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Objective: The objective of this study was to analyze the risk of malignancy and the histopathology of telomerase reverse transcriptase promoter (TERT) mutated cytologically indeterminate thyroid nodules (ITN). Methods: A PUBMED search of molecularly tested ITN was conducted and data on TERT mutated ITN with histopathology correlation were extracted. Results: Twenty-six manuscripts (published between 2014 and 2022) reported on 77 TERT mutated ITN. Sixty-five nodules were malignant (84 %), with 16 (25 %) described with high-risk histopathology, 5 (8 %) described as low-risk, and most without any description. Isolated TERT mutations were malignant in 26/30 ITNs (87 %) with 9 (35 %) described as high risk and none described as low risk. TERT + RAS mutated ITNs were malignant in 29/34 ITNs (85 %) with 3 (10 %) described as high risk and 4 (14 %) described as low risk. Finally, all 5 TERT + BRAFV600E mutated nodules were malignant and 3/5 (60 %) were described as high risk. Conclusion: TERT mutated ITNs have a high risk of malignancy (84 %), and the current data does not show a difference in malignancy rate between isolated TERT mutations and TERT + RAS co-mutated ITNs. When described, TERT + RAS co-mutated ITNs did not have a higher rate of high-risk histopathology as compared to isolated TERT mutated lesions. Most TERT mutated ITNs did not have a description of histopathology risk and the oncologic outcomes, including rate of recurrence, metastases, and disease specific survival, are unknown. Further data is needed to determine if TERT mutated ITNs should be subjected to aggressive initial treatment.

Background: Metastatic prostate cancer (CaP) treatments are evolving rapidly but without evidence-based biomarkers to predict responses, and to maximize remissions and survival. Objective: To determine the activity of androgen receptor (AR), the target for default first-line systemic treatment, in localized treatment-naïve CaP and its association with clinical risk factors, molecular markers, CaP subtypes, and predictors of treatment response. Design, setting, and participants: We examined 452 bona fide AR target genes in clinical-grade expression profiles from 6532 such CaPs collected between 2013 and 2017 by US physicians ordering the Decipher RP test. Results were validated in three independent smaller cohorts (n = 73, 90, and 127) and clinical CaP AR ChIP-Seq data. Association with CaP differentiation and progression was analyzed in independent datasets. Outcome measurements and statistical analysis: Unsupervised clustering of CaPs based on AR target gene expression was aligned with clinical variables, differentiation scores, molecular subtypes, and predictors of response to hormonal therapy, radiotherapy, and chemotherapy. AR target gene sets were analyzed via Gene Set Enrichment Analysis for differentiation and treatment resistance, Ingenuity Pathway Analysis for associated biology, and Cistrome for genomic AR binding site (ARBS) composition. Results and limitations: Expression of eight AR target gene subsignatures gave rise to five CaP clusters, which were preferentially associated with CaP molecular subtypes, differentiation, and predictors of treatment response rather than with clinical variables. Subsignatures differed in contribution to CaP progression, luminal/basal differentiation, CaP biology, and ARBS composition. Validation in prospective trials and optimized quantitation are needed for clinical implementation. Conclusions: Measurement of AR activity patterns in treatment-naïve CaP may serve as a first branch of an evidence-based decision tree to optimize personalized treatment plans. Patient summary: Treatment options for metastatic prostate cancer are increasing without information needed to choose the right treatment for the right patient. We found variation in the behavior of the target for the default first-line therapy before treatment, which may help optimize treatment plans.

Background: Hypoxia is associated with a poor prognosis in prostate cancer. This work aimed to derive and validate a hypoxia-related mRNA signature for localized prostate cancer. Method: Hypoxia genes were identified in vitro via RNA-sequencing and combined with in vivo gene co-expression analysis to generate a signature. The signature was independently validated in eleven prostate cancer cohorts and a bladder cancer phase III randomized trial of radiotherapy alone or with carbogen and nicotinamide (CON). Results: A 28-gene signature was derived. Patients with high signature scores had poorer biochemical recurrence free survivals in six of eight independent cohorts of prostatectomy-treated patients (Log rank test P

Global changes in chromatin accessibility may drive cancer progression by reprogramming transcription factor (TF) binding. In addition, histone acetylation readers such as bromodomain-containing protein 4 (BRD4) have been shown to associate with these TFs and contribute to aggressive cancers including prostate cancer (PC). Here, we show that chromatin accessibility defines castration-resistant prostate cancer (CRPC). We show that the deregulation of androgen receptor (AR) expression is a driver of chromatin relaxation and that AR/androgen-regulated bromodomain-containing proteins (BRDs) mediate this effect. We also report that BRDs are overexpressed in CRPCs and that ATAD2 and BRD2 have prognostic value. Finally, we developed gene stratification signature (BROMO-10) for bromodomain response and PC prognostication, to inform current and future trials with drugs targeting these processes. Our findings provide a compelling rational for combination therapy targeting bromodomains in selected patients in which BRD-mediated TF binding is enhanced or modified as cancer progresses.

Management of men with prostate cancer is fraught with uncertainty as physicians and patients balance efficacy with potential toxicity and diminished quality of life. Utilization of genomics as a prognostic biomarker has improved the informed decision-making process by enabling more rationale treatment choices. Recently investigations have begun to determine whether genomic information from tumor transcriptome data can be used to impact clinical decision-making beyond prognosis. Here we discuss the potential of genomics to alter management of a patient who presented with high-risk prostate adenocarcinoma. We suggest that this information help selecting patients for advanced imaging, chemotherapies, or clinical trial.