Abstract 264: Development of companion tests based on continuous markers: Illustration with blood-based tumor mutational burden in NSCLC cancer patients treated with atezolizumab
Authors: Julia Gilhodes, Frédérique Penault-Llorca, Julien Mazières, Maurice Pérol, Christos Chouaid, Eve Leconte, Jean-Pierre Delord, Thomas Filleron
Journal: Cancer Research (2021)

Abstract As illustrated in the setting of immune checkpoint inhibitors (ICI), a crucial goal in the development of new therapeutics is to propose a companion diagnostic test. This challenge is particularly complex with a continuous marker for which a threshold is needed. We analyzed data that have demonstrated that blood-based tumor mutational burden (bTMB) may identify patients who may benefit from atezolizumab in second-line and higher NSCLC (Gandara, Nat Med 2018). Three approaches to determine a threshold for bTMB were used in the POPLAR dataset: 1) ROC curve to discriminate best overall response in the ICI arm (Youden index), 2) treatment effect on PFS among subgroups of high bTMB patients according to several thresholds, 3) marker-by-treatment predictiveness curves for PFS taking into consideration both arms. Effectiveness of predictive models was assessed on the OAK dataset using several utility measures (treatment effects, proportion of patients affected by the classification). Table 1 presents the threshold and the rate of patients in each bTMB subgroup for each approach. Treatment effects in bTMB groups were similar for the three methods, but the p-value for interaction between treatment and bTMB was the most significant for the predictive approach. Predictiveness analysis reclassified 72 patients (11.2%) as bTMB high compared to ROC analysis . In this reclassified subgroup, 6-month PFS rates were 6.65% [2.23; 19.8] (Control) and 23.40% [13.95; 39.3] (ICI). Marker-by-treatment predictiveness curves should be favoured over analyses on the experimental arm only. Prognostic approaches may not be useful for treatment selection. A prognostic value doesn't ensure that treatment effects will differ depending on marker value. Moreover, treatment interaction modelling at different timepoints is particulary relevant when proportional hazards assumption is violated. Pros and cons of each approach will be presented. Treatment effects within bTMB groups and interaction between treatment and bTMB in the OAK cohortDichotomization approachThresholdN (%)Treatment armHR for treatmentpinteractionROCbTMB&amp;lt;14451 (70.2%)Docetaxel1.00 [0.83; 1.22]0.035AtezolizumabbTMB≥14191 (29.8%)Docetaxel0.69 [0.50; 0.93]AtezolizumabSubpopbTMB&amp;lt;12426 (66.4%)Docetaxel1.00 [0.82; 1.23]0.029AtezolizumabbTMB≥12216 (33.6%)Docetaxel0.71 [0.53; 0.95]AtezolizumabMarker-treatment predictiveness curvesbTMB&amp;lt;10379 (59.0%)Docetaxel1.04 [0.84; 1.29]0.018AtezolizumabbTMB≥10263 (41.0%)Docetaxel0.72 [0.55; 0.93]Atezolizumab Citation Format: Julia Gilhodes, Frédérique Penault-Llorca, Julien Mazières, Maurice Pérol, Christos Chouaid, Eve Leconte, Jean-Pierre Delord, Thomas Filleron. Development of companion tests based on continuous markers: Illustration with blood-based tumor mutational burden in NSCLC cancer patients treated with atezolizumab [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 264.