The role of mass spectrometry-based serum proteomics signatures in predicting clinical outcomes in cancer patients treated with immune checkpoint inhibitors(ICI)Background Although immune checkpoint inhibitors (ICI) have changed the therapeutic scheme for multiple cancers, only a subset of patients experiences durable benefit. As current tumor markers such as PD-L1 show limited reliability in predicting clinical outcomes, we have explored the predictive role of markers representative of the host immune response on a systemic level present in the circulating proteome. Mass spectrometry allows for analysis of the proteome without the specific identification of individual proteins and protein isoforms. Here, we analyzed the most recent studies using mass spectrometry-based serum proteomics in predicting response to ICI treatment.

Method A systematic literature search on Pubmed and major oncology scientific meetings was conducted up to November 18, 2020.

Result Classifier algorithms trained with data sets from advanced stages of lung cancer (Primary Immune Response, Host Immune Classifier) and melanoma (BDX008, Immune Checkpoint Blockade) were used to stratify patients into groups with favorable and unfavorable treatment outcome. Patients with unfavorable predictive markers had worse prognosis when treated with ICI-single agent therapy. For patients treated with ICI alone or with chemotherapy as frontline or beyond, mass spectrometry-based serum proteomic signatures were shown to be a reliable predictive marker for survival outcomes (hazard ratios 0.15-0.5) independent of PD-L1 expression level.

Conclusion Mass spectrometry-based serum proteomic tests reliably identify patients expected to have a worse prognosis. These patients can benefit from frontline aggressive treatment strategy combining ICI and chemotherapy rather than the standard of care ICI monotherapy.

Cancer type Advanced stage NSCLCTreatmentLine of therapyClassifiernumber of patients included (n)number of patients in each classifier, n(%)Survival outcome (OS, month)HR [95%CI]reference
Advanced stage NSCLC Immunotherapy (nivolumab) 2nd line PIR 116 Not resistant 75 (65%) 17.3 0.48 [0.30-0.77], p=0.002 Mirte Muller, et al. 
     Resistant 41(35%) 6.0   
     Sensitive 32 (28%) 11.1 0.58 [0.38-0.87], p=0.009  
     Not sensitive 84 (72%) 4.3   
 Immunotherapy ± Chemotherapy All lines First line single agent immunotherapy (pembrolizumab) First line Combination (immunotherapy/chemotherapy) All line HIC 284 Hot 196 (69%) Not reached 0.38 [0.27-0.53], p<0.001 R. Brian Mitchell, et al. 
     Cold 88 (31%) 5.0   
  First line single agent immunotherapy (pembrolizumab)  117 Hot 80 (68%) 16.8 0.36 [0.22-0.58] p<0.001  
     Cold 37 (32%) 2.8   
  First line Combination (immunotherapy/chemotherapy)  161 Hot 113 (70%) Not reached 0.41 [0.26-0.67], p= 0.0003  
     Cold 48 (30%) 6.4   
 Immunotherapy (either pembrolizumab or nivolumab) All line HIC 47 Hot 32 (68%) Not reached 0.34 [0.10-1.18] p= 0.089 YK Chae, et al. 
     Cold 15 (32%) 16.5   
Unresectable Melanoma Immunotherapy (nivolumab) 2nd line BDX008 119 72 (61%) 2-year survival : 55% vs 21 % 3-year survival : 51% vs 14% 0.38 [0.19-0.55], p < 0.001 J. Weber, et al. 
     47 (39%)    
   ICB 119 sensitive 34 (29%) 2-year survival : 67% vs 33% 3-year survival : 58% vs 28% 0.37 [0.19-0.71], p = 0.002 J. Weber, et al. 
     resistant 85 (71%)    
Cancer type Advanced stage NSCLCTreatmentLine of therapyClassifiernumber of patients included (n)number of patients in each classifier, n(%)Survival outcome (OS, month)HR [95%CI]reference
Advanced stage NSCLC Immunotherapy (nivolumab) 2nd line PIR 116 Not resistant 75 (65%) 17.3 0.48 [0.30-0.77], p=0.002 Mirte Muller, et al. 
     Resistant 41(35%) 6.0   
     Sensitive 32 (28%) 11.1 0.58 [0.38-0.87], p=0.009  
     Not sensitive 84 (72%) 4.3   
 Immunotherapy ± Chemotherapy All lines First line single agent immunotherapy (pembrolizumab) First line Combination (immunotherapy/chemotherapy) All line HIC 284 Hot 196 (69%) Not reached 0.38 [0.27-0.53], p<0.001 R. Brian Mitchell, et al. 
     Cold 88 (31%) 5.0   
  First line single agent immunotherapy (pembrolizumab)  117 Hot 80 (68%) 16.8 0.36 [0.22-0.58] p<0.001  
     Cold 37 (32%) 2.8   
  First line Combination (immunotherapy/chemotherapy)  161 Hot 113 (70%) Not reached 0.41 [0.26-0.67], p= 0.0003  
     Cold 48 (30%) 6.4   
 Immunotherapy (either pembrolizumab or nivolumab) All line HIC 47 Hot 32 (68%) Not reached 0.34 [0.10-1.18] p= 0.089 YK Chae, et al. 
     Cold 15 (32%) 16.5   
Unresectable Melanoma Immunotherapy (nivolumab) 2nd line BDX008 119 72 (61%) 2-year survival : 55% vs 21 % 3-year survival : 51% vs 14% 0.38 [0.19-0.55], p < 0.001 J. Weber, et al. 
     47 (39%)    
   ICB 119 sensitive 34 (29%) 2-year survival : 67% vs 33% 3-year survival : 58% vs 28% 0.37 [0.19-0.71], p = 0.002 J. Weber, et al. 
     resistant 85 (71%)    

PIR (Primary Immune Response) ; Sensitive, Intermediate, Resistant/not resistant = sensitive + intermediate, not sensitive = resistant + intermediate, HIC (Host Immune Classifier) ; Hot, Cold, BDX008 ; +, - ICB (Immune Checkpoint Blockade); sensitive, resistant

Citation Format: Yoonhee Choi, Jin Young Hwang, Won Kyung Hur, Myungwoo Nam, Leeseul Kim, Yeun Ho Lee, William Cheng, Eugene Kim, Emma Yu, Chan Mi Jung, William Han Bae, Young Kwang Chae. The role of mass spectrometry-based serum proteomics signatures in predicting clinical outcomes in cancer patients treated with immune check point inhibitors (ICI) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 673.