Comprehensive molecular and pathological evaluation of transitional mesothelioma assisted by deep learning approach: a multi institutional study of the International Mesothelioma Panel from MESOPATH Reference Center.

Histological subtypes of malignant pleural mesothelioma are a major prognostic indicator and decision denominator for all therapeutic strategies. In ambiguous case a rare transitional [TM) pattern may be diagnosed by pathologists either as epithelioid (EM), biphasic (BM) or sarcomatoid (SM) mesothelioma. The aims of this study were to better characterize the TM subtype from a morphological, immunohistochemical, molecular standpoint; deep learning of pathological slides was applied to this cohort. METHODS: A random selection of 49 representative digitalized sections from surgical biopsies of TM were reviewed by 16 panelists. We evaluated BAP1 expression and p16 homozygous deletion [HD]. We conducted a comprehensive integrated transcriptomic analysis. Unsupervised deep learning algorithm was trained to classify tumors. RESULTS: The 16 panelists recorded 784 diagnoses on the 49 cases. Whilst Kappa value of 0.42 is moderate, the presence of a TM component was diagnosed in 51%. In 49%, the reviewers classified the lesion as EM in 53%, SM in 33%, or BM in 14%. Median survival was 6.7 months. Loss of BAP1 observed in 44% was less frequent in TM than in EM and BM. p16 HD was higher in TM 73% followed by BM (63%) and SM (46%). RNA sequencing unsupervised clustering analysis showed that TM grouped together and were closer to SM than to EM. Deep learning analysis achieved a 94% accuracy for TM identification CONCLUSION: These results demonstrated that TM pattern should be classified in non-epithelioid mesothelioma at minimum as a subgroup of SM type.