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...
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| Hauptverfasser: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
December 2024
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| In: |
JCO precision oncology
Year: 2024, Heft: 8, Pages: 1-12 |
| ISSN: | 2473-4284 |
| DOI: | 10.1200/PO.23.00555 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1200/PO.23.00555 Verlag, kostenfrei, Volltext: https://ascopubs.org/doi/10.1200/PO.23.00555 
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| Verfasserangaben: | Petros Christopoulos, MD, PhD; Michal Harel, PhD; Kimberly McGregor, MD; Yehuda Brody, PhD; Igor Puzanov, MD; Jair Bar, MD, PhD; Yehonatan Elon, PhD; Itamar Sela, PhD; Ben Yellin, PhD; Coren Lahav, MSc; Shani Raveh, PhD; Anat Reiner-Benaim, PhD; Niels Reinmuth, MD; Hovav Nechushtan, MD; David Farrugia, MD; Ernesto Bustinza-Linares, MD; Yanyan Lou, MD; Raya Leibowitz, MD, PhD; Iris Kamer, PhD; Alona Zer Kuch, MD; Mor Moskovitz, MD; Adva Levy-Barda, PhD; Ina Koch, PhD; Michal Lotem, MD; Rivka Katzenelson, MD; Abed Agbarya, MD; Gillian Price, MD; Helen Cheley, RN; Mahmoud Abu-Amna, MD; Tom Geldart, MD; Maya Gottfried, MD; Ella Tepper, MD; Andreas Polychronis, MD; Ido Wolf, MD; Adam P. Dicker, MD, PhD; David P. Carbone, MD; and David R. Gandara, MD |
| Zusammenfassung: | 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. |
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| Beschreibung: | Online veröffentlicht am 21. März 2024 Gesehen am 01.07.2025 |
| Beschreibung: | Online Resource |
| ISSN: | 2473-4284 |
| DOI: | 10.1200/PO.23.00555 |