Malone, Shawn

Importance The ideal duration of androgen deprivation therapy (ADT) for treating localized prostate cancer is unknown due to variable adherence and treatment durations tested in clinical trials. Objective To determine the ideal duration of ADT for patients with prostate cancer treated with radiotherapy. Data Sources This individual patient data meta-analysis of 13 randomized phase 3 clinical trials evaluated the use of radiotherapy alone or with ADT. It included patients with a median follow-up of 11.3 (IQR, 9.5-14.5) years and ADT duration of 0 to 36 months. Most patients (7392 [72%]) included had National Comprehensive Cancer Network high-risk or very high-risk disease. Study Selection For this meta-analysis, a systematic literature search from 1980 to 2020 was performed in trial registries (Cochrane Central Register of Controlled Trials and ClinicalTrials.gov), MEDLINE (1966-2020), Embase (1982-2020), Web of Science, and Scopus to identify trials. Data Extraction and Synthesis Intention-to-treat and as-treated analyses were performed. The number needed to treat to prevent 1 distant metastasis at 10 years was calculated based on prognostic risk group. The analyses were conducted from January 5 to August 15, 2023. Main Outcomes and Measures The primary end point for this study was overall survival, defined as time to death or last follow-up from randomization. Secondary end points included biochemical recurrence, distant metastasis (DM), prostate cancer–specific mortality, and other-cause mortality. Results The median (IQR) age among the 10 266 male patients was 70 (65-74) years. Longer durations of ADT were associated with nonlinear improvement in relative benefits of DM, prostate cancer–specific mortality, and overall survival, with reduced estimated benefits beyond 9 to 12 months of ADT based on the end point. There was a near-linear increase in other-cause mortality associated with long-term ADT use (hazard ratio, 1.28; 95% CI, 1.09-1.50; P = .002 for 28 vs 0 months of ADT). The optimal ADT duration based on 10-year DM was 0, 6, 12 months, and undefined for patients with 1 intermediate-risk factor, 2 or more intermediate-risk factors, and National Comprehensive Cancer Network high-risk and very high-risk disease, respectively. Conclusions and Relevance The results of this meta-analysis suggest that, for men with localized prostate cancer treated with definitive radiotherapy and ADT, there are relative and absolute benefits from increasing durations of ADT that help provide individualized risk estimates.

PURPOSE The sequencing of androgen-deprivation therapy (ADT) with radiotherapy (RT) may affect outcomes for prostate cancer in an RT-field size-dependent manner. Herein, we investigate the impact of ADT sequencing for men receiving ADT with prostate-only RT (PORT) or whole-pelvis RT (WPRT). MATERIALS AND METHODS Individual patient data from 12 randomized trials that included patients receiving neoadjuvant/concurrent or concurrent/adjuvant short-term ADT (4-6 months) with RT for localized disease were obtained from the Meta-Analysis of Randomized trials in Cancer of the Prostate consortium. Inverse probability of treatment weighting (IPTW) was performed with propensity scores derived from age, initial prostate-specific antigen, Gleason score, T stage, RT dose, and mid-trial enrollment year. Metastasis-free survival (primary end point) and overall survival (OS) were assessed by IPTW-adjusted Cox regression models, analyzed independently for men receiving PORT versus WPRT. IPTW-adjusted Fine and Gray competing risk models were built to evaluate distant metastasis (DM) and prostate cancer–specific mortality. RESULTS Overall, 7,409 patients were included (6,325 neoadjuvant/concurrent and 1,084 concurrent/adjuvant) with a median follow-up of 10.2 years (interquartile range, 7.2-14.9 years). A significant interaction between ADT sequencing and RT field size was observed for all end points ( P interaction < .02 for all) except OS. With PORT (n = 4,355), compared with neoadjuvant/concurrent ADT, concurrent/adjuvant ADT was associated with improved metastasis-free survival (10-year benefit 8.0%, hazard ratio [HR], 0.65; 95% CI, 0.54 to 0.79; P < .0001), DM (subdistribution HR, 0.52; 95% CI, 0.33 to 0.82; P = .0046), prostate cancer–specific mortality (subdistribution HR, 0.30; 95% CI, 0.16 to 0.54; P < .0001), and OS (HR, 0.69; 95% CI, 0.57 to 0.83; P = .0001). However, in patients receiving WPRT (n = 3,049), no significant difference in any end point was observed in regard to ADT sequencing except for worse DM (HR, 1.57; 95% CI, 1.20 to 2.05; P = .0009) with concurrent/adjuvant ADT. CONCLUSION ADT sequencing exhibits a significant impact on clinical outcomes with a significant interaction with field size. Concurrent/adjuvant ADT should be the standard of care where short-term ADT is indicated in combination with PORT.

PURPOSE To compare the predictive ability of mapping algorithms derived using cross-sectional and longitudinal data. METHODS This methodological assessment used data from a randomized controlled noninferiority trial of patients with low-risk prostate cancer, conducted by NRG Oncology (ClinicalTrials.gov identifier: NCT00331773 ), which examined the efficacy of conventional schedule versus hypofractionated radiation therapy (three-dimensional conformal external beam radiation therapy/IMRT). Health-related quality-of-life data were collected using the Expanded Prostate Cancer Index Composite (EPIC), and health utilities were obtained using EuroQOL-5D-3L (EQ-5D) at baseline and 6, 12, 24, and 60 months postintervention. Mapping algorithms were estimated using ordinary least squares regression models through five-fold cross-validation in baseline cross-sectional data and combined longitudinal data from all assessment periods; random effects specifications were also estimated in longitudinal data. Predictive performance was compared using root mean square error. Longitudinal predictive ability of models obtained using baseline data was examined using mean absolute differences in the reported and predicted utilities. RESULTS A total of 267 (and 199) patients in the estimation sample had complete EQ-5D and EPIC domain (and subdomain) data at baseline and at all subsequent assessments. Ordinary least squares models using combined data showed better predictive ability (lowest root mean square error) in the validation phase for algorithms with EPIC domain/subdomain data alone, whereas models using baseline data outperformed other specifications in the validation phase when patient covariates were also modeled. The mean absolute differences were lower for models using EPIC subdomain data compared with EPIC domain data and generally decreased as the time of assessment increased. CONCLUSION Overall, mapping algorithms obtained using baseline cross-sectional data showed the best predictive performance. Furthermore, these models demonstrated satisfactory longitudinal predictive ability.

277 Background: The prognostic importance of local failure (LF) after definitive radiotherapy (RT) in patients with NCCN intermediate- (IRPCa) and high-risk prostate cancer (HRPCa) remains unclear, particularly given the likelihood that occult distant metastases (DMs) at presentation may be the true driver of natural history. Here, we leverage individual patient data (IPD) from 18 randomized control trials (RCTs) to evaluate the prognostic impact of LF and the kinetics of DM after RT. Methods: IPD for 18 RCTs were obtained from the Meta-Analysis of Randomized trials in Cancer of the Prostate (MARCAP) Consortium, comprising a total of 12533 patients (6288 HRPCa & 6245 IRPCa). Multivariable Cox proportional hazards (PH) models were developed to evaluate the relationship between overall survival (OS), PCa-specific survival (PCSS), DM-free survival (DMFS) & LF as a time-dependent covariate, adjusted for clinicodemographic parameters. Markov PH models, defined via transitions between 4 states, were developed to evaluate the aforementioned relationship. Proportional hazards assumption was imposed and examined for both models. Time is from randomization. Results: Median follow-up was 9.1 years. There were 795 (13%) LF & 1288 (21%) DM events for patients with HRPCa; these numbers were 449 (7%) & 451 (7%) for IRPCa. For HRPCa & IRPCa, 81% and 81% of DMs developed from a clinically relapse-free state (cRFS), with a median time of 46 and 60 months, respectively (p < 0.0001). 39% & 13% of DM events occurred within 2 years after RT for HRPCa & IRPCa, respectively. At later time points, DM events were more likely to emerge after an LF event for both HRPCa (9% vs. 34% between 0-2 vs. 8-10 years post-RT, p = 0.001) and IRPCa (10% vs. 34% between 0-2 vs. 8-10 years post-RT, p = 0.008). LF was significantly associated with OS (hazard ratio [HR] 1.17, 95% confidence interval [CI] 1.06–1.30), PCSS (HR 2.02, 95% CI 1.75-2.33) & DMFS (HR 1.94, 95% CI 1.75–2.15) (p < 0.01 for all) in patients with HRPCa. LF was also significantly associated with DMFS (HR 1.57, 95% CI 1.36–1.81) but not OS in patients with IRPCa. Patients who had not transitioned to the LF state had a significantly lower HR of transitioning to a PCa-specific death state than those who transitioned to the LF state (HR 0.32, 95% CI 0.21–0.50, p < 0.001). Conclusions: LF is an independent prognosticator of OS, PCSS & DMFS in HRPCa and of DMFS in IRPCa. The predominant mode of DM development is from the cRFS state, underscoring the importance of accurate upfront staging & systemic therapy. However, particularly at late time points, an increasing proportion of DM events originated after diagnosis of a LF, constituting a “second wave” of DM events. This suggests that optimizing local control is also important, though the majority of DM events appear prior to a clinically-detected LF.

60 Background: Mapping algorithms informing economic evaluations are often derived using baseline data from clinical trials. It is unclear if these algorithms can predict health utilities accurately in post-intervention data. Thus, this study examines the longitudinal predictive ability of mapping algorithms derived from baseline trial data and explores the factors associated with prediction errors. Methods: This methodological study utilized data from an international, multicenter, randomized controlled trial of patients with low-risk prostate cancer (PC), conducted by NRG Oncology (NCT00331773). In addition to patient demographic and clinical data, this study utilized PRO data collected at baseline and 6, 12 and 24 months post-intervention. The Expanded Prostate Cancer Index Composite (EPIC) questionnaire measures health-related quality-of-life (HRQoL) and has four domains (urinary, sexual, hormonal, and bowel) and two subdomains per domain (function and bother); EuroQOL-5D-3L (EQ5D) captures health utilities. Ordinary Least Squares (OLS) regression models were used to map EPIC scores to EQ5D utilities in the baseline data through 5-fold cross-validation. Predictive performance was tested in the post-intervention data; predicted and reported utilities were compared using t-tests, and the absolute prediction error was modeled using fixed effects, as a function of baseline demographic and clinical covariates, as well as observed and predicted EQ5D utilities. Results: A total of 267 (199) patients had complete EQ5D and EPIC domain (or subdomain) data at baseline and all subsequent assessments. In the EPIC domain sample, mean ± standard deviation observed EQ5D utility was 0.90±0.13 at baseline, 0.92±0.11 at 6 months, 0.90±0.13 at 12 months and 0.89±0.14 at 24 months. Mean absolute differences (MDs) between reported and predicted were lower for models using EPIC subdomain data compared to EPIC domain data, and generally decreased as the time of assessment increased. The mapping functions over-predicted utilities for patients in perfect health while the prediction errors were increasingly negative for lower reported EQ5D scores. According to the fixed effects model for EPIC domain data, lower observed and predicted baseline EQ5D scores, and time of assessment were significant predictors of the absolute prediction error; for EPIC subdomain data, lower observed and predicted baseline EQ5D scores, hormonal bother and function, and bowel function significantly predicted the absolute prediction error. Conclusions: This study is the first to demonstrate the longitudinal validity of EPIC questionnaire, and builds upon existing research on longitudinal validity of mapping functions. The low MDs in prediction errors in post-intervention data indicate that the mapping functions are sensitive to treatment effect, thereby increasing confidence in their use in economic evaluations in PC.

PURPOSEThere remains a lack of clarity regarding the influence of sequencing of androgen deprivation therapy (ADT) and radiotherapy (RT) on outcomes in prostate cancer (PCa). Herein, we evaluate the optimal sequencing of ADT with prostate-directed RT in localized PCa.METHODSMEDLINE (1966-2018), Embase (1982-2018), ClinicalTrials.gov, and conference proceedings (1990-2018) were searched to identify randomized trials evaluating the sequencing, but not duration, of ADT with RT. Two randomized phase III trials were identified, and individual patient data were obtained: Ottawa 0101 and NRG Oncology's Radiation Therapy Oncology Group 9413. Ottawa 0101 randomly assigned patients to neoadjuvant or concurrent versus concurrent or adjuvant short-term ADT. Radiation Therapy Oncology Group 9413, a 2 × 2 factorial trial, included a random assignment of neoadjuvant or concurrent versus adjuvant short-term ADT. The neoadjuvant or concurrent ADT arms of both trials were combined into the neoadjuvant group, and the arms receiving adjuvant ADT were combined into the adjuvant group. The primary end point of this meta-analysis was progression-free survival (PFS).RESULTSThe median follow-up was 14.9 years. Overall, 1,065 patients were included (531 neoadjuvant and 534 adjuvant). PFS was significantly improved in the adjuvant group (15-year PFS, 29% v 36%, hazard ratio [HR], 1.25 [95% CI, 1.07 to 1.47], P = .01). Biochemical failure (subdistribution HR [sHR], 1.37 [95% CI, 1.12 to 1.68], P = .002), distant metastasis (sHR, 1.40 [95% CI, 1.00 to 1.95], P = .04), and metastasis-free survival (HR, 1.17 [95% CI, 1.00 to 1.37], P = .050) were all significantly improved in the adjuvant group. There were no differences in late grade ≥ 3 gastrointestinal (2% v 3%, P = .33) or genitourinary toxicity (5% v 5%, P = .76) between groups.CONCLUSIONThe sequencing of ADT with prostate-directed RT has significant association with long-term PFS and MFS in localized PCa. Our findings favor use of an adjuvant over a neoadjuvant approach, without any increase in long-term toxicity.

5584 Background: The timing of systemic therapy in relation to radiotherapy (RT) is important in most malignancies. In contrast, androgen deprivation therapy (ADT) has largely been investigated in relation to its duration rather than its sequencing with RT. Herein, we conduct the first combined individual patient analysis of two phase III randomized trials to determine the optimal timing of ADT with RT in localized prostate cancer (PCa). Methods: Individual patient data was obtained from the Malone et al trial (JCO 2019), which randomized patients to receive neoadjuvant/concurrent or concurrent/adjuvant ADT for 6 months with prostate only RT. This was combined with the prostate only RT arms of RTOG 9413 that randomized patients to 4 months of neoadjuvant/concurrent or adjuvant ADT. The neoadjuvant/concurrent arms of both trials were combined into the “neoadjuvant” group, and the concurrent/adjuvant (Malone) and adjuvant arm (RTOG 9413) were combined in the “adjuvant” group. The Kaplan-Meier method was used to estimate overall survival (OS) and progression-free survival (PFS). Cumulative incidence of distant metastasis (DM), PCa-specific mortality (PCSM) and biochemical failure (BF) were calculated using the Fine-Gray method with non-PCa deaths as competing events. Late genitourinary (GU) and gastrointestinal (GI) toxicity are also reported. Results: The median follow-up was 14.9 years (yrs) and 1065 patients were included (n=531 neoadjuvant, 534 adjuvant). Groups were well balanced for all baseline characteristics. Adjuvant ADT was superior to neoadjuvant ADT in terms of BF (15yr: 33% vs 43%, HR: 1.37 (95%CI: 1.12-1.68), p=0.002), DM (15yr: 12% vs 18%, HR: 1.40 (95%CI: 1.00-1.95), p=0.04), and PFS (15yr: 36% vs 29%, HR: 1.25 (95%CI: 1.07-1.47), p=0.01). Adjuvant ADT yielded lower PCSM (15yr: 15% vs 20%, HR: 1.29 (95%CI: 0.95-1.75), p=0.10), but did not reach statistical significance. This approached statistical significance in high risk PCa (HR 1.39 (95%CI 1.00-1.93), p=0.053). OS was not significantly different between arms (15yr: 39% vs 34%, HR: 1.11 (95%CI: 0.95-1.30), p=0.20). There was no significant difference in either late grade ≥3 GI (p=0.21) or GU (p=0.98) toxicity. Conclusions: We demonstrate for the first time that sequencing of ADT with RT significantly impacts long-term oncologic outcomes in localized PCa, favoring an adjuvant rather than neoadjuvant approach, without increasing late toxicity. This data has important implications to ongoing and future clinical trial design. Clinical trial information: NCT00769548 .

55 Background: To compare the predictive ability of health utility mapping algorithms derived using cross-sectional and longitudinal data specific to the Expanded Prostate Cancer Index Composite (EPIC). Methods: This mapping study utilized data from an international, multicenter, randomized controlled trial of patients with low-risk prostate cancer conducted by NRG Oncology (NCT00331773). Health-related quality-of-life (HRQoL) data were collected using EPIC, and health utilities were obtained using EuroQOL-5D (EQ5D) at baseline and 6, 12 and 24 months post-intervention. Data were split into an estimation sample (70%) and a validation sample (30%). Ordinary Least Squares (OLS) regression models were estimated using baseline cross-sectional data as well as pooled data from all assessment periods. Random effects (RE) specifications that explicitly model the longitudinal nature of the data were also estimated. Candidate models were selected based on root mean square error (RMSE). Results: A total of 196 (147) patients in the estimation sample had complete EQ5D and EPIC domain (subdomain) data at all time points. OLS models using combined data outperformed the counter-part RE models as well as OLS models using baseline data in the five-fold cross-validation. Addition of covariates to the models resulted in improved predictive ability. In the external validation, when only EPIC domain/ subdomain data are available, the OLS model using combined data predicted EQ5D utilities better than the counterpart RE model and OLS model using baseline data (RMSE=0.121108 & 0.111345). OLS model using baseline data outperformed other model types for algorithms with EPIC domains and demographics (RMSE=0.121757), while RE models outperformed the other two model types for algorithms with EPIC subdomains and demographic data, (0.112782) and for algorithms with EPIC domains/ subdomains, demographics, and clinical covariates (RMSE=0.123589 & 0.163093). Conclusions: While algorithms using pooled data outperformed other model types in internal validation, RE models showed better predictive ability in external validation for algorithms with covariates. Clinical trial information: NCT00331773.