Mitochondrial DNA mutations in extremely preterm infants with bronchopulmonary dysplasia.

Bronchopulmonary dysplasia (BPD) is a serious chronic lung disease affecting extremely preterm infants. While mitochondrial dysfunction has been investigated in various medical conditions, limited research has explored mitochondrial DNA (mtDNA) gene mutations, specifically in BPD. This study aimed to evaluate mitochondrial mtDNA gene mutations in extremely preterm infants with BPD. In this prospective observational study, we enrolled a cohort of extremely preterm infants diagnosed with BPD. Clinical data were collected to provide comprehensive patient profiles. Peripheral blood mononuclear cells were isolated from whole-blood samples obtained within a defined timeframe. Subsequently, mtDNA extraction and sequencing using next-generation sequencing technology were performed to identify mtDNA gene mutations. Among the cohort of ten extremely preterm infants with BPD, mtDNA sequencing revealed the presence of mutations in seven patients, resulting in a total of twenty-one point mutations. Notably, many of these mutations were identified in loci associated with critical components of the respiratory chain complexes, vital for proper mitochondrial function and cellular energy production. This pilot study provides evidence of mtDNA point mutations in a subset of extremely preterm infants with BPD. These findings suggest a potential association between mitochondrial dysfunction and the pathogenesis of BPD. Further extensive investigations are warranted to unravel the mechanisms underlying mtDNA mutations in BPD.

Invasiveness of Upper Tract Urothelial Carcinoma: Clinical Significance and Integrative Diagnostic Strategy.

PURPOSE: In this study, we aimed to determine the clinicopathologic, radiologic, and molecular significance of the tumor invasiveness to further stratify the patients with high-grade (HG) upper tract urothelial carcinoma (UTUC) who can be treated less aggressively. MATERIALS AND METHODS: Clinicopathologic and radiologic characteristics of 166 surgically resected HG UTUC (48 noninvasive, and 118 invasive) cases were evaluated. Six noninvasive UTUC cases with intratumoral tumor grade heterogeneity were selected for whole-exome sequencing (WES) to understand the underlying molecular pathophysiology. Barcode-tagging sequencing was done for validation of the target genes from WES data. RESULTS: Patients with noninvasive UTUC showed no cancer-specific death with better cancer-specific survival (p < 0.001) and recurrence-free survival (p < 0.001) compared to the patients with invasive UTUC. Compared to the invasive UTUC, noninvasive UTUC was correlated to a low grade (LG) on the preoperative abdominal computed tomography (CT) grading system (p < 0.001), histologic intratumoral tumor grade heterogeneity (p=0.018), discrepancy in preoperative urine cytology diagnosis (p=0.018), and absence of urothelial carcinoma in situ (p < 0.001). WES of the heterogeneous components showed mutually shared HRAS and FGFR3 mutations shared between the HG and LG components. HRAS mutation was associated with the lower grade on preoperative abdominal CT and intratumoral tumor grade heterogeneity (p=0.045 and p < 0.001, respectively), whereas FGFR3 mutation was correlated to the absence of carcinoma in situ (p < 0.001). CONCLUSION: According to our comprehensive analysis, HG noninvasive UTUC can be preoperatively suspected based on distinct preoperative radiologic, cytologic, histologic, and molecular features. Noninvasive HG UTUC shows excellent prognosis and thus should be treated less aggressively.

A new model using deep learning to predict recurrence after surgical resection of lung adenocarcinoma.

This study aimed to develop a deep learning (DL) model for predicting the recurrence risk of lung adenocarcinoma (LUAD) based on its histopathological features. Clinicopathological data and whole slide images from 164 LUAD cases were collected and used to train DL models with an ImageNet pre-trained efficientnet-b2 architecture, densenet201, and resnet152. The models were trained to classify each image patch into high-risk or low-risk groups, and the case-level result was determined by multiple instance learning with final FC layer's features from a model from all patches. Analysis of the clinicopathological and genetic characteristics of the model-based risk group was performed. For predicting recurrence, the model had an area under the curve score of 0.763 with 0.750, 0.633 and 0.680 of sensitivity, specificity, and accuracy in the test set, respectively. High-risk cases for recurrence predicted by the model (HR group) were significantly associated with shorter recurrence-free survival and a higher stage (both, p < 0.001). The HR group was associated with specific histopathological features such as poorly differentiated components, complex glandular pattern components, tumor spread through air spaces, and a higher grade. In the HR group, pleural invasion, necrosis, and lymphatic invasion were more frequent, and the size of the invasion was larger (all, p < 0.001). Several genetic mutations, including TP53 (p = 0.007) mutations, were more frequently found in the HR group. The results of stages I-II were similar to those of the general cohort. DL-based model can predict the recurrence risk of LUAD and identify the presence of the TP53 gene mutation by analyzing histopathologic features.

Artificial intelligence algorithm for neoplastic cell percentage estimation and its application to copy number variation in urinary tract cancer.

Background: Bladder cancer is characterized by frequent mutations, which provide potential therapeutic targets for most patients. The effectiveness of emerging personalized therapies depends on an accurate molecular diagnosis, for which the accurate estimation of the neoplastic cell percentage (NCP) is a crucial initial step. However, the established method for determining the NCP, manual counting by a pathologist, is time-consuming and not easily executable. Methods: To address this, artificial intelligence (AI) models were developed to estimate the NCP using nine convolutional neural networks and the scanned images of 39 cases of urinary tract cancer. The performance of the AI models was compared to that of six pathologists for 119 cases in the validation cohort. The ground truth value was obtained through multiplexed immunofluorescence. The AI model was then applied to 41 cases in the application cohort that underwent next-generation sequencing testing, and its impact on the copy number variation (CNV) was analyzed. Results: Each AI model demonstrated high reliability, with intraclass correlation coefficients (ICCs) ranging from 0.82 to 0.88. These values were comparable or better to those of pathologists, whose ICCs ranged from 0.78 to 0.91 in urothelial carcinoma cases, both with and without divergent differentiation/ subtypes. After applying AI-driven NCP, 190 CNV (24.2%) were reclassified with 66 (8.4%) and 78 (9.9%) moved to amplification and loss, respectively, from neutral/minor CNV. The neutral/minor CNV proportion decreased by 6%. Conclusions: These results suggest that AI models could assist human pathologists in repetitive and cumbersome NCP calculations.