Identification of a Radiosensitivity Molecular Signature Induced by Enzalutamide in Hormone-sensitive and Hormone-resistant Prostate Cancer Cells

Compartmentalization of androgen receptors at endogenous genes in living cells

A wide range of nuclear proteins are involved in the spatio-temporal organization of the genome through diverse biological processes such as gene transcription and DNA replication. Upon stimulation by testosterone and translocation to the nucleus, multiple androgen receptors (ARs) accumulate in microscopically discernable foci which are irregularly distributed in the nucleus. Here, we investigated the formation and physical nature of these foci, by combining novel fluorescent labeling techniques to visualize a defined chromatin locus of AR-regulated genes-PTPRN2 or BANP-simultaneously with either AR foci or individual AR molecules. Quantitative colocalization analysis showed evidence of AR foci formation induced by R1881 at both PTPRN2 and BANP loci. Furthermore, single-particle tracking (SPT) revealed three distinct subdiffusive fractional Brownian motion (fBm) states: immobilized ARs were observed near the labeled genes likely as a consequence of DNA-binding, while the intermediate confined state showed a similar spatial behavior but with larger displacements, suggesting compartmentalization by liquid-liquid phase separation (LLPS), while freely mobile ARs were diffusing in the nuclear environment. All together, we show for the first time in living cells the presence of AR-regulated genes in AR foci.

Advances in PARP Inhibitors for Prostate Cancer

Poly-adenosine diphosphate-ribose polymerase plays an essential role in cell function by regulating apoptosis, genomic stability and DNA repair. PARPi is a promising drug class that has gained significant traction in the last decade with good outcomes in different cancers. Several trials have sought to test its effectiveness in metastatic castration resistant prostate cancer (mCRPC). We conducted a comprehensive literature review to evaluate the current role of PARPi in this setting. To this effect, we conducted queries in the PubMed, Embase and Cochrane databases. We reviewed and compared all major contemporary publications on the topic. In particular, recent phase II and III studies have also demonstrated the benefits of olaparib, rucaparib, niraparib, talazoparib in CRPC. Drug effectiveness has been assessed through radiological progression or overall response. Given the notion of synthetic lethality and potential synergy with other oncological therapies, several trials are looking to integrate PARPi in combined therapies. There remains ongoing controversy on the need for genetic screening prior to treatment initiation as well as the optimal patient population, which would benefit most from PARPi. PARPi is an important asset in the oncological arsenal for mCRPC. New combinations with PARPi may improve outcomes in earlier phases of prostate cancer.

Enhanced Antitumor Efficacy of Radium-223 and Enzalutamide in the Intratibial LNCaP Prostate Cancer Model

Radium-223 dichloride and enzalutamide are indicated for metastatic castration-resistant prostate cancer and their combination is currently being investigated in a large phase 3 clinical trial. Here, we evaluated the antitumor efficacy of radium-223, enzalutamide, and their combination in the intratibial LNCaP model mimicking prostate cancer metastasized to bone. In vitro experiments revealed that the combination of radium-223 and enzalutamide inhibited LNCaP cell proliferation and showed synergistic efficacy. The combination of radium-223 and enzalutamide also demonstrated enhanced in vivo antitumor efficacy, as determined by measuring serum PSA levels in the intratibial LNCaP model. A decreasing trend in the total area of tumor-induced abnormal bone was associated with the combination treatment. The serum levels of the bone formation marker PINP and the bone resorption marker CTX-I were lowest in the combination treatment group and markedly decreased compared with vehicle group. Concurrent administration of enzalutamide did not impair radium-223 uptake in tumor-bearing bone or the ability of radium-223 to inhibit tumor-induced abnormal bone formation. In conclusion, combination treatment with radium-223 and enzalutamide demonstrated enhanced antitumor efficacy without compromising the integrity of healthy bone. The results support the ongoing phase 3 trial of this combination.

Drivers of Radioresistance in Prostate Cancer

Prostate cancer (PCa) is the second most commonly diagnosed cancer worldwide. Radiotherapy remains one of the first-line treatments in localised disease and may be used as monotherapy or in combination with other treatments such as androgen deprivation therapy or radical prostatectomy. Despite advancements in delivery methods and techniques, radiotherapy has been unable to totally overcome radioresistance resulting in treatment failure or recurrence of previously treated PCa. Various factors have been linked to the development of tumour radioresistance including abnormal tumour vasculature, oxygen depletion, glucose and energy deprivation, changes in gene expression and proteome alterations. Understanding the biological mechanisms behind radioresistance is essential in the development of therapies that are able to produce both initial and sustained response to radiotherapy. This review will investigate the different biological mechanisms utilised by PCa tumours to drive radioresistance.

A radiation resistance related index for biochemical recurrence and tumor immune environment in prostate cancer patients

PURPOSE

To establish and verify a novel radiation resistance related index for predicting biochemical recurrence and tumor immune environment in prostate cancer (PCa) patients.

MATERIALS AND METHODS

The transcriptome information of PCa were obtained from GEO and TCGA portal. We identified radiation resistance related genes (RRGs) between radioresistant and radiosensitive PCa cells. We conducted multivariate Cox analysis to construct a novel radiation resistance related index for predicting biochemical recurrence (BCR)-free survival (BCRFS). Internal and external validations were conducted. Preliminary experimental verifications were performed.

RESULTS

We identified 194 differentially expressed RRGs and three radiation resistance related molecular clusters for PCa. Moreover, we established a novel radiation resistance related index and succeeded in conducting internal and external validations. High-risk populations meant significantly worse BCRFS in training, testing and validating cohort. The area under receiver operating characteristic curve were 0.809, 0.698, and 0.712 in training, testing, and validating cohort. The immune microenvironment was significantly different between high and low-risk score patients. Preliminary experiment identified and validated three potential biomarkers related to radiation resistance (ZNF695, TM4SF19, CCDC3) of PCa.

CONCLUSIONS

This study successfully established and verified a novel radiation resistance related index, which had an excellent performance in predicting BCR and tumor immune microenvironment in patients with PCa.

Androgen Receptor Gene Pathway Upregulation and Radiation Resistance in Oligometastatic Prostate Cancer

Stereotactic ablative body radiotherapy (SABR) is currently used as a salvage intervention for men with oligometastatic prostate cancer (PC), and increasingly so since the results of the Stereotactic Ablative Body Radiotherapy for the Comprehensive Treatment of Oligometastatic Cancers (SABR-COMET) trial reported a significant improvement in overall survival with SABR. The addition of androgen deprivation therapy (ADT) to localised prostate radiotherapy improves survival as it sensitises PC to radiotherapy-induced cell death. The importance of the androgen receptor (AR) gene pathway in the development of resistance to radiotherapy is well established. In this review paper, we will examine the data to determine how we can overcome the upregulation of the AR pathway and suggest a strategy for improving outcomes in men with oligometastatic hormone-sensitive PC.

Inhibiting 3βHSD1 to eliminate the oncogenic effects of progesterone in prostate cancer

Prostate cancer continuously progresses following deprivation of circulating androgens originating from the testis and adrenal glands, indicating the existence of oncometabolites beyond androgens. In this study, mass-spectrometry-based screening of clinical specimens and a retrospective analysis on the clinical data of prostate cancer patients indicate the potential oncogenic effects of progesterone in patients. High doses of progesterone activate canonical and non-canonical androgen receptor (AR) target genes. Physiological levels of progesterone facilitate cell proliferation via GATA2. Inhibitors of 3β-hydroxysteroid dehydrogenase 1 (3βHSD1) has been discovered and shown to suppress the generation of progesterone, eliminating its transient and accumulating oncogenic effects. An increase in progesterone is associated with poor clinical outcomes in patients and may be used as a predictive biomarker. Overall, we demonstrate that progesterone acts as an oncogenic hormone in prostate cancer, and strategies to eliminate its oncogenic effects may benefit prostate cancer patients.

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High doses of progesterone activate canonical and non-canonical AR signaling

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Progesterone of physiological levels exerts its chronic oncogenic effect via GATA2

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Targeting 3βHSD1 to suppress progesterone synthesis blocks its oncogenic effects

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Serum progesterone might be a predictive biomarker for abiraterone response

Combination of Radiosensitivity Gene Signature and PD-L1 Status Predicts Clinical Outcome of Patients With Locally Advanced Head and Neck Squamous Cell Carcinoma: A Study Based on The Cancer Genome Atlas Dataset

Aim: The aim of our study was to investigate the potential predictive value of the combination of radiosensitivity gene signature and PD-L1 expression for the prognosis of locally advanced head and neck squamous cell carcinoma (HNSCC).

Methods: The cohort was selected from The Cancer Genome Atlas (TCGA) and classified into the radiosensitive (RS) group and radioresistant (RR) group by a radiosensitivity-related gene signature. The cohort was also grouped as PD-L1-high or PD-L1-low based on PD-L1 mRNA expression. The least absolute shrinkage and selection operator (lasso)-based Cox model was used to select hub survival genes. An independent validation cohort was obtained from the Gene Expression Omnibus (GEO) database.

Results: We selected 288 locally advanced HNSCC patients from TCGA. The Kaplan–Meier method found that the RR and PD-L1-high group had a worse survival than others (p = 0.033). The differentially expressed gene (DEG) analysis identified 553 upregulated genes and 486 downregulated genes (p < 0.05, fold change >2) between the RR and PD-L1-high group and others. The univariate Cox analysis of each DEG and subsequent lasso-based Cox model revealed five hub survival genes (POU4F1, IL34, HLF, CBS, and RNF165). A further hub survival gene-based risk score model was constructed, which was validated by an external cohort. We observed that a higher risk score predicted a worse prognosis (p = 0.0013). The area under the receiver operating characteristic curve (AUC) plots showed that this risk score model had good prediction value (1-year AUC = 0.684, 2-year AUC = 0.702, and 3-year AUC = 0.688). Five different deconvolution methods all showed that the B cells were lower in the RR and PD-L1-high group (p < 0.05). Finally, connectivity mapping analysis showed that the histone deacetylase (HDAC) inhibitor trichostatin A might have the potential to reverse the phenotype of RR and PD-L1-high in locally advanced HNSCC (p < 0.05, false discovery rate <0.1).

Conclusion: The combination of 31-gene signature and the PD-L1 mRNA expression had a potential predictive value for the prognosis of locally advanced HNSCC who had RT. The B cells were lower in the RR and PD-L1-high group. The identified risk gene signature of locally advanced HNSCC and the potential therapeutic drug trichostatin A for the RR and PD-L1-high group are worth being further studied in a prospective homogenous cohort.

DNA methylome in visceral adipose tissue can discriminate patients with and without colorectal cancer

Adipose tissue dysfunction, particularly the visceral (VAT) compartment, has been proposed to play a relevant role in colorectal cancer (CRC) development and progression. Epigenetic mechanisms could be involved in this association. The current study aimed to evaluate if specific epigenetic marks in VAT are associated with colorectal cancer (CRC) to identify epigenetic hallmarks of adipose tissue-related CRC. Epigenome-wide DNA methylation was evaluated in VAT from 25 healthy participants and 29 CRC patients, using the Infinium HumanMethylation450K BeadChip. The epigenome-wide methylation analysis identified 170,184 sites able to perfectly separate the CRC and healthy samples. The differentially methylated CpG sites (DMCpGs) showed a global trend for increased methylated levels in CRC with respect to healthy group. Most of the genes encoded by the DMCpGs belonged to metabolic pathways and cell cycle, insulin resistance, and adipocytokine signalling, as well as tumoural transformation processes. In gene-specific analyses, involved genes biologically relevant for the development of CRC include PTPRN2, MAD1L1, TNXB, DIP2C, INPP5A, HDCA4, PRDM16, RPTOR, ATP11A, TBCD, PABPC3, and IER2. The methylation level of some of them showed a discriminatory capacity for detecting CRC higher than 90%, showing IER2 to have the highest capacity. This study reveals that a specific methylation pattern of VAT is associated with CRC. Some of the epigenetic marks identified could provide useful tools for the prediction and personalized treatment of CRC connected to excess adiposity.

Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance

Resistance to ionizing radiation, a first-line therapy for many cancers, is a major clinical challenge. Personalized prediction of tumor radiosensitivity is not currently implemented clinically due to insufficient accuracy of existing machine learning classifiers. Despite the acknowledged role of tumor metabolism in radiation response, metabolomics data is rarely collected in large multi-omics initiatives such as The Cancer Genome Atlas (TCGA) and consequently omitted from algorithm development. In this study, we circumvent the paucity of personalized metabolomics information by characterizing 915 TCGA patient tumors with genome-scale metabolic Flux Balance Analysis models generated from transcriptomic and genomic datasets. Metabolic biomarkers differentiating radiation-sensitive and -resistant tumors are predicted and experimentally validated, enabling integration of metabolic features with other multi-omics datasets into ensemble-based machine learning classifiers for radiation response. These multi-omics classifiers show improved classification accuracy, identify clinical patient subgroups, and demonstrate the utility of personalized blood-based metabolic biomarkers for radiation sensitivity. The integration of machine learning with genome-scale metabolic modeling represents a significant methodological advancement for identifying prognostic metabolite biomarkers and predicting radiosensitivity for individual patients.

Dosimetric Analysis of the Short-Ranged Particle Emitter 161Tb for Radionuclide Therapy of Metastatic Prostate Cancer

Simple Summary

A tremendous effort and rapid development of the prostate-specific membrane antigen (PSMA)-targeting radio ligands for radionuclide therapy has resulted in encouraging response rates for advanced prostate cancer. Different radionuclides have been utilized or suggested as suitable candidates. In this study, a dynamic model of metastatic progress was developed and utilized to estimate a radiopharmaceutical’s potential of obtaining metastatic control of advanced prostate cancer. The simulations performed demonstrated the advantage of utilizing radionuclides with short-range particle emission, i.e., alpha-emitters and low-energy electrons. The recently-proposed beta-emitting radionuclide terbium-161 demonstrates great potential of being a future candidate towards targeted radionuclide therapy of advanced prostate cancer. This is in line with recent encouraging preclinical results and development of upscaling the product quality. Recently, the first in-human application with a [¹⁶¹Tb]Tb-DOTATOC also demonstrated good SPECT image quality, which can enable dosimetry calculations for new ¹⁶¹Tb-based radiopharmaceuticals.

Abstract

The aim of this study was to analyze the required absorbed doses to detectable metastases (D_req) when using radionuclides with prostate specific membrane antigen (PSMA)-targeting radioligands to achieve a high probability for metastatic control. The Monte Carlo based analysis was performed for the clinically-used radionuclides yttrium-90, iodine-131, lutetium-177, and actinium-225, and the newly-proposed low-energy electron emitter terbium-161. It was demonstrated that metastatic formation rate highly influenced the metastatic distribution. Lower values generated few large detectable metastases, as in the case with oligo metastases, while high values generated a distribution of multiple small detectable metastases, as observed in patients with diffused visualized metastases. With equal number of detectable metastases, the total metastatic volume burden was 4–6 times higher in the oligo metastatic scenario compared to the diffusely visualized scenario. The D_req was around 30% higher for the situations with 20 detectable metastases compared to one detectable metastasis. The D_req for iodine-131 and yttrium-90 was high (920–3300 Gy). The D_req for lutetium-177 was between 560 and 780 Gy and considerably lower D_req were obtained for actinium-225 and terbium-161, with 240–330 Gy and 210–280 Gy, respectively. In conclusion, the simulations demonstrated that terbium-161 has the potential for being a more effective targeted radionuclide therapy for metastases using PSMA ligands.

Elucidation of the Genomic-Epigenomic Interaction Landscape of Aggressive Prostate Cancer

Background

Majority of prostate cancer (PCa) deaths are attributed to localized high-grade aggressive tumours which progress rapidly to metastatic disease. A critical unmet need in clinical management of PCa is discovery and characterization of the molecular drivers of aggressive tumours. The development and progression of aggressive PCa involve genetic and epigenetic alterations occurring in the germline, somatic (tumour), and epigenomes. To date, interactions between genes containing germline, somatic, and epigenetic mutations in aggressive PCa have not been characterized. The objective of this investigation was to elucidate the genomic-epigenomic interaction landscape in aggressive PCa to identify potential drivers aggressive PCa and the pathways they control. We hypothesized that aggressive PCa originates from a complex interplay between genomic (both germline and somatic mutations) and epigenomic alterations. We further hypothesized that these complex arrays of interacting genomic and epigenomic factors affect gene expression, molecular networks, and signaling pathways which in turn drive aggressive PCa.

Methods

We addressed these hypotheses by performing integrative data analysis combining information on germline mutations from genome-wide association studies with somatic and epigenetic mutations from The Cancer Genome Atlas using gene expression as the intermediate phenotype.

Results

The investigation revealed signatures of genes containing germline, somatic, and epigenetic mutations associated with aggressive PCa. Aberrant DNA methylation had effect on gene expression. In addition, the investigation revealed molecular networks and signalling pathways enriched for germline, somatic, and epigenetic mutations including the STAT3, PTEN, PCa, ATM, AR, and P53 signalling pathways implicated in aggressive PCa.

Conclusions

The study demonstrated that integrative analysis combining diverse omics data is a powerful approach for the discovery of potential clinically actionable biomarkers, therapeutic targets, and elucidation of oncogenic interactions between genomic and epigenomic alterations in aggressive PCa.