Lynch syndrome (LS) is a cancer-predisposing genetic disease mediated by pathogenic mutations in DNA mismatch repair (MMR) genes MLH1, MSH2, MSH6, and PMS2. Accumulating evidence demonstrates that there is significant biological heterogeneity across MMR genes. Compared to MLH1 and MSH2, PMS2 variant carriers have a much lower risk for LS-related cancers. Tumors in MLH1 and MSH2 variant carriers often display MMR deficiency (dMMR) and/or high microsatellite instability (MSI-H), two predictive biomarkers for immunotherapy efficacy. However, tumors in PMS2 variant carriers are largely microsatellite stable (MSS) instead of MSI. Therefore, the optimal management of cancer patients with LS requires the integration of disease stage, MMR gene penetrance, dMMR/MSI status, and tumor mutational burden (TMB). In this work, we presented a locally advanced lung cancer patient with dMMR/MSI-H/TMB-H tumor and selective loss of PMS2 by immunohistochemistry. Germline testing revealed a rare PMS2 splicing variant (c.1144+1G>A) in the proband and his healthy daughter. The diagnosis of LS was made based on genetic analysis of this variant and literature review. Given the incomplete penetrance of PMS2, the proband and the carrier received tailored genetic counseling. To reduce cancer risk, the proband received four cycles of nivolumab plus chemotherapy and achieved a disease-free survival of sixteen months.
AimsMicroscopic examination is a basic diagnostic technology for colorectal cancer (CRC), but it is very laborious. We developed a dual resolution deep learning network with self-attention mechanism (DRSANet) which combines context and details for CRC binary classification and localisation in whole slide images (WSIs), and as a computer-aided diagnosis (CAD) to improve the sensitivity and specificity of doctors’ diagnosis.MethodsRepresentative regions of interest (ROI) of each tissue type were manually delineated in WSIs by pathologists. Based on the same coordinates of centre position, patches were extracted at different magnification levels from the ROI. Specifically, patches from low magnification level contain contextual information, while from high magnification level provide important details. A dual-inputs network was designed to learn context and details simultaneously, and self-attention mechanism was used to selectively learn different positions in the images to enhance the performance.ResultsIn classification task, DRSANet outperformed the benchmark networks which only depended on the high magnification patches on two test set. Furthermore, in localisation task, DRSANet demonstrated a better localisation capability of tumour area in WSI with less areas of misidentification.ConclusionsWe compared DRSANet with benchmark networks which only use the patches from high magnification level. Experimental results reveal that the performance of DRSANet is better than the benchmark networks. Both context and details should be considered in deep learning method.
Background
Paired basic amino acid-cleaving enzyme 4 (PACE4) belongs to the family of proprotein convertase and is essential for tumor progression, whereas its role in cancer remains controversial and little is known about its role in nasopharyngeal carcinoma (NPC). The aim of this study was to examine if the expression of PACE4 is a prognostic biomarker for patients with NPC.
Methods
Immunofluorescence (IF) and immunohistochemistry (IHC) were used to analyze PACE4 expression in NPC cell line CNE1 and 172 clinicopathologically characterized NPC tissues. The data were analyzed by Chi-square test, Kaplan–Meier plots, and Cox proportional hazards regression model.
Results
IF and IHC staining results showed that PACE4 was mainly located in the cytoplasm of NPC cell line (CNE1) and NPC tissues. Expression of PACE4 was observed in 46/172 (26.7%) of NPC tissues. Further analysis showed that expression of PACE4 was positively associated with late N stage, distant metastasis, and late clinical stage (
P
<0.05). High expression of PACE4 predicted shorter 5-year overall survival of patients with NPC, especially for the patients in advanced stage (32.7% vs 77.3%,
P
<0.001). Furthermore, multivariate analysis showed that PACE4 expression may serve as a potential prognostic factor for NPC.
Conclusion
Our results suggest that PACE4 may play a crucial role in tumor progression and may serve as a valuable prognostic biomarker for patients with NPC.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.