Abstract Library

#4134 Assessment of the current and emerging criteria for the histopathological classification of lung neuroendocrine tumors in the lungNENomics project

Introduction: The lungNENomics project analysed 300 lung neuroendocrine tumors (NETs), of which 259 cases were pathologically reviewed by six expert pathologists.

Conference:

Presenting Author: Mathian E

Authors: Mathian E, Drouet Y, Sexton-Oates A, Papotti M, Pelosi G,

Keywords: Lung neuroendocrine neoplasm, Pathology, Deep-learning, Ki-67, PHH3,

#3845 The lungNENomics project – A comprehensive multidisciplinary characterisation of pulmonary carcinoids

Introduction: Lung neuroendocrine tumors (LNETs) are subdivided into low-grade (typical) and intermediate-grade (atypical) carcinoids, at higher risk of developing metastases. We have previously identified 3 LNETs molecular groups with different prognoses, as well as “supra-carcinoids” with molecular profiles of higher-grade neuroendocrine carcinomas. Despite promising results, there is still no consensus on their differential diagnosis and treatment and little is known about how some tumors evolve toward more aggressive phenotypes.

Conference:

Presenting Author:

Authors: Foll M, Sexton-Oates A, Mathian E, Alcala N, Mangiante L,

Keywords: lung carcinoid, deep learning, genomic, evolution, multi-omics, spatial analyses,

#3235 A molecular map of lung neuroendocrine neoplasms

Introduction: Lung neuroendocrine neoplasms (LNEN) are understudied cancers because of their rarity, and the sample sizes of the genomic studies are limited. Recently, we performed the first multi-omic characterization of atypical carcinoids, unveiling the existence of the supra-carcinoids and providing the missing pieces for a complete molecular characterization of LNEN.

Conference: 18th Annual ENETS Concerence (2021)

Presenting Author:

Authors: Mathian E, Gabriel A, Mangiante L, Voegele C, Cahais V,

Keywords: carcinoid, lung cancer, neuroendocrine neoplasm, rare cancers, omics, tumor map,