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

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

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1037-1053
Number of pages17
JournalJournal of Thoracic Oncology
Volume15
Issue number6
DOIs
Publication statusPublished - Jun 2020

Keywords

  • Histology
  • Mesothelioma
  • Surgery
  • Systemic treatment

Fingerprint

Dive into the research topics of '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'. Together they form a unique fingerprint.

Cite this