Clinical predictors of treatment efficacy and a prognostic nomogram in patients with lung adenocarcinoma receiving immune checkpoint inhibitors: a retrospective study

Hu, Fang; Jin, Peng; Niu, Yiming; Mao, Xiaowei; Zhao, Yupei; Jiang, Liyan

doi:10.21037/jtd-22-1270

Cited by 2 publications

(1 citation statement)

References 40 publications

(49 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As shown in Figure 4A and 4B , the predictive power of our DCB model is more effective and robust than other reported genetic-mutation feature based signatures for patients with melanoma. Previous studies have revealed that clinical characteristics may affect the outcome of ICIs ( 50 , 51 ). In our model, we found that adding gender, age and clinical stage to the model did not significantly improve its prediction power ( Figure S4 ).…”

Section: Discussionmentioning

confidence: 99%

An enhanced genetic mutation-based model for predicting the efficacy of immune checkpoint inhibitors in patients with melanoma

Pan

Tang²,

Wang³

et al. 2023

Front. Oncol.

View full text Add to dashboard Cite

BackgroundProgrammed death ligand 1 (PD-L1) and tumor mutation burden (TMB) have been developed as biomarkers for the treatment of immune checkpoint inhibitors (ICIs). However, some patients who are TMB-high or PD-L1-high remained resistant to ICIs therapy. Therefore, a more clinically applicable and effective model for predicting the efficacy of ICIs is urgently needed.MethodsIn this study, genomic data for 466 patients with melanoma treated with ICIs from seven independent cohorts were collected and used as training and validation cohorts (training cohort n = 300, validation cohort1 n = 61, validation cohort2 n = 105). Ten machine learning classifiers, including Random Forest classifier, Stochastic Gradient Descent (SGD) classifier and Linear Support Vector Classifier (SVC), were subsequently evaluated. ResultsThe Linear SVC with a 186-gene mutation-based set was screened to construct the durable clinical benefit (DCB) model. Patients predicted to have DCB (pDCB) were associated with a better response to the treatment of ICIs in the validation cohort1 (AUC=0.838) and cohort2 (AUC=0.993). Compared with TMB and other reported genetic mutation-based signatures, the DCB model showed greater predictive power. Furthermore, we explored the genomic features in determining the benefits of ICIs treatment and found that patients with pDCB were associated with higher tumor immunogenicity. ConclusionThe DCB model constructed in this study can effectively predict the efficacy of ICIs treatment in patients with melanoma, which will be helpful for clinical decision-making.

show abstract

Section: Discussionmentioning

confidence: 99%