Dataset Information

Five-Factor Prognostic Model for Survival of Post-Platinum Patients with Metastatic Urothelial Carcinoma Receiving PD-L1 Inhibitors.

ABSTRACT:

Purpose

A prognostic model for overall survival of post-platinum patients with metastatic urothelial carcinoma receiving PD-1/PD-L1 inhibitors is necessary as existing models were constructed in the chemotherapy setting.

Materials and methods

Patient level data were used from phase I/II trials evaluating PD-L1 inhibitors following platinum based chemotherapy for metastatic urothelial carcinoma. The derivation data set consisted of 2 phase I/II trials evaluating atezolizumab (405). Two phase I/II trials that evaluated avelumab (242) and durvalumab (198) comprised the validation data sets. Cox regression analyses evaluated the association of candidate prognostic factors with overall survival. Stepwise selection was used to select an optimal model using the derivation data set. Discrimination and calibration were assessed in the avelumab and durvalumab data sets.

Results

The 5 prognostic factors identified in the optimal model using the atezolizumab derivation data set were ECOG-PS (1 vs 0, HR 1.80, 95% CI 1.36-2.36), liver metastasis (HR 1.55, 95% CI 1.20-2.00), platelet count (HR 2.22; 95% CI 1.54-3.18), neutrophil-to-lymphocyte ratio (HR 1.94, 95% CI 1.57-2.40) and lactate dehydrogenase (HR 1.60, 95% CI 1.28-1.99). There was robust discrimination of survival between low, intermediate and high risk groups. The c-statistic was 0.692 in the derivation and 0.671 and 0.773 in the avelumab and durvalumab validation data sets, respectively. A web based interactive tool was developed to calculate the expected survival probabilities based on risk factors.

Conclusions

A validated 5-factor model has satisfactory prognostic performance for survival across 3 PD-L1 inhibitors to treat metastatic urothelial carcinoma after platinum therapy and may assist in stratification, interpreting and designing trials incorporating PD-1/PD-L1 inhibitors in the post-platinum setting.

SUBMITTER: Sonpavde G

PROVIDER: S-EPMC7655635 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundImmune checkpoint inhibitors (ICI) are extremely expensive and most patients with metastatic urothelial carcinoma (mUC) do not benefit significantly from their use.ObjectiveWe performed a systematic review and meta-analysis to determine response rates and survival outcomes on patients with mUC progressing despite prior platinum-based chemotherapy receiving ICI stratified by biomarker status.MethodsWe performed a comprehensive literature search for all articles in PubMed and Embase up to 06/15/2019 to identify all studies pertaining to programmed death-ligand 1 (PD-L1) and programmed death 1 (PD-1) receptor targeted therapies for mUC that reported biomarkers. Given that biomarkers are reported on different scales and with different metrics, we defined each biomarker as either positive or negative using the definitions implemented in each individual trial. We meta-analyzed the data, reconstructed overall (OS) and progression-free survival (PFS) curves, and analyzed response rates by biomarker status. OS and PFS were analyzed in a pooled Kaplan-Meier analysis and pseudo-individualized patient data (IPD) extracted.ResultsWe identified 1429 manuscripts of which 8 met inclusion criteria, with a total of 1837 treated patients with outcomes data. On proportional hazards survival analysis, patients in the biomarker negative group were associated with a lower PFS (HR 1.48, 95% CI: 1.18 - 1.85, p < 0.001) and lower OS (HR 1.54, 95% CI: 1.32 - 1.80, p < 0.001) when compared to the biomarker positive group. Response data was available for 1641 patients and random effects proportion show complete response in 8% and 3% in biomarker positive and negative patients, respectively.ConclusionsICI therapy for metastatic UC post platinum therapy has a higher overall response rate, OS and PFS in patients who are biomarker positive compared to those who are negative. However, some patients who are biomarker negative do achieve complete responses. A better biomarker for patient selection is essential before biomarkers can be used to stratify candidates for ICI therapy.