A phase II trial of apalutamide for intermediate-risk prostate cancer and molecular correlates.

OBJECTIVES: To determine whether 6 months of preoperative apalutamide for intermediate-risk prostate cancer (IRPCa) reduces the aggregate postoperative radiotherapy risk and to evaluate associations of molecular perturbations with clinical outcomes in this study cohort. PATIENTS AND METHODS: Between May 2018 and February 2020, eligible patients with IRPCa (Gleason 3 + 4 or 4 + 3 and clinical T2b-c or prostate-specific antigen level of 10-20 ng/mL) were treated with apalutamide 240 mg/day for 6 months followed by radical prostatectomy (RP) in this single-arm, phase II trial. The primary endpoint was presence of any adverse pathological feature at risk of pelvic radiation (pathological T stage after neoadjuvant therapy [yp]T3 or ypN1 or positive surgical margins). Translational studies, including germline and somatic DNA alterations and RNA and protein expression, were performed on post-apalutamide RP specimens, and assessed for associations with clinical outcomes. RESULTS: A total of 40 patients underwent a RP, and only one patient discontinued apalutamide prior to 6 months. In all, 40% had adverse pathological features at time of RP, and the 3-year biochemical recurrence (BCR) rate was 15%, with 27.5% being not evaluable. Genomic alterations frequently seen in metastatic PCas, such as androgen receptor (AR), tumour protein p53 (TP53), phosphatase and tensin homologue (PTEN), or BReast CAncer associated gene (BRCA1/2) were underrepresented in this localised cohort. Adverse pathological features and BCR at 3-years were associated with increased expression of select cell cycle (e.g., E2F targets: adjusted P value [Padj] < 0.001, normalised enrichment score [NES] 2.47) and oxidative phosphorylation (Padj < 0.001, NES 1.62) pathways. CONCLUSIONS: Preoperative apalutamide did not reduce the aggregate postoperative radiation risk to the pre-specified threshold in unselected men with IRPCa. However, transcriptomic analysis identified key dysregulated pathways in tumours associated with adverse pathological outcomes and BCR, which warrant future study. Further investigation of preoperative therapy is underway for men with high-risk PCa.

RPPA SPACE: an R package for normalization and quantitation of Reverse-Phase Protein Array data.

SUMMARY: Reverse-Phase Protein Array (RPPA) is a robust high-throughput, cost-effective platform for quantitatively measuring proteins in biological specimens. However, converting raw RPPA data into normalized, analysis-ready data remains a challenging task. Here, we present the RPPA SPACE (RPPA Superposition Analysis and Concentration Evaluation) R package, a substantially improved successor to SuperCurve, to meet that challenge. SuperCurve has been used to normalize over 170 000 samples to date. RPPA SPACE allows exclusion of poor-quality samples from the normalization process to improve the quality of the remaining samples. It also features a novel quality-control metric, 'noise', that estimates the level of random errors present in each RPPA slide. The noise metric can help to determine the quality and reliability of the data. In addition, RPPA SPACE has simpler input requirements and is more flexible than SuperCurve, it is much faster with greatly improved error reporting. AVAILABILITY AND IMPLEMENTATION: The standalone RPPA SPACE R package, tutorials and sample data are available via https://rppa.space/, CRAN (https://cran.r-project.org/web/packages/RPPASPACE/index.html) and GitHub (https://github.com/MD-Anderson-Bioinformatics/RPPASPACE). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Methylation-eQTL analysis in cancer research.

MOTIVATION: DNA methylation is a key epigenetic factor regulating gene expression. While promoter methylation has been well studied, recent publications have revealed that functionally important methylation also occurs in intergenic and distal regions, and varies across genes and tissue types. Given the growing importance of inter-platform integrative genomic analyses, there is an urgent need to develop methods to discover and characterize gene-level relationships between methylation and expression. RESULTS: We introduce a novel sequential penalized regression approach to identify methylation-expression quantitative trait loci (methyl-eQTLs), a term that we have coined to represent, for each gene and tissue type, a sparse set of CpG loci best explaining gene expression and accompanying weights indicating direction and strength of association. Using TCGA and MD Anderson colorectal cohorts to build and validate our models, we demonstrate our strategy better explains expression variability than current commonly used gene-level methylation summaries. The methyl-eQTLs identified by our approach can be used to construct gene-level methylation summaries that are maximally correlated with gene expression for use in integrative models, and produce a tissue-specific summary of which genes appear to be strongly regulated by methylation. Our results introduce an important resource to the biomedical community for integrative genomics analyses involving DNA methylation. AVAILABILITY AND IMPLEMENTATION: We produce an R Shiny app (https://rstudio-prd-c1.pmacs.upenn.edu/methyl-eQTL/) that interactively presents methyl-eQTL results for colorectal, breast and pancreatic cancer. The source R code for this work is provided in the Supplementary Material. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The Structure-Function Relationship of Angular Estrogens and Estrogen Receptor Alpha to Initiate Estrogen-Induced Apoptosis in Breast Cancer Cells.

High-dose synthetic estrogen therapy was the standard treatment of advanced breast cancer for three decades until the discovery of tamoxifen. A range of substituted triphenylethylene synthetic estrogens and diethylstilbestrol were used. It is now known that low doses of estrogens can cause apoptosis in long-term estrogen deprived (LTED) breast cancer cells resistant to antiestrogens. This action of estrogen can explain the reduced breast cancer incidence in postmenopausal women over 60 who are taking conjugated equine estrogens and the beneficial effect of low-dose estrogen treatment of patients with acquired aromatase inhibitor resistance in clinical trials. To decipher the molecular mechanism of estrogens at the estrogen receptor (ER) complex by different types of estrogens-planar [17ß-estradiol (E2)] and angular triphenylethylene (TPE) derivatives-we have synthesized a small series of compounds with either no substitutions on the TPE phenyl ring containing the antiestrogenic side chain of endoxifen or a free hydroxyl. In the first week of treatment with E2 the LTED cells undergo apoptosis completely. By contrast, the test TPE derivatives act as antiestrogens with a free para-hydroxyl on the phenyl ring that contains an antiestrogenic side chain in endoxifen. This inhibits early E2-induced apoptosis if a free hydroxyl is present. No substitution at the site occupied by the antiestrogenic side chain of endoxifen results in early apoptosis similar to planar E2 The TPE compounds recruit coregulators to the ER differentially and predictably, leading to delayed apoptosis in these cells. SIGNIFICANCE STATEMENT: In this paper we investigate the role of the structure-function relationship of a panel of synthetic triphenylethylene (TPE) derivatives and a novel mechanism of estrogen-induced cell death in breast cancer, which is now clinically relevant. Our study indicates that these TPE derivatives, depending on the positioning of the hydroxyl groups, induce various conformations of the estrogen receptor's ligand-binding domain, which in turn produces differential recruitment of coregulators and subsequently different apoptotic effects on the antiestrogen-resistant breast cancer cells.

Back to the Colorectal Cancer Consensus Molecular Subtype Future.

PURPOSE OF REVIEW: This review seeks to provide an informed prospective on the advances in molecular profiling and analysis of colorectal cancer (CRC). The goal is to provide a historical context and current summary on how advances in gene and protein sequencing technology along with computer capabilities led to our current bioinformatic advances in the field. RECENT FINDINGS: An explosion of knowledge has occurred regarding genetic, epigenetic, and biochemical alterations associated with the evolution of colorectal cancer. This has led to the realization that CRC is a heterogeneous disease with molecular alterations often dictating natural history, response to treatment, and outcome. The consensus molecular subtypes (CMS) classification classifies CRC into four molecular subtypes with distinct biological characteristics, which may form the basis for clinical stratification and subtype-based targeted intervention. This review summarizes new developments of a field moving "Back to the Future." CRC molecular subtyping will better identify key subtype specific therapeutic targets and responses to therapy.

TCGASpliceSeq a compendium of alternative mRNA splicing in cancer.

TCGA's RNASeq data represent one of the largest collections of cancer transcriptomes ever assembled. RNASeq technology, combined with computational tools like our SpliceSeq package, provides a comprehensive, detailed view of alternative mRNA splicing. Aberrant splicing patterns in cancers have been implicated in such processes as carcinogenesis, de-differentiation and metastasis. TCGA SpliceSeq (http://bioinformatics.mdanderson.org/TCGASpliceSeq) is a web-based resource that provides a quick, user-friendly, highly visual interface for exploring the alternative splicing patterns of TCGA tumors. Percent Spliced In (PSI) values for splice events on samples from 33 different tumor types, including available adjacent normal samples, have been loaded into TCGA SpliceSeq. Investigators can interrogate genes of interest, search for the genes that show the strongest variation between or among selected tumor types, or explore splicing pattern changes between tumor and adjacent normal samples. The interface presents intuitive graphical representations of splicing patterns, read counts and various statistical summaries, including percent spliced in. Splicing data can also be downloaded for inclusion in integrative analyses. TCGA SpliceSeq is freely available for academic, government or commercial use.

PathwaysWeb: a gene pathways API with directional interactions, expanded gene ontology, and versioning.

UNLABELLED: PathwaysWeb is a resource-based, well-documented web system that provides publicly available information on genes, biological pathways, Gene Ontology (GO) terms, gene-gene interaction networks (importantly, with the directionality of interactions) and links to key-related PubMed documents. The PathwaysWeb API simplifies the construction of applications that need to retrieve and interrelate information across multiple, pathway-related data types from a variety of original data sources. PathwaysBrowser is a companion website that enables users to explore the same integrated pathway data. The PathwaysWeb system facilitates reproducible analyses by providing access to all versions of the integrated datasets. Although its GO subsystem includes data for mouse, PathwaysWeb currently focuses on human data. However, pathways for mouse and many other species can be inferred with a high success rate from human pathways. AVAILABILITY AND IMPLEMENTATION: PathwaysWeb can be accessed via the Internet at http://bioinformatics.mdanderson.org/main/PathwaysWeb:Overview. CONTACT: jmmelott@mdanderson.org SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Proteomic Features of Colorectal Cancer Identify Tumor Subtypes Independent of Oncogenic Mutations and Independently Predict Relapse-Free Survival.

BACKGROUND: The directed study of the functional proteome in colorectal cancer (CRC) has identified critical protein markers and signaling pathways; however, the prognostic relevance of many of these proteins remains unclear. METHODS: We determined the prognostic implications of the functional proteome in 263 CRC tumor samples from patients treated at MD Anderson Cancer Center (MDACC) and 462 patients from The Cancer Genome Atlas (TCGA) to identify patterns of protein expression that drive tumorigenesis. A total of 163 validated proteins were analyzed by reverse phase protein array (RPPA). Unsupervised hierarchical clustering of the tumor proteins from the MDACC cohort was performed, and clustering was validated using RPPA data from TCGA CRC. Cox regression was used to identify predictors of tumor recurrence. RESULTS: Clustering revealed dichotomization, with subtype A notable for a high epithelial-mesenchymal transition (EMT) protein signature, while subtype B was notable for high Akt/TSC/mTOR pathway components. Survival data were only available for the MDACC cohort and were used to evaluate prognostic relevance of these protein signatures. Group B demonstrated worse relapse-free survival (hazard ratio 2.11, 95% confidence interval 1.04-4.27, p = 0.039), although there was no difference in known genomic drivers between the two proteomic groups. Proteomic grouping and stage were significant predictors of recurrence on multivariate analysis. Eight proteins were found to be significant predictors of tumor recurrence on multivariate analysis: Collagen VI, FOXO3a, INPP4B, LcK, phospho-PEA15, phospho-PRAS40, Rad51, phospho-S6. CONCLUSION: CRC can be classified into distinct subtypes by proteomic features independent of common oncogenic driver mutations. Proteomic analysis has identified key biomarkers with prognostic importance, however these findings require further validation in an independent cohort.

iBAG: integrative Bayesian analysis of high-dimensional multiplatform genomics data.

MOTIVATION: Analyzing data from multi-platform genomics experiments combined with patients' clinical outcomes helps us understand the complex biological processes that characterize a disease, as well as how these processes relate to the development of the disease. Current data integration approaches are limited in that they do not consider the fundamental biological relationships that exist among the data obtained from different platforms. Statistical Model: We propose an integrative Bayesian analysis of genomics data (iBAG) framework for identifying important genes/biomarkers that are associated with clinical outcome. This framework uses hierarchical modeling to combine the data obtained from multiple platforms into one model. RESULTS: We assess the performance of our methods using several synthetic and real examples. Simulations show our integrative methods to have higher power to detect disease-related genes than non-integrative methods. Using the Cancer Genome Atlas glioblastoma dataset, we apply the iBAG model to integrate gene expression and methylation data to study their associations with patient survival. Our proposed method discovers multiple methylation-regulated genes that are related to patient survival, most of which have important biological functions in other diseases but have not been previously studied in glioblastoma. AVAILABILITY: http://odin.mdacc.tmc.edu/∼vbaladan/. CONTACT: veera@mdanderson.org SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

RETRACTED: Liu et al. Prediction of Ovarian Cancer Response to Therapy Based on Deep Learning Analysis of Histopathology Images. Cancers 2023, 15, 4044.

The journal and authors wish to retract the article entitled 'Prediction of Ovarian Cancer Response to Therapy Based on Deep Learning Analysis of Histopathology Images' cited above [...].

PCA-Plus: Enhanced principal component analysis with illustrative applications to batch effects and their quantitation.

Background: Principal component analysis (PCA), a standard approach to analysis and visualization of large datasets, is commonly used in biomedical research for detecting similarities and differences among groups of samples. We initially used conventional PCA as a tool for critical quality control of batch and trend effects in multi-omic profiling data produced by The Cancer Genome Atlas (TCGA) project of the NCI. We found, however, that conventional PCA visualizations were often hard to interpret when inter-batch differences were moderate in comparison with intra-batch differences; it was also difficult to quantify batch effects objectively. We, therefore, sought enhancements to make the method more informative in those and analogous settings. Results: We have developed algorithms and a toolbox of enhancements to conventional PCA that improve the detection, diagnosis, and quantitation of differences between or among groups, e.g., groups of molecularly profiled biological samples. The enhancements include (i) computed group centroids; (ii) sample-dispersion rays; (iii) differential coloring of centroids, rays, and sample data points; (iii) trend trajectories; and (iv) a novel separation index (DSC) for quantitation of differences among groups. Conclusions: PCA-Plus has been our most useful single tool for analyzing, visualizing, and quantitating batch effects, trend effects, and class differences in molecular profiling data of many types: mRNA expression, microRNA expression, DNA methylation, and DNA copy number. An early version of PCA-Plus has been used as the central graphical visualization in our MBatch package for near-real-time surveillance of data for analysis working groups in more than 70 TCGA, PanCancer Atlas, PanCancer Analysis of Whole Genomes, and Genome Data Analysis Network projects of the NCI. The algorithms and software are generic, hence applicable more generally to other types of multivariate data as well. PCA-Plus is freely available in a down-loadable R package at our MBatch website.

FuncPEP v2.0: An Updated Database of Functional Short Peptides Translated from Non-Coding RNAs.

Over the past decade, there have been reports of short novel functional peptides (less than 100 aa in length) translated from so-called non-coding RNAs (ncRNAs) that have been characterized using mass spectrometry (MS) and large-scale proteomics studies. Therefore, understanding the bivalent functions of some ncRNAs as transcripts that encode both functional RNAs and short peptides, which we named ncPEPs, will deepen our understanding of biology and disease. In 2020, we published the first database of functional peptides translated from non-coding RNAs-FuncPEP. Herein, we have performed an update including the newly published ncPEPs from the last 3 years along with the categorization of host ncRNAs. FuncPEP v2.0 contains 152 functional ncPEPs, out of which 40 are novel entries. A PubMed search from August 2020 to July 2023 incorporating specific keywords was performed and screened for publications reporting validated functional peptides derived from ncRNAs. We did not observe a significant increase in newly discovered functional ncPEPs, but a steady increase. The novel identified ncPEPs included in the database were characterized by a wide array of molecular and physiological parameters (i.e., types of host ncRNA, species distribution, chromosomal density, distribution of ncRNA length, identification methods, molecular weight, and functional distribution across humans and other species). We consider that, despite the fact that MS can now easily identify ncPEPs, there still are important limitations in proving their functionality.

Prediction of Ovarian Cancer Response to Therapy Based on Deep Learning Analysis of Histopathology Images.

BACKGROUND: Ovarian cancer remains the leading gynecological cause of cancer mortality. Predicting the sensitivity of ovarian cancer to chemotherapy at the time of pathological diagnosis is a goal of precision medicine research that we have addressed in this study using a novel deep-learning neural network framework to analyze the histopathological images. METHODS: We have developed a method based on the Inception V3 deep learning algorithm that complements other methods for predicting response to standard platinum-based therapy of the disease. For the study, we used histopathological H&E images (pre-treatment) of high-grade serous carcinoma from The Cancer Genome Atlas (TCGA) Genomic Data Commons portal to train the Inception V3 convolutional neural network system to predict whether cancers had independently been labeled as sensitive or resistant to subsequent platinum-based chemotherapy. The trained model was then tested using data from patients left out of the training process. We used receiver operating characteristic (ROC) and confusion matrix analyses to evaluate model performance and Kaplan-Meier survival analysis to correlate the predicted probability of resistance with patient outcome. Finally, occlusion sensitivity analysis was piloted as a start toward correlating histopathological features with a response. RESULTS: The study dataset consisted of 248 patients with stage 2 to 4 serous ovarian cancer. For a held-out test set of forty patients, the trained deep learning network model distinguished sensitive from resistant cancers with an area under the curve (AUC) of 0.846 ± 0.009 (SE). The probability of resistance calculated from the deep-learning network was also significantly correlated with patient survival and progression-free survival. In confusion matrix analysis, the network classifier achieved an overall predictive accuracy of 85% with a sensitivity of 73% and specificity of 90% for this cohort based on the Youden-J cut-off. Stage, grade, and patient age were not statistically significant for this cohort size. Occlusion sensitivity analysis suggested histopathological features learned by the network that may be associated with sensitivity or resistance to the chemotherapy, but multiple marker studies will be necessary to follow up on those preliminary results. CONCLUSIONS: This type of analysis has the potential, if further developed, to improve the prediction of response to therapy of high-grade serous ovarian cancer and perhaps be useful as a factor in deciding between platinum-based and other therapies. More broadly, it may increase our understanding of the histopathological variables that predict response and may be adaptable to other cancer types and imaging modalities.

SPOP Mutations Target STING1 Signaling in Prostate Cancer and Create Therapeutic Vulnerabilities to PARP Inhibitor-Induced Growth Suppression.

PURPOSE: Speckle-type POZ protein (SPOP) is important in DNA damage response (DDR) and maintenance of genomic stability. Somatic heterozygous missense mutations in the SPOP substrate-binding cleft are found in up to 15% of prostate cancers. While mutations in SPOP predict for benefit from androgen receptor signaling inhibition (ARSi) therapy, outcomes for patients with SPOP-mutant (SPOPmut) prostate cancer are heterogeneous and targeted treatments for SPOPmut castrate-resistant prostate cancer (CRPC) are lacking. EXPERIMENTAL DESIGN: Using in silico genomic and transcriptomic tumor data, proteomics analysis, and genetically modified cell line models, we demonstrate mechanistic links between SPOP mutations, STING signaling alterations, and PARP inhibitor vulnerabilities. RESULTS: We demonstrate that SPOP mutations are associated with upregulation of a 29-gene noncanonical (NC) STING (NC-STING) signature in a subset of SPOPmut, treatment-refractory CRPC patients. We show in preclinical CRPC models that SPOP targets and destabilizes STING1 protein, and prostate cancer-associated SPOP mutations result in upregulated NC-STING-NF-κB signaling and macrophage- and tumor microenvironment (TME)-facilitated reprogramming, leading to tumor cell growth. Importantly, we provide in vitro and in vivo mechanism-based evidence that PARP inhibitor (PARPi) treatment results in a shift from immunosuppressive NC-STING-NF-κB signaling to antitumor, canonical cGAS-STING-IFNß signaling in SPOPmut CRPC and results in enhanced tumor growth inhibition. CONCLUSIONS: We provide evidence that SPOP is critical in regulating immunosuppressive versus antitumor activity downstream of DNA damage-induced STING1 activation in prostate cancer. PARPi treatment of SPOPmut CRPC alters this NC-STING signaling toward canonical, antitumor cGAS-STING-IFNß signaling, highlighting a novel biomarker-informed treatment strategy for prostate cancer.

Model averaging strategies for structure learning in Bayesian networks with limited data.

BACKGROUND: Considerable progress has been made on algorithms for learning the structure of Bayesian networks from data. Model averaging by using bootstrap replicates with feature selection by thresholding is a widely used solution for learning features with high confidence. Yet, in the context of limited data many questions remain unanswered. What scoring functions are most effective for model averaging? Does the bias arising from the discreteness of the bootstrap significantly affect learning performance? Is it better to pick the single best network or to average multiple networks learnt from each bootstrap resample? How should thresholds for learning statistically significant features be selected? RESULTS: The best scoring functions are Dirichlet Prior Scoring Metric with small λ and the Bayesian Dirichlet metric. Correcting the bias arising from the discreteness of the bootstrap worsens learning performance. It is better to pick the single best network learnt from each bootstrap resample. We describe a permutation based method for determining significance thresholds for feature selection in bagged models. We show that in contexts with limited data, Bayesian bagging using the Dirichlet Prior Scoring Metric (DPSM) is the most effective learning strategy, and that modifying the scoring function to penalize complex networks hampers model averaging. We establish these results using a systematic study of two well-known benchmarks, specifically ALARM and INSURANCE. We also apply our network construction method to gene expression data from the Cancer Genome Atlas Glioblastoma multiforme dataset and show that survival is related to clinical covariates age and gender and clusters for interferon induced genes and growth inhibition genes. CONCLUSIONS: For small data sets, our approach performs significantly better than previously published methods.

Bayesian ensemble methods for survival prediction in gene expression data.

MOTIVATION: We propose a Bayesian ensemble method for survival prediction in high-dimensional gene expression data. We specify a fully Bayesian hierarchical approach based on an ensemble 'sum-of-trees' model and illustrate our method using three popular survival models. Our non-parametric method incorporates both additive and interaction effects between genes, which results in high predictive accuracy compared with other methods. In addition, our method provides model-free variable selection of important prognostic markers based on controlling the false discovery rates; thus providing a unified procedure to select relevant genes and predict survivor functions. RESULTS: We assess the performance of our method several simulated and real microarray datasets. We show that our method selects genes potentially related to the development of the disease as well as yields predictive performance that is very competitive to many other existing methods. AVAILABILITY: http://works.bepress.com/veera/1/.

Fibroblast Growth Factor Receptor 1 Drives the Metastatic Progression of Prostate Cancer.

BACKGROUND: No curative therapy is currently available for metastatic prostate cancer (PCa). The diverse mechanisms of progression include fibroblast growth factor (FGF) axis activation. OBJECTIVE: To investigate the molecular and clinical implications of fibroblast growth factor receptor 1 (FGFR1) and its isoforms (α/ß) in the pathogenesis of PCa bone metastases. DESIGN, SETTING, AND PARTICIPANTS: In silico, in vitro, and in vivo preclinical approaches were used. RNA-sequencing and immunohistochemical (IHC) studies in human samples were conducted. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: In mice, bone metastases (chi-square/Fisher's test) and survival (Mantel-Cox) were assessed. In human samples, FGFR1 and ladinin 1 (LAD1) analysis associated with PCa progression were evaluated (IHC studies, Fisher's test). RESULTS AND LIMITATIONS: FGFR1 isoform expression varied among PCa subtypes. Intracardiac injection of mice with FGFR1-expressing PC3 cells reduced mouse survival (α, p < 0.0001; ß, p = 0.032) and increased the incidence of bone metastases (α, p < 0.0001; ß, p = 0.02). Accordingly, IHC studies of human castration-resistant PCa (CRPC) bone metastases revealed significant enrichment of FGFR1 expression compared with treatment-naïve, nonmetastatic primary tumors (p = 0.0007). Expression of anchoring filament protein LAD1 increased in FGFR1-expressing PC3 cells and was enriched in human CRPC bone metastases (p = 0.005). CONCLUSIONS: FGFR1 expression induces bone metastases experimentally and is significantly enriched in human CRPC bone metastases, supporting its prometastatic effect in PCa. LAD1 expression, found in the prometastatic PCa cells expressing FGFR1, was also enriched in CRPC bone metastases. Our studies support and provide a roadmap for the development of FGFR blockade for advanced PCa. PATIENT SUMMARY: We studied the role of fibroblast growth factor receptor 1 (FGFR1) in prostate cancer (PCa) progression. We found that PCa cells with high FGFR1 expression increase metastases and that FGFR1 expression is increased in human PCa bone metastases, and identified genes that could participate in the metastases induced by FGFR1. These studies will help pinpoint PCa patients who use fibroblast growth factor to progress and will benefit by the inhibition of this pathway.

ATR Inhibition Induces CDK1-SPOP Signaling and Enhances Anti-PD-L1 Cytotoxicity in Prostate Cancer.

PURPOSE: Despite significant benefit for other cancer subtypes, immune checkpoint blockade (ICB) therapy has not yet been shown to significantly improve outcomes for men with castration-resistant prostate cancer (CRPC). Prior data have shown that DNA damage response (DDR) deficiency, via genetic alteration and/or pharmacologic induction using DDR inhibitors (DDRi), may improve ICB response in solid tumors in part due to induction of mitotic catastrophe and innate immune activation. Discerning the underlying mechanisms of this DDRi-ICB interaction in a prostate cancer-specific manner is vital to guide novel clinical trials and provide durable clinical responses for men with CRPC. EXPERIMENTAL DESIGN: We treated prostate cancer cell lines with potent, specific inhibitors of ATR kinase, as well as with PARP inhibitor, olaparib. We performed analyses of cGAS-STING and DDR signaling in treated cells, and treated a syngeneic androgen-indifferent, prostate cancer model with combined ATR inhibition and anti-programmed death ligand 1 (anti-PD-L1), and performed single-cell RNA sequencing analysis in treated tumors. RESULTS: ATR inhibitor (ATRi; BAY1895433) directly repressed ATR-CHK1 signaling, activated CDK1-SPOP axis, leading to destabilization of PD-L1 protein. These effects of ATRi are distinct from those of olaparib, and resulted in a cGAS-STING-initiated, IFN-ß-mediated, autocrine, apoptotic response in CRPC. The combination of ATRi with anti-PD-L1 therapy resulted in robust innate immune activation and a synergistic, T-cell-dependent therapeutic response in our syngeneic mouse model. CONCLUSIONS: This work provides a molecular mechanistic rationale for combining ATR-targeted agents with immune checkpoint blockade for patients with CRPC. Multiple early-phase clinical trials of this combination are underway.

Optimizing the diagnosis and management of ductal prostate cancer.

Ductal adenocarcinoma (DAC) is the most common variant histological subtype of prostate carcinoma and has an aggressive clinical course. DAC is usually characterized and treated as high-risk prostatic acinar adenocarcinoma (PAC). However, DAC has a different biology to that of acinar disease, which often poses a challenge for both diagnosis and management. DAC can be difficult to identify using conventional diagnostic modalities such as serum PSA levels and multiparametric MRI, and the optimal management for localized DAC is unknown owing to the rarity of the disease. Following definitive therapy for localized disease with radical prostatectomy or radiotherapy, the majority of DACs recur with visceral metastases at low PSA levels. Various systemic therapies that have been shown to be effective in high-risk PAC have limited use in treating DAC. Although current understanding of the biology of DAC is limited, genomic analyses have provided insights into the pathology behind its aggressive behaviour and potential future therapeutic targets.