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

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.