TMB相关免疫浸润调控评分(MOTIF)预测免疫治疗响应并指导联合增效

Patients with high tumor mutational burden(TMB)levels do not consistently respond to immune checkpoint inhibitors(ICIs),possibly because a high TMB level does not necessarily result in adequate infiltration of CD8^(+)T cells.Using bulk ribonucleic acid sequencing(RNA-seq)data from 9311 tumor samples across 30 cancer types,we developed a novel tool called the modulator of TMB-associated immune infiltration(MOTIF),which comprises genes that can determine the extent of CD8^(+)T cell infiltration prompted by a certain TMB level.We confirmed that MOTIF can accurately reflect the integrity and defects of the cancer-immunity cycle.By analyzing 84 human single-cell RNA-seq datasets from 32 types of solid tumors,we revealed that MOTIF can provide insights into the diverse roles of various cell types in the modulation of CD8^(+)T cell infiltration.Using pretreatment RNA-seq data from 13 ICI-treated cohorts,we validated the use of MOTIF in predicting CD8^(+)T cell infiltration and ICI efficacy.Among the components of MOTIF,we identified EMC3 as a negative regulator of CD8^(+)T cell infiltration,which was validated via in vivo studies.Additionally,MOTIF provided guidance for the potential combinations of programmed death 1 blockade with certain immunostimulatory drugs to facilitate CD8^(+)T cell infiltration and improve ICI efficacy.

Delayed treatment effect predicting(DTEP)model for guiding immuno-oncology trial designs

Background:The presence of delayed treatment effects(DTE)is common in immuno-oncology trials.However,conventional trial designs often overlook the potential presence of DTE,which can result in an underestimation of the required sample size and loss of statistical power.Conversely,when there is actually no apparent delay in treatment effects,alternative trial designs for addressing DTE may lead to an over-estimation of sample size and unnecessary prolongation of the trial duration.To mitigate this challenge,we propose the use of a DTE predicting(DTEP)model to better guide immuno-oncology trial designs.Methods:The DTEP model was developed and validated using data from 147 pub-lished randomized immuno-oncology trials.The eligible trials were divided into a training set(approximately 75%of the trials)and a test set(approximately 25%).We employed linear discriminant analysis(LDA)to develop the DTEP model for pre-dicting the DTE status using baseline characteristics available at the trial design stage.The receiver operating characteristic(ROC)curve was utilized to assess the ability of the model to distinguish between trials with and without DTE.We further re-conducted the JUPITER-02 trial in a simulation setting,employing three design approaches to assess the potential benefits of utilizing the DTEP model.Results:Baseline characteristics available during the trial design stage,including cancer type,line of treatment,and experimental and control arm regimens were incorporated,and high accuracy in predicting the DTE status in both the training set(area under the operating characteristic curve(AUC),0.79;95%confidence interval(CI),0.71-0.88)and test set(AUC,0.78;95%CI,0.66-0.90)was achieved.Notably,the model successfully predicted the DTE status in two randomized trials among the test sets that were conducted by our team(ESCORT-1st(absence of DTE)and JUPITER-02(presence of DTE)).In silico re-conduct of the JUPITER-02 trial further showed that the statistical power would be markedly improved when trial designs were guided by the DTEP model.Conclusions:The DTEP model can significantly enhance the precision and effectiveness of immuno-oncology trial designs,thereby facilitating the discovery of effective im-munotherapeutics in a more streamlined and expedited manner.