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TP53 loss-of-function is commonly found in aggressive prostate cancer. However, a highly-efficient therapy for this tumor subtype is still lacking. In this study, we investigated the relationship between TP53 mutation status and autophagy in prostate cancer and assessed the efficacy of autophagy inhibitors on TP53-deficient tumors.We first evaluated the expression patterns of p53 and autophagy-related proteins, namely LC3B, ULK1 and BECLIN1, as well as their relationship in treatment-naïve and castration-resistant prostate cancer specimens through immunohistochemistry. Subsequently, we generated a Trp53-deleted genetically-engineered mouse model, established prostate tumor organoid lines from the mice and assessed the efficacy of autophagy inhibitors in overcoming Enzalutamide resistance in the tumor organoid model. We also investigated the impact of TP53 re-expression in modulating responses to autophagy inhibitors using LNCaP cell line, which harbored a TP53 missense mutation. Lastly, we attempted to identify potential autophagy-related genes that were crucial for TP53-deficient tumor maintenance.TP53 loss-of-function was associated with increased levels of autophagy-related proteins in aggressive prostate cancers and Trp53-deleted genetically-engineered mouse-derived tumors. Moreover, the generated androgen receptor-independent tumor organoids were highly vulnerable to autophagy inhibition. Upon TP53 re-expression, not only did the surviving LNCaP cells demonstrate resistance, but they also showed growth advantage in response to autophagy inhibition. Lastly, PEX14, an important peroxisomal regulator was differentially upregulated in aggressive tumors with TP53 loss-of-function mutations, thus implying the importance of peroxisome turnover in this tumor subtype.Our results support the potential use of autophagy inhibitors in prostate cancers that contain TP53 loss-of-function mutations.

Index of Supplementary Data

Supplementary Table 3: Transcriptomic analyses of the human patient samples A. TCGA Interactome B. SU2C Interactome C. Protein activity TCGA D. Protein Activity SU2C

Prioritizing treatments for individual patients with cancer remains challenging, and performing coclinical studies using patient-derived models in real time is often unfeasible. To circumvent these challenges, we introduce OncoLoop, a precision medicine framework that predicts drug sensitivity in human tumors and their preexisting high-fidelity (cognate) model(s) by leveraging drug perturbation profiles. As a proof of concept, we applied OncoLoop to prostate cancer using genetically engineered mouse models (GEMM) that recapitulate a broad spectrum of disease states, including castration-resistant, metastatic, and neuroendocrine prostate cancer. Interrogation of human prostate cancer cohorts by Master Regulator (MR) conservation analysis revealed that most patients with advanced prostate cancer were represented by at least one cognate GEMM-derived tumor (GEMM-DT). Drugs predicted to invert MR activity in patients and their cognate GEMM-DTs were successfully validated in allograft, syngeneic, and patient-derived xenograft (PDX) models of tumors and metastasis. Furthermore, OncoLoop-predicted drugs enhanced the efficacy of clinically relevant drugs, namely, the PD-1 inhibitor nivolumab and the AR inhibitor enzalutamide.Significance:OncoLoop is a transcriptomic-based experimental and computational framework that can support rapid-turnaround coclinical studies to identify and validate drugs for individual patients, which can then be readily adapted to clinical practice. This framework should be applicable in many cancer contexts for which appropriate models and drug perturbation data are available.This article is highlighted in the In This Issue feature, p. 247

Supplementary Table 4: OncoMatch A. OncoMatch TCGA B. OncoMatch SU2C

Detailed Materials and Methods

Supplementary Table 6. OncoLoop summary table

Supplementary Table 2: Transcriptomic analyses of the GEMMs A. GEMMs Interactome B. Protein activity C. Cluster analyses D. Pathway analyses

Figure S1: Genomic alterations in prostate cancer represented in the GEMMs (related to Fig. 2). Figure S2: Additional phenotypic analyses of the GEMMs (related to Fig. 2). Figure S3: Phenotypic analysis of allograft and organoid models (related to Fig. 2). Figure S4: Additional transcriptomic analyses of the GEMMs (related to Fig. 3). Figure S5: Analyses of AR activity in GEMMS (related to Fig. 3). Figure S6: Regulatory sub-networks of the GEMM clusters (related to Fig. 3). Figure S7. MR-match of prostate cancer cells lines to human PCa (related to Figs. 5). Figure S8. Drug perturbation protein activity profiles from DU145 cells (related to Figs. 5, 6, 7). Figure S9. LNCaP Pharmacotyping to patients and GEMMs (related to Figs. 5). Figure S10: Additional validation of drug candidates (related to Figs 6, 7). Figure S11: Additional validation of drug candidates (related to Figs 6).

Supplementary Table 7. Transcriptomic analyses of the PDX models A. PDX interactome B. OncoTreat for drugs to PDX using DU145 cell perturbation data

Supplementary Table 5. Drug perturbation and OncoTreat analysis A. Summary of drugs and concentrations used B. Drug perturbation data for DU145 C. OncoTreat for drugs to SU2C patients using DU145 D. OncoTreat for drugs to GEMMs using DU145

Supplementary Table 1: Phenotypic analysis of the GEMMs

Video of mouse procedures for post-surgical steps

Video of mouse surgical procedures for transurethral delivery of Adenovirus to bladder

Video of mouse procedures for intravesical delivery of 4-hydroxy tamoxifen into bladder

Detailed procedures to accompany the videos

To study the progression of bladder cancer from non–muscle-invasive to muscle-invasive disease, we have developed a novel toolkit that uses complementary approaches to achieve gene recombination in specific cell populations in the bladder urothelium in vivo, thereby allowing us to generate a new series of genetically engineered mouse models (GEMM) of bladder cancer. One method is based on the delivery of adenoviruses that express Cre recombinase in selected cell types in the urothelium, and a second uses transgenic drivers in which activation of inducible Cre alleles can be limited to the bladder urothelium by intravesicular delivery of tamoxifen. Using both approaches, targeted deletion of the Pten and p53 tumor suppressor genes specifically in basal urothelial cells gave rise to muscle-invasive bladder tumors. Furthermore, preinvasive lesions arising in basal cells displayed upregulation of molecular pathways related to bladder tumorigenesis, including proinflammatory pathways. Cross-species analyses comparing a mouse gene signature of early bladder cancer with a human signature of bladder cancer progression identified a conserved 28-gene signature of early bladder cancer that is associated with poor prognosis for human bladder cancer and that outperforms comparable gene signatures. These findings demonstrate the relevance of these GEMMs for studying the biology of human bladder cancer and introduce a prognostic gene signature that may help to stratify patients at risk for progression to potentially lethal muscle-invasive disease.Significance:Analyses of bladder cancer progression in a new series of genetically engineered mouse models has identified a gene signature of poor prognosis in human bladder cancer.

Video of mouse surgical procedures for delivery of Adenovirus into the bladder lumen.

This file contains an index of all supplementary materials, and the supplementary figures, legends, and tables.

Purpose: Previously, FTY720 was found to possess potent anticancer effects on various types of cancer. In the present study, we aimed to first verify the role of Runx2 in prostate cancer progression and metastasis, and, subsequently, assessed if FTY720 could modulate Runx2 expression, thus interfering downstream events regulated by this protein.Experimental Design: First, the association between Runx2 and prostate cancer progression was assessed using localized prostate cancer specimens and mechanistic investigation of Runx2-induced cancer aggressiveness was then carried out. Subsequently, the effect of FTY720 on Runx2 expression and transcriptional activity was investigated using PC-3 cells, which highly expressed Runx2 protein. Last, the involvement of Runx2 in FTY720-induced anticancer effects was evaluated by modulating Runx2 expression in various prostate cancer cell lines.Results: Runx2 nuclear expression was found to be up-regulated in prostate cancer and its expression could be used as a predictor of metastasis in prostate cancer. Further mechanistic studies indicated that Runx2 accelerated prostate cancer aggressiveness through promotion of cadherin switching, invasion toward collagen I, and Akt activation. Subsequently, we found that FTY720 treatment down-regulated Runx2 expression and its transcriptional activity, as well as inhibited its regulated downstream events. More importantly, silencing Runx2 in PC-3 enhanced FTY720-induced anticancer effects as well as cell viability inhibition, whereas overexpressing Runx2 in 22Rv1 that expressed very low endogenous Runx2 protein conferred resistance in the same events.Conclusion: This study provided a novel mechanism for the anticancer effect of FTY720 on advanced prostate cancer, thus highlighting the therapeutic potential of this drug in treating this disease.

Supplementary Data from Suppression of Androgen-Independent Prostate Cancer Cell Aggressiveness by FTY720: Validating Runx2 as a Potential Antimetastatic Drug Screening Platform

Supplementary of differential gene expression and pathway analyses

Supplementary Figure S1 from Up-Regulation of TWIST in Prostate Cancer and Its Implication as a Therapeutic Target

Androgen-independent metastatic prostate cancer is the main obstacle in the treatment of this cancer. Unlike a majority of solid cancers, prostate cancer usually shows poor response to chemotherapeutic drugs. In this study, we have shown a potential novel target, TWIST, a highly conserved bHLH transcription factor, in the treatment of prostate cancer. Using malignant and nonmalignant prostate tissues, we found that TWIST expression was highly expressed in the majority (90%) of prostate cancer tissues but only in a small percentage (6.7%) of benign prostate hyperplasia. In addition, the TWIST expression levels were positively correlated with Gleason grading and metastasis, indicating its role in the development and progression of prostate cancer. Furthermore, down-regulation of TWIST through small interfering RNA in androgen-independent prostate cancer cell lines, DU145 and PC3, resulted in increased sensitivity to the anticancer drug taxol-induced cell death which was associated with decreased Bcl/Bax ratio, leading to activation of the apoptosis pathway. More importantly, inactivation of TWIST suppressed migration and invasion abilities of androgen-independent prostate cancer cells, which was correlated with induction of E-cadherin expression as well as morphologic and molecular changes associated with mesenchymal to epithelial transition. These results were further confirmed on the androgen-dependent LNCaP cells ectopically expressing the TWIST protein. Our results have identified TWIST as a critical regulator of prostate cancer cell growth and suggest a potential therapeutic approach to inhibit the growth and metastasis of androgen-independent prostate cancer through inactivation of the TWIST gene.

Prioritizing treatments for individual patients with cancer remains challenging, and performing coclinical studies using patient-derived models in real time is often unfeasible. To circumvent these challenges, we introduce OncoLoop, a precision medicine framework that predicts drug sensitivity in human tumors and their preexisting high-fidelity (cognate) model(s) by leveraging drug perturbation profiles. As a proof of concept, we applied OncoLoop to prostate cancer using genetically engineered mouse models (GEMM) that recapitulate a broad spectrum of disease states, including castration-resistant, metastatic, and neuroendocrine prostate cancer. Interrogation of human prostate cancer cohorts by Master Regulator (MR) conservation analysis revealed that most patients with advanced prostate cancer were represented by at least one cognate GEMM-derived tumor (GEMM-DT). Drugs predicted to invert MR activity in patients and their cognate GEMM-DTs were successfully validated in allograft, syngeneic, and patient-derived xenograft (PDX) models of tumors and metastasis. Furthermore, OncoLoop-predicted drugs enhanced the efficacy of clinically relevant drugs, namely, the PD-1 inhibitor nivolumab and the AR inhibitor enzalutamide. Significance: OncoLoop is a transcriptomic-based experimental and computational framework that can support rapid-turnaround coclinical studies to identify and validate drugs for individual patients, which can then be readily adapted to clinical practice. This framework should be applicable in many cancer contexts for which appropriate models and drug perturbation data are available. This article is highlighted in the In This Issue feature, p. 247

At present, prioritizing cancer treatments at the individual patient level remains challenging, and performing co-clinical studies using patient-derived models in real-time is often not feasible. To circumvent these challenges, we introduce OncoLoop, a precision medicine framework to predict and validate drug sensitivity in human tumors and their pre-existing high-fidelity (cognate) model(s) by leveraging perturbational profiles of clinically-relevant oncology drugs. As proof-of-concept, we applied OncoLoop to prostate cancer (PCa) using a series of genetically-engineered mouse models (GEMMs) that recapitulate a broad spectrum of disease states, including castration-resistant, metastatic, and neuroendocrine prostate cancer. Interrogation of published cohorts using Master Regulator (MR) conservation analysis revealed that most patients were represented by at least one cognate GEMM-derived tumor (GEMM-DT). Drugs predicted to invert MR activity in patients and their cognate GEMM-DTs were successfully validated, including in two cognate allografts and one cognate patient-derived xenograft (PDX). OncoLoop is a highly generalizable framework that can be extended to other cancers and potentially other diseases.Significance StatementOncoLoop is a transcriptomic-based experimental and computational framework that can support rapid-turnaround co-clinical studies to identify and validate drugs for individual patients, which can then be readily adapted to clinical practice. This framework should be applicable in many cancer contexts for which appropriate models and drug perturbation data are available.

In the Special Issue on Prostate Cancer, we have invited 25 researchers or clinicians from prostate cancer community to review the cutting-edge topics in this field. In particular, the mini-reviews have covered various basic science and clinical aspects in prostate cancer, including prostate epithelial stem cells or progenitors, androgen and androgen receptor pathways, tumor modeling, genomics, different cell-autonomous and non-cell-autonomous mechanisms as well as various clinical issues encompassing diagnosis, risk stratification and treatments.

Prostate cancer is frequently characterized as a multifocal disease with great intratumoral heterogeneity as well as a high propensity to metastasize to bone. Consequently, modeling prostate tumor has remained a challenging task for researchers in this field. In the past decades, genomic advances have led to the identification of key molecular alterations in prostate cancer. Moreover, resistance towards second-generation androgen-deprivation therapy, namely abiraterone and enzalutamide has unveiled androgen receptor-independent diseases with distinctive histopathological and clinical features. In this review, we have critically evaluated the commonly used preclinical models of prostate cancer with respect to their capability of recapitulating the key genomic alterations, histopathological features and bone metastatic potential of human prostate tumors. In addition, we have also discussed the potential use of the emerging organoid models in prostate cancer research, which possess clear advantages over the commonly used preclinical tumor models. We anticipate that no single model can faithfully recapitulate the complexity of prostate cancer, and thus, propose the use of a cost- and time-efficient integrated tumor modeling approach for future prostate cancer investigations.