Endocrine Pathology

, Volume 25, Issue 2, pp 151-164

First online:

Unraveling Tumor Grading and Genomic Landscape in Lung Neuroendocrine Tumors

  • Giuseppe PelosiAffiliated withDepartment of Pathology and Laboratory Medicine, Fondazione IRCCS Istituto Nazionale dei TumoriDepartment of Biomedical and Clinical Sciences Luigi Sacco, Università degli StudiDipartimento di Patologia Diagnostica e Laboratorio, Fondazione IRCCS Istituto Nazionale dei Tumori Email author 
  • , Mauro PapottiAffiliated withDepartment of Oncology, Università degli Studi of Turin at San Luigi Hospital
  • , Guido RindiAffiliated withInstitute of Anatomic Pathology, Gemelli Hospital and Università Cattolica del Sacro Cuore
  • , Aldo ScarpaAffiliated withDepartment of Pathology and Diagnostics, Università degli StudiARC-NET Research Centre, Università degli Studi

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Currently, grading in lung neuroendocrine tumors (NETs) is inherently defined by the histological classification based on cell features, mitosis count, and necrosis, for which typical carcinoids (TC) are low-grade malignant tumors with long life expectation, atypical carcinoids (AC) intermediate-grade malignant tumors with more aggressive clinical behavior, and large cell NE carcinomas (LCNEC) and small cell lung carcinomas (SCLC) high-grade malignant tumors with dismal prognosis. While Ki-67 antigen labeling index, highlighting the proportion of proliferating tumor cells, has largely been used in digestive NETs for assessing prognosis and assisting therapy decisions, the same marker does not play an established role in the diagnosis, grading, and prognosis of lung NETs. Next generation sequencing techniques (NGS), thanks to their astonishing ability to process in a shorter timeframe up to billions of DNA strands, are radically revolutionizing our approach to diagnosis and therapy of tumors, including lung cancer. When applied to single genes, panels of genes, exome, or the whole genome by using either frozen or paraffin tissues, NGS techniques increase our understanding of cancer, thus realizing the bases of precision medicine. Data are emerging that TC and AC are mainly altered in chromatin remodeling genes, whereas LCNEC and SCLC are also mutated in cell cycle checkpoint and cell differentiation regulators. A common denominator to all lung NETs is a deregulation of cell proliferation, which represents a biological rationale for morphologic (mitoses and necrosis) and molecular (Ki-67 antigen) parameters to successfully serve as predictors of tumor behavior (i.e., identification of pathological entities with clinical correlation). It is envisaged that a novel grading system in lung NETs based on the combined assessment of mitoses, necrosis, and Ki-67 LI may offer a better stratification of prognostic classes, realizing a bridge between molecular alterations, morphological features, and clinical behavior.


Lung Neuroendocrine Tumors Carcinoma Carcinoid Typical Atypical LCNEC SCLC Grading Next generation sequencing Cell cycle Chromatin Remodeling Prognosis Survival