Distinct mesenchymal cell states mediate prostate cancer progression.

Alterations in tumor stroma influence prostate cancer progression and metastatic potential. However, the molecular underpinnings of this stromal-epithelial crosstalk are largely unknown. Here, we compare mesenchymal cells from four genetically engineered mouse models (GEMMs) of prostate cancer representing different stages of the disease to their wild-type (WT) counterparts by single-cell RNA sequencing (scRNA-seq) and, ultimately, to human tumors with comparable genotypes. We identified 8 transcriptionally and functionally distinct stromal populations responsible for common and GEMM-specific transcriptional programs. We show that stromal responses are conserved in mouse models and human prostate cancers with the same genomic alterations. We noted striking similarities between the transcriptional profiles of the stroma of murine models of advanced disease and those of of human prostate cancer bone metastases. These profiles were then used to build a robust gene signature that can predict metastatic progression in prostate cancer patients with localized disease and is also associated with progression-free survival independent of Gleason score. Taken together, this offers new evidence that stromal microenvironment mediates prostate cancer progression, further identifying tissue-based biomarkers and potential therapeutic targets of aggressive and metastatic disease.

The Prostate Cancer Androgen Receptor Cistrome in African American Men Associates with Upregulation of Lipid Metabolism and Immune Response.

African-American (AA) men are more likely to be diagnosed with and die from prostate cancer than European American (EA) men. Despite the central role of the androgen receptor (AR) transcription factor in prostate cancer, little is known about the contribution of epigenetics to observed racial disparities. We performed AR chromatin immunoprecipitation sequencing on primary prostate tumors from AA and EA men, finding that sites with greater AR binding intensity in AA relative to EA prostate cancer are enriched for lipid metabolism and immune response genes. Integration with transcriptomic and metabolomic data demonstrated coinciding upregulation of lipid metabolism gene expression and increased lipid levels in AA prostate cancer. In a metastatic prostate cancer cohort, upregulated lipid metabolism associated with poor prognosis. These findings offer the first insights into ancestry-specific differences in the prostate cancer AR cistrome. The data suggest a model whereby increased androgen signaling may contribute to higher levels of lipid metabolism, immune response, and cytokine signaling in AA prostate tumors. Given the association of upregulated lipogenesis with prostate cancer progression, our study provides a plausible biological explanation for the higher incidence and aggressiveness of prostate cancer observed in AA men. SIGNIFICANCE: With immunotherapies and inhibitors of metabolic enzymes in clinical development, the altered lipid metabolism and immune response in African-American men provides potential therapeutic opportunities to attenuate racial disparities in prostate cancer.

Building Tools for Machine Learning and Artificial Intelligence in Cancer Research: Best Practices and a Case Study with the PathML Toolkit for Computational Pathology.

Imaging datasets in cancer research are growing exponentially in both quantity and information density. These massive datasets may enable derivation of insights for cancer research and clinical care, but only if researchers are equipped with the tools to leverage advanced computational analysis approaches such as machine learning and artificial intelligence. In this work, we highlight three themes to guide development of such computational tools: scalability, standardization, and ease of use. We then apply these principles to develop PathML, a general-purpose research toolkit for computational pathology. We describe the design of the PathML framework and demonstrate applications in diverse use cases. PathML is publicly available at www.pathml.com.

PKCλ/ι inhibition activates an ULK2-mediated interferon response to repress tumorigenesis.

The interferon (IFN) pathway is critical for cytotoxic T cell activation, which is central to tumor immunosurveillance and successful immunotherapy. We demonstrate here that PKCλ/ι inactivation results in the hyper-stimulation of the IFN cascade and the enhanced recruitment of CD8+ T cells that impaired the growth of intestinal tumors. PKCλ/ι directly phosphorylates and represses the activity of ULK2, promoting its degradation through an endosomal microautophagy-driven ubiquitin-dependent mechanism. Loss of PKCλ/ι results in increased levels of enzymatically active ULK2, which, by direct phosphorylation, activates TBK1 to foster the activation of the STING-mediated IFN response. PKCλ/ι inactivation also triggers autophagy, which prevents STING degradation by chaperone-mediated autophagy. Thus, PKCλ/ι is a hub regulating the IFN pathway and three autophagic mechanisms that serve to maintain its homeostatic control. Importantly, single-cell multiplex imaging and bioinformatics analysis demonstrated that low PKCλ/ι levels correlate with enhanced IFN signaling and good prognosis in colorectal cancer patients.

Temporal evolution of cellular heterogeneity during the progression to advanced AR-negative prostate cancer.

Despite advances in the development of highly effective androgen receptor (AR)-directed therapies for the treatment of men with advanced prostate cancer, acquired resistance to such therapies frequently ensues. A significant subset of patients with resistant disease develop AR-negative tumors that lose their luminal identity and display neuroendocrine features (neuroendocrine prostate cancer (NEPC)). The cellular heterogeneity and the molecular evolution during the progression from AR-positive adenocarcinoma to AR-negative NEPC has yet to be characterized. Utilizing a new genetically engineered mouse model, we have characterized the synergy between Rb1 loss and MYCN (encodes N-Myc) overexpression which results in the formation of AR-negative, poorly differentiated tumors with high metastatic potential. Single-cell-based approaches revealed striking temporal changes to the transcriptome and chromatin accessibility which have identified the emergence of distinct cell populations, marked by differential expression of Ascl1 and Pou2f3, during the transition to NEPC. Moreover, global DNA methylation and the N-Myc cistrome are redirected following Rb1 loss. Altogether, our data provide insight into the progression of prostate adenocarcinoma to NEPC.

ELOVL5 Is a Critical and Targetable Fatty Acid Elongase in Prostate Cancer.

The androgen receptor (AR) is the key oncogenic driver of prostate cancer, and despite implementation of novel AR targeting therapies, outcomes for metastatic disease remain dismal. There is an urgent need to better understand androgen-regulated cellular processes to more effectively target the AR dependence of prostate cancer cells through new therapeutic vulnerabilities. Transcriptomic studies have consistently identified lipid metabolism as a hallmark of enhanced AR signaling in prostate cancer, yet the relationship between AR and the lipidome remains undefined. Using mass spectrometry-based lipidomics, this study reveals increased fatty acyl chain length in phospholipids from prostate cancer cells and patient-derived explants as one of the most striking androgen-regulated changes to lipid metabolism. Potent and direct AR-mediated induction of ELOVL fatty acid elongase 5 (ELOVL5), an enzyme that catalyzes fatty acid elongation, was demonstrated in prostate cancer cells, xenografts, and clinical tumors. Assessment of mRNA and protein in large-scale data sets revealed ELOVL5 as the predominant ELOVL expressed and upregulated in prostate cancer compared with nonmalignant prostate. ELOVL5 depletion markedly altered mitochondrial morphology and function, leading to excess generation of reactive oxygen species and resulting in suppression of prostate cancer cell proliferation, 3D growth, and in vivo tumor growth and metastasis. Supplementation with the monounsaturated fatty acid cis-vaccenic acid, a direct product of ELOVL5 elongation, reversed the oxidative stress and associated cell proliferation and migration effects of ELOVL5 knockdown. Collectively, these results identify lipid elongation as a protumorigenic metabolic pathway in prostate cancer that is androgen-regulated, critical for metastasis, and targetable via ELOVL5. SIGNIFICANCE: This study identifies phospholipid elongation as a new metabolic target of androgen action that is critical for prostate tumor metastasis.

Metabolomics of Prostate Cancer Gleason Score in Tumor Tissue and Serum.

Gleason score, a measure of prostate tumor differentiation, is the strongest predictor of lethal prostate cancer at the time of diagnosis. Metabolomic profiling of tumor and of patient serum could identify biomarkers of aggressive disease and lead to the development of a less-invasive assay to perform active surveillance monitoring. Metabolomic profiling of prostate tissue and serum samples was performed. Metabolite levels and metabolite sets were compared across Gleason scores. Machine learning algorithms were trained and tuned to predict transformation or differentiation status from metabolite data. A total of 135 metabolites were significantly different (P adjusted < 0.05) in tumor versus normal tissue, and pathway analysis identified one sugar metabolism pathway (P adjusted = 0.03). Machine learning identified profiles that predicted tumor versus normal tissue (AUC of 0.82 ± 0.08). In tumor tissue, 25 metabolites were associated with Gleason score (unadjusted P < 0.05), 4 increased in high grade while the remainder were enriched in low grade. While pyroglutamine and 1,5-anhydroglucitol were correlated (0.73 and 0.72, respectively) between tissue and serum from the same patient, no metabolites were consistently associated with Gleason score in serum. Previously reported as well as novel metabolites with differing abundance were identified across tumor tissue. However, a "metabolite signature" for Gleason score was not obtained. This may be due to study design and analytic challenges that future studies should consider. IMPLICATIONS: Metabolic profiling can distinguish benign and neoplastic tissues. A novel unsupervised machine learning method can be utilized to achieve this distinction.

Inhibition of O-GlcNAc Transferase Renders Prostate Cancer Cells Dependent on CDK9.

O-GlcNAc transferase (OGT) is a nutrient-sensitive glycosyltransferase that is overexpressed in prostate cancer, the most common cancer in males. We recently developed a specific and potent inhibitor targeting this enzyme, and here, we report a synthetic lethality screen using this compound. Our screen identified pan-cyclin-dependent kinase (CDK) inhibitor AT7519 as lethal in combination with OGT inhibition. Follow-up chemical and genetic approaches identified CDK9 as the major target for synthetic lethality with OGT inhibition in prostate cancer cells. OGT expression is regulated through retention of the fourth intron in the gene and CDK9 inhibition blunted this regulatory mechanism. CDK9 phosphorylates carboxy-terminal domain (CTD) of RNA Polymerase II to promote transcription elongation. We show that OGT inhibition augments effects of CDK9 inhibitors on CTD phosphorylation and general transcription. Finally, the combined inhibition of both OGT and CDK9 blocked growth of organoids derived from patients with metastatic prostate cancer, but had minimal effects on normal prostate spheroids. We report a novel synthetic lethal interaction between inhibitors of OGT and CDK9 that specifically kills prostate cancer cells, but not normal cells. Our study highlights the potential of combining OGT inhibitors with other treatments to exploit cancer-specific vulnerabilities. IMPLICATIONS: The primary contribution of OGT to cell proliferation is unknown, and in this study, we used a compound screen to indicate that OGT and CDK9 collaborate to sustain a cancer cell-specific pro-proliferative program. A better understanding of how OGT and CDK9 cross-talk will refine our understanding of this novel synthetic lethal interaction.