Predicting response to immunotherapy in gastric cancer via assessing perineural invasion-mediated inflammation in tumor microenvironment.

BACKGROUND: The perineural invasion (PNI)-mediated inflammation of the tumor microenvironment (TME) varies among gastric cancer (GC) patients and exhibits a close relationship with prognosis and immunotherapy. Assessing the neuroinflammation of TME is important in predicting the response to immunotherapy in GC patients. METHODS: Fifteen independent cohorts were enrolled in this study. An inflammatory score was developed and validated in GC. Based on PNI-related prognostic inflammatory signatures, patients were divided into Clusters A and B using unsupervised clustering. The characteristics of clusters and the potential regulatory mechanism of key genes were verified by RT-PCR, western-blot, immunohistochemistry and immunofluorescence in cell and tumor tissue samples.The neuroinflammation infiltration (NII) scoring system was developed based on principal component analysis (PCA) and visualized in a nomogram together with other clinical characteristics. RESULTS: Inflammatory scores were higher in GC patients with PNI compared with those without PNI (P < 0.001). NII.clusterB patients with PNI had abundant immune cell infiltration in the TME but worse prognosis compared with patients in the NII.clusterA patients with PNI and non-PNI subgroups. Higher immune checkpoint expression was noted in NII.clusterB-PNI. VCAM1 is a specific signature of NII.clusterB-PNI, which regulates PD-L1 expression by affecting the phosphorylation of STAT3 in GC cells. Patients with PNI and high NII scores may benefit from immunotherapy. Patients with low nomogram scores had a better prognosis than those with high nomogram scores. CONCLUSIONS: Inflammation mediated by PNI is one of the results of tumor-nerve crosstalk, but its impact on the tumor immune microenvironment is complex. Assessing the inflammation features of PNI is a potential method in predicting the response of immunotherapy effectively.

Multi-modal analysis for accurate prediction of preoperative stage and indications of optimal treatment in gastric cancer.

BACKGROUND: Accurate preoperative clinical staging of gastric cancer helps determine therapeutic strategies. However, no multi-category grading models for gastric cancer have been established. This study aimed to develop multi-modal (CT/EHRs) artificial intelligence (AI) models for predicting tumor stages and optimal treatment indication based on preoperative CT images and electronic health records (EHRs) in patients with gastric cancer. METHODS: This retrospective study enrolled 602 patients with a pathological diagnosis of gastric cancer from Nanfang hospital retrospectively and divided them into training (n = 452) and validation sets (n = 150). A total of 1326 features were extracted of which 1316 radiomic features were extracted from the 3D CT images and 10 clinical parameters were obtained from electronic health records (EHRs). Four multi-layer perceptrons (MLPs) whose input was the combination of radiomic features and clinical parameters were automatically learned with the neural architecture search (NAS) strategy. RESULTS: Two two-layer MLPs identified by NAS approach were employed to predict the stage of the tumor showed greater discrimination with the average ACC value of 0.646 for five T stages, 0.838 for four N stages than traditional methods with ACC of 0.543 (P value = 0.034) and 0.468 (P value = 0.021), respectively. Furthermore, our models reported high prediction accuracy for the indication of endoscopic resection and the preoperative neoadjuvant chemotherapy with the AUC value of 0.771 and 0.661, respectively. CONCLUSIONS: Our multi-modal (CT/EHRs) artificial intelligence models generated with the NAS approach have high accuracy for tumor stage prediction and optimal treatment regimen and timing, which could facilitate radiologists and gastroenterologists to improve diagnosis and treatment efficiency.

Incidence of and risk factors for liver damage in patients with HIV-1 mono-infection receiving antiretroviral therapy.

OBJECTIVES: The study aimed to investigate the incidence of and risk factors for liver damage in patients with human immunodeficiency virus type-1 (HIV-1) mono-infection receiving antiretroviral therapy (ART). METHODS: We retrospectively analyzed the clinical data of patients who were diagnosed with HIV-1 infection and initiated ART from January to December 2017. Among them, 382 patients with HIV-1 mono-infection and normal baseline liver function were included in the analysis. The incidence of liver damage at each follow-up point, and possible risk factors for liver damage were evaluated via COX regression survival analyses. RESULTS: The overall incidence of liver damage (grade I-IV) was 27.23% (interquartile range [IQR]: 26.38%-28.72%). Grade I liver damage was most common and accounted for 22.13% of cases (IQR: 21.06%-24.04%), while grade II liver damage accounted for 3.40% of cases (IQR: 3.19%-4.26%). COX regression and survival analyses revealed that baseline body mass index (BMI), alanine aminotransferase (ALT) level, CD4+ T cell count, HIV-1 viral load, and the antiretroviral regimen were significantly correlated with the occurrence of liver damage. Moreover, baseline ALT levels and HIV-1 viral load were identified as independent risk factors for liver damage in patients with HIV-1 mono-infection. CONCLUSION: Liver damage is common in patients with HIV-1 mono-infection undergoing ART. Patients with risk factors for liver damage should be well-informed before the initiation of ART, and liver function should be closely monitored during ART even in patients with normal liver function before ART.