Plasma Proteome-Based Test for First-line Treatment Selection in Metastatic Non–Small Cell Lung Cancer

In the tumor microenvironment (Tumor Microenvironment, TME), Immune Checkpoint Inhibitors (ICIs) targeting PD-1 or PD-L1 enhance the body’s natural ability to eliminate cancer cells by disrupting the inhibitory receptor-ligand interaction. However, even so, critical trials indicate that the response rate for monotherapy is <50%, with a median Progression-Free Survival (PFS) between 5 to 8 months. Consequently, understanding the complex interactions between tumor characteristics, the influence of the microenvironment, and immune system factors, personalized treatment decisions based on individual patient variability and host immune factors have become increasingly important.

In recent years, although the contribution of PD-L1 testing in treatment decisions has been evident, there is significant variability in actual clinical practice. For example, about 75% of patients with high PD-L1 expressing tumors are treated with PD-1/PD-L1 inhibitors alone in the first-line (1L) treatment setting, with the remaining 25% of patients opting for combination chemotherapy. Therefore, existing PD-L1 scoring and other biomarkers, such as Tumor Mutational Burden (TMB), do not provide the high predictive value needed for optimized treatment decisions on an individual patient basis. Current treatment strategies for metastatic non-small cell lung cancer (mNSCLC) have limitations, necessitating a more precise approach to personalize treatment decisions.

A research group led by Professor Petros Christopoulos published an important paper titled “Plasma Proteome-Based Test for First-Line Treatment Selection in Metastatic Non–Small Cell Lung Cancer” in the JCO Precision Oncology journal in 2024. In this study, the research team developed a machine learning algorithm based on pre-treatment plasma proteome profiles—Prophet—and conducted a blinded validation aimed at improving individualized treatment selection for mNSCLC. The study included 540 patients who received PD-1/PD-L1 inhibitor-based treatment and an additional 85 patients who received chemotherapy, with all plasma and clinical data collected pre-treatment. Plasma proteomics analysis was performed using the SomaScan assay v4.1.

The results demonstrated a strong correlation between the Prophet test and clinical benefit (CB), with high concordance (r2 = 0.98, p < .001) between predicted and observed CB, categorizing patients into Prophet positive or negative groups. This further stratified patient outcomes beyond PD-L1 expression levels. Prophet successfully distinguished patients with high tumor PD-L1 levels (≥50%) who were Prophet-negative. For these patients, the outcome of combined immunotherapy and chemotherapy was significantly better than immunotherapy alone, both in overall survival (OS) and PFS (Hazard Ratio [HR] = 0.23 [95% Confidence Interval, CI, 0.1 to 0.51], p = .0003). Conversely, the statistics for Prophet-positive patients showed comparable treatment efficacy between monotherapy and combination chemotherapy (HR, 0.78 [95% CI, 0.42 to 1.44], p = .424).

The conclusion drawn from the paper is that the plasma proteome-based test, combined with standard PD-L1 testing, can differentiate patient subgroups with differing outcomes during PD-1/PD-L1 inhibitor-based treatment. These data suggest that this approach can enhance the precision of first-line treatment for mNSCLC. The study indicates that a simple blood-based genomics test can act as a composite biomarker alongside tumor PD-L1 testing to optimize the treatment decisions for patients with advanced non-small cell lung cancer initially,