Evaluation of 30 DNA damage response and 6 mismatch repair gene mutations as biomarkers for immunotherapy outcomes across multiple solid tumor types

Objective:DNA damage response(DDR)genes have low mutation rates,which may restrict their clinical applications in predicting the outcomes of immune checkpoint inhibitor(ICI)treatment.Thus,a systemic analysis of multiple DDR genes is needed to identify potential biomarkers of ICI efficacy.Methods:A total of 39,631 patients with mutation data were selected from the cBioPortal database.A total of 155 patients with mutation data were obtained from the Fudan University Shanghai Cancer Center(FUSCC).A total of 1,660 patients from the MSK-IMPACT cohort who underwent ICI treatment were selected for survival analysis.A total of 249 patients who underwent ICI treatment from the Dana-Farber Cancer Institute(DFCI)cohort were obtained from a published dataset.The Cancer Genome Atlas(TCGA)level 3 RNA-Seq version 2 RSEM data for gastric cancer were downloaded from cBioPortal.Results:Six MMR and 30 DDR genes were included in this study.Six MMR and 20 DDR gene mutations were found to predict the therapeutic efficacy of ICI,and most of them predicted the therapeutic efficacy of ICI,in a manner dependent on TMB,except for 4 combined DDR gene mutations,which were associated with the therapeutic efficacy of ICI independently of the TMB.Single MMR/DDR genes showed low mutation rates;however,the mutation rate of all the MMR/DDR genes associated with the therapeutic efficacy of ICI was relatively high,reaching 10%–30%in several cancer types.Conclusions:Coanalysis of multiple MMR/DDR mutations aids in selecting patients who are potential candidates for immunotherapy.

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.

Predicting the 7^(th)Day Efficacy of Acupoint Application of Chinese Herbs(Xiao Zhong Zhi Tong Tie)in Patients with Diarrhea——A Machine-Learning Model Based on XGBoost Algorithm

Objective:Extreme gradient boosting(XGBoost)was used to predict the 7^(th)day efficacy of the acupoint application(AP)of Chinese herbs(Xiao Zhong Zhi Tong Tie)in patients with diarrhea.Materials and Methods:We consecutively collected medical records of patients with diarrhea nationwide on the Chun Bo Wan Xiang cloud platform from August 22 to November 5,2020.Demographic and clinical data and the fecal properties were included in this study.We established the XGBoost model to predict the 7^(th)day efficacy of AP in patients with diarrhea.The XGBoost model was evaluated using the area under the receiver operating characteristic(ROC)curve(AUC).We next compared the performance of XGBoost with that of artificial neural network(ANN),ANN+boosting,ANN+bagging,and support vector machine(SVM).Results:The XGBoost model provided a prediction accuracy of 84.86%(95%confidence interval=82.74%to 86.81%)and the ROC curve analysis showed an AUC of 0.81.The top-three variables with the highest importance are age,duration of diarrhea,and region(North).Our study revealed that XGBoost was not superior to ANN,ANN+boosting,ANN+bagging,and SVM.Conclusions:The established XGBoost model for predicting the 7^(th)day efficacy of AP in patients with diarrhea exhibited good accuracy and precision,which can be used for efficacy prediction.