[The French mesothelioma network from 1998 to 2013].

International audienceMesothelioma is a rare disease less than 0.3% of cancers in France, very aggressive and resistant to the majority of conventional therapies. Asbestos exposure is nearly the only recognized cause of mesothelioma in men observed in 80% of case. In 1990, the projections based on mortality predicted a raise of incidence in mesothelioma for the next three decades. Nowadays, the diagnosis of this cancer is based on pathology, but the histological presentation frequently heterogeneous, is responsible for numerous pitfalls and major problems of early detection toward effective therapy. Facing such a diagnostic, epidemiological and medico-legal context, a national and international multidisciplinary network has been progressively set up in order to answer to epidemiological survey, translational or academic research questions. Moreover, in response to the action of the French Cancer Program (action 23.1) a network of pathologists was organized for expert pathological second opinion using a standardized procedure of certification for mesothelioma diagnosis. We describe the network organization and show the results during this last 15years period of time from 1998-2013. These results show the major impact on patient's management, and confirm the interest of this second opinion to provide accuracy of epidemiological data, quality of medico-legal acknowledgement and accuracy of clinical diagnostic for the benefit of patients. We also show the impact of these collaborative efforts for creating a high quality clinicobiological, epidemiological and therapeutic data collection for improvement of the knowledge of this dramatic disease

Comprehensive Molecular and Pathologic Evaluation of Transitional Mesothelioma Assisted by Deep Learning Approach: A Multi-Institutional Study of the International Mesothelioma Panel from the MESOPATH Reference Center

International audienceAbstractIntroductionHistologic subtypes of malignant pleural mesothelioma are a major prognostic indicator and decision denominator for all therapeutic strategies. In an ambiguous case, a rare transitional mesothelioma (TM) pattern may be diagnosed by pathologists either as epithelioid mesothelioma (EM), biphasic mesothelioma (BM), or sarcomatoid mesothelioma (SM). This study aimed to better characterize the TM subtype from a histological, immunohistochemical, and molecular standpoint. Deep learning of pathologic slides was applied to this cohort.MethodsA random selection of 49 representative digitalized sections from surgical biopsies of TM was reviewed by 16 panelists. We evaluated BAP1 expression and CDKN2A (p16) homozygous deletion. We conducted a comprehensive, integrated, transcriptomic analysis. An unsupervised deep learning algorithm was trained to classify tumors.ResultsThe 16 panelists recorded 784 diagnoses on the 49 cases. Even though a Kappa value of 0.42 is moderate, the presence of a TM component was diagnosed in 51%. In 49% of the histological evaluation, 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 homozygous deletion was higher in TM (73%), followed by BM (63%) and SM (46%). RNA sequencing unsupervised clustering analysis revealed that TM grouped together and were closer to SM than to EM. Deep learning analysis achieved 94% accuracy for TM identification.ConclusionThese results revealed that the TM pattern should be classified as non-EM or at minimum as a subgroup of the SM type