Concomitant Prediction of the Ki67 and PIT-1 Expression in Pituitary Adenoma Using Different Radiomics Models.

OBJECTIVES: To preoperatively predict the high expression of Ki67 and positive pituitary transcription factor 1 (PIT-1) simultaneously in pituitary adenoma (PA) using three different radiomics models. METHODS: A total of 247 patients with PA (training set: n = 198; test set: n = 49) were included in this retrospective study. The imaging features were extracted from preoperative contrast-enhanced T1WI (T1CE), T1-weighted imaging (T1WI), and T2-weighted imaging (T2WI). Feature selection was performed using Spearman's rank correlation coefficient and least absolute shrinkage and selection operator (LASSO). The classic machine learning (CML), deep learning (DL), and deep learning radiomics (DLR) models were constructed using logistic regression (LR), support vector machine (SVM), and multi-layer perceptron (MLP) algorithms. The area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, specificity, accuracy, negative predictive value (NPV) and positive predictive value (PPV) were calculated for the training and test sets. In addition, combined with clinical characteristics, the best CML and the best DL models (SVM classifier), the DL radiomics nomogram (DLRN) was constructed to aid clinical decision-making. RESULTS: Seven CML features, 96 DL features, and 107 DLR features were selected to construct CML, DL and DLR models. Compared to CML and DL model, the DLR model had the best performance. The AUC, sensitivity, specificity, accuracy, NPV and PPV were 0.827, 0.792, 0.800, 0.796, 0.800 and 0.792 in the test set, respectively. CONCLUSIONS: Compared with CML and DL models, the DLR model shows the best performance in predicting the Ki67 and PIT-1 expression in PAs simultaneously.

Gut microbiota composition and metabolic characteristics in patients with Craniopharyngioma.

BACKGROUND: Emerging evidence suggests that the gut microbiota is associated with various intracranial neoplastic diseases. It has been observed that alterations in the gut microbiota are present in gliomas, meningiomas, and pituitary neuroendocrine tumors (Pit-NETs). However, the correlation between gut microbiota and craniopharyngioma (CP), a rare embryonic malformation tumor in the sellar region, has not been previously mentioned. Consequently, this study aimed to investigate the gut microbiota composition and metabolic patterns in CP patients, with the goal of identifying potential therapeutic approaches. METHODS: We enrolled 15 medication-free and non-operated patients with CP and 15 healthy controls (HCs), conducting sequential metagenomic and metabolomic analyses on fecal samples to investigate changes in the gut microbiota of CP patients. RESULTS: The composition of gut microbiota in patients with CP compared to HCs show significant discrepancies at both the genus and species levels. The CP group exhibits greater species diversity. And the metabolic patterns between the two groups vary markedly. CONCLUSIONS: The gut microbiota composition and metabolic patterns in patients with CP differ significantly from the healthy population, presenting potential new therapeutic opportunities.

Specific TP53 subtype as biomarker for immune checkpoint inhibitors in lung adenocarcinoma.

BACKGROUND: Although TP53 co-mutation with KRAS/ATM/EGFR/STK11 have been proved to have predictive value for response to immune checkpoint inhibitors (ICIs), not all TP53 mutations are equal in this context. As the main part of TP53 mutant types, Missense and Nonsense alternations in TP53 as independent factors to predict the response to ICIs within Lung Adenocarcinoma (LUAD) patients have not yet been reported. METHODS: An integrated analysis based on multiple-dimensional data types including genomic, transcriptomic, proteomic and clinical data from published lung adenocarcinoma data and local database of LUAD taking immune checkpoint inhibitors. Gene set enrichment analysis (GSEA) was used to determine potentially relevant gene expression signatures between specific subgroups. Single-sample GSEA (GSVA) is conducted to calculate the score for enrichment of a set of genes regulating DNA damage repair (DDR) pathway. FINDINGS: The TP53-missense-mutation group showed increased PD-L1 (CD274) level and enriched IFN-γ signatures compared with the TP53-wild-type subgroup, but no differences were noted in patients with nonsense-mutant vs wild-type p53. Furthermore, a group of suppressor Immune cells like M2 Macrophage and Neutrophils are found enriched in nonsense group. On the other-side, both TP53 missense and nonsense mutations are associated with elevated TMB and neoantigen levels and contribute equally to DNA damage repair deficiency. The distribution regarding to multi-dimensional factors determining the efficacy of ICIs finally transformed into diverse clinical benefits for LUAD. TP53 missense but not -nonsense Mutants are associated with better clinical benefits taking antiPD-1/1L. However, all such TP53 subgroups responds well to nivolumab (antiPD-L1) plus ipilimumab (antiCTLA-4) therapy. INTERPRETATION: Our study demonstrated that not all TP53 mutations are equal in predicting efficacy in patients with LUAD treated with ICIs. Multi-center data showed that TP53 missense and nonsense mutations were significantly different in terms of associations with PD-L1 expression, IFN-γ signatures and TME composition. Special attention should be paid to potential TP53 mutation heterogeneity when evaluating TP53 status as biomarker for ICIs. FUNDING: The study was supported by Key Lab System Project of Guangdong Science and Technology Department - Guangdong Provincial Key Lab of Translational Medicine in Lung Cancer (Grant No. 2017B030314120, to Yi-Long WU).