ECE2022 Poster Presentations Endocrine-Related Cancer (41 abstracts)
Spliceosomic landscape across the histological subtypes of lung neuroendocrine neoplasms: a new layer to disentangle tumor Heterogeneity
Ricardo Blázquez-Encinas 1,2,3 , Víctor García Vioque 1,2,3 , Alexandra Sexton-Oates 4 , María Trinidad Moreno Montilla 1,2,3 , Emilia Alors-Pérez 1,2,3 , Federica Mangili 1,2,3,5 , Nicolas Alcala 4 , Matthieu Foll 4 , Alejandro Ibañez Costa 1,2,3 , Lynnette Fernández Cuesta 4 & Justo P. Castaño 1,2,3,6
1Maimonides Institute for Biomedical Research of Cordoba (IMIBIC), Córdoba, Spain; 2Reina Sofía University Hospital, Córdoba, Spain; 3University of Córdoba, Cell Biology, Physiology and Immunology, Córdoba, Spain; 4International Agency for Research on Cancer, Rare Cancers Genomics, Lyon, France; 5University of Milan, Clinical Sciences and Community Health, Milan, Italy; 6CIBER Physiopathology of Obesity and Nutrition, Spain
Lung neuroendocrine neoplasms (LungNENs) are highly heterogeneous tumors, which are classified by the WHO according to their histological grade into low grade: Typical Carcinoids (TC) and intermediate grade Atypical Carcinoids (AC), and high-grade: Large Cell Neuroendocrine Carcinoma (LCNEC) and Small Cell Lung Cancer (SCLC). Recently, a number of studies have tried to untangle the molecular features that define each subtype by applying different approaches, including genomic, transcriptomic and epigenomic analyses, which have provided useful information to better classify and understand LungNENs. However, an emerging layer of complexity tightly linked to every hallmark of cancer remains to be explored in detail in LungNENs: the spliceosomic landscape. Indeed, the status of the alternative splicing pattern and its underlying machinery (spliceosome components and splicing factors) is heavily altered in many tumors, where it relates to cancer development and progression. Thus, here we aimed to analyze the expression of the splicing machinery and the alternative splicing patterns in LungNENs, compare them among the different subtypes, and explore their relationship with their pathophysiological phenotype. To achieve this aim, RNA-seq data of 284 LungNENs samples were analyzed, comprising: 164 TC and AC, 69 LCNEC and 51 SCLC. Specifically, gene expression was calculated with DESeq-2 and alternative splicing events and isoforms were quantified using SUPPA2. We used Principal Component Analysis and Uniform Manifold Approximation and Projection to cluster the samples according to their spliceosomic landscape. Geneset Enrichment Analysis was used to study molecular pathways involved in groups of genes. Results revealed the splicing machinery has clearly distinct expression patterns among the four subgroups of LungNENs, which enable to precisely discriminate them. Moreover, each subgroup displayed a specific profile of alternative splicing events, where certain genes were enriched in relevant biological pathways. Our analyses represent the first comparative study of the spliceosomic landscape of the different histological subgroups of LungNENs and provide new tools and original information to gain further insight to understand LungNEN heterogeneity.
Volume 81
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Endocrine Abstracts
ISSN 1470-3947 (print) | ISSN 1479-6848 (online)
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