ECE2020 Oral Communications Young Investigators (12 abstracts)
Identification of transcriptome profiles in paraffin samples using 3’ RNA-sequencing for the prognostic assessment of adrenocortical carcinoma
Anne Jouinot 1,2 , Mathilde Sibony 1,3 , Lindsay Jeanpierre 1 , Amandine Septier 1 , Daniel De Murat 1 , Roberta Armignacco 1 , Karine Perlemoine 1 , Brigitte Izac 1 , Franck Letourneur 1 , Bruno Ragazzon 1 , Karen Leroy 4 , Sebastien Gaujoux 1,5 , Bertrand Dousset 1,5 , Lionel Groussin 1,2 , Rossella Libe 1,2 , Jerome Bertherat 1,2 & Guillaume Assié 1,2
1 Université de Paris, Institut Cochin, INSERM, CNRS, Paris, France; 2AP-HP Hôpital Cochin, Endocrinology, Paris, France; 3AP-HP Hôpital Cochin, Pathology, Paris, France; 4AP-HP Hôpital Cochin, Genetics and Molecular Biology, Paris, France; 5AP-HP Hôpital Cochin, Digestive and endocrine surgery, Paris, France
Background: Adrenocortical cancer (ACC) is an aggressive tumor with heterogeneous prognosis. Previous genomic studies have demonstrated the importance of molecular classification for the prognostic assessment. Among molecular markers, transcriptome profiles “C1A” (steroid and proliferative signature) and “C1B” (immune signature) show the strongest association with outcome. However these markers are determined so far only from frozen tissue samples, since paraffin-induced RNA degradation prevents the determination of transcriptome profiles using standard technologies on paraffin samples. This limitation hampers the integration of such markers in the routine of pathology departments. The aim of this study was to determine transcriptome profiles from paraffin samples, using a dedicated protocol of RNA-sequencing.
Methods: Tumor RNA was extracted both from frozen (Qiagen) and paraffin (Promega) tissues in 48 ACC patients from Cochin hospital. For 5 patients, several samples (2 to 4) from macroscopically different primary tumor regions were analyzed, including 2 with one area presenting a high Weiss score (> 3) like an ACC and another area with a low Weiss score (< 3) like a benign adenoma. Transcriptome was determined using RNA-sequencing of 3’-end transcripts that are more resistant to RNA degradation- in paraffin samples (QuantSeq, Lexogen and Illumina). An unsupervised non-negative matrix factorization consensus clustering was performed on the top most variable genes to classify the transcriptome profiles. These transcriptome profiles were then compared with known prognostic expression marker (BUB1B-PINK1 differential expression assessed by RT-qPCR) obtained from frozen samples. Association between groups was assessed with Fisher’s test. Association with overall survival (OS) and disease-free survival (DFS) was tested using log-rank test.
Results: Sufficient quality of RNA-sequencing (>1 000 000 reads, Q30 > 85%) was obtained for 54/55 paraffin samples (98%). Unsupervised clustering identified 2 main subgroups: one (26 patients, 55%) enriched in proliferative genes was characterized as “C1A”, and the other (21 patients, 45%) showing immune and inflammatory signature was characterized as “C1B”. Transcriptome profiles were strongly associated with BUB1B-PINK1 marker (P < 10−5), DFS (5-year DFS of 45% and 95% in “C1A” and “C1B” subgroups respectively, P < 10−3) and OS (5-year OS of 55% and 95% in “C1A” and “C1B” subgroups respectively, P = 0.007). Transcriptome-based classification was stable in different tumor regions from the same patient.
Conclusion: The 3’ RNA-sequencing protocol successfully classified “C1A” and “C1B” transcriptome profiles and represents a convenient solution for the determination of gene-expression-based prognostic classification from paraffin samples.
Volume 70
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Endocrine Abstracts
ISSN 1470-3947 (print) | ISSN 1479-6848 (online)
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