Tumor response and progression-free survival as potential surrogate endpoints for overall survival in extensive stage small-cell lung cancer: findings on the basis of North Central Cancer Treatment Group trials.
BACKGROUND
The authors investigated the putative surrogate endpoints of best response, complete response (CR), confirmed response, and progression-free survival (PFS) for associations with overall survival (OS), and as possible surrogate endpoints for OS.


METHODS
Individual patient data from 870 untreated extensive stage small-cell lung cancer patients participating in 6 single-arm (274 patients) and 3 randomized trials (596 patients) were pooled. Patient-level associations between putative surrogate endpoints and OS were assessed by Cox models using landmark analyses. Trial-level surrogacy of putative surrogate endpoints were assessed by the association of treatment effects on OS and individual putative surrogate endpoints. Trial-level surrogacy measures included: R(2) from weighted least squares regression model, Spearman correlation coefficient, and R(2) from bivariate survival model (Copula R(2) ).


RESULTS
Median OS and PFS were 9.6 (95% confidence interval [CI], 9.1-10.0) and 5.5 (95% CI, 5.2-5.9) months, respectively; best response, CR, and confirmed response rates were 44%, 22%, and 34%, respectively. Patient-level associations showed that PFS status at 4 months was a strong predictor of subsequent survival (hazard ratio [HR], 0.42; 95% CI, 0.35-0.51; concordance index 0.63; P < .01), with 6-month PFS being the strongest (HR, 0.41; 95% CI, 0.35-0.49; concordance index, 0.66, P < .01). At the trial level, PFS showed the highest level of surrogacy for OS (weighted least squares R(2) = 0.79; Copula R(2) = 0.80), explaining 79% of the variance in OS. Tumor response endpoints showed lower surrogacy levels (weighted least squares R(2) ≤0.48).


CONCLUSIONS
PFS was strongly associated with OS at both the patient and trial levels. PFS also shows promise as a potential surrogate for OS, but further validation is needed using data from a larger number of randomized phase 3 trials.
Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota. foster.nathan@mayo.edu
Nathan R
NR
Foster
Yingwei
Y
Qi
Qian
Q
Shi
James E
JE
Krook
John W
JW
Kugler
James R
JR
Jett
Julian R
JR
Molina
Steven E
SE
Schild
Alex A
AA
Adjei
Sumithra J
SJ
Mandrekar
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D015415:Biomarkers / Q000032:analysis; D014408:Biomarkers, Tumor / Q000032:analysis; D017322:Clinical Trials, Phase II as Topic / Q000706:statistics & numerical data; D017326:Clinical Trials, Phase III as Topic / Q000706:statistics & numerical data; D018572:Disease-Free Survival; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000175:diagnosis / Q000401:mortality* / Q000628:therapy*; D008297:Male; D008875:Middle Aged; D011379:Prognosis; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data; D055752:Small Cell Lung Carcinoma / Q000175:diagnosis / Q000401:mortality* / Q000628:therapy*; D016019:Survival Analysis; D016896:Treatment Outcome; D014481:United States / Q000453:epidemiology
D015415:Biomarkers; D014408:Biomarkers, Tumor