Background: Combination therapy with immune checkpoint inhibitors (ICIs) has been applied in the clinic to achieve synergistic effects and to improve clinical efficacy. Compared with monotherapy, combination therapy has promising efficacy against various advanced cancers. To further verify the effectiveness of combination therapy, we conducted a meta-analysis of the efficacy and safety of nivolumab (NIVO) and NIVO plus ipilimumab (IPI) in advanced cancer. Methods: Electronic databases (PubMed, EMbase, and The Cochrane Library) were systematically searched for applicable studies published in English between January 1990 and June 2019. Relevant outcomes included objective response rate (ORR), disease control rate (DCR), median progression-free survival (mPFS), median overall survival (mOS), and grade 3-4 adverse events (AEs).

Background and Aims: Radiotherapy is an increasingly essential therapeutic strategy in the management of hepatocellular carcinoma (HCC). Nevertheless, resistance to radiotherapy is one of the primary obstacles to successful treatment outcomes. Hence, we aim to elucidate the mechanisms underlying radioresistance and identify reliable biotargets that would be inhibited to enhance the efficacy of radiotherapy in HCC. Approach and Results: From a label‐free quantitative proteome screening, we identified transfer RNA (tRNA; guanine‐N [7]‐) methyltransferase 1 (METTL1), a key enzyme for N7‐methylguanosine (m7G) tRNA modification, as an essential driver for HCC cells radioresistance. We reveal that METTL1 promotes DNA double‐strand break (DSB) repair and renders HCC cells resistant to ionizing radiation (IR) using loss‐of‐function and gain‐of‐function assays in vitro and in vivo. Mechanistically, METTL1‐mediated m7G tRNA modification selectively regulates the translation of DNA‐dependent protein kinase catalytic subunit or DNA ligase IV with higher frequencies of m7G‐related codons after IR treatment, thereby resulting in the enhancement of nonhomologous end‐joining (NHEJ)–mediated DNA DSB repair efficiency. Clinically, high METTL1 expression in tumor tissue is significantly correlated with poor prognosis in radiotherapy‐treated patients with HCC. Conclusions: Our findings show that METTL1 is a critical enhancer for HCC cell NHEJ‐based DNA repair following IR therapy. These findings give insight into the role of tRNA modification in messenger RNA translation control in HCC radioresistance.

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Background: Lenvatinib and transarterial chemoembolization (TACE) are first-line treatments for unresectable hepatocellular carcinoma (HCC), but the objective response rate (ORR) is not satisfactory. We aimed to predict the response to lenvatinib combined with TACE before treatment for unresectable HCC using machine learning (ML) algorithms based on clinical data. Methods: Patients with unresectable HCC receiving the combination therapy of lenvatinib combined with TACE from two medical centers were retrospectively collected from January 2020 to December 2021. The response to the combination therapy was evaluated over the following 4–12 weeks. Five types of ML algorithms were applied to develop the predictive models, including classification and regression tree (CART), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), random forest (RF), and support vector machine (SVM). The performance of the models was assessed by the receiver operating characteristic (ROC) curve and area under the receiver operating characteristic curve (AUC). The Shapley Additive exPlanation (SHAP) method was applied to explain the model. Results: A total of 125 unresectable HCC patients were included in the analysis after the inclusion and exclusion criteria, among which 42 (33.6%) patients showed progression disease (PD), 49 (39.2%) showed stable disease (SD), and 34 (27.2%) achieved partial response (PR). The nonresponse group (PD + SD) included 91 patients, while the response group (PR) included 34 patients. The top 40 most important features from all 64 clinical features were selected using the recursive feature elimination (RFE) algorithm to develop the predictive models. The predictive power was satisfactory, with AUCs of 0.74 to 0.91. The SVM model and RF model showed the highest accuracy (86.5%), and the RF model showed the largest AUC (0.91, 95% confidence interval (CI): 0.61–0.95). The SHAP summary plot and decision plot illustrated the impact of the top 40 features on the efficacy of the combination therapy, and the SHAP force plot successfully predicted the efficacy at the individualized level. Conclusions: A new predictive model based on clinical data was developed using ML algorithms, which showed favorable performance in predicting the response to lenvatinib combined with TACE for unresectable HCC. Combining ML with SHAP could provide an explicit explanation of the efficacy prediction.

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