Core classification of head and neck squamous cell carcinomas: Correlations between morphology, DNA ploidy and HPV infection

Extracting interpretable features for pathologists using weakly supervised learning to predict p16 expression in oropharyngeal cancer

One drawback of existing artificial intelligence (AI)-based histopathological prediction models is the lack of interpretability. The objective of this study is to extract p16-positive oropharyngeal squamous cell carcinoma (OPSCC) features in a form that can be interpreted by pathologists using AI model. We constructed a model for predicting p16 expression using a dataset of whole-slide images from 114 OPSCC biopsy cases. We used the clustering-constrained attention-based multiple-instance learning (CLAM) model, a weakly supervised learning approach. To improve performance, we incorporated tumor annotation into the model (Annot-CLAM) and achieved the mean area under the receiver operating characteristic curve of 0.905. Utilizing the image patches on which the model focused, we examined the features of model interest via histopathologic morphological analysis and cycle-consistent adversarial network (CycleGAN) image translation. The histopathologic morphological analysis evaluated the histopathological characteristics of image patches, revealing significant differences in the numbers of nuclei, the perimeters of the nuclei, and the intercellular bridges between p16-negative and p16-positive image patches. By using the CycleGAN-converted images, we confirmed that the sizes and densities of nuclei are significantly converted. This novel approach improves interpretability in histopathological morphology-based AI models and contributes to the advancement of clinically valuable histopathological morphological features.

Mutation analysis of tumor necrosis factor alpha-induced protein 3 gene in Hodgkin lymphoma

AIMS

Survival and proliferation of Hodgkin and Reed-Sternberg (HRS) cells, the malignant cells of classical Hodgkin lymphoma (CHL), are dependent on constitutive activation of nuclear factor kB (NF-κB). A20, encoded by TNF alpha-induced protein 3 (TNFAIP₃₎, one of the inhibitors of NF-kB, was found to be inactivated by deletions and/or point mutations in CHL.

METHODS

TNFAIP₃ mutations were examined in 37 patients with CHL by using PCR and direct sequencing. In addition, protein expression of A20 was evaluated by immunohistochemistry. Epstein-Barr virus (EBV) status of HL samples was determined by EBV EBER chromogenic in situ hybridization (ISH).

RESULTS

We identified 8 mutation positive cases in a collective of 37 investigated cases (22%). Mutations were most frequent in the nodular sclerosis subtype. Our results revealed the tendency that cases harboring A20 mutations were negative for A20 staining. None of A20 mutation-positive CHL cases showed EBV infection.

CONCLUSIONS

Our study confirms the involvement of the TNFAIP₃ tumor suppressor gene in CHL. A20 may represent a suppressor of human lymphoma and provide a critical molecular link between chronic inflammation and cancer. None of A20 mutation-positive CHL cases showed EBV infection. This fact suggests complementing functions of TNFAIP₃ inactivation and EBV infection in CHL pathogenesis and may represent an interesting point of further investigations.