BAZ2A-mediated repression via H3K14ac-marked enhancers promotes prostate cancer stem cells.

Prostate cancer (PCa) is one of the most prevalent cancers in men. Cancer stem cells are thought to be associated with PCa relapse. Here, we show that BAZ2A is required for PCa cells with a cancer stem-like state. BAZ2A genomic occupancy in PCa cells coincides with H3K14ac-enriched chromatin regions. This association is mediated by BAZ2A-bromodomain (BAZ2A-BRD) that specifically binds H3K14ac. BAZ2A associates with inactive enhancers marked by H3K14ac and repressing transcription of genes frequently silenced in aggressive and poorly differentiated PCa. BAZ2A-mediated repression is also linked to EP300 that acetylates H3K14ac. BAZ2A-BRD mutations or treatment with inhibitors abrogating BAZ2A-BRD/H3K14ac interaction impair PCa stem cells. Furthermore, pharmacological inactivation of BAZ2A-BRD impairs Pten-loss oncogenic transformation of prostate organoids. Our findings indicate a role of BAZ2A-BRD in PCa stem cell features and suggest potential epigenetic-reader therapeutic strategies to target BAZ2A in aggressive PCa.

Analysis of Prognostic Values of Various PET Metrics in Preoperative 18F-FDG PET for Early-Stage Bronchial Carcinoma for Progression-Free and Overall Survival: Significantly Increased Glycolysis Is a Predictive Factor.

The purpose of this study was to assess various volume-based PET quantification metrics, including metabolic tumor volume and total lesion glycolysis (TLG) with different thresholds, as well as background activity-based PET metrics (background-subtracted lesion activity [BSL] and background-subtracted volume) as prognostic markers for progression-free and overall survival (PFS and OS, respectively) in early-stage I and II non-small cell lung cancer (NSCLC) after resection. Methods: Patients (n = 133) underwent an adequate 18F-FDG PET/CT scan before surgery between January 2003 and December 2010. All PET activity metrics showed a skewed distribution and were log-transformed before calculation of the Pearson correlation coefficients. Survival tree analysis was used to discriminate between high- and low-risk patients and to select the most important prognostic markers. The Akaike information criterion was used to compare 2 univariate models. Results: Within the study time, 36 patients died from NSCLC and 26 patients from other causes. At the end of follow-up, 70 patients were alive, with 67 patients being free of disease. All log-transformed PET metrics showed a strong linear association, with a Pearson correlation coefficient between 0.703 and 0.962. After multiple testing corrections, only 1 prognostic marker contributed a significant split point in the survival tree analysis. Of 10 potential predictors including 7 PET metrics, a BSL greater than 6,852 (P = 0.017) was chosen as split point, assigning 13 patients into a high-risk group. If BSL was removed from the set of predictors, a 42% TLG (TLG42%) of greater than 4,204 (P = 0.023) was chosen as split point. When a dichotomized BSL or TLG42% variable was used for a univariate Cox model, the Akaike information criterion difference of both models was smaller than 2; therefore, the data do not provide evidence that 1 of the 2 prognostic factors is superior. Conclusion: Volume-based PET metrics correlate with PFS and OS and could be used for risk assessment in stage I-II NSCLC. The different PET metrics assessed in this study showed a high correlation; therefore, it is not surprising that there was no significant difference to predict PFS or OS within this study. Overall, patients with large and metabolically active tumors should be considered high risk and might need further treatment after resection. Because all analysis steps were done with the same data, these results should be validated on new patient data.

18F-FDG PET/CT of Non-Small Cell Lung Carcinoma Under Neoadjuvant Chemotherapy: Background-Based Adaptive-Volume Metrics Outperform TLG and MTV in Predicting Histopathologic Response.

UNLABELLED: Assessment of tumor response after chemotherapy using (18)F-FDG PET metrics is gaining acceptance. Several studies have suggested that the parameters metabolically active tumor volume (MTV) and total lesion glycolysis (TLG) are superior to SUVmax for measuring tumor burden. However, the measurement of MTV and TLG is still controversial; the most common method uses an absolute threshold of 42% of SUVmax Recently, we implemented a background-adaptive method to determine the background-subtracted lesion activity (BSL) and the background-subtracted volume (BSV). In this study, we investigated the correlation between such PET metrics and histopathologic response in non-small cell lung carcinoma (NSCLC). METHODS: Forty-four NSCLC patients were retrospectively identified. Their PET/CT data on both types of scan before and after neoadjuvant chemotherapy were analyzed regarding SUVmax, MTV, TLG, BSL, and BSV, as well as the relative changes in these parameters. The tumor regression score as an indicator of histopathologic response was scored on hematoxylin- and eosin-stained sections of the surgical specimens using a 4-tiered scale (scores 1-4). The correlation between score and the absolute and relative PET metrics after chemotherapy was analyzed using Spearman rank correlation tests. RESULTS: Tumors that demonstrated a good response after neoadjuvant chemotherapy had significantly lower (18)F-FDG activity than nonresponding tumors (scores 3 and 4: SUVmax, 4.2 [range, 1.8-7.9] vs. scores 1 and 2: SUVmax, 8.1 [range, 1.4-40.4]; P = 0.001). The same was found for change in SUVmax and score (P = 0.001). PET volume metrics based on a 42% fixed threshold for SUVmax did not correlate with score (TLG, P = 0.505; MTV, P = 0.386). However, both of the background activity-based PET volume metrics-BSL and BSV-significantly correlated with score (P < 0.001 each). CONCLUSION: PET volume metrics based on background-adaptive methods correlate better with histopathologic tumor regression score in NSCLC patients under neoadjuvant chemotherapy than algorithms and methods using a fixed threshold (42% SUVmax).