Purpose

Both the performance characteristics of prostate-specific membrane antigen positron emission tomography and insurance approval improves with increasing prostate-specific antigen (PSA) level causing some physicians to delay post-radical prostatectomy salvage radiation therapy (sRT) after PSA failure. Yet, it is unknown for men with at most one high-risk factor (ie, pT3/4 or prostatectomy [p] Gleason score 8-10) whether a PSA level exists above which initiating sRT is associated with increased all-cause mortality (ACM)-risk and was investigated.

Methods

Using a multinational database of 25,551 patients with pT2-4N0 or NXM0 prostate cancer, multivariable Cox regression analysis evaluated whether an association with a significant increase in ACM-risk existed when sRT was delivered above a prespecified PSA level beginning at 0.10 ng/mL and in 0.05 increments up to 0.50 ng/mL versus at or below that level. The model was adjusted for age at and year of radical prostatectomy, established prostate cancer prognostic factors, institution, and the time-dependent use of androgen deprivation therapy.

Results

After a median follow-up of 6.00 years, patients who received sRT at a PSA level >0.25 ng/mL had a significantly higher ACM-risk (AHR, 1.49; 95% CI, 1.11 to 2.00; P = .008) compared with men who received sRT when the PSA was ≤0.25 mg/mL. This elevated ACM-risk remained significant for all PSA cutpoints up to 0.50 ng/mL but was not significant at PSA cutpoint values below 0.25 ng/mL.

Conclusion

Among patients with at most one high-risk factor, initiating sRT above a PSA level of 0.25 ng/mL was associated with increased ACM-risk.

Objectives

To prospectively evaluate the performance of a pre-specified statistical model based on four kallikrein markers in blood (total prostate-specific antigen [PSA], free PSA, intact PSA, and human kallikrein-related peptidase 2), commercially available as the 4Kscore, in predicting Gleason Grade Group (GG) ≥2 prostate cancer at biopsy in an international multicentre study at three academic medical centres, and whether microseminoprotein-β (MSP) adds predictive value.

Patients and methods

A total of 984 men were prospectively enrolled at three academic centres. The primary outcome was GG ≥2 on prostate biopsy. Three pre-specified statistical models were used: a base model including PSA, age, digital rectal examination and prior negative biopsy; a model that added free PSA to the base model; and the 4Kscore.

Results

A total of 947 men were included in the final analysis and 273 (29%) had GG ≥2 on prostate biopsy. The base model area under the receiver operating characteristic curve of 0.775 increased to 0.802 with the addition of free PSA, and to 0.824 for the 4Kscore. Adding MSP to the 4Kscore model yielded an increase (0.014-0.019) in discrimination. In decision-curve analysis of clinical utility, the 4Kscore showed a benefit starting at a 7.5% threshold.

Conclusion

A prospective multicentre evaluation of a pre-specified model based on four kallikrein markers (4Kscore) with the addition of MSP improves the predictive discrimination for GG ≥2 prostate cancer on biopsy and could be used to inform biopsy decision-making.

Context

Multimodal treatment for men with locally advanced prostate cancer (PCa) using neoadjuvant/adjuvant systemic therapy, surgery, and radiation therapy is being increasingly explored. There is also interest in the oncologic benefit of treating the primary tumor in the setting of metastatic PCa (mPCa).

Objective

To perform a review of the literature regarding the treatment of the primary tumor in the setting of mPCa.

Evidence acquisition

Medline, PubMed, and Scopus electronic databases were queried for English language articles from January 1990 to September 2014. Prospective and retrospective studies were included.

Evidence synthesis

There is no published randomized controlled trial (RCT) comparing local therapy and systemic therapy to systemic therapy alone in the treatment of mPCa. Prospective studies of men with locally advanced PCa and retrospective studies of occult node-positive PCa have consistently shown the addition of local therapy to a multimodal treatment regimen improves outcomes. Molecular and genomic evidence further suggests the primary tumor may have an active role in mPCa.

Conclusions

Treatment of the primary tumor in mPCa is being increasingly explored. While preclinical, translational, and retrospective evidence supports local therapy in advanced disease, further prospective studies are under way to evaluate this multimodal approach and identify the patients most likely to benefit from the inclusion of local therapy in the setting of metastatic disease.

Patient summary

In this review we explored preclinical and clinical evidence for treatment of the primary tumor in metastatic prostate cancer (mPCa). We found evidence to support clinical trials investigating mPCa therapy that includes local treatment of the primary tumor. Currently, treating the primary tumor in mPCa is controversial and lacks high-level evidence sufficient for routine recommendation.

Identifying the earliest somatic changes in prostate cancer can give important insights into tumor evolution and aids in stratifying high- from low-risk disease. We integrated whole genome, transcriptome and methylome analysis of early-onset prostate cancers (diagnosis ≤55 years). Characterization across 292 prostate cancer genomes revealed age-related genomic alterations and a clock-like enzymatic-driven mutational process contributing to the earliest mutations in prostate cancer patients. Our integrative analysis identified four molecular subgroups, including a particularly aggressive subgroup with recurrent duplications associated with increased expression of ESRP1, which we validate in 12,000 tissue microarray tumors. Finally, we combined the patterns of molecular co-occurrence and risk-based subgroup information to deconvolve the molecular and clinical trajectories of prostate cancer from single patient samples.

There is substantial heterogeneity among primary prostate cancers, evident in the spectrum of molecular abnormalities and its variable clinical course. As part of The Cancer Genome Atlas (TCGA), we present a comprehensive molecular analysis of 333 primary prostate carcinomas. Our results revealed a molecular taxonomy in which 74% of these tumors fell into one of seven subtypes defined by specific gene fusions (ERG, ETV1/4, and FLI1) or mutations (SPOP, FOXA1, and IDH1). Epigenetic profiles showed substantial heterogeneity, including an IDH1 mutant subset with a methylator phenotype. Androgen receptor (AR) activity varied widely and in a subtype-specific manner, with SPOP and FOXA1 mutant tumors having the highest levels of AR-induced transcripts. 25% of the prostate cancers had a presumed actionable lesion in the PI3K or MAPK signaling pathways, and DNA repair genes were inactivated in 19%. Our analysis reveals molecular heterogeneity among primary prostate cancers, as well as potentially actionable molecular defects.

(Cell Reports 23, 313–326; April 3, 2018) In the originally published version of this article, the author list contained two errors. Specifically, David J. Kwiatkowski was misspelled as David J. Kwaitkowski, and William Y. Kim was inadvertently written as William T. Kim. Both names have been corrected online. The authors regret this error.

(Immunity 48, 812–830.e1–e14; April 17, 2018) In the originally published version of this article, the authors neglected to include Younes Mokrab and Aaron M. Newman as co-authors and misspelled the names of authors Charles S. Rabkin and Ilya Shmulevich. The author names have been corrected here and online. In addition, the concluding sentence of the subsection “Immune Signature Compilation” in the Method Details in the original published article was deemed unclear because it did not specify differences among the gene set scoring methods. The concluding sentences now reads “Gene sets from Bindea et al., Senbabaoglu et al., and the MSigDB C7 collection were scored using single-sample gene set enrichment (ssGSEA) analysis (Barbie et al., 2009), as implemented in the GSVA R package (Hänzelmann et al., 2013). All other signatures were scored using methods found in the associated citations.”

(Cell 173, 371–385.e1–e9; April 5, 2018) It has come to our attention that we made two errors in preparation of this manuscript. First, in the STAR Methods, under the subheading of “Hypermutators and Immune Infiltrates” within the “Quantification and Statistical Analysis” section, we inadvertently referred to Figures S7A–S7C for data corresponding to sample stratification by hypermutator status alone in the last sentence. It should have referred to Figure S6A–S6C. Second, the lists of highly frequent missense mutations for COAD (colon adenocarcinoma) and READ (rectum adenocarcinoma) displayed in Figure S7 were incorrect because when we ordered the mutations in the initial analysis, we mistakenly combined the two cancer types COAD and READ for analysis, despite the fact that they were listed as two separate cancer types in the x-axis of the figure. After re-ordering the mutations by frequency for COAD and READ independently, information on highly frequent missense mutations for each of these cancer types is different and updated now in the revised Figure S7. These errors don't change the major conclusions of the paper and have been corrected online. We apologize for any confusion they may have caused. [Figure-presented]

The role of enhancers, a key class of non-coding regulatory DNA elements, in cancer development has increasingly been appreciated. Here, we present the detection and characterization of a large number of expressed enhancers in a genome-wide analysis of 8928 tumor samples across 33 cancer types using TCGA RNA-seq data. Compared with matched normal tissues, global enhancer activation was observed in most cancers. Across cancer types, global enhancer activity was positively associated with aneuploidy, but not mutation load, suggesting a hypothesis centered on "chromatin-state" to explain their interplay. Integrating eQTL, mRNA co-expression, and Hi-C data analysis, we developed a computational method to infer causal enhancer-gene interactions, revealing enhancers of clinically actionable genes. Having identified an enhancer ∼140 kb downstream of PD-L1, a major immunotherapy target, we validated it experimentally. This study provides a systematic view of enhancer activity in diverse tumor contexts and suggests the clinical implications of enhancers.