476P - A model relating overall survival to tumor growth inhibition in renal cell carcinoma

Date 27 September 2014
Event ESMO 2014
Session Poster Display session
Topics Drug Development
Renal Cell Cancer
Translational Research
Presenter Francois Mercier
Citation Annals of Oncology (2014) 25 (suppl_4): iv146-iv164. 10.1093/annonc/mdu331
Authors F. Mercier1, B. Houk2, L. Claret3, P. Milligan4, R. Bruno5
  • 1Consulting Services, Pharsight, 68920 - Wintzenheim/FR
  • 2Clinical Pharmacology, Pfizer, La Jolla/US
  • 3Consulting Services, Pharsight, Marseille/FR
  • 4Pharmacometrics, Pfizer, Sandwich/GB
  • 5Consulting Services, Pharsight, FR-13013 - Marseille/FR

Abstract

Aim

Tumor growth inhibition (TGI) metrics estimated with longitudinal tumor size (TS) models have been shown to be predictive of overall survival (OS) in a variety of tumor types.

Methods

TS data from 2490 patients with 1st line or refractory RCC who received temsirolimus, interferon, sunitinib, sorafenib or axitinib in 10 Phase 2 or Phase 3 studies were used. TGI metrics (Early tumor shrinkage (ETS) at week 8, 10, 12, time to growth (TTG)) as well as baseline prognostic factors were tested in a multivariate log-normal model of OS. Model performances were evaluated by posterior predictive check of the OS and hazard ratio distributions.

Results

TTG was the best TGI metric to predict OS (days), but Week 8 ETS, an earlier measure, had satisfactory performance, and was employed due to its ease of clinical utility. The parameter estimates of the model with Week 8 ETS are:

Parameter Estimate (SE) p-value
Intercept 8.07 (0.270) <0.001
TS ratio at week 8 -1.99 (0.135) <0.001
Baseline hemoglobin (g/L) 0.133 (0.111) <0.001
Baseline ECOG = 1 -0.400 (0. 048) <0.001
Baseline ECOG = (2, 3) -0.163 (0.077) 0.033
Log(# baseline metastases) -0.209 (0.032) <0.001
Baseline cor. calcium (mg/dL) -0.104 (0.019) <0.001
Time from diagnosis (day) 8.0E-5 (1.7E-5) <0.001
Baseline LDH (U/L) -3.7E-4 (9.2E-5) <0.001
Lung metastases (yes) -0.138 (0.046) 0.002
Log(scale) -0.107 (0.020) <0.001

This model was then used in simulation mode to define a clinically relevant ETS target for future Phase II studies with investigational treatments.

Conclusions

A TGI-based OS model is proposed for patients with RCC. The model demonstrates good performance when fitted to data from 10 different Phase II and Phase III clinical trials. Simulations with this model help with identification of relevant ETS targets in clinical trial design.

Disclosure

F. Mercier:is employed by Pharsight Consulting Services (a division of Certara, L.P.), a company that received financial support from Pfizer to complete this work; B. Houk: is employed by Pfizer; L. Claret: is employed by Pharsight Consulting Services (a division of Certara, L.P.), a company that received financial support from Pfizer to complete this work; P. Milligan: is employed by Pfizer; R. Bruno: is employed by Pharsight Consulting Services (a division of Certara, L.P.), a company that received financial support from Pfizer to complete this work.