Gafita Andrei, Martin Andrew J, Emmett Louise, Eiber Matthias, Iravani Amir, Fendler Wolfgang P, Buteau James, Sandhu Shahneen, Azad Arun A, Herrmann Ken, Stockler Martin R, Davis Ian D, Hofman Michael S
Division of Nuclear Medicine and Molecular Imaging, The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Johns Hopkins Theranostics Center, Baltimore, MD, USA.
NHMRC Clinical Trials Center, University of Sydney, Sydney, NSW, Australia; ANZUP Cancer Trials Group, Sydney, NSW, Australia.
Eur Urol Oncol. 2025 Feb;8(1):21-28. doi: 10.1016/j.euo.2024.03.009. Epub 2024 Apr 6.
BACKGROUND: Prognostic models have been developed using data from a multicentre noncomparative study to forecast the likelihood of a 50% reduction in prostate-specific antigen (PSA50), longer prostate-specific antigen (PSA) progression-free survival (PFS), and longer overall survival (OS) in patients with metastatic castration-resistant prostate cancer receiving [Lu]Lu-PSMA radioligand therapy. The predictive utility of the models to identify patients likely to benefit most from [Lu]Lu-PSMA compared with standard chemotherapy has not been established. OBJECTIVE: To determine the predictive value of the models using data from the randomised, open-label, phase 2, TheraP trial (primary objective) and to evaluate the clinical net benefit of the PSA50 model (secondary objective). DESIGN, SETTING, AND PARTICIPANTS: All 200 patients were randomised in the TheraP trial to receive [Lu]Lu-PSMA-617 (n = 99) or cabazitaxel (n = 101) between February 2018 and September 2019. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Predictive performance was investigated by testing whether the association between the modelled outcome classifications (favourable vs unfavourable outcome) was different for patients randomised to [Lu]Lu-PSMA versus cabazitaxel. The clinical benefit of the PSA50 model was evaluated using a decision curve analysis. RESULTS AND LIMITATIONS: The probability of PSA50 in patients classified as having a favourable outcome was greater in the [Lu]Lu-PSMA-617 group than in the cabazitaxel group (odds ratio 6.36 [95% confidence interval {CI} 1.69-30.80] vs 0.96 [95% CI 0.32-3.05]; p = 0.038 for treatment-by-model interaction). The PSA50 rate in patients with a favourable outcome for [Lu]Lu-PSMA-617 versus cabazitaxel was 62/88 (70%) versus 31/85 (36%). The decision curve analysis indicated that the use of the PSA50 model had a clinical net benefit when the probability of a PSA response was ≥30%. The predictive performance of the models for PSA PFS and OS was not established (treatment-by-model interaction: p = 0.36 and p = 0.41, respectively). CONCLUSIONS: A previously developed outcome classification model for PSA50 was demonstrated to be both predictive and prognostic for the outcome after [Lu]Lu-PSMA-617 versus cabazitaxel, while the PSA PFS and OS models had purely prognostic value. The models may aid clinicians in defining strategies for patients with metastatic castration-resistant prostate cancer who failed first-line chemotherapy and are eligible for [Lu]Lu-PSMA-617 and cabazitaxel. PATIENT SUMMARY: In this report, we validated previously developed statistical models that can predict a response to Lu-PSMA radioligand therapy in patients with advanced prostate cancer. We found that the statistical models can predict patient survival, and aid in determining whether Lu-PSMA therapy or cabazitaxel yields a higher probability to achieve a serum prostate-specific antigen response.
背景：已利用一项多中心非对照研究的数据开发了预后模型，以预测接受[镥]镥-PSMA放射性配体治疗的转移性去势抵抗性前列腺癌患者前列腺特异性抗原降低50%（PSA50）、更长的前列腺特异性抗原（PSA）无进展生存期（PFS）以及更长总生存期（OS）的可能性。与标准化疗相比，这些模型识别可能从[镥]镥-PSMA中获益最多患者的预测效用尚未确立。 目的：利用随机、开放标签、2期TheraP试验的数据确定这些模型的预测价值（主要目的），并评估PSA50模型的临床净效益（次要目的）。 设计、背景和参与者：在2018年2月至2019年9月期间，TheraP试验中的所有200名患者被随机分配接受[镥]镥-PSMA-617（n = 99）或卡巴他赛（n = 101）。 结局测量和统计分析：通过测试随机分配至[镥]镥-PSMA与卡巴他赛的患者中，模型化结局分类（有利结局与不利结局）之间的关联是否不同来研究预测性能。使用决策曲线分析评估PSA50模型的临床效益。 结果和局限性：在[镥]镥-PSMA-617组中，分类为具有有利结局的患者出现PSA50的概率高于卡巴他赛组（优势比6.36 [95%置信区间{CI} 1.69 - 30.80] 对比0.96 [95% CI 0.32 - 3.05]；治疗与模型交互作用p = 0.038）。[镥]镥-PSMA-617与卡巴他赛相比，具有有利结局患者的PSA50率为62/88（70%）对比31/85（36%）。决策曲线分析表明，当PSA反应概率≥30%时，使用PSA50模型具有临床净效益。未确立这些模型对PSA PFS和OS的预测性能（治疗与模型交互作用：分别为p = 0.36和p = 0.41）。 结论：先前开发的PSA50结局分类模型被证明对[镥]镥-PSMA-617与卡巴他赛治疗后的结局具有预测性和预后性，而PSA PFS和OS模型仅具有预后价值。这些模型可能有助于临床医生为一线化疗失败且有资格接受[镥]镥-PSMA-617和卡巴他赛治疗的转移性去势抵抗性前列腺癌患者制定策略。 患者总结：在本报告中，我们验证了先前开发的统计模型，该模型可预测晚期前列腺癌患者对镥-PSMA放射性配体治疗的反应。我们发现这些统计模型可预测患者生存期，并有助于确定镥-PSMA治疗或卡巴他赛实现血清前列腺特异性抗原反应的概率更高。
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