TY - JOUR
T1 - Plasma Proteome-Based Test for First-Line Treatment Selection in Metastatic Non-Small Cell Lung Cancer
AU - Christopoulos, Petros
AU - Harel, Michal
AU - McGregor, Kimberly
AU - Brody, Yehuda
AU - Puzanov, Igor
AU - Bar, Jair
AU - Elon, Yehonatan
AU - Sela, Itamar
AU - Yellin, Ben
AU - Lahav, Coren
AU - Raveh, Shani
AU - Reiner-Benaim, Anat
AU - Reinmuth, Niels
AU - Nechushtan, Hovav
AU - Farrugia, David
AU - Bustinza-Linares, Ernesto
AU - Lou, Yanyan
AU - Leibowitz, Raya
AU - Kamer, Iris
AU - Zer Kuch, Alona
AU - Moskovitz, Mor
AU - Levy-Barda, Adva
AU - Koch, Ina
AU - Lotem, Michal
AU - Katzenelson, Rivka
AU - Agbarya, Abed
AU - Price, Gillian
AU - Cheley, Helen
AU - Abu-Amna, Mahmoud
AU - Geldart, Tom
AU - Gottfried, Maya
AU - Tepper, Ella
AU - Polychronis, Andreas
AU - Wolf, Ido
AU - Dicker, Adam P.
AU - Carbone, David P.
AU - Gandara, David R.
N1 - Publisher Copyright:
© 2024 by American Society of Clinical Oncology.
PY - 2024/3/1
Y1 - 2024/3/1
N2 - PURPOSECurrent 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 METHODSWe 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.RESULTSOur 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).CONCLUSIONPlasma 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.
AB - PURPOSECurrent 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 METHODSWe 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.RESULTSOur 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).CONCLUSIONPlasma 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.
UR - http://www.scopus.com/inward/record.url?scp=85199027028&partnerID=8YFLogxK
U2 - 10.1200/PO.23.00555
DO - 10.1200/PO.23.00555
M3 - Article
C2 - 38513170
AN - SCOPUS:85199027028
SN - 2473-4284
VL - 8
JO - JCO Precision Oncology
JF - JCO Precision Oncology
M1 - e2300555
ER -
