Use of an Integrative Genomics Approach to Identify Metastatic NSCLC Patients Benefiting From the Addition of Chemotherapy to Immune Checkpoint Inhibitors.

[BACKGROUND] Not all non-small cell lung cancer (NSCLC) patients benefit from chemotherapy when added to immune checkpoint inhibitors (ICI). We used comprehensive genomic profiling coupled with a computational biology model (Cellworks) to create an algorithm to identify patients who may benefit from adding chemotherapy to ICI (ICI+C).

[PATIENTS AND METHODS] The algorithm (Therapy Response Index, or TRI) was trained in a retrospective cohort of 553 NSCLC patients from the U.S. Veteran's Health Administration (VHA) system who received comprehensive genomic profiling. The TRI, computational model and clinical threshold were locked and validated in 710 advanced NSCLC front-line patients receiving either ICI or ICI+C, obtained from the Flatiron Health-Foundation Medicine NSCLC clinico-genomic database.

[RESULTS] The classifier was significantly associated with OS in a multivariate analysis. (LR P = .0355). Patients with a low TRI (TRI ≤ 32) received an estimated incremental benefit in median OS of ∼ 3 months with ICI+C (LR P = .04). In contrast, patients with a high TRI (TRI > 32) showed no improvement in median OS when receiving ICI+C. A statistical test of interaction between TRI and chemotherapy met prespecified criteria of P < .25 (LR P = .1361), suggesting that TRI may be predictive of chemotherapy benefit.

[CONCLUSIONS] TRI was predictive of OS and incremental chemotherapy benefit in patients with NSCLC receiving ICI or ICI+C. These results support the use of the classifier to identify patients who may benefit from ICI+C and those unlikely to respond to ICI alone, independent of PD-L1 levels.