A novel decision-making tool for first-line treatment selection in metastatic non-small cell lung cancer based on plasma proteome profiling
Authors: Christopoulos Petros , Harel Michal , Lahav Coren , Sela Itamar , Dahan Nili , Reinmuth Niels , Koch Ina , Zer Alona , Moskovitz Mor , Levy-Barda Adva , Lotem Michal , Nechushtan Hovav , Katzenelson Rivka , Agbarya Abed , Abu-Amna Mahmoud , Gottfried Maya , Tepper Ella , Ambrosone Christine B. , Wolf Ido , Lou Yanyan , Leibowitz Raya , Dicker Adam P. , Carbone David P. , Gandara David 
Published: December 2022

Importance

Advanced stage non-small cell lung cancer (NSCLC) patients with no driver mutations are typically treated with immune checkpoint inhibitor (ICI)-based therapy, either in the form of monotherapy or concurrently with chemotherapy, while treatment modality selection is based mainly on programmed death ligand 1 (PD-L1) expression levels in the tumor. However, PD-L1 assays are only moderately predictive of therapeutic benefit.

Objective

To develop a novel decision-making tool for physicians treating NSCLC patients on whether to administer immune checkpoint inhibitor (ICI) therapy alone or in combination with chemotherapy.

Design, setting, and participants

This multicenter observational study includes patients from an ongoing clinical trial (PROPHETIC;NCT04056247). Patients were recruited from 13 different centers (total n=425; 58 patients were excluded) from June 2016 and June 2021. Plasma samples were obtained prior to treatment initiation, and deep proteomic profiling was conducted. PROphet® computational model for predicting clinical benefit (CB) probability at 12 months was developed based on the plasma proteomic profile. The model performance was validated in a blinded manner. Following validation, training and prediction was performed over the entire cohort using cross-validation methodology. The patients were divided into four groups based on their PD-L1 expression level combined with their CB probability, and the survival outcome was examined for each group. The data were analyzed from July to October 2022.

Main outcome and measures

Clinical benefit from ICI-based treatment, overall survival (OS) and progression-free survival (PFS).

Results

The model displayed strong predictive capability with an AUC of 0.78 (p-value = 5.00e-05), outperforming a PD-L1-based predictive model (AUC = 0.62; p-value 2.76e-01), and exhibited a significant difference in OS and PFS between patients with low and high CB probabilities. When combining CB probability with PD-L1 expression levels, four patient subgroups were identified; (i) patients with PD-L1≥50% and a negative PROphet result who significantly benefit from ICI-chemotherapy combination therapy compared to ICI monotherapy; (ii) patients with PD-L1≥50% and a positive PROphet result who benefit similarly from either treatment modalities; (iii) patients with PD-L1<50% and a negative PROphet result who do not benefit from either treatment modalities; (iv) patients with PD-L1<50% and a positive PROphet score who benefit from combination therapy.

Conclusions and relevance

The PROphet® model displayed good performance for prediction of CB at 12 months based on a plasma sample obtained prior to treatment. Our findings further demonstrate a potential clinical utility for informing treatment decisions for NSCLC patients treated with ICIs by adding resolution to the PD-L1 biomarker currently used to guide treatment selection, thereby enabling to select the most suitable treatment modality for each patient.