ECE2023 Oral Communications Oral Communications 6: Endocrine-related Cancer (6 abstracts)
Tumor microenvironment of adrenocortical carcinoma dissected by single-cell RNA-sequencing
Anne Jouinot 1,2 , Yoann Martin 1 , Thomas Foulonneau 1 , Yanis Bendjelal 1 , Philip Calvet 1 , Florian Violon 1,3 , Mathilde Sibony 1,3 , De Murat Daniel 1 , Roberta Armignacco 1 , Karine Perlemoine 1 , Franck Letourneur 1 , Brigitte Izac 1 , Muriel Andrieu 1 , Annabel Berthon 1 , Bruno Ragazzon 1 , Lionel Groussin 1,2 , Rossella Libe 1,2 , Jerome Bertherat 1,2 & Guillaume Assié 1,2
1Institut Cochin, INSERM U1016, CNRS UMR8104, Paris Cité University, Paris, France; 2Endocrinology, AP-HP Hôpital Cochin, Paris, France; 3Pathology, AP-HP Hôpital Cochin, Paris, France
Background: Molecular classification is important for diagnosis and prognosis of adrenocortical tumors (ACT). Transcriptome profiles separate benign ACT (“C2” cluster) from carcinomas (ACC) and identify two groups of ACC, “C1A” (“steroid” and “proliferation” signatures) and “C1B” (“immune” signature), of poor and better prognosis respectively. However, these signatures were characterized at the tissue level (“bulk”) and our knowledge of the cellular composition of ACC is limited. The aim of this study was to dissect the tumor microenvironment composition in “C1A” and “C1B” ACC.
Methods: We performed single-nuclei RNA-sequencing (10×) of ~170,000 cells from human normal adrenal (n=4), benign ACT (n=14) and ACC (n=20). Bioinformatic analyses were conducted using CellRanger and Seurat pipelines to construct a single-cell atlas of normal and tumoral adrenal cortex. This atlas was then used to estimate the cell proportions (Cibersortx) in “bulk” transcriptome data of 216 ACC patients: a microarray dataset (ENSAT 2014, n=46), a full-length RNA-sequencing dataset (TCGA, n=78) and a 3’ RNA-sequencing dataset (ENSAT 2021, n=92). Finally, we tested the association of tumor microenvironment composition with hormone secretion and outcome.
Results: Microenvironment of steroid cells was composed of fibroblasts (4.6% of total cells), endothelial cells (5.1%), myeloid cells (9.9%) and lymphoid cells (1.3%). Comparing “C1A” and “C1B”, only minor differences were observed for proportions of fibroblasts (2.4% and 2.8% respectively, P<0.001), endothelial cells (4.0% and 3.3%, P<0.001) and myeloid cells (10.4% and 13.3%, P<0.001). In ACC microenvironment, fibroblasts, endothelial cells and myeloid cells corresponded mainly to cancer-associated fibroblasts (expressing PDGFRB and FN1), tumor-associated endothelial cells (expressing ANGPT2 and VWF) and tumor-associated macrophages (expressing CD163 and F13A1) respectively. Comparing “C1A” and “C1B”, a subpopulation of immune cells was enriched in “C1B” ACC. Deconvolution in bulk transcriptomes showed that this population is associated with non-cortisol secreting tumors (t-test P=0.002, P<0.001 and P=0.03), longer disease-free survival (Logrank P=0.006, P<0.001 and P<0.001) and longer overall survival (Logrank P=0.004, P=0.006 and P<0.001 in ENSAT 2014, TCGA and ENSAT 2021 datasets respectively).
Conclusion: A specific population of immune cells may be repressed by glucocorticoid secretion, leading to immune system escape in poor prognosis “C1A” ACC. This may impact immunotherapy strategies in this cancer.
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Endocrine Abstracts
ISSN 1470-3947 (print) | ISSN 1479-6848 (online)
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