scholarly journals A single institution study evaluating outcomes of PD-L1 high KRAS-mutant advanced non-small cell lung cancer (NSCLC) patients treated with first line immune checkpoint inhibitors

2021 ◽  
Vol 27 ◽  
pp. 100330 ◽  
Author(s):  
Adi Kartolo ◽  
Harriet Feilotter ◽  
Wilma Hopman ◽  
Andrea S. Fung ◽  
Andrew Robinson
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Li Wang ◽  
Zhixuan Ren ◽  
Bentong Yu ◽  
Jian Tang

Abstract Introduction Immune checkpoint inhibitors (ICIs) have become a frontier in the field of clinical technology for advanced non-small cell lung cancer (NSCLC). Currently, the predictive biomarker of ICIs mainly including the expression of PD-L1, TMB, TIICs, MMR and MSI-H. However, there are no official biomarkers to guide the treatment of ICIs and to determine the prognosis. Therefore, it is essential to explore a systematic nomogram to predict the prognosis of ICIs treatment in NSCLC Methods In this work, we obtained gene expression and clinical data of NSCLC patients from the TCGA database. Immune-related genes (IRGs) were downloaded from the ImmPort database. The detailed clinical annotation and response data of 240 advanced NSCLC patients who received ICIs treatment were obtained from the cBioPortal for Cancer Genomics. Kaplan–Meier survival analysis was used to perform survival analyses, and selected clinical variables to develop a novel nomogram. The prognostic significance of FGFR4 was validated by another cohort in cBioPortal for Cancer Genomics. Results 3% of the NSCLC patients harbored FGFR4 mutations. The mutation of FGFR4 were confirmed to be associated with PD-L1, and TMB. Patients harbored FGFR4 mutations were found to have a better prolonged progression-free survival (PFS) to ICIs treatment (FGFR4: P = 0.0209). Here, we built and verified a novel nomogram to predict the prognosis of ICIs treatment for NSCLC patients. Conclusion Our results showed that FGFR4 could serve as novel biomarkers to predict the prognosis of ICIs treatment of advanced NSCLC. Our systematic prognostic nomogram showed a great potential to predict the prognosis of ICIs for advanced NSCLC patients.


2020 ◽  
Author(s):  
Dantong Sun ◽  
Lu Tian ◽  
Yan Zhu ◽  
Yang Wo ◽  
Qiaoling Liu ◽  
...  

Abstract Introduction Patients with advanced non-small cell lung cancer (NSCLC) benefit from treatment with immune checkpoint inhibitors (ICIs). Biomarkers such as programmed death-ligand 1 (PD-L1), the tumor mutational burden (TMB) and the mismatch repair (MMR) status are used to predict the prognosis of ICIs therapy. Nevertheless, novel biomarkers need to be further investigated, and a systematic prognostic model is needed for the evaluation of the survival risks of ICIs treatment.Methods A cohort of 240 patients who received ICIs from the cBioPortal for Cancer Genomics was evaluated in this research. Clinical information and targeted sequencing data were acquired for analyses. The Kaplan-Meier plot method was used to perform survival analyses, and selected variables were then confirmed by a novel nomogram constructed by the “rms” package of R software.Results Seven percent of the NSCLC patients harbored ARID1A mutations, while 4% of the NSCLC patients harbored ARID1B mutations. Mutations in ARID1A and ARID1B were confirmed to be associated with sensitivity to ICIs. Patients harboring these mutations were found to have a better response to treatment (ARID1A: P=0.045; ARID1B: P=0.034) and prolonged progression-free survival (ARID1B: P=0.032). Here, a novel nomogram was constructed to predict the prognosis of ICIs treatment. Elevation of the TMB, enhanced expression of PD-L1 and activation of the antigen presentation process and cellular immunity were found to be correlated with ARID1A and ARID1B mutations.Conclusion ARID1A and ARID1B could serve as novel biomarkers for the prognosis and sensitivity to ICIs of advanced NSCLC.


2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e21218-e21218
Author(s):  
Leeseul Kim ◽  
Young Kwang Chae ◽  
Chan Mi Jung ◽  
Emma Yu ◽  
Alice Daeun Lee ◽  
...  

e21218 Background: Early recognition of immune-related adverse events (irAEs) of immune checkpoint inhibitors(ICI) is important. Circulating proteome reflects host response to diseases and is being explored as a marker for response to immunotherapy. We previously have reported that a serum-based proteomics test, Primary Immune Response (PIR) demonstrated a trend that PIR-sensitive patients are more likely to tolerate ICI treatment longer without developing irAEs in non-small cell lung cancer (NSCLC) patients. The VeriStrat test is another serum-based proteomic assay, which was reported to be predictive of survival outcomes for all treatment regimens and lines of therapy including ICI in NSCLC. We explored the associations between the VeriStrat test and developing irAEs in NSCLC patients treated with ICI. Methods: Data of 70 consented NSCLC patients treated with any regimens and lines of therapy including ICI were collected. Samples were grouped into either VeriStrat ‘Good’(VS-G) or VeriStrat ‘Poor’(VS-P). We analyzed the durations from the immunotherapy initiation to each episode of irAE and each irAE above grade 2 using log-rank test. IrAEs were graded according to Common Terminology Criteria for Adverse Events (CTCAE) v5.0. Results: Among the 70 patients, 18 patients (25%) experienced one or more irAEs. There was no significant difference in ‘Time to first irAE’ between VS-G and VS-P (p = 0.72, HR = 0.82, 95% CI = 0.29-2.32). Among 48 VS-G patients, 12(25%) had one or more irAE and 5(10%)had irAE graded over 2. Among 22 VS-P patients, 6(27%) had one or more irAE and 2(9%) had irAE graded over 2. There was no significant difference between VS-G and VS-P groups in the development of irAE and irAE graded over 2. Conclusions: There was no statistically significant association between the VeriStrat test and the development of irAEs. Further studies are warranted to investigate proper serum based proteomic assay to predict the development of irAE.


2017 ◽  
Vol 12 (1) ◽  
pp. S1316
Author(s):  
Laura Mezquita ◽  
Melinda Charrier ◽  
Edouard Auclin ◽  
Louise Dupraz ◽  
Jordi Remon ◽  
...  

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