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136 A plasma proteomic based predictive biomarker for response to immunotherapy in NSCLC
  1. Michal Harel1,
  2. Coren Lahav1,
  3. Ido Wolf2,
  4. Ella Tepper3,
  5. Maya Gottfried4,
  6. Raya Leibowitz5,
  7. Mahmoud Abu-Amna6,
  8. Abed Agbarya7,
  9. Rivka Katzenelson8,
  10. Michal Lotem9,
  11. Mor Moskovitz10,
  12. Alona Zer11,
  13. Ina Koch12,
  14. Niels Reinmuth12,
  15. Adam Dicker13,
  16. David Gandara14,
  17. Petros Christopoulos15 and
  18. David Carbone16
  1. 1Oncohost LTD, Binyamina, Israel
  2. 2Tel Aviv Sourasky Medical Center, Tel Aviv Yafo, Israel
  3. 3Assuta Ramat HaHayal Hospital, Tel Aviv Yafo, Israel
  4. 4Meir Medical Center, Kfar Saba, Israel
  5. 5Shamir Medical Center, Zerifin, Israel
  6. 6Emek Medical Center, Afula, Israel
  7. 7Bnai-Zion Medical Center, Haifa, Israel
  8. 8Kaplan Medical Center, Rehovot, Israel
  9. 9Hadassah Medical Center, Jerusalem, Israel
  10. 10Rabin Medical center, Petah Tikva, Israel
  11. 11Rambam Health Care Campus, Haifa, Israel
  12. 12Asklepios Klinik Gauting GmbH, Munich-Gauting, Germany
  13. 13Thomas Jefferson University, Philadelphia, PA, USA
  14. 14UC Davis Comprehensive Cancer Center, Sacramento, CA, USA
  15. 15Thoraxklinik Heidelberg gGmbH, Heidelberg, Germany
  16. 16The Ohio State University, Columbus, OH, USA


Background To date, predicting response to immune checkpoint blockade (ICB) therapy in non-small cell lung cancer (NSCLC) patients is based on tumor PD-L1 levels. However, available assays are only moderately predictive, and most require a tumor biopsy. Here, we describe a novel machine learning-based biomarker model that analyzes proteomic profiles in blood plasma to predict ICB response in NSCLC patients.

Methods We collected plasma samples and clinical data from 339 ICB-treated NSCLC patients via a multi-center clinical study (PROPHETIC; NCT04056247; approved by local IRB committees from each site), 60% of them received combination of ICB-chemotherapy and the rest received ICB alone. Patients displaying disease progression were classified as non-responders and the rest as responders. Proteomic profiling was performed by the SOMAscan assay. A machine-learning-based model for clinical response prediction was developed based on protein expression level in patient’s plasma. Using a proprietary algorithm, we identified Response Associated Proteins (RAPs), that serve as potential indicators of clinical response depending on their plasma level in the individual patient. The output of the model provides a patient-specific response probability for 3, 6, and 12 months after starting treatment.

Results The RAP-based model displayed strong predictive power over the first year of ICB-based therapy, as indicated by area under the curve (AUC) of the receiver operating characteristics (ROC) plot of 0.71, 0.77 and 0.78 for 3-, 6- and 12-months following treatment initiation, respectively, and a high goodness of fit between predicted response probability and observed response rate (R2 = 0.97). Patients with low and high response probability predictions displayed a significant difference in overall survival and progression-free survival. The RAP-based model outperformed a PD-L1-based model (AUC of 0.5, 0.6 and 0.55 for 3-, 6- and 12- months, respectively). Notably, in a subgroup of patients with PD-L1-high tumors (>50% PD-L1) receiving monotherapy, patients with high response probability predictions survived significantly longer than patients with low response probability predictions (p-value 0.0002, 0.0036 and 0.0115 for 3-, 6- and 12-months, respectively) who had similar overall survival as patients with PD-L1-low tumors (<50% PD-L1).

Conclusions Altogether, we have developed a novel predictive model for ICB response in NSCLC patients based on proteomic profiling of blood plasma. The model offers two main clinical utilities. First, it provides response predictions for three time points over the first year of treatment. Second, it identifies a subgroup of high PD-L1 patients who may benefit more from combination therapy.

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