Abstract
BackgroundThe development of immune checkpoint inhibitors (ICIs) is a revolutionary milestone in the field of immune-oncology. However, the low response rate is the major problem of ICI treatment. The recent studies showed that response rate to single-agent programmed cell death protein 1 (PD-1)/programmed cell death-ligand 1 (PD-L1) inhibition in unselected non-small cell lung cancer (NSCLC) patients is 25% so that researchers defined several biomarkers to predict the response of immunotherapy in ICIs treatment. Common biomarkers like tumor mutational burden (TMB) and PD-L1 expression have several limitations, such as low accuracy and inadequately validated cutoff value.MethodsTwo published and an unpublished ICIs treatment NSCLC cohorts with 129 patients were collected and divided into a training cohort (n = 53), a validation cohort (n = 22), and two independent test cohorts (n = 34 and n = 20). We identified six immune-related pathways whose mutational status was significantly associated with overall survival after ICIs treatment. Then these pathways mutational status combined with TMB, PD-L1 expression and intratumor heterogeneity were incorporated to build a Bayesian-regularization neural networks (BRNN) model to predict the ICIs treatment response.ResultsWe firstly proved that TMB, PD-L1, and mutant-allele tumor heterogeneity (MATH) were independent biomarkers. The survival analysis of six immune-related pathways revealed the mutational status could distinguish overall survival after ICIs treatment. When predicting immunotherapy efficacy, the overall accuracy of area under curve (AUC) in validation cohort reaches 0.85, outperforming previous predictors in either sensitivity or specificity. And the AUC in two independent test cohorts reach 0.74 and 0.80.ConclusionWe developed a pathway-model that could predict the efficacy of ICIs in NSCLC patients. Our study made a significant contribution to solving the low prediction accuracy of immunotherapy of single biomarker. With the accumulation of larger data sets, further studies are warranted to refine the predictive performance of the approach.
Highlights
Immunotherapy is emerging as a beneficial tool for cancer treatment by activating the immune system to produce antitumor effects [1]
The previous studies documented that higher tumor mutational burden (TMB) or programmed cell death-ligand 1 (PD-L1) expression correlated with better outcomes as compared with lower TMB or progressive disease (PD)-L1 expression [11, 12, 25, 40]
TMB of some patients was more than 10 but PD-L1 expression was less than 25% (Figure 1A)
Summary
Immunotherapy is emerging as a beneficial tool for cancer treatment by activating the immune system to produce antitumor effects [1]. To identify patients who are more likely to respond to PD-1/PD-L1 blockade as well as other immunotherapeutics, researchers defined several biomarkers to predict the response of immunotherapy in cancer treatment. Patients with a higher TMB or higher PD-L1 expression have a higher likelihood of immunotherapy response Another novel statistical value, mutant-allele tumor heterogeneity (MATH), has been documented that is as a measure of intratumor genetic heterogeneity and can be used as a biomarker to predict the response of treatment for patients [13,14,15,16]. The recent studies showed that response rate to single-agent programmed cell death protein 1 (PD-1)/programmed cell death-ligand 1 (PD-L1) inhibition in unselected non-small cell lung cancer (NSCLC) patients is 25% so that researchers defined several biomarkers to predict the response of immunotherapy in ICIs treatment. Common biomarkers like tumor mutational burden (TMB) and PD-L1 expression have several limitations, such as low accuracy and inadequately validated cutoff value
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
