Explore the vast range of titles available.

Key Points Patients with high-risk prostate cancer have a significant chance of developing systemic or local recurrence, and are at higher risk for symptoms and/or death from the disease Definitions vary for what constitutes high-risk disease in localized prostate cancer, but are historically based on clinicopathological findings including clinical stage, Gleason score, and PSA The literature is limited as a consequence of variations in definition, lack of prospective randomized trials, limitations in statistical plan (underpowered studies), the need for long-term follow-up, and suboptimal end points Several key principles for radiotherapy have been established, including the importance of dose, and the addition of androgen-deprivation therapy Optimal surgical management requires completely removing the gland itself, confirming negative margins intraoperatively, and discussing the potential need for post-operative radiotherapy Treatment of potential lymph-node involvement, either surgically or with extended pelvic radiation, is favoured in high-risk disease, but lacks level I evidence High-risk prostate cancer includes a heterogeneous group of patients with a range of prognoses, with some that can be fatal. The optimal management of this patient subgroup is evolving. We critically evaluate the existing literature focused on defining the high-risk population, the management of patients with high-risk prostate cancer, and future directions to optimize care. Approximately 15% of patients with prostate cancer are diagnosed with high-risk disease. However, the current definitions of high-risk prostate cancer include a heterogeneous group of patients with a range of prognoses. Some have the potential to progress to a lethal phenotype that can be fatal, while others can be cured with treatment of the primary tumour alone. The optimal management of this patient subgroup is evolving. A refined classification scheme is needed to enable the early and accurate identification of high-risk disease so that more-effective treatment paradigms can be developed. We discuss several principles established from clinical trials, and highlight other questions that remain unanswered. This Review critically evaluates the existing literature focused on defining the high-risk population, the management of patients with high-risk prostate cancer, and future directions to optimize care.

Background: We aimed to assess the prognostic relevance of the new Grade Groups in Prostate Cancer (PCa) within a large cohort of European men treated with radical prostatectomy (RP). Methods: Data from 27 122 patients treated with RP at seven European centers were analyzed. We investigated the prognostic performance of the new Grade Groups (based on Gleason score 3+3, 3+4, 4+3, 8 and 9–10) on biopsy and RP specimen, adjusted for established clinical and pathological characteristics. Multivariable Cox proportional hazards regression models assessed the association of new Grade Groups with biochemical recurrence (BCR). Prognostic accuracies of the models were assessed using Harrell’s C-index. Results: Median follow-up was 29 months (interquartile range, 13–54). The 4-year estimated BCR-free survival (bRFS) for biopsy Grade Groups 1–5 were 91.3, 81.6, 69.8, 60.3 and 44.4%, respectively. The 4-year estimated bRFS for RP Grade Groups 1–5 were 96.1%, 86.7%, 67.0%, 63.1% and 41.0%, respectively. Compared with Grade Group 1, all other Grade Groups based both on biopsy and RP specimen were independently associated with a lower bRFS (all P <0.01). Adjusted pairwise comparisons revealed statistically differences between all Grade Groups, except for group 3 and 4 on RP specimen ( P =0.10). The discriminations of the multivariable base prognostic models based on the current three-tier and the new five-tier systems were not clinically different (0.3 and 0.9% increase in discrimination for clinical and pathological model). Conclusions: We validated the independent prognostic value of the new Grade Groups on biopsy and RP specimen from European PCa men. However, it does not improve the accuracies of prognostic models by a clinically significant margin. Nevertheless, this new classification may help physicians and patients estimate disease aggressiveness with a user-friendly, clinically relevant and reproducible method.

Purpose of ReviewThis review will examine the taxonomy of PCa subclasses across disease states, explore the relationship among specific alterations, and highlight current clinical relevance.Recent FindingsProstate cancer (PCa) is driven by multiple genomic alterations, with distinct patterns and clinical implications. Alterations occurring early in the timeline of the disease define core subtypes of localized, treatment-naive PCa. With time, an increase in number and severity of genomic alterations adds molecular complexity and is associated with progression to metastasis. These later events are not random and are influenced by the underlying subclasses.SummaryAll the subclasses of localized disease initially respond to androgen deprivation therapy (ADT), but with progression to castrate-resistant PCa (CRPC), mechanisms of resistance against ADT shift the molecular landscape. In CRPC, resistance mechanisms largely define the biology and sub-classification of these cancers, while clinical relevance and opportunities for precision therapy are still being defined.

The eighth edition of the American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) Staging Manual has been updated and improved to ensure the highest degree of clinical relevance and to improve its utility for patient evaluation and clinical research. Major changes include: 1) pathologically organ-confined disease is now considered pT2 and is no longer subclassified by extent of involvement or laterality, 2) tumor grading now includes both the Gleason score (as in the seventh edition criteria) and the grade group (introduced in the eighth edition criteria), 3) prognostic stage group III includes select, organ-confined disease based on prostate-specific antigen and Gleason/grade group status, and 4) 2 statistical prediction models are included in the staging manual. The AJCC will continue to critically analyze emerging prostate cancer biomarkers and tools for their ability to prognosticate and guide treatment decision making with the highest level of accuracy and confidence for patients and physicians.

Reconstructing the evolutionary history of metastases is critical for understanding their basic biological principles and has profound clinical implications. Genome-wide sequencing data has enabled modern phylogenomic methods to accurately dissect subclones and their phylogenies from noisy and impure bulk tumour samples at unprecedented depth. However, existing methods are not designed to infer metastatic seeding patterns. Here we develop a tool, called Treeomics, to reconstruct the phylogeny of metastases and map subclones to their anatomic locations. Treeomics infers comprehensive seeding patterns for pancreatic, ovarian, and prostate cancers. Moreover, Treeomics correctly disambiguates true seeding patterns from sequencing artifacts; 7% of variants were misclassified by conventional statistical methods. These artifacts can skew phylogenies by creating illusory tumour heterogeneity among distinct samples. In silico benchmarking on simulated tumour phylogenies across a wide range of sample purities (15–95%) and sequencing depths (25-800 × ) demonstrates the accuracy of Treeomics compared with existing methods. Tumours frequently metastasize to multiple anatomical sites and understanding how these different metastases evolve may be important for therapy. Here, the authors develop a method—Treeomics—that can construct phylogenies from multiple metastases from next-generation sequencing data.

The conventional way of classifying metastatic cancers according to their organ of origin is denying people access to drugs that could help them. The conventional way of classifying metastatic cancers according to their organ of origin is denying people access to drugs that could help them. A radiologist and an assistant wearing face masks check a screen during cryotherapy treatment on a patient with kidney cancer

Prostate cancer is the second most common cancer in men worldwide 1 . Over the past decade, large-scale integrative genomics efforts have enhanced our understanding of this disease by characterizing its genetic and epigenetic landscape in thousands of patients 2 , 3 . However, most tumours profiled in these studies were obtained from patients from Western populations. Here we produced and analysed whole-genome, whole-transcriptome and DNA methylation data for 208 pairs of tumour tissue samples and matched healthy control tissue from Chinese patients with primary prostate cancer. Systematic comparison with published data from 2,554 prostate tumours revealed that the genomic alteration signatures in Chinese patients were markedly distinct from those of Western cohorts: specifically, 41% of tumours contained mutations in FOXA1 and 18% each had deletions in ZNF292 and CHD1 . Alterations of the genome and epigenome were correlated and were predictive of disease phenotype and progression. Coding and noncoding mutations, as well as epimutations, converged on pathways that are important for prostate cancer, providing insights into this devastating disease. These discoveries underscore the importance of including population context in constructing comprehensive genomic maps for disease. Genomic, transcriptomic and DNA methylation data from tissue samples from 208 Chinese patients with prostate cancer define the landscape of alterations in this population, and comparison with data from Western cohorts suggests that the disease may stratify into different molecular subtypes.

Cancer-linked mutations A large-scale genetic analysis of more than 400 breast, lung, ovarian and prostate cancer samples has identified thousands of cancer-related mutations. Kan et al . analysed DNA from cancer patients and identified 2,576 somatic mutations across 1,507 coding genes. Of these, 77 are thought to be significantly mutated, implying possible pathogenic roles for protein kinases, G protein-coupled receptors and other potential therapeutic targets. These authors performed a large-scale study in which they identified 2,576 somatic mutations across 1,507 coding genes from 441 breast, lung, ovarian and prostate cancer types and subtypes. The study provides an overview of the mutational spectra across major human cancers, implies an expanded role for Gα subunits in multiple cancer types and identifies several potential therapeutic targets. The systematic characterization of somatic mutations in cancer genomes is essential for understanding the disease and for developing targeted therapeutics 1 . Here we report the identification of 2,576 somatic mutations across ∼1,800 megabases of DNA representing 1,507 coding genes from 441 tumours comprising breast, lung, ovarian and prostate cancer types and subtypes. We found that mutation rates and the sets of mutated genes varied substantially across tumour types and subtypes. Statistical analysis identified 77 significantly mutated genes including protein kinases, G-protein-coupled receptors such as GRM8 , BAI3 , AGTRL1 (also called APLNR ) and LPHN3 , and other druggable targets. Integrated analysis of somatic mutations and copy number alterations identified another 35 significantly altered genes including GNAS , indicating an expanded role for gα subunits in multiple cancer types. Furthermore, our experimental analyses demonstrate the functional roles of mutant GNAO1 (a Gα subunit) and mutant MAP2K4 (a member of the JNK signalling pathway) in oncogenesis. Our study provides an overview of the mutational spectra across major human cancers and identifies several potential therapeutic targets.

Digital pathology provides a possibility for computational analysis of histological slides and automatization of routine pathological tasks. Histological slides are very heterogeneous concerning staining, sections’ thickness, and artifacts arising during tissue processing, cutting, staining, and digitization. In this study, we digitally reproduce major types of artifacts. Using six datasets from four different institutions digitized by different scanner systems, we systematically explore artifacts’ influence on the accuracy of the pre-trained, validated, deep learning-based model for prostate cancer detection in histological slides. We provide evidence that any histological artifact dependent on severity can lead to a substantial loss in model performance. Strategies for the prevention of diagnostic model accuracy losses in the context of artifacts are warranted. Stress-testing of diagnostic models using synthetically generated artifacts might be an essential step during clinical validation of deep learning-based algorithms.

Genome alteration signatures reflect recurring patterns caused by distinct endogenous or exogenous mutational events during the evolution of cancer. Signatures of single base substitution (SBS) have been extensively studied in different types of cancer. Copy number alterations are important drivers for the progression of multiple cancer. However, practical tools for studying the signatures of copy number alterations are still lacking. Here, a user-friendly open source bioinformatics tool “sigminer” has been constructed for copy number signature extraction, analysis and visualization. This tool has been applied in prostate cancer (PC), which is particularly driven by complex genome alterations. Five copy number signatures are identified from human PC genome with this tool. The underlying mutational processes for each copy number signature have been illustrated. Sample clustering based on copy number signature exposure reveals considerable heterogeneity of PC, and copy number signatures show improved PC clinical outcome association when compared with SBS signatures. This copy number signature analysis in PC provides distinct insight into the etiology of PC, and potential biomarkers for PC stratification and prognosis.