Association of KDR mutation with better clinical outcomes in pan-cancer for immune checkpoint inhibitors.

Kinase insert domain receptor (KDR) activation is associated with the immunosuppressive microenvironment. However, the efficacy of immunotherapy in patients with KDR mutations is still unclear. To investigate the relationship between KDR gene mutations and the prognosis of pan-cancer, and whether immune checkpoint inhibitors (ICIs) may improve the prognosis of patients with KDR mutations, we analyzed public cohorts of pan-cancer immunotherapeutic patients including genomic and clinical data.Further analysis was performed on an internal validation data set including 67 non-small cell lung cancer. Through bioinformatics analysis, potential mechanism was studied in TCGA data. We found better responses to ICIs in patients with KDR mutation from pan-cancer public datasets (objective response rate [ORR], 45.0% vs 25.1%, P=0.0058; progression-free survival [PFS], P=0.039, HR=0.586, 95% CI 0.353-0.973) and validation cohort (overall survival (OS), P=0.05, HR=0.62; 95% CI, 0.38-1.00). Our NSCLC cohort verified the value of KDR mutation in predicting better clinical outcomes, including ORR (70.0% vs 22.81%, P=0.0057) and PFS (HR=0.158; 95% CI, 0.045-0.773, P=0.007). KDR mutation was associated with tumor mutation burden high, neoantigen burden and immune cellular activities. Meanwhile, KDR mutation was indicative of an immune-hot status, characterized by higher expression of PD-L1 and abundance of cytotoxic lymphocytes. KDR mutations may be potential positive predictors for pan-cancer received ICIs.

Deleterious AHNAK2 Mutation as a Novel Biomarker for Immune Checkpoint Inhibitors in Non-Small Cell Lung Cancer.

Immune checkpoint inhibitors (ICIs) have exhibited promising efficacy in non-small cell lung cancer (NSCLC), but the response occurs in only a minority of patients. In clinic, biomarkers such as TMB (tumor mutation burden) and PD-L1 (programmed cell death 1 ligand 1) still have their limitations in predicting the prognosis of ICI treatment. Hence, reliable predictive markers for ICIs are urgently needed. A public immunotherapy dataset with clinical information and mutational data of 75 NSCLC patients was obtained from cBioPortal as the discovery cohort, and another immunotherapy dataset of 249 patients across multiple cancer types was collected as the validation. Integrated bioinformatics analysis was performed to explore the potential mechanism, and immunohistochemistry studies were used to verify it. AHNAK nucleoprotein 2 (AHNAK2) was reported to have pro-tumor growth effects across multiple cancers, while its role in tumor immunity was unclear. We found that approximately 11% of the NSCLC patients harbored AHNAK2 mutations, which were associated with promising outcomes to ICI treatments (ORR, p = 0.013). We further found that AHNAK2 deleterious mutation (del-AHNAK2 mut) possessed better predictive function in NSCLC than non-deleterious AHNAK2 mutation (PFS, OS, log-rank p < 0.05), potentially associated with stronger tumor immunogenicity and an activated immune microenvironment. This work identified del-AHNAK2 mut as a novel biomarker to predict favorable ICI response in NSCLC.

Deep Learning-Based Computed Tomography Imaging to Diagnose the Lung Nodule and Treatment Effect of Radiofrequency Ablation.

This study aimed to detect and diagnose the lung nodules as early as possible to effectively treat them, thereby reducing the burden on the medical system and patients. A lung computed tomography (CT) image segmentation algorithm was constructed based on the deep learning convolutional neural network (CNN). The clinical data of 69 patients with lung nodules diagnosed by needle biopsy and pathological comprehensive diagnosis at hospital were collected for specific analysis. The CT image segmentation algorithm was used to distinguish the nature and volume of lung nodules and compared with other computer aided design (CAD) software (Philips ISP). 69 patients with lung nodules were treated by radiofrequency ablation (RFA). The results showed that the diagnostic sensitivity of the CT image segmentation algorithm based on the CNN was obviously higher than that of the Philips ISP for solid nodules <5 mm (63 cases vs. 33 cases) (P < 0.05); it was the same result for the subsolid nodule <5 mm (33 case vs. 5 cases) (P < 0.05) that was slightly higher for solid and subsolid nodules with a diameter of 5-10 mm (37 cases vs. 28 cases) (P < 0.05). In addition, the CNN algorithm can reach all detection for calcified nodules and pleural nodules (7 cases; 5 cases), and the diagnostic sensitivities were much better than those of Philips ISP (2 cases; 3 cases) (P < 0.05). Patients with pulmonary nodules treated by RFA were in good postoperative condition, with a half-year survival rate of 100% and a one-year survival rate of 72.4%. Therefore, it could be concluded that the CT image segmentation algorithm based on the CNN could effectively detect and diagnose the lung nodules early, and the RFA could effectively treat the lung nodules.

Individualized predictions of early isolated distal deep vein thrombosis in patients with acute ischemic stroke: a retrospective study.

BACKGROUND: Although isolated distal deep vein thrombosis (IDDVT) is a clinical complication for acute ischemic stroke (AIS) patients, very few clinicians value it and few methods can predict early IDDVT. This study aimed to establish and validate an individualized predictive nomogram for the risk of early IDDVT in AIS patients. METHODS: This study enrolled 647 consecutive AIS patients who were randomly divided into a training cohort (n = 431) and a validation cohort (n = 216). Based on logistic analyses in training cohort, a nomogram was constructed to predict early IDDVT. The nomogram was then validated using area under the receiver operating characteristic curve (AUROC) and calibration plots. RESULTS: The multivariate logistic regression analysis revealed that age, gender, lower limb paralysis, current pneumonia, atrial fibrillation and malignant tumor were independent risk factors of early IDDVT; these variables were integrated to construct the nomogram. Calibration plots revealed acceptable agreement between the predicted and actual IDDVT probabilities in both the training and validation cohorts. The nomogram had AUROC values of 0.767 (95% CI: 0.742-0.806) and 0.820 (95% CI: 0.762-0.869) in the training and validation cohorts, respectively. Additionally, in the validation cohort, the AUROC of the nomogram was higher than those of the other scores for predicting IDDVT. CONCLUSIONS: The present nomogram provides clinicians with a novel and easy-to-use tool for the prediction of the individualized risk of IDDVT in the early stages of AIS, which would be helpful to initiate imaging examination and interventions timely.

Coffee consumption and risk of lung cancer: a meta-analysis.

Epidemiologic studies have evaluated the potential association between coffee consumption and lung cancer risk. However, results were inconsistent. To clarify the role of coffee in lung cancer, we conducted a meta-analysis on this topic. We searched PubMed and EMBASE databases (from 1966 to January 2009) and the reference lists of retrieved articles. Study-specific risk estimates were pooled using random-effects model. Five prospective studies and 8 case-control studies involving 5347 lung cancer cases and 104,911 non-cases were included in this meta-analysis. The combined results indicated a significant positive association between highest coffee intake and lung cancer [relative risk (RR)=1.27, 95% confidence interval (CI)=1.04-1.54). Furthermore, an increase in coffee consumption of 2 cups/day was associated with a 14% increased risk of developing lung cancer (RR=1.14, 95% CI=1.04-1.26). In stratified analyses, the highest coffee consumption was significantly associated with increased risk of lung cancer in prospective studies, studies conducted in America and Japan, but borderline significantly associated with decreased risk of lung cancer in non-smokers. In addition, decaffeinated coffee drinking was associated with decreased lung cancer risk, although the number of studies on this topic was relative small. In conclusion, results from this meta-analysis indicate that high or an increased consumption of coffee may increase the risk of lung cancer. Because the residual confounding effects of smoking or other factors may still exist, these results should be interpreted with caution.

Green tea, black tea consumption and risk of lung cancer: a meta-analysis.

Studies investigating the association of green tea and black tea consumption with lung cancer risk have reported inconsistent findings. To provide a quantitative assessment of this association, we conducted a meta-analysis on the topic. Studies were identified by a literature search in PubMed from 1966 to November 2008 and by searching the reference lists of relevant studies. Summary relative risk (RR) estimates and their corresponding 95% confidence intervals (CIs) were calculated based on random-effects model. Our meta-analysis included 22 studies provided data on consumption of green tea or black tea, or both related to lung cancer risk. For green tea, the summary RR indicated a borderline significant association between highest green tea consumption and reduced risk of lung cancer (RR=0.78, 95% CI=0.61-1.00). Furthermore, an increase in green tea consumption of two cups/day was associated with an 18% decreased risk of developing lung cancer (RR=0.82, 95% CI=0.71-0.96). For black tea, no statistically significant association was observe through the meta-analysis (highest versus non/lowest, RR=0.86, 95% CI=0.70-1.05; an increment of two cups/day, RR=0.82, 95% CI=0.65-1.03). In conclusion, our data suggest that high or an increase in consumption of green tea but not black tea may be related to the reduction of lung cancer risk.