Integrative molecular analysis of lung neuroendocrine neoplasms with different Ki-67 indices identifies a molecular transition group between low- and high-grade neoplasms (A22)

Category: Basic Science

Special category: A - Basic Science - Genetics, Epigenetics, miRNAs, Omics

Presenting author: PhD Michele Simbolo

Introduction: In the 2021 World Health Organization (WHO) classification of lung cancers, lung neuroendocrine neoplasms (LNENs) are classified as low-grade, intermediate-grade and high-grade based on mitotic count but not Ki-67 index, as for GEP-NENs.

Aim(s): In order to test a hypothetical Ki-67-based classification supported by genomic and transcriptomic data, we performed the molecular characterization of a well-known series of LNETs with a very broad range of Ki67 indices.

Materials and methods: For this purpose, 126 LNETs were assessed for transcriptomic profiling of 20,815 genes and genomic alterations in 409 genes. Two approaches were used: 1) supervised, where samples were grouped in 4 categories (G1, G2, G3 and NEC) according to Ki-67 index, and 2) unsupervised.

Results: Using the supervised approach, increased alteration in TP53 and/or RB1 genes and TML values from G1 to NEC (p<0.001) was observed while alteration in MEN1 was enriched in the G2 group (p=0.04). A double transcriptomic profile emerged in the G2 group, one related to the G1 group and the other to the G3/NEC group. A secretory environment supported by CAFs and inflammatory processes was highlighted in the G3 group. Conversely, using the unsupervised approach, a molecular transition group characterized by MEN1 alteration and intermediate survival outcome was identified together with 4 supra-carcinoids in the NEC group.

Conclusion: This study highlighted the presence of a transitional molecular entity ranging from low- to high-grade LNEN overcoming Ki-67-based classification. Furthermore, molecular features of rare entities such as LNET G3 and supra-carcinoids were identified elucidating the relationship between LNEN categories.

Keywords: lung neuroendocrine tumor, net grade 3, next generation sequencing, transcriptomics