The lungNENomics project – A comprehensive multidisciplinary characterisation of pulmonary carcinoids

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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.

Aim(s): The goal of the lungNENomics project is to fully characterize pulmonary carcinoids using a multidisciplinary approach that includes detailed clinical and histopathological evaluation, deep learning image analysis, multi-omics and spatial data, evolutionary genomics, and organoid models.

Materials and methods: We have generated WGS and/or RNA sequencing and DNA methylation array data (n>200), Digital Spatial proteomics (n=60), Visium Spatial Gene Expression (n=4), multi-regional sequencing (n=26), patient-derived tumor organoids (n=10) and quantitative histopathological image analyses using deep learning (n=350).

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,