Impact of PROphet Test in Changing Physicians' Therapeutic Decision-Making for Checkpoint Immunotherapy in Non-Small-Cell Lung Cancer.

PURPOSE: Immune Checkpoint Inhibitor (ICI) regimens are approved for first-line treatment of metastatic nononcogene-driven NSCLC. Guidelines do not differentiate which patients with PD-L1 ≥ 50% should receive ICI monotherapy. The clinically validated PROphet NSCLC plasma proteomic-based test is designed to inform this therapeutic decision. METHODS: One hundred oncologists were presented with 3 "virtual" metastatic NSCLC cases with PD-L1 scores and asked to recommend an approved first-line regimen. They then watched an online educational webinar on the PROphetNSCLC test. Postwebinar, the same cases were represented with the addition of a PROphet result, and oncologists again recommended a first-line regimen. Responses were compared to assess the impact on first-line treatment selection. RESULTS: Treatment recommendation changed in 39.6% of PROphet-tested cases, with 93% of physicians changing at least 1 case. In the PD-L1 ≥ 50% group, 89% of physicians changed their recommendation, followed by 77%, in PD-L1 < 1%, and 36% in PD-L1 1% to 49%. ​In the PD-L1 ≥ 50%, PROphet POSITIVE group, the recommendation for ICI monotherapy increased from 60% to 89%. ​For the PD-L1 ≥ 50%, PROphet NEGATIVE group, the recommendation for monotherapy dropped from 60% to 9%. In the PD-L1 < 1%, PROphet NEGATIVE group, 35% of patients were spared toxicity from ICI compared to 11% in PROphet untested cases. CONCLUSION: Adding PROphet to PD-L1 expression impacted therapeutic decision making in first-line NSCLC. PROphet identifies those predicted to have an overall survival benefit from ICI monotherapy versus combination versus chemotherapy, improving the probability of efficacy and reducing toxicity for some patients.

Biological insights from plasma proteomics of non-small cell lung cancer patients treated with immunotherapy.

Introduction: Immune checkpoint inhibitors have made a paradigm shift in the treatment of non-small cell lung cancer (NSCLC). However, clinical response varies widely and robust predictive biomarkers for patient stratification are lacking. Here, we characterize early on-treatment proteomic changes in blood plasma to gain a better understanding of treatment response and resistance. Methods: Pre-treatment (T0) and on-treatment (T1) plasma samples were collected from 225 NSCLC patients receiving PD-1/PD-L1 inhibitor-based regimens. Plasma was profiled using aptamer-based technology to quantify approximately 7000 plasma proteins per sample. Proteins displaying significant fold changes (T1:T0) were analyzed further to identify associations with clinical outcomes using clinical benefit and overall survival as endpoints. Bioinformatic analyses of upregulated proteins were performed to determine potential cell origins and enriched biological processes. Results: The levels of 142 proteins were significantly increased in the plasma of NSCLC patients following ICI-based treatments. Soluble PD-1 exhibited the highest increase, with a positive correlation to tumor PD-L1 status, and, in the ICI monotherapy dataset, an association with improved overall survival. Bioinformatic analysis of the ICI monotherapy dataset revealed a set of 30 upregulated proteins that formed a single, highly interconnected network, including CD8A connected to ten other proteins, suggestive of T cell activation during ICI treatment. Notably, the T cell-related network was detected regardless of clinical benefit. Lastly, circulating proteins of alveolar origin were identified as potential biomarkers of limited clinical benefit, possibly due to a link with cellular stress and lung damage. Conclusions: Our study provides insights into the biological processes activated during ICI-based therapy, highlighting the potential of plasma proteomics to identify mechanisms of therapy resistance and biomarkers for outcome.

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

PURPOSE: Current guidelines for the management of metastatic non-small cell lung cancer (NSCLC) without driver mutations recommend checkpoint immunotherapy with PD-1/PD-L1 inhibitors, either alone or in combination with chemotherapy. This approach fails to account for individual patient variability and host immune factors and often results in less-than-ideal outcomes. To address the limitations of the current guidelines, we developed and subsequently blindly validated a machine learning algorithm using pretreatment plasma proteomic profiles for personalized treatment decisions. PATIENTS AND METHODS: We conducted a multicenter observational trial (ClinicalTrials.gov identifier: NCT04056247) of patients undergoing PD-1/PD-L1 inhibitor-based therapy (n = 540) and an additional patient cohort receiving chemotherapy (n = 85) who consented to pretreatment plasma and clinical data collection. Plasma proteome profiling was performed using SomaScan Assay v4.1. RESULTS: Our test demonstrates a strong association between model output and clinical benefit (CB) from PD-1/PD-L1 inhibitor-based treatments, evidenced by high concordance between predicted and observed CB (R2 = 0.98, P < .001). The test categorizes patients as either PROphet-positive or PROphet-negative and further stratifies patient outcomes beyond PD-L1 expression levels. The test successfully differentiates between PROphet-negative patients exhibiting high tumor PD-L1 levels (≥50%) who have enhanced overall survival when treated with a combination of immunotherapy and chemotherapy compared with immunotherapy alone (hazard ratio [HR], 0.23 [95% CI, 0.1 to 0.51], P = .0003). By contrast, PROphet-positive patients show comparable outcomes when treated with immunotherapy alone or in combination with chemotherapy (HR, 0.78 [95% CI, 0.42 to 1.44], P = .424). CONCLUSION: Plasma proteome-based testing of individual patients, in combination with standard PD-L1 testing, distinguishes patient subsets with distinct differences in outcomes from PD-1/PD-L1 inhibitor-based therapies. These data suggest that this approach can improve the precision of first-line treatment for metastatic NSCLC.