Single-cell ATAC and RNA sequencing reveal pre-existing and persistent cells associated with prostate cancer relapse

A distinct subset of urothelial cells with enhanced EMT features promotes chemotherapy resistance and cancer recurrence by increasing COL4A1-ITGB1 mediated angiogenesis

Drug resistance and tumor recurrence remain clinical challenges in the treatment of urothelial carcinoma (UC). However, the underlying mechanism is not fully understood. Here, we performed single-cell RNA sequencing and identified a subset of urothelial cells with epithelial-mesenchymal transition (EMT) features (EMT-UC), which is significantly correlated with chemotherapy resistance and cancer recurrence. To validate the clinical significance of EMT-UC, we constructed EMT-UC like cells by introducing overexpression of two markers, Zinc Finger E-Box Binding Homeobox 1 (ZEB1) and Desmin (DES), and examined their histological distribution characteristics and malignant phenotypes. EMT-UC like cells were mainly enriched in UC tissues from patients with adverse prognosis and exhibited significantly elevated EMT, migration and gemcitabine tolerance in vitro. However, EMT-UC was not specifically identified from tumorous tissues, certain proportion of them were also identified in adjacent normal tissues. Tumorous EMT-UC highly expressed genes involved in malignant behaviors and exhibited adverse prognosis. Additionally, tumorous EMT-UC was associated with remodeled tumor microenvironment (TME), which exhibited high angiogenic and immunosuppressive potentials compared with the normal counterparts. Furthermore, a specific interaction of COL4A1 and ITGB1 was identified to be highly enriched in tumorous EMT-UC, and in the endothelial component. Targeting the interaction of COL4A1 and ITGB1 with specific antibodies significantly suppressed tumorous angiogenesis and alleviated gemcitabine resistance of UC. Overall, our findings demonstrated that the driven force of chemotherapy resistance and recurrence of UC was EMT-UC mediated COL4A1-ITGB1 interaction, providing a potential target for future UC treatment.

SLC4A4 is a novel driver of enzalutamide resistance in prostate cancer

The androgen receptor signaling inhibitor (ARSI) enzalutamide (Enz) has shown critical efficacy in the treatment of advanced prostate cancer (PCa). However, the development of drug resistance is a significant factor contributing to mortality in PCa patients. We aimed to explore the key mechanisms of Enz-resistance. Through analysis of GEO databases, we identified SLC4A4 as a novel driver in Enz resistance. Long-term Enz treatment leads to the up-regulation of SLC4A4, which in turn mediates P53 lactylation via the NF-κB/STAT3/SLC4A4 axis, ultimately leading to the development of Enz resistance and progression of PCa. SLC4A4 knockdown overcomes Enz resistance both in vitro and in vivo. Hence, our results suggest that targeting SLC4A4 could be a promising therapeutic strategy for Enz resistance. STATEMENT OF SIGNIFICANCE: SLC4A4 is a novel driver of enzalutamide resistance.

A Zwitterionic Detergent and Catalyst-Based Single-Cell Proteomics Using a Loss-Free Microhole-Collection Disc

Recent advances in single-cell proteomics have solved many bottlenecks, such as throughput, sample recovery, and scalability via nanoscale sample handling. In this study, we aimed for a sensitive mass spectrometry (MS) analysis capable of handling single cells with a conventional mass spectrometry workflow without additional equipment. We achieved seamless cell lysis and TMT labeling in a micro-HOLe Disc (microHOLD) by developing a mass-compatible single solution based on a zwitterionic detergent and a catalyst for single-cell lysis and tandem mass tag labeling without a heat incubation step. This method was developed to avoid peptide loss by surface adsorption and buffer or tube changes by collecting tandem mass tag-labeled peptide through microholes placed in the liquid chromatography injection vials in a single solution. We successfully applied the microHOLD single-cell proteomics method for the analysis of proteome reprogramming in hormone-sensitive prostate cells to develop castration-resistant prostate cancer cells. This novel single-cell proteomics method is not limited by cutting-edge nanovolume handling equipment and achieves high throughput and ultrasensitive proteomics analysis of limited samples, such as single cells.

Metastatic hormone-naïve prostate cancer: a distinct biological entity

Metastatic hormone-naïve prostate cancer (mHNPC) is often the initial form of presentation for metastatic prostate cancer and encompasses a heterogeneous patient population with high inter-patient heterogeneity in prognosis and response to therapy. A more precise treatment of mHNPC, guided by evidence-based biomarkers, remains an unmet medical need. In addition, the limited number of representative laboratory models of mHNPC hampers the translation of basic research into clinical applications. We provide a comprehensive overview of the clinical and biological features that characterize mHNPC, highlight molecular data that could explain the unique prognostic characteristics of mHNPC, and identify key open questions.

Androgen receptor-mediated pharmacogenomic expression quantitative trait loci: implications for breast cancer response to AR-targeting therapy

BACKGROUND

Endocrine therapy is the most important treatment modality of breast cancer patients whose tumors express the estrogen receptor α (ERα). The androgen receptor (AR) is also expressed in the vast majority (80-90%) of ERα-positive tumors. AR-targeting drugs are not used in clinical practice, but have been evaluated in multiple trials and preclinical studies.

METHODS

We performed a genome-wide study to identify hormone/drug-induced single nucleotide polymorphism (SNP) genotype - dependent gene-expression, known as PGx-eQTL, mediated by either an AR agonist (dihydrotestosterone) or a partial antagonist (enzalutamide), utilizing a previously well characterized lymphoblastic cell line panel. The association of the identified SNPs-gene pairs with breast cancer phenotypes were then examined using three genome-wide association (GWAS) studies that we have published and other studies from the GWAS catalog.

RESULTS

We identified 13 DHT-mediated PGx-eQTL loci and 23 Enz-mediated PGx-eQTL loci that were associated with breast cancer outcomes post ER antagonist or aromatase inhibitors (AI) treatment, or with pharmacodynamic (PD) effects of AIs. An additional 30 loci were found to be associated with cancer risk and sex-hormone binding globulin levels. The top loci involved the genes IDH2 and TMEM9, the expression of which were suppressed by DHT in a PGx-eQTL SNP genotype-dependent manner. Both of these genes were overexpressed in breast cancer and were associated with a poorer prognosis. Therefore, suppression of these genes by AR agonists may benefit patients with minor allele genotypes for these SNPs.

CONCLUSIONS

We identified AR-related PGx-eQTL SNP-gene pairs that were associated with risks, outcomes and PD effects of endocrine therapy that may provide potential biomarkers for individualized treatment of breast cancer.

The implications of single-cell RNA-seq analysis in prostate cancer: unraveling tumor heterogeneity, therapeutic implications and pathways towards personalized therapy

In recent years, advancements in single-cell and spatial transcriptomics, which are highly regarded developments in the current era, particularly the emerging integration of single-cell and spatiotemporal transcriptomics, have enabled a detailed molecular comprehension of the complex regulation of cell fate. The insights obtained from these methodologies are anticipated to significantly contribute to the development of personalized medicine. Currently, single-cell technology is less frequently utilized for prostate cancer compared with other types of tumors. Starting from the perspective of RNA sequencing technology, this review outlined the significance of single-cell RNA sequencing (scRNA-seq) in prostate cancer research, encompassing preclinical medicine and clinical applications. We summarize the differences between mouse and human prostate cancer as revealed by scRNA-seq studies, as well as a combination of multi-omics methods involving scRNA-seq to highlight the key molecular targets for the diagnosis, treatment, and drug resistance characteristics of prostate cancer. These studies are expected to provide novel insights for the development of immunotherapy and other innovative treatment strategies for castration-resistant prostate cancer. Furthermore, we explore the potential clinical applications stemming from other single-cell technologies in this review, paving the way for future research in precision medicine.

CRISPR genome-wide screening identifies PAK1 as a critical driver of ARSI cross-resistance in prostate cancer progression

Next-generation androgen receptor signaling inhibitors (ARSIs), such as enzalutamide (Enza) and darolutamide (Daro), are initially effective for the treatment of advanced prostate cancer (PCa) and castration-resistant prostate cancer (CRPC). However, patients often relapse and develop cross-resistance, which consequently makes drug resistance an inevitable cause of CRPC-related mortality. By conducting a comprehensive analysis of GEO datasets, CRISPR genome-wide screening results, ATAC-seq data, and RNA-seq data, we systemically identified PAK1 as a significant contributor to ARSI cross-resistance due to the activation of the PAK1/RELA/hnRNPA1/AR-V7 axis. Inhibition of PAK1 followed by suppression of NF-κB pathways and AR-V7 expression effectively overcomes ARSI cross-resistance. Our findings indicate that PAK1 represents a promising therapeutic target gene for the treatment of ARSI cross-resistant PCa patients in the clinic. STATEMENT OF SIGNIFICANCE: PAK1 drives ARSI cross-resistance in prostate cancer progression.

An Extracellular Matrix Overlay Model for Bioluminescence Microscopy to Measure Single-Cell Heterogeneous Responses to Antiandrogens in Prostate Cancer Cells

Prostate cancer (PCa) displays diverse intra-tumoral traits, impacting its progression and treatment outcomes. This study aimed to refine PCa cell culture conditions for dynamic monitoring of androgen receptor (AR) activity at the single-cell level. We introduced an extracellular matrix-Matrigel (ECM-M) culture model, enhancing cellular tracking during bioluminescence single-cell imaging while improving cell viability. ECM-M notably tripled the traceability of poorly adherent PCa cells, facilitating robust single-cell tracking, without impeding substrate permeability or AR response. This model effectively monitored AR modulation by antiandrogens across various PCa cell lines. Single-cell imaging unveiled heterogeneous antiandrogen responses within populations, correlating non-responsive cell proportions with drug IC50 values. Integrating ECM-M culture with the PSEBC-TSTA biosensor enabled precise characterization of ARi responsiveness within diverse cell populations. Our ECM-M model stands as a promising tool to assess heterogeneous single-cell treatment responses in cancer, offering insights to link drug responses to intracellular signaling dynamics. This approach enhances our comprehension of the nuanced and dynamic nature of PCa treatment responses.

Localized high-risk prostate cancer harbors an androgen receptor low subpopulation susceptible to HER2 inhibition

Patients diagnosed with localized high-risk prostate cancer have higher rates of recurrence, and the introduction of neoadjuvant intensive hormonal therapies seeks to treat occult micrometastatic disease by their addition to definitive treatment. Sufficient profiling of baseline disease has remained a challenge in enabling the in-depth assessment of phenotypes associated with exceptional vs. poor pathologic responses after treatment. In this study, we report comprehensive and integrative gene expression profiling of 37 locally advanced prostate tumors prior to six months of androgen deprivation therapy (ADT) plus the androgen receptor (AR) inhibitor enzalutamide prior to radical prostatectomy. A robust transcriptional program associated with HER2 activity was positively associated with poor outcome and opposed AR activity, even after adjusting for common genomic alterations in prostate cancer including PTEN loss and expression of the TMPRSS2:ERG fusion. Patients experiencing exceptional pathologic responses demonstrated lower levels of HER2 and phospho-HER2 by immunohistochemistry of biopsy tissues. The inverse correlation of AR and HER2 activity was found to be a universal feature of all aggressive prostate tumors, validated by transcriptional profiling an external cohort of 121 patients and immunostaining of tumors from 84 additional patients. Importantly, the AR activity-low, HER2 activity-high cells that resist ADT are a pre-existing subset of cells that can be targeted by HER2 inhibition alone or in combination with enzalutamide. In summary, we show that prostate tumors adopt an AR activity-low prior to antiandrogen exposure that can be exploited by treatment with HER2 inhibitors.

Mechanism-centric regulatory network identifies NME2 and MYC programs as markers of Enzalutamide resistance in CRPC

Heterogeneous response to Enzalutamide, a second-generation androgen receptor signaling inhibitor, is a central problem in castration-resistant prostate cancer (CRPC) management. Genome-wide systems investigation of mechanisms that govern Enzalutamide resistance promise to elucidate markers of heterogeneous treatment response and salvage therapies for CRPC patients. Focusing on the de novo role of MYC as a marker of Enzalutamide resistance, here we reconstruct a CRPC-specific mechanism-centric regulatory network, connecting molecular pathways with their upstream transcriptional regulatory programs. Mining this network with signatures of Enzalutamide response identifies NME2 as an upstream regulatory partner of MYC in CRPC and demonstrates that NME2-MYC increased activities can predict patients at risk of resistance to Enzalutamide, independent of co-variates. Furthermore, our experimental investigations demonstrate that targeting MYC and its partner NME2 is beneficial in Enzalutamide-resistant conditions and could provide an effective strategy for patients at risk of Enzalutamide resistance and/or for patients who failed Enzalutamide treatment.

CLIC3 interacts with NAT10 to inhibit N4-acetylcytidine modification of p21 mRNA and promote bladder cancer progression

Chromatin accessibility plays important roles in revealing the regulatory networks of gene expression, while its application in bladder cancer is yet to be fully elucidated. Chloride intracellular channel 3 (CLIC3) protein has been reported to be associated with the progression of some tumors, whereas the specific mechanism of CLIC3 in tumor remains unclear. Here, we screened for key genes in bladder cancer through the identification of transcription factor binding site clustered region (TFCR) on the basis of chromatin accessibility and TF motif. CLIC3 was identified by joint profiling of chromatin accessibility data with TCGA database. Clinically, CLIC3 expression was significantly elevated in bladder cancer and was negatively correlated with patient survival. CLIC3 promoted the proliferation of bladder cancer cells by reducing p21 expression in vitro and in vivo. Mechanistically, CLIC3 interacted with NAT10 and inhibited the function of NAT10, resulting in the downregulation of ac4C modification and stability of p21 mRNA. Overall, these findings uncover an novel mechanism of mRNA ac4C modification and CLIC3 may act as a potential therapeutic target for bladder cancer.

TooManyCellsInteractive: A visualization tool for dynamic exploration of single-cell data

BACKGROUND

As single-cell sequencing technologies continue to advance, the growing volume and complexity of the ensuing data present new analytical challenges. Large cellular populations from single-cell atlases are more difficult to visualize and require extensive processing to identify biologically relevant subpopulations. Managing these workflows is also laborious for technical users and unintuitive for nontechnical users.

RESULTS

We present TooManyCellsInteractive (TMCI), a browser-based JavaScript application for interactive exploration of cell populations. TMCI provides an intuitive interface to visualize and manipulate a radial tree representation of hierarchical cell subpopulations and allows users to easily overlay, filter, and compare biological features at multiple resolutions. Here we describe the software architecture and demonstrate how we used TMCI in a pan-cancer analysis to identify unique survival pathways among drug-tolerant persister cells.

CONCLUSIONS

TMCI will facilitate exploration and visualization of large-scale sequencing data in a user-friendly way. TMCI is freely available at https://github.com/schwartzlab-methods/too-many-cells-interactive. An example tree from data within this article is available at https://tmci.schwartzlab.ca/.

Phenotype plasticity and altered sensitivity to chemotherapeutic agents in aggressive prostate cancer cells

In 2023, approximately 288,300 new diagnoses of prostate cancer will occur, with 34,700 disease-related deaths. Death from prostate cancer is associated with metastasis, enabled by progression of tumor phenotypes and successful extracapsular extension to reach Batson's venous plexus, a specific route to the spine and brain. Using a mouse-human tumor xenograft model, we isolated an aggressive muscle invasive cell population of prostate cancer, called DU145J7 with a distinct biophysical phenotype, elevated histone H3K27, and increased matrix metalloproteinase 14 expression as compared to the non-aggressive parent cell population called DU145WT. Our goal was to determine the sensitivities to known chemotherapeutic agents of the aggressive cells as compared to the parent population. High-throughput screening was performed with 5,578 compounds, comprising of approved and investigational drugs for oncology. Eleven compounds were selected for additional testing, which revealed that vorinostat, 5-azacitidine, and fimepinostat (epigenetic inhibitors) showed 2.6-to-7.5-fold increases in lethality for the aggressive prostate cancer cell population as compared to the parent, as judged by the concentration of drug to inhibit 50% cell growth (IC50). On the other hand, the DU145J7 cells were 2.2-to-4.0-fold resistant to mitoxantrone, daunorubicin, and gimatecan (topoisomerase inhibitors) as compared to DU145WT. No differences in sensitivities between cell populations were found for docetaxel or pirarubicin. The increased sensitivity of DU145J7 prostate cancer cells to chromatin modifying agents suggests a therapeutic vulnerability occurs after tumor cells invade into and through muscle. Future work will determine which epigenetic modifiers and what combinations will be most effective to eradicate early aggressive tumor populations.

Genomic characterization of IDH-mutant astrocytoma progression to grade 4 in the treatment setting

As the progression of low-grade diffuse astrocytomas into grade 4 tumors significantly impacts patient prognosis, a better understanding of this process is of paramount importance for improved patient care. In this project, we analyzed matched IDH-mutant astrocytomas before and after progression to grade 4 from six patients (discovery cohort) with genome-wide sequencing, 21 additional patients with targeted sequencing, and 33 patients from Glioma Longitudinal AnalySiS cohort for validation. The Cancer Genome Atlas data from 595 diffuse gliomas provided supportive information. All patients in our discovery cohort received radiation, all but one underwent chemotherapy, and no patient received temozolomide (TMZ) before progression to grade 4 disease. One case in the discovery cohort exhibited a hypermutation signature associated with the inactivation of the MSH2 and DNMT3A genes. In other patients, the number of chromosomal rearrangements and deletions increased in grade 4 tumors. The cell cycle checkpoint gene CDKN2A, or less frequently RB1, was most commonly inactivated after receiving both chemo- and radiotherapy when compared to other treatment groups. Concomitant activating PDGFRA/MET alterations were detected in tumors that acquired a homozygous CDKN2A deletion. NRG3 gene was significantly downregulated and recurrently altered in progressed tumors. Its decreased expression was associated with poorer overall survival in both univariate and multivariate analysis. We also detected progression-related alterations in RAD51B and other DNA repair pathway genes associated with the promotion of error-prone DNA repair, potentially facilitating tumor progression. In our retrospective analysis of patient treatment and survival timelines (n = 75), the combination of postoperative radiation and chemotherapy (mainly TMZ) outperformed radiation, especially in the grade 3 tumor cohort, in which it was typically given after primary surgery. Our results provide further insight into the contribution of treatment and genetic alterations in cell cycle, growth factor signaling, and DNA repair-related genes to tumor evolution and progression.

A Bayesian method to infer copy number clones from single-cell RNA and ATAC sequencing

Single-cell RNA and ATAC sequencing technologies enable the examination of gene expression and chromatin accessibility in individual cells, providing insights into cellular phenotypes. In cancer research, it is important to consistently analyze these states within an evolutionary context on genetic clones. Here we present CONGAS+, a Bayesian model to map single-cell RNA and ATAC profiles onto the latent space of copy number clones. CONGAS+ clusters cells into tumour subclones with similar ploidy, rendering straightforward to compare their expression and chromatin profiles. The framework, implemented on GPU and tested on real and simulated data, scales to analyse seamlessly thousands of cells, demonstrating better performance than single-molecule models, and supporting new multi-omics assays. In prostate cancer, lymphoma and basal cell carcinoma, CONGAS+ successfully identifies complex subclonal architectures while providing a coherent mapping between ATAC and RNA, facilitating the study of genotype-phenotype maps and their connection to genomic instability.

Fanconi anemia pathway regulation by FANCI in prostate cancer

Prostate cancer is one of the leading causes of death among men worldwide, and thus, research on the genetic factors enabling the formation of treatment-resistant cancer cells is crucial for improving patient outcomes. Here, we report a cell line-specific dependence on FANCI and related signaling pathways to counteract the effects of DNA-damaging chemotherapy in prostate cancer. Our results reveal that FANCI depletion results in significant downregulation of Fanconi anemia (FA) pathway members in prostate cancer cells, indicating that FANCI is an important regulator of the FA pathway. Furthermore, we found that FANCI silencing reduces proliferation in p53-expressing prostate cancer cells. This extends the evidence that inactivation of FANCI may convert cancer cells from a resistant state to an eradicable state under the stress of DNA-damaging chemotherapy. Our results also indicate that high expression of FA pathway genes correlates with poorer survival in prostate cancer patients. Moreover, genomic alterations of FA pathway members are prevalent in prostate adenocarcinoma patients; mutation and copy number information for the FA pathway genes in seven patient cohorts (N = 1,732 total tumor samples) reveals that 1,025 (59.2%) tumor samples have an alteration in at least one of the FA pathway genes, suggesting that genomic alteration of the pathway is a prominent feature in patients with the disease.

Spatial transcriptomics in development and disease

The proper functioning of diverse biological systems depends on the spatial organization of their cells, a critical factor for biological processes like shaping intricate tissue functions and precisely determining cell fate. Nonetheless, conventional bulk or single-cell RNA sequencing methods were incapable of simultaneously capturing both gene expression profiles and the spatial locations of cells. Hence, a multitude of spatially resolved technologies have emerged, offering a novel dimension for investigating regional gene expression, spatial domains, and interactions between cells. Spatial transcriptomics (ST) is a method that maps gene expression in tissue while preserving spatial information. It can reveal cellular heterogeneity, spatial organization and functional interactions in complex biological systems. ST can also complement and integrate with other omics methods to provide a more comprehensive and holistic view of biological systems at multiple levels of resolution. Since the advent of ST, new methods offering higher throughput and resolution have become available, holding significant potential to expedite fresh insights into comprehending biological complexity. Consequently, a rapid increase in associated research has occurred, using these technologies to unravel the spatial complexity during developmental processes or disease conditions. In this review, we summarize the recent advancement of ST in historical, technical, and application contexts. We compare different types of ST methods based on their principles and workflows, and present the bioinformatics tools for analyzing and integrating ST data with other modalities. We also highlight the applications of ST in various domains of biomedical research, especially development and diseases. Finally, we discuss the current limitations and challenges in the field, and propose the future directions of ST.

Recent advancements in single-cell metabolic analysis for pharmacological research

Cellular heterogeneity is crucial for understanding tissue biology and disease pathophysiology. Pharmacological research is being advanced by single-cell metabolic analysis, which offers a technique to identify variations in RNA, proteins, metabolites, and drug molecules in cells. In this review, the recent advancement of single-cell metabolic analysis techniques and their applications in drug metabolism and drug response are summarized. High-precision and controlled single-cell isolation and manipulation are provided by microfluidics-based methods, such as droplet microfluidics, microchamber, open microfluidic probe, and digital microfluidics. They are used in tandem with variety of detection techniques, including optical imaging, Raman spectroscopy, electrochemical detection, RNA sequencing, and mass spectrometry, to evaluate single-cell metabolic changes in response to drug administration. The advantages and disadvantages of different techniques are discussed along with the challenges and future directions for single-cell analysis. These techniques are employed in pharmaceutical analysis for studying drug response and resistance pathway, therapeutic targets discovery, and in vitro disease model evaluation.

Unveiling novel insights in prostate cancer through single-cell RNA sequencing

Single-cell RNA sequencing (scRNA-seq) is a cutting-edge technology that provides insights at the individual cell level. In contrast to traditional bulk RNA-seq, which captures gene expression at an average level and may overlook important details, scRNA-seq examines each individual cell as a fundamental unit and is particularly well-suited for identifying rare cell populations. Analogous to a microscope that distinguishes various cell types within a tissue sample, scRNA-seq unravels the heterogeneity and diversity within a single cell species, offering great potential as a leading sequencing method in the future. In the context of prostate cancer (PCa), a disease characterized by significant heterogeneity and multiple stages of progression, scRNA-seq emerges as a powerful tool for uncovering its intricate secrets.

Lineage Plasticity and Stemness Phenotypes in Prostate Cancer: Harnessing the Power of Integrated "Omics" Approaches to Explore Measurable Metrics

Prostate cancer (PCa), the most frequent and second most lethal cancer type in men in developed countries, is a highly heterogeneous disease. PCa heterogeneity, therapy resistance, stemness, and lethal progression have been attributed to lineage plasticity, which refers to the ability of neoplastic cells to undergo phenotypic changes under microenvironmental pressures by switching between developmental cell states. What remains to be elucidated is how to identify measurements of lineage plasticity, how to implement them to inform preclinical and clinical research, and, further, how to classify patients and inform therapeutic strategies in the clinic. Recent research has highlighted the crucial role of next-generation sequencing technologies in identifying potential biomarkers associated with lineage plasticity. Here, we review the genomic, transcriptomic, and epigenetic events that have been described in PCa and highlight those with significance for lineage plasticity. We further focus on their relevance in PCa research and their benefits in PCa patient classification. Finally, we explore ways in which bioinformatic analyses can be used to determine lineage plasticity based on large omics analyses and algorithms that can shed light on upstream and downstream events. Most importantly, an integrated multiomics approach may soon allow for the identification of a lineage plasticity signature, which would revolutionize the molecular classification of PCa patients.

ASCL1-mediated ferroptosis resistance enhances the progress of castration-resistant prostate cancer to neurosecretory prostate cancer

Neuroendocrine prostate cancer (NEPC) is a multi-resistant variant of prostate cancer (PCa) that frequently emerges in castration-resistant prostate cancer (CRPC). NEPC is usually associated with tumor aggression, hormone therapy resistance, and poor clinical outcome. However, the mechanisms underlying the trans-differentiation from CRPC to NEPC have not been elucidated. Achaete-scute complex-like 1 (ASCL1) plays a role in neuronal commitment and differentiation and olfactory and autonomic neuron generation. This study revealed that ASCL1 was regulated by the SRY-box transcription factor 2 (SOX2) and highly expressed in NEPC cells, which was closely related to poor prognosis. Moreover, ASCL1 overexpression significantly enhanced CRPC progression to NEPC by resisting ferroptosis. Mechanically, ferroptosis resistance was mediated by CAMP-responsive element binding protein 1 (CREB1) phosphorylation, promoted by substantially upregulated ASCL1 in NEPC cells. In addition, upregulated SOX2 induced PCa cell differentiation into neuroendocrine tumors by mediating their lineage changes. In conclusion, inhibiting the ferroptosis resistance mediated by ASCL1 could provide a new NEPC therapeutic target and increase patient survival.

Metformin escape in prostate cancer by activating the PTGR1 transcriptional program through a novel super-enhancer

The therapeutic efficacy of metformin in prostate cancer (PCa) appears uncertain based on various clinical trials. Metformin treatment failure may be attributed to the high frequency of transcriptional dysregulation, which leads to drug resistance. However, the underlying mechanism is still unclear. In this study, we found evidences that metformin resistance in PCa cells may be linked to cell cycle reactivation. Super-enhancers (SEs), crucial regulatory elements, have been shown to be associated with drug resistance in various cancers. Our analysis of SEs in metformin-resistant (MetR) PCa cells revealed a correlation with Prostaglandin Reductase 1 (PTGR1) expression, which was identified as significantly increased in a cluster of cells with metformin resistance through single-cell transcriptome sequencing. Our functional experiments showed that PTGR1 overexpression accelerated cell cycle progression by promoting progression from the G0/G1 to the S and G2/M phases, resulting in reduced sensitivity to metformin. Additionally, we identified key transcription factors that significantly increase PTGR1 expression, such as SRF and RUNX3, providing potential new targets to address metformin resistance in PCa. In conclusion, our study sheds new light on the cellular mechanism underlying metformin resistance and the regulation of the SE-TFs-PTGR1 axis, offering potential avenues to enhance metformin's therapeutic efficacy in PCa.

The Genomic and Epigenomic Landscape of Double-Negative Metastatic Prostate Cancer

Systemic targeted therapy in prostate cancer is primarily focused on ablating androgen signaling. Androgen deprivation therapy and second-generation androgen receptor (AR)-targeted therapy selectively favor the development of treatment-resistant subtypes of metastatic castration-resistant prostate cancer (mCRPC), defined by AR and neuroendocrine (NE) markers. Molecular drivers of double-negative (AR-/NE-) mCRPC are poorly defined. In this study, we comprehensively characterized treatment-emergent mCRPC by integrating matched RNA sequencing, whole-genome sequencing, and whole-genome bisulfite sequencing from 210 tumors. AR-/NE- tumors were clinically and molecularly distinct from other mCRPC subtypes, with the shortest survival, amplification of the chromatin remodeler CHD7, and PTEN loss. Methylation changes in CHD7 candidate enhancers were linked to elevated CHD7 expression in AR-/NE+ tumors. Genome-wide methylation analysis nominated Krüppel-like factor 5 (KLF5) as a driver of the AR-/NE- phenotype, and KLF5 activity was linked to RB1 loss. These observations reveal the aggressiveness of AR-/NE- mCRPC and could facilitate the identification of therapeutic targets in this highly aggressive disease.

SIGNIFICANCE

Comprehensive characterization of the five subtypes of metastatic castration-resistant prostate cancer identified transcription factors that drive each subtype and showed that the double-negative subtype has the worst prognosis.

Advancements in MRI-Based Radiomics and Artificial Intelligence for Prostate Cancer: A Comprehensive Review and Future Prospects

The use of multiparametric magnetic resonance imaging (mpMRI) has become a common technique used in guiding biopsy and developing treatment plans for prostate lesions. While this technique is effective, non-invasive methods such as radiomics have gained popularity for extracting imaging features to develop predictive models for clinical tasks. The aim is to minimize invasive processes for improved management of prostate cancer (PCa). This study reviews recent research progress in MRI-based radiomics for PCa, including the radiomics pipeline and potential factors affecting personalized diagnosis. The integration of artificial intelligence (AI) with medical imaging is also discussed, in line with the development trend of radiogenomics and multi-omics. The survey highlights the need for more data from multiple institutions to avoid bias and generalize the predictive model. The AI-based radiomics model is considered a promising clinical tool with good prospects for application.

Single-cell profiling in multiple myeloma: insights, problems, and promises

In a short time, single-cell platforms have become the norm in many fields of research, including multiple myeloma (MM). In fact, the large amount of cellular heterogeneity in MM makes single-cell platforms particularly attractive because bulk assessments can miss valuable information about cellular subpopulations and cell-to-cell interactions. The decreasing cost and increasing accessibility of single-cell platform, combined with breakthroughs in obtaining multiomics data for the same cell and innovative computational programs for analyzing data, have allowed single-cell studies to make important insights into MM pathogenesis; yet, there is still much to be done. In this review, we will first focus on the types of single-cell profiling and the considerations for designing a single-cell profiling experiment. Then, we will discuss what have learned from single-cell profiling about myeloma clonal evolution, transcriptional reprogramming, and drug resistance, and about the MM microenvironment during precursor and advanced disease.

Single-cell omics traces the heterogeneity of prostate cancer cells and the tumor microenvironment

Prostate cancer is one of the more heterogeneous tumour types. In recent years, with the rapid development of single-cell sequencing and spatial transcriptome technologies, researchers have gained a more intuitive and comprehensive understanding of the heterogeneity of prostate cancer. Tumour-associated epithelial cells; cancer-associated fibroblasts; the complexity of the immune microenvironment, and the heterogeneity of the spatial distribution of tumour cells and other cancer-promoting molecules play a crucial role in the growth, invasion, and metastasis of prostate cancer. Single-cell multi-omics biotechnology, especially single-cell transcriptome sequencing, reveals the expression level of single cells with higher resolution and finely dissects the molecular characteristics of different tumour cells. We reviewed the recent literature on prostate cancer cells, focusing on single-cell RNA sequencing. And we analysed the heterogeneity and spatial distribution differences of different tumour cell types. We discussed the impact of novel single-cell omics technologies, such as rich omics exploration strategies, multi-omics joint analysis modes, and deep learning models, on future prostate cancer research. In this review, we have constructed a comprehensive catalogue of single-cell omics studies in prostate cancer. This article aimed to provide a more thorough understanding of the diagnosis and treatment of prostate cancer. We summarised and proposed several key issues and directions on applying single-cell multi-omics and spatial transcriptomics to understand the heterogeneity of prostate cancer. Finally, we discussed single-cell omics trends and future directions in prostate cancer.

Exploring the role of differentially expressed metabolic genes and their mechanisms in bone metastatic prostate cancer

Background

Approximately 10-20% of patients diagnosed with prostate cancer (PCa) evolve into castration-resistant prostate cancer (CRPC), while nearly 90% of patients with metastatic CRPC (mCRPC) exhibit osseous metastases (BM). These BM are intimately correlated with the stability of the tumour microenvironment.

Purpose

This study aspires to uncover the metabolism-related genes and the underlying mechanisms responsible for bone metastatic prostate cancer (BMPCa).

Methods

Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets of PCa and BM were analyzed through R Studio software to identify differentially expressed genes (DEGs). The DEGs underwent functional enrichment via Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO), with key factors screened by a random forest utilized to establish a prognostic model for PCa. The study explored the relationship between DEGs and the stability of the immune microenvironment. The action and specificity of CRISP3 in PCa was validated through western blot analysis, CCK-8 assay, scratch assay, and cellular assay.

Results

The screening of GEO and TCGA datasets resulted in the identification of 199 co-differential genes. Three DEGs, including DES, HBB, and SLPI, were selected by random forest classification model and cox regression model. Immuno-infiltration analysis disclosed that a higher infiltration of naïve B cells and resting CD4 memory T cells occurred in the high-expression group of DES, whereas infiltration of resting M1 macrophages and NK cells was greater in the low-expression group of DES. A significant infiltration of neutrophils was observed in the high-expression group of HBB, while greater infiltration of gamma delta T cells and M1 macrophages was noted in the low-expression group of HBB. Resting dendritic cells, CD8 T cells, and resting T regulatory cells (Tregs) infiltrated significantly in the high-expression group of SLPI, while only resting mast cells infiltrated significantly in the low-expression group of SLPI. CRISP3 was established as a critical gene in BMPCa linked to DES expression. Targeting CRISP3, d-glucopyranose may impact tumour prognosis. During the mechanistic experiments, it was established that CRISP3 can advance the proliferation and metastatic potential of PCa by advancing epithelial-to-mesenchymal transition (EMT).

Conclusion

By modulating lipid metabolism and maintaining immunological and microenvironmental balance, DES, HBB, and SLPI suppress prostate cancer cell growth. The presence of DES-associated CRISP3 is a harbinger of unfavorable outcomes in prostate cancer and may escalate tumor proliferation and metastatic capabilities by inducing epithelial-mesenchymal transition.

Enzalutamide-induced signatures revealed by epigenetic plasticity using single-cell multi-omics sequencing in prostate cancer

Prostate cancer is morphologically and molecularly heterogeneous, which poses obstacles for early diagnosis and treatment. Advancements in understanding the heterogeneity of prostate cancer will help navigate through these challenges and ultimately benefit patients. In this study, we integrated single-cell sequencing for transposase-accessible chromatin and whole transcriptome in prostate cancer cell lines, aiming to decode the epigenetic plasticity upon enzalutamide (ENZ) treatment. By comparing the cell populations representing early-treatment response or resistance to the initial tumor cells, we identified seven signature gene sets; they present consistent trends of chromatin closing co-occurred with down-regulated genes during early response and chromatin opening with up-regulated genes upon maintaining drug resistance. In the molecular signatures, we found genes ZNF337, MAPK15, and ESRRG are favorable in progression-free prognosis during early response, while genes CCDC150, CCDC18, and POC1A marked poor prognosis underpinning the pre-existing drug resistance in The Cancer Genome Atlas prostate adenocarcinoma cohort. Ultimately, drug-target analyses nominated combinatory drug candidates to either enhance early-treatment response or potentially overcome ENZ resistance. Together, our integrative, single-cell multi-omics approach in pre-clinical models is effective in identifying informative signatures from complex molecular events, illustrating diverse drug responses in prostate cancer, and invoking novel combinatory drug strategies to inform clinical decision making.

Experimental in vitro, ex vivo and in vivo models in prostate cancer research

Androgen deprivation therapy has a central role in the treatment of advanced prostate cancer, often causing initial tumour remission before increasing independence from signal transduction mechanisms of the androgen receptor and then eventual disease progression. Novel treatment approaches are urgently needed, but only a fraction of promising drug candidates from the laboratory will eventually reach clinical approval, highlighting the demand for critical assessment of current preclinical models. Such models include standard, genetically modified and patient-derived cell lines, spheroid and organoid culture models, scaffold and hydrogel cultures, tissue slices, tumour xenograft models, patient-derived xenograft and circulating tumour cell eXplant models as well as transgenic and knockout mouse models. These models need to account for inter-patient and intra-patient heterogeneity, the acquisition of primary or secondary resistance, the interaction of tumour cells with their microenvironment, which make crucial contributions to tumour progression and resistance, as well as the effects of the 3D tissue network on drug penetration, bioavailability and efficacy.

Enhancer profiling identifies epigenetic markers of endocrine resistance and reveals therapeutic options for metastatic castration-resistant prostate cancer patients

Androgen Receptor (AR) signaling inhibitors, including enzalutamide, are treatment options for patients with metastatic castration-resistant prostate cancer (mCRPC), but resistance inevitably develops. Using metastatic samples from a prospective phase II clinical trial, we epigenetically profiled enhancer/promoter activities with H3K27ac chromatin immunoprecipitation followed by sequencing, before and after AR-targeted therapy. We identified a distinct subset of H3K27ac-differentially marked regions that associated with treatment responsiveness. These data were successfully validated in mCRPC patient-derived xenograft models (PDX). In silico analyses revealed HDAC3 as a critical factor that can drive resistance to hormonal interventions, which we validated in vitro . Using cell lines and mCRPC PDX tumors in vitro , we identified drug-drug synergy between enzalutamide and the pan-HDAC inhibitor vorinostat, providing therapeutic proof-of-concept. These findings demonstrate rationale for new therapeutic strategies using a combination of AR and HDAC inhibitors to improve patient outcome in advanced stages of mCRPC.

Clinical testing of transcriptome-wide expression profiles in high-risk localized and metastatic prostate cancer starting androgen deprivation therapy: an ancillary study of the STAMPEDE abiraterone Phase 3 trial

Metastatic and high-risk localized prostate cancer respond to hormone therapy but outcomes vary. Following a pre-specified statistical plan, we used Cox models adjusted for clinical variables to test associations with survival of multi-gene expression-based classifiers from 781 patients randomized to androgen deprivation with or without abiraterone in the STAMPEDE trial. Decipher score was strongly prognostic (p<2×10-5) and identified clinically-relevant differences in absolute benefit, especially for localized cancers. In metastatic disease, classifiers of proliferation, PTEN or TP53 loss and treatment-persistent cells were prognostic. In localized disease, androgen receptor activity was protective whilst interferon signaling (that strongly associated with tumor lymphocyte infiltration) was detrimental. Post-Operative Radiation-Therapy Outcomes Score was prognostic in localized but not metastatic disease (interaction p=0.0001) suggesting the impact of tumor biology on clinical outcome is context-dependent on metastatic state. Transcriptome-wide testing has clinical utility for advanced prostate cancer and identified worse outcomes for localized cancers with tumor-promoting inflammation.

Single-nucleus RNA and ATAC sequencing uncovers the molecular and cellular characteristics in the musk gland of Chinese forest musk deer (Moschus berezovskii)

The Chinese forest musk deer (FMD; Moschus berezovskii) is an endangered artiodactyl mammal. Musk secreted by the musk gland of male has extremely high economic and medicinal value. However, the molecular and cellular characteristics of the musk gland have not been studied. Here, we investigated the diversity and transcriptional composition of musk gland cell types and the effect of cell type-specific chromatin accessibility on gene expression using single-nucleus RNA sequencing (snRNA-seq) and single-nucleus ATAC sequencing (snATAC-seq) association analysis. Based on uniform manifold approximation and projection (UMAP) analysis, we identified 13 cell types from the musk gland, which included two different acinar cells (cluster 0 and cluster 10). Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicated that many pathways related to musk secretion were enriched in acinar cells. Our analysis also revealed acinar cell core transcription factors and core target genes, and further constructed acinar cell-specific regulatory networks. In cluster 0, 11 core target genes (Nedd4l, Adcy9, Akr1c1, Vapb, Me1, Acsl1, Acss3, Srd5a1, Scnn1a, Acadm, and Nceh1) possibly related to musk secretion were regulated by 24 core transcription factors (SP3, NFIC, NR6A1, EHF, RUNX1, TFAP2A, RREB1, GRHL2, NFIB, ELF1, MAX, KLF5, REL, HES1, POU2F3, TFDP1, NR2C1, ATF7, MEIS1, NR4A2, NFIA, PBX1, ZNF652, and NFKB1). In cluster 10, four core target genes (Akr1c1, Pcca, Atp1b1, and Sgk1) possibly related to musk secretion were regulated by 10 core transcription factors (BARX2, EHF, PBX1, RUNX1, NFIB, FOXP1, KLF3, KLF6, ETV6, and NR3C2). Moreover, the credibility of snRNA-seq and snATAC-seq data was verified by fluorescence in situ hybridization and immunohistochemistry. Finally, cell communication analysis demonstrated that the two types of acinar cells mainly have communications in musk secretion-related processes. In conclusion, we provided important insights and invaluable resources for the molecular and cellular characteristics of the musk gland, which will lay a foundation for the study of musk secretion mechanism in the future.

Prognostic implication of heterogeneity and trajectory progression induced by enzalutamide in prostate cancer

BACKGROUND

Enzalutamide, as a second-generation endocrine therapy drug for prostate cancer (PCa), is prominent representative among the synthetic androgen receptor antagonists. Currently, there is lack of enzalutamide-induced signature (ENZ-sig) for predicting progression and relapse-free survival (RFS) in PCa.

METHODS

Enzalutamide-induced candidate markers were derived from single-cell RNA sequencing analysis integrating three enzalutamide-stimulated models (0-, 48-, and 168-h enzalutamide stimulation). ENZ-sig was constructed on the basis of candidate genes that were associated with RFS in The Cancer Genome Atlas leveraging least absolute shrinkage and selection operator method. The ENZ-sig was further validated in GSE70768, GSE94767, E-MTAB-6128, DFKZ, GSE21034, and GSE70769 datasets. Biological enrichment analysis was used to discover the underlying mechanism between high ENZ-sig and low ENZ-sig in single-cell RNA sequencing and bulk RNA sequencing.

RESULTS

We identified a heterogenous subgroup that induced by enzalutamide stimulation and found 53 enzalutamide-induced candidate markers that are related to trajectory progression and enzalutamide-stimulated. The candidate genes were further narrowed down into 10 genes that are related to RFS in PCa. A 10-gene prognostic model (ENZ-sig)-IFRD1, COL5A2, TUBA1A, CFAP69, TMEM388, ACPP, MANEA, FOSB, SH3BGRL, and ST7-was constructed for the prediction of RFS in PCa. The effective and robust predictability of ENZ-sig was verified in six independent datasets. Biological enrichment analysis revealed that differentially expressed genes in high ENZ-sig were more activated in cell cycle-related pathway. High-ENZ-sig patients were more sensitive to cell cycle-targeted drugs (MK-1775, AZD7762, and MK-8776) than low-ENZ-sig patients in PCa.

CONCLUSIONS

Our results provided evidence and insight on the potential utility of ENZ-sig in PCa prognosis and combination therapy strategy of enzalutamide and cell cycle-targeted compounds in treating PCa.

Extensive androgen receptor enhancer heterogeneity in primary prostate cancers underlies transcriptional diversity and metastatic potential

Androgen receptor (AR) drives prostate cancer (PCa) development and progression. AR chromatin binding profiles are highly plastic and form recurrent programmatic changes that differentiate disease stages, subtypes and patient outcomes. While prior studies focused on concordance between patient subgroups, inter-tumor heterogeneity of AR enhancer selectivity remains unexplored. Here we report high levels of AR chromatin binding heterogeneity in human primary prostate tumors, that overlap with heterogeneity observed in healthy prostate epithelium. Such heterogeneity has functional consequences, as somatic mutations converge on commonly-shared AR sites in primary over metastatic tissues. In contrast, less-frequently shared AR sites associate strongly with AR-driven gene expression, while such heterogeneous AR enhancer usage also distinguishes patients' outcome. These findings indicate that epigenetic heterogeneity in primary disease is directly informative for risk of biochemical relapse. Cumulatively, our results illustrate a high level of AR enhancer heterogeneity in primary PCa driving differential expression and clinical impact.

Integrating Micro and Nano Technologies for Cell Engineering and Analysis: Toward the Next Generation of Cell Therapy Workflows

The emerging field of cell therapy offers the potential to treat and even cure a diverse array of diseases for which existing interventions are inadequate. Recent advances in micro and nanotechnology have added a multitude of single cell analysis methods to our research repertoire. At the same time, techniques have been developed for the precise engineering and manipulation of cells. Together, these methods have aided the understanding of disease pathophysiology, helped formulate corrective interventions at the cellular level, and expanded the spectrum of available cell therapeutic options. This review discusses how micro and nanotechnology have catalyzed the development of cell sorting, cellular engineering, and single cell analysis technologies, which have become essential workflow components in developing cell-based therapeutics. The review focuses on the technologies adopted in research studies and explores the opportunities and challenges in combining the various elements of cell engineering and single cell analysis into the next generation of integrated and automated platforms that can accelerate preclinical studies and translational research.

Spatial transcriptomics technology in cancer research

In recent years, spatial transcriptomics (ST) technologies have developed rapidly and have been widely used in constructing spatial tissue atlases and characterizing spatiotemporal heterogeneity of cancers. Currently, ST has been used to profile spatial heterogeneity in multiple cancer types. Besides, ST is a benefit for identifying and comprehensively understanding special spatial areas such as tumor interface and tertiary lymphoid structures (TLSs), which exhibit unique tumor microenvironments (TMEs). Therefore, ST has also shown great potential to improve pathological diagnosis and identify novel prognostic factors in cancer. This review presents recent advances and prospects of applications on cancer research based on ST technologies as well as the challenges.

scATACpipe: A nextflow pipeline for comprehensive and reproducible analyses of single cell ATAC-seq data

Single cell ATAC-seq (scATAC-seq) has become the most widely used method for profiling open chromatin landscape of heterogeneous cell populations at a single-cell resolution. Although numerous software tools and pipelines have been developed, an easy-to-use, scalable, reproducible, and comprehensive pipeline for scATAC-seq data analyses is still lacking. To fill this gap, we developed scATACpipe, a Nextflow pipeline, for performing comprehensive analyses of scATAC-seq data including extensive quality assessment, preprocessing, dimension reduction, clustering, peak calling, differential accessibility inference, integration with scRNA-seq data, transcription factor activity and footprinting analysis, co-accessibility inference, and cell trajectory prediction. scATACpipe enables users to perform the end-to-end analysis of scATAC-seq data with three sub-workflow options for preprocessing that leverage 10x Genomics Cell Ranger ATAC software, the ultra-fast Chromap procedures, and a set of custom scripts implementing current best practices for scATAC-seq data preprocessing. The pipeline extends the R package ArchR for downstream analysis with added support to any eukaryotic species with an annotated reference genome. Importantly, scATACpipe generates an all-in-one HTML report for the entire analysis and outputs cluster-specific BAM, BED, and BigWig files for visualization in a genome browser. scATACpipe eliminates the need for users to chain different tools together and facilitates reproducible and comprehensive analyses of scATAC-seq data from raw reads to various biological insights with minimal changes of configuration settings for different computing environments or species. By applying it to public datasets, we illustrated the utility, flexibility, versatility, and reliability of our pipeline, and demonstrated that our scATACpipe outperforms other workflows.

Transcriptional profiling of matched patient biopsies clarifies molecular determinants of enzalutamide-induced lineage plasticity

The androgen receptor (AR) signaling inhibitor enzalutamide (enza) is one of the principal treatments for metastatic castration-resistant prostate cancer (CRPC). Several emergent enza clinical resistance mechanisms have been described, including lineage plasticity in which the tumors manifest reduced dependency on the AR. To improve our understanding of enza resistance, herein we analyze the transcriptomes of matched biopsies from men with metastatic CRPC obtained prior to treatment and at progression (n = 21). RNA-sequencing analysis demonstrates that enza does not induce marked, sustained changes in the tumor transcriptome in most patients. However, three patients' progression biopsies show evidence of lineage plasticity. The transcription factor E2F1 and pathways linked to tumor stemness are highly activated in baseline biopsies from patients whose tumors undergo lineage plasticity. We find a gene signature enriched in these baseline biopsies that is strongly associated with poor survival in independent patient cohorts and with risk of castration-induced lineage plasticity in patient-derived xenograft models, suggesting that tumors harboring this gene expression program may be at particular risk for resistance mediated by lineage plasticity and poor outcomes.

Prostate cancer in omics era

Recent advances in omics technology have prompted extraordinary attempts to define the molecular changes underlying the onset and progression of a variety of complex human diseases, including cancer. Since the advent of sequencing technology, cancer biology has become increasingly reliant on the generation and integration of data generated at these levels. The availability of multi-omic data has transformed medicine and biology by enabling integrated systems-level approaches. Multivariate signatures are expected to play a role in cancer detection, screening, patient classification, assessment of treatment response, and biomarker identification. This review reports current findings and highlights a number of studies that are both novel and groundbreaking in their application of multi Omics to prostate cancer.

Spatially resolved clonal copy number alterations in benign and malignant tissue

Defining the transition from benign to malignant tissue is fundamental to improving early diagnosis of cancer1. Here we use a systematic approach to study spatial genome integrity in situ and describe previously unidentified clonal relationships. We used spatially resolved transcriptomics2 to infer spatial copy number variations in >120,000 regions across multiple organs, in benign and malignant tissues. We demonstrate that genome-wide copy number variation reveals distinct clonal patterns within tumours and in nearby benign tissue using an organ-wide approach focused on the prostate. Our results suggest a model for how genomic instability arises in histologically benign tissue that may represent early events in cancer evolution. We highlight the power of capturing the molecular and spatial continuums in a tissue context and challenge the rationale for treatment paradigms, including focal therapy.

Future directions for precision oncology in prostate cancer

Clinical genomic testing is becoming routine in prostate cancer, as biomarker-driven therapies such as poly-ADP ribose polymerase (PARP) inhibitors and anti-PD1 immunotherapy are now approved for select men with castration-resistant prostate cancer harboring alterations in DNA repair genes. Challenges for precision medicine in prostate cancer include an overall low prevalence of actionable genomic alterations and a still limited understanding of the impact of tumor heterogeneity and co-occurring alterations on treatment response and outcomes across diverse patient populations. Expanded tissue-based technologies such as whole-genome sequencing, transcriptome analysis, epigenetic analysis, and single-cell RNA sequencing have not yet entered the clinical realm and could potentially improve upon our understanding of how molecular features of tumors, intratumoral heterogeneity, and the tumor microenvironment impact therapy response and resistance. Blood-based technologies including cell-free DNA, circulating tumor cells (CTCs), and extracellular vesicles (EVs) are less invasive molecular profiling resources that could also help capture intraindividual tumor heterogeneity and track dynamic changes that occur in the context of specific therapies. Furthermore, molecular imaging is an important biomarker tool within the framework of prostate cancer precision medicine with a capability to detect heterogeneity across metastases and potential therapeutic targets less invasively. Here, we review recent technological advances that may help promote the future implementation and value of precision oncology testing for patients with advanced prostate cancer.

Second generation androgen receptor antagonists and challenges in prostate cancer treatment

Prostate cancer is a hormone-dependent malignancy, whose onset and progression are closely related to the activity of the androgen receptor (AR) signaling pathway. Due to this critical role of AR signaling in driving prostate cancer, therapy targeting the AR pathway has been the mainstay strategy for metastatic prostate cancer treatment. The utility of these agents has expanded with the emergence of second-generation AR antagonists, which began with the approval of enzalutamide in 2012 by the United States Food and Drug Administration (FDA). Together with apalutamide and darolutamide, which were approved in 2018 and 2019, respectively, these agents have improved the survival of patients with prostate cancer, with applications for both androgen-dependent and castration-resistant disease. While patients receiving these drugs receive a benefit in the form of prolonged survival, they are not cured and ultimately progress to lethal neuroendocrine prostate cancer (NEPC). Here we summarize the current state of AR antagonist development and highlight the emerging challenges of their clinical application and the potential resistance mechanisms, which might be addressed by combination therapies or the development of novel AR-targeted therapies.

The Role of Epigenetic Change in Therapy-Induced Neuroendocrine Prostate Cancer Lineage Plasticity

The androgen receptor (AR) signaling pathway is critical for growth and differentiation of prostate cancer cells. For that reason, androgen deprivation therapy with medical or surgical castration is the principal treatment for metastatic prostate cancer. More recently, new potent AR signaling inhibitors (ARSIs) have been developed. These drugs improve survival for men with metastatic castration-resistant prostate cancer (CRPC), the lethal form of the disease. However, ARSI resistance is nearly universal. One recently appreciated resistance mechanism is lineage plasticity or switch from an AR-driven, luminal differentiation program to an alternate differentiation program. Importantly, lineage plasticity appears to be increasing in incidence in the era of new ARSIs, strongly implicating AR suppression in this process. Lineage plasticity and shift from AR-driven tumors occur on a continuum, ranging from AR-expressing tumors with low AR activity to AR-null tumors that have activation of alternate differentiation programs versus the canonical luminal program found in AR-driven tumors. In many cases, AR loss coincides with the activation of a neuronal program, most commonly exemplified as therapy-induced neuroendocrine prostate cancer (t-NEPC). While genetic events clearly contribute to prostate cancer lineage plasticity, it is also clear that epigenetic events-including chromatin modifications and DNA methylation-play a major role. Many epigenetic factors are now targetable with drugs, establishing the importance of clarifying critical epigenetic factors that promote lineage plasticity. Furthermore, epigenetic marks are readily measurable, demonstrating the importance of clarifying which measurements will help to identify tumors that have undergone or are at risk of undergoing lineage plasticity. In this review, we discuss the role of AR pathway loss and activation of a neuronal differentiation program as key contributors to t-NEPC lineage plasticity. We also discuss new epigenetic therapeutic strategies to reverse lineage plasticity, including those that have recently entered clinical trials.

Single cell cancer epigenetics

Bulk sequencing methodologies have allowed us to make great progress in cancer research. Unfortunately, these techniques lack the resolution to fully unravel the epigenetic mechanisms that govern tumor heterogeneity. Consequently, many novel single cell-sequencing methodologies have been developed over the past decade, allowing us to explore the epigenetic components that regulate different aspects of cancer heterogeneity, namely: clonal heterogeneity, tumor microenvironment (TME), spatial organization, intratumoral differentiation programs, metastasis, and resistance mechanisms. In this review, we explore the different sequencing techniques that enable researchers to study different aspects of epigenetics (DNA methylation, chromatin accessibility, histone modifications, DNA-protein interactions, and chromatin 3D architecture) at the single cell level, their potential applications in cancer, and their current technical limitations.

From Omics to Multi-Omics Approaches for In-Depth Analysis of the Molecular Mechanisms of Prostate Cancer

Cancer arises following alterations at different cellular levels, including genetic and epigenetic modifications, transcription and translation dysregulation, as well as metabolic variations. High-throughput omics technologies that allow one to identify and quantify processes involved in these changes are now available and have been instrumental in generating a wealth of steadily increasing data from patient tumors, liquid biopsies, and from tumor models. Extensive investigation and integration of these data have led to new biological insights into the origin and development of multiple cancer types and helped to unravel the molecular networks underlying this complex pathology. The comprehensive and quantitative analysis of a molecule class in a biological sample is named omics and large-scale omics studies addressing different prostate cancer stages have been performed in recent years. Prostate tumors represent the second leading cancer type and a prevalent cause of cancer death in men worldwide. It is a very heterogenous disease so that evaluating inter- and intra-tumor differences will be essential for a precise insight into disease development and plasticity, but also for the development of personalized therapies. There is ample evidence for the key role of the androgen receptor, a steroid hormone-activated transcription factor, in driving early and late stages of the disease, and this led to the development and approval of drugs addressing diverse targets along this pathway. Early genomic and transcriptomic studies have allowed one to determine the genes involved in prostate cancer and regulated by androgen signaling or other tumor-relevant signaling pathways. More recently, they have been supplemented by epigenomic, cistromic, proteomic and metabolomic analyses, thus, increasing our knowledge on the intricate mechanisms involved, the various levels of regulation and their interplay. The comprehensive investigation of these omics approaches and their integration into multi-omics analyses have led to a much deeper understanding of the molecular pathways involved in prostate cancer progression, and in response and resistance to therapies. This brings the hope that novel vulnerabilities will be identified, that existing therapies will be more beneficial by targeting the patient population likely to respond best, and that bespoke treatments with increased efficacy will be available soon.

The Integration of Metabolomics with Other Omics: Insights into Understanding Prostate Cancer

Our understanding of prostate cancer (PCa) has shifted from solely caused by a few genetic aberrations to a combination of complex biochemical dysregulations with the prostate metabolome at its core. The role of metabolomics in analyzing the pathophysiology of PCa is indispensable. However, to fully elucidate real-time complex dysregulation in prostate cells, an integrated approach based on metabolomics and other omics is warranted. Individually, genomics, transcriptomics, and proteomics are robust, but they are not enough to achieve a holistic view of PCa tumorigenesis. This review is the first of its kind to focus solely on the integration of metabolomics with multi-omic platforms in PCa research, including a detailed emphasis on the metabolomic profile of PCa. The authors intend to provide researchers in the field with a comprehensive knowledge base in PCa metabolomics and offer perspectives on overcoming limitations of the tool to guide future point-of-care applications.

Breaking the Immune Complexity of the Tumor Microenvironment Using Single-Cell Technologies

Tumors are not a simple aggregate of transformed cells but rather a complicated ecosystem containing various components, including infiltrating immune cells, tumor-related stromal cells, endothelial cells, soluble factors, and extracellular matrix proteins. Profiling the immune contexture of this intricate framework is now mandatory to develop more effective cancer therapies and precise immunotherapeutic approaches by identifying exact targets or predictive biomarkers, respectively. Conventional technologies are limited in reaching this goal because they lack high resolution. Recent developments in single-cell technologies, such as single-cell RNA transcriptomics, mass cytometry, and multiparameter immunofluorescence, have revolutionized the cancer immunology field, capturing the heterogeneity of tumor-infiltrating immune cells and the dynamic complexity of tenets that regulate cell networks in the tumor microenvironment. In this review, we describe some of the current single-cell technologies and computational techniques applied for immune-profiling the cancer landscape and discuss future directions of how integrating multi-omics data can guide a new "precision oncology" advancement.