Background:
Multiple studies have indicated that obese and overweight patients may experience favourable survival outcomes during treatment with an immune checkpoint inhibitor (ICI). However, most prior studies evaluated ICI-treated patients without a control cohort, and utilised categorised BMI with inconsistent body mass index (BMI) cut point definitions.
Methods:
This secondary analysis pooled individual patient data from the intention-to-treat population of the IMpower130 and IMpower150 RCTs comparing chemoimmunotherapy versus chemotherapy. The potentially non-linear relationship between BMI and chemoimmunotherapy treatment effect was evaluated using Multivariable Fractional Polynomial Interaction (MFPI). Specifically, the first-degree FP and flex3 variant of the MFPI algorithm in stratified (by RCT) Cox proportional hazards models was applied. Sensitivity analyses explored a range of other techniques, such as restricted cubic splines.
Results:
A total of 1282 patients were included. From the MFPI analysis, BMI was not significantly associated with chemoimmunotherapy treatment effect with respect to either OS (p=0.71) or PFS (p=0.35) (Figure). This was supported by sensitivity analyses using other techniques.
Conclusions:
There was no association between high BMI and enhanced chemoimmunotherapy treatment benefit in front-line treatment of advanced non-squamous NSCLC. Such findings provide countering evidence against the much-reported phenomenon of the “obesity paradox” with ICI during treatment of advanced NSCLC.
Figure: Treatment effect estimates (chemoimmunotherapy versus chemotherapy) with continuous BMI by the method of Multivariate Fractional Polynomial Interaction with Cox proportional-hazard regression models stratified by trial. The shaded area is the 95% CIs of the treatment effect estimated as hazard ratios (HRs) (black solid line). Vertical dotted lines mark the 5th and 95th percentile of BMI distribution of the pooled analysis cohort, at 19.8 kg/m2 and 34.7 kg/m2 respectively.
Acknowledgement:
This work is based on research using data from data contributors Roche that has been made available through Vivli, Inc. Vivli has not contributed to or approved, and is not in any way responsible for, the contents of this publication