Objectives: The objective of this study was to assess risk of neurological toxicities following the use of different immune checkpoint inhibitor (ICI) regimens in solid tumors. Methods: Pubmed, Embase, and ClinicalTrials.gov databases were searched for publications, and data were analyzed using Review Manager 5.3 software to compare the risk of immune-related and nonspecific neurological complications potentially triggered by ICIs to controls. Results: In total 23 randomized clinical trials comprising 11,687 patients were included in this meta-analysis. Patients with PD-L1 (OR, 0.29; 95% confidence interval [CI], 0.18-0.48; P<0.01) or programmed cell-death protein 1 (PD-1) inhibitor (OR, 0.21; 95% CI, 0.14-0.31; P<0.01) were less likely to develop any-grade peripheral neuropathy than chemotherapy, while the risk of grade 3-5 was also smaller for PD-1 inhibitor (OR, 0.16; 95% CI, 0.05-0.54; P=0.003). Combination therapy with CTLA4 and PD-1 inhibitor did not significantly increase the risk of any-grade (OR, 0.83; 95% CI, 0.21-3.32; P>0.05) or grade 3-5 (OR, 1.4; 95% CI, 0.2-9.61; P>0.05) peripheral neuropathy compared to monotherapy with CTLA4 or PD-1 inhibitor. However, difference in risk of immune-related adverse events (irAEs) involving central nervous system did not reach statistical significance in patients with different ICI regimens compared those under chemotherapy. Additionally, risk of experiencing paresthesia was in line with that of peripheral neuropathy (OR, 0.42; 95% CI, 0.28-0.62; P<0.01). Conclusions: This meta-analysis shows that PD-L1/PD-1 and CTLA4 inhibitor have decreased risk of peripheral neuropathy compared to chemotherapy, while combination therapy with CTLA4 and PD-1 inhibitor have no difference in neurological toxicities compared to monotherapy with CTLA4 or PD-1 inhibitor.

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Background: Breast cancer is one of the most common cancer in women and a proportion of patients experiences brain metastases with poor prognosis. The study aimed to construct a novel predictive clinical model to evaluate the overall survival (OS) of patients with postoperative brain metastasis of breast cancer (BCBM) and validate its effectiveness. Methods: From 2010 to 2020, a total of 310 female patients with BCBM were diagnosed in The Affiliated Cancer Hospital of Xinjiang Medical University, and they were randomly assigned to the training cohort and the validation cohort. Data of another 173 BCBM patients were collected from the Surveillance, Epidemiology, and End Results Program (SEER) database as an external validation cohort. In the training cohort, the least absolute shrinkage and selection operator (LASSO) Cox regression model was used to determine the fundamental clinical predictive indicators and the nomogram was constructed to predict OS. The model capability was assessed using receiver operating characteristic, C-index, and calibration curves. Kaplan–Meier survival analysis was performed to evaluate clinical effectiveness of the risk stratification system in the model. The accuracy and prediction capability of the model were verified using the validation and SEER cohorts. Results: LASSO Cox regression analysis revealed that lymph node metastasis, molecular subtype, tumor size, chemotherapy, radiotherapy, and lung metastasis were statistically significantly correlated with BCBM. The C-indexes of the survival nomogram in the training, validation, and SEER cohorts were 0.714, 0.710, and 0.670, respectively, which showed good prediction capability. The calibration curves demonstrated that the nomogram had great forecast precision, and a dynamic diagram was drawn to increase the maneuverability of the results. The Risk Stratification System showed that the OS of low-risk patients was considerably better than that of high-risk patients (P < 0.001). Conclusion: The nomogram prediction model constructed in this study has a good predictive value, which can effectively evaluate the survival rate of patients with postoperative BCBM.

Cancer screening provides the opportunity to detect cancer early, ideally before symptom onset and metastasis, and offers an increased opportunity for a better prognosis. The ideal biomarkers for cancer screening should discriminate individuals who have not developed invasive cancer yet but are destined to do so from healthy subjects. However, most cancers lack effective screening recommendations. Therefore, further studies on novel screening strategies are urgently required. Here, our proof-of-concept study shows blood platelets could be a platform for liquid biopsy-based early cancer detection. By using a simple suboptimal inoculation melanoma mouse model, we identified differentially expressed RNAs in platelet signatures of mice injected with a suboptimal number of cancer cells (eDEGs) compared with mice with macroscopic melanomas and negative controls. These RNAs were strongly enriched in pathways related to immune response and regulation. Moreover, 36 genes selected from the eDEGs via bioinformatics analyses were verified in a mouse validation cohort via quantitative real-time PCR. LASSO regression was employed to generate the prediction models with gene expression signatures as the best predictors for occult tumor progression in mice. The prediction models showed great diagnostic efficacy and predictive value in our murine validation cohort, and could discriminate mice with occult tumors from control group (area under curve (AUC) of 0.935 (training data) and 0.912 (testing data)) (gene signature including Cd19, Cdkn1a, S100a9, Tap1, and Tnfrsf1b) and also from macroscopic tumor group (AUC of 0.920 (training data) and 0.936 (testing data)) (gene signature including Ccr7, Cd4, Kmt2d, and Ly6e). Our study provides evidence for potential clinical relevance of blood platelets as a platform for liquid biopsy-based early detection of cancer. Furthermore, the eDEGs are mostly immune-related, not tumor-specific. Hence it is possible platelets-based liquid biopsy could enable simultaneous early detection of cancers from multiple organs of origin. It is also feasible to determine the origin of cancer since platelet profiles are influenced by tumor type.

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