Validation of Prognostic and Predictive Models for Therapeutic Response in Patients Treated with [177Lu]Lu-PSMA-617 Versus Cabazitaxel for Metastatic Castration-resistant Prostate Cancer (TheraP): A Post Hoc Analysis from a Randomised, Open-label, Phase 2 Trial

Andrei Gafita; Andrew J Martin; Louise Emmett; Matthias Eiber; Amir Iravani; Wolfgang P Fendler; James Buteau; Shahneen Sandhu; Arun A Azad; Ken Herrmann; Martin R Stockler; Ian D Davis; Michael S Hofman

doi:10.1016/j.euo.2024.03.009

Validation of Prognostic and Predictive Models for Therapeutic Response in Patients Treated with [¹⁷⁷Lu]Lu-PSMA-617 Versus Cabazitaxel for Metastatic Castration-resistant Prostate Cancer (TheraP): A Post Hoc Analysis from a Randomised, Open-label, Phase 2 Trial

Eur Urol Oncol. 2024 Apr 6:S2588-9311(24)00085-3. doi: 10.1016/j.euo.2024.03.009. Online ahead of print.

Authors

Affiliations

¹ 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. Electronic address: [email protected].
² NHMRC Clinical Trials Center, University of Sydney, Sydney, NSW, Australia; ANZUP Cancer Trials Group, Sydney, NSW, Australia.
³ ANZUP Cancer Trials Group, Sydney, NSW, Australia; Department of Theranostics and Nuclear Medicine, St Vincent's Hospital, Sydney, NSW, Australia; Faculty of Medicine, UNSW Sydney, Sydney, NSW, Australia.
⁴ Department of Nuclear Medicine, Technical University Munich, Klinikum rechts der Isar, Munich, Germany.
⁵ ANZUP Cancer Trials Group, Sydney, NSW, Australia; Prostate Cancer Theranostics and Imaging Center of Excellence (ProsTIC), Molecular Imaging and Therapeutic Nuclear Medicine, Cancer Imaging, Peter MacCallum Cancer Center, Melbourne, VIC, Australia; Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia; Department of Radiology, University of Washington, Seattle, WA, USA.
⁶ Department of Nuclear Medicine, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany.
⁷ ANZUP Cancer Trials Group, Sydney, NSW, Australia; Prostate Cancer Theranostics and Imaging Center of Excellence (ProsTIC), Molecular Imaging and Therapeutic Nuclear Medicine, Cancer Imaging, Peter MacCallum Cancer Center, Melbourne, VIC, Australia; Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia.
⁸ ANZUP Cancer Trials Group, Sydney, NSW, Australia; Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia; Department of Medical Oncology, Peter MacCallum Cancer Center, Melbourne, VIC, Australia.
⁹ ANZUP Cancer Trials Group, Sydney, NSW, Australia; Monash University, Melbourne, VIC, Australia; Eastern Health, Melbourne, VIC, Australia.

PMID: 38584037
DOI: 10.1016/j.euo.2024.03.009

Abstract

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 [¹⁷⁷Lu]Lu-PSMA radioligand therapy. The predictive utility of the models to identify patients likely to benefit most from [¹⁷⁷Lu]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 [¹⁷⁷Lu]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 [¹⁷⁷Lu]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 [¹⁷⁷Lu]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 [¹⁷⁷Lu]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 [¹⁷⁷Lu]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 [¹⁷⁷Lu]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.

Keywords: Lu-PSMA; Nomogram; Predictive models; Prostate-specific membrane antigen positron emission tomography; Theranostics.