Related Experiment Video
Updated: Apr 18, 2026
Author Spotlight: Advancements in Molecular Biomarker Testing for Non-Squamous Non-Small Cell Lung Cancer
Published on: September 8, 2023
Use of an Integrative Genomics Approach to Identify Metastatic NSCLC Patients Benefiting From the Addition of
Mark Klein1, Drew Watson2, Michael Castro2
1Primary and Specialty Integrated Care Community, Minneapolis VA Health Care System, Minneapolis, MN; Department of Medicine, University of Minnesota, Minneapolis, MN.
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.
Insights
A new algorithm, the Therapy Response Index (TRI), predicts which non-small cell lung cancer (NSCLC) patients benefit from adding chemotherapy to immune checkpoint inhibitors (ICI). TRI identifies patients likely to gain survival benefits from ICI plus chemotherapy (ICI+C).
Area of Science:
- Oncology
- Computational Biology
- Genomics
Background:
- Not all non-small cell lung cancer (NSCLC) patients benefit from chemotherapy when added to immune checkpoint inhibitors (ICI).
- Predictive biomarkers are needed to guide treatment decisions for NSCLC patients receiving ICI therapy.
Purpose of the Study:
- To develop and validate an algorithm to identify NSCLC patients who may benefit from the addition of chemotherapy to ICI (ICI+C).
Main Methods:
- A computational biology model (Cellworks) was used to create the Therapy Response Index (TRI) algorithm.
- The TRI was trained on 553 NSCLC patients and validated on 710 advanced NSCLC patients from clinico-genomic databases.
Main Results:
- The TRI classifier was significantly associated with overall survival (OS) in NSCLC patients.
- Patients with a low TRI (≤32) showed an incremental benefit of ~3 months in median OS with ICI+C.
- Patients with a high TRI (>32) did not show improved OS with ICI+C, suggesting TRI predicts chemotherapy benefit.
Conclusions:
- The TRI algorithm is predictive of OS and incremental chemotherapy benefit in NSCLC patients receiving ICI or ICI+C.
- TRI can guide the selection of patients for ICI+C therapy, identifying those likely to respond and those who may not benefit.
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