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

Salle, Francoise Galateau; Le Stang, Nolwenn; Tirode, Franck; Courtiol, Pierre; Nicholson, Andrew G; Tsao, Ming-Sound; Tazelaar, Henry D; Churg, Andrew; Dacic, Sanja; Roggli, Victor; Pissaloux, Daniel; Maussion, Charles; Moarii, Matahi; Beasley, Mary Beth; Begueret, Hugues; Chapel, David B; Copin, Marie Christine; Gibbs, Allen R; Klebe, Sonja; Lantuejoul, Sylvie; Nabeshima, Kazuki; Vignaud, Jean-Michel; Attanoos, Richard; Brcic, Luka; Capron, Frederique; Chirieac, Lucian R; Damiola, Francesca; Sequeiros, Ruth; Cazes, Aurelie; Damotte, Diane; Foulet, Armelle; Giusiano-Courcambeck, Sophie; Hiroshima, Kenzo; Hofman, Veronique; Husain, Aliya N; Kerr, Keith; Marchevsky, Alberto; Paindavoine, Severine; Picquenot, Jean Michel; Rouquette, Isabelle; Sagan, Christine; Sauter, Jennifer; Thivolet, Francoise; Brevet, Marie; Rouvier, Philippe; Travis, William D; Planchard, Gaetane; Weynand, Birgit; Clozel, Thomas; Wainrib, Gilles; Fernandez-Cuesta, Lynnette; Pairon, Jean-Claude; Rusch, Valerie; Girard, Nicolas

doi:10.1016/j.jtho.2020.01.025

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

Author:

Salle, Francoise Galateau

Le Stang, Nolwenn ; Tirode, Franck ; Courtiol, Pierre ; Nicholson, Andrew G ; Tsao, Ming-Sound ; Tazelaar, Henry D ; Churg, Andrew ; Dacic, Sanja ; Roggli, Victor ; Pissaloux, Daniel ; Maussion, Charles ; Moarii, Matahi ; Beasley, Mary Beth ; Begueret, Hugues ; Chapel, David B ; Copin, Marie Christine ; Gibbs, Allen R ; Klebe, Sonja ; Lantuejoul, Sylvie ; Nabeshima, Kazuki ; Vignaud, Jean-Michel ; Attanoos, Richard ; Brcic, Luka ; Capron, Frederique ; Chirieac, Lucian R ; Damiola, Francesca ; Sequeiros, Ruth ; Cazes, Aurelie ; Damotte, Diane ; Foulet, Armelle ; Giusiano-Courcambeck, Sophie ; Hiroshima, Kenzo ; Hofman, Veronique ; Husain, Aliya N ; Kerr, Keith ; Marchevsky, Alberto ; Paindavoine, Severine ; Picquenot, Jean Michel ; Rouquette, Isabelle ; Sagan, Christine ; Sauter, Jennifer ; Thivolet, Francoise ; Brevet, Marie ; Rouvier, Philippe ; Travis, William D ; Planchard, Gaetane ; Weynand, Birgit ; Clozel, Thomas ; Wainrib, Gilles ; Fernandez-Cuesta, Lynnette ; Pairon, Jean-Claude ; Rusch, Valerie ; Girard, Nicolas

Keywords:

Science & Technology, Life Sciences & Biomedicine, Oncology, Respiratory System, Mesothelioma, Histology, Surgery, Systemic treatment, MALIGNANT PLEURAL MESOTHELIOMA, SARCOMATOID MESOTHELIOMA, BAP1 IMMUNOHISTOCHEMISTRY, DIFFERENTIAL-DIAGNOSIS, EXTRAPLEURAL PNEUMONECTOMY, P16 FISH, LUNG, GUIDELINES, CARCINOMA, BENIGN, Deep Learning, Homozygote, Humans, Lung Neoplasms, Sequence Deletion, Tumor Suppressor Proteins, Ubiquitin Thiolesterase, 1102 Cardiorespiratory Medicine and Haematology, 1103 Clinical Sciences, 1112 Oncology and Carcinogenesis, Oncology & Carcinogenesis, 3202 Clinical sciences, 3211 Oncology and carcinogenesis

Abstract:

INTRODUCTION: Histologic 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. METHODS: A 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. RESULTS: The 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. CONCLUSION: These results revealed that the TM pattern should be classified as non-EM or at minimum as a subgroup of the SM type.