2024-06-27 カロリンスカ研究所(KI)
<関連情報>
- https://news.ki.se/new-genetic-markers-for-adrenal-cancer-may-predict-survival
- https://www.esmoopen.com/article/S2059-7029(24)01386-3/fulltext
副腎皮質癌の包括的ゲノム解析により、患者の生存に関連する遺伝子プロファイルが明らかになった Comprehensive genomic analysis of adrenocortical carcinoma reveals genetic profiles associated with patient survival
A. Sun-Zhang,C.C. Juhlin,T. Carling,…,M. Schott,C. Larsson,S. Bajalica-Lagercrantz
ESMO Open Published:June 26, 2024
DOI:https://doi.org/10.1016/j.esmoop.2024.103617
Graphical abstract
Highlights
- A 45-gene signature for ACC was constructed using a multi-omics pipeline.
- Patients with the 45-gene signature had significantly worse prognosis even after adjusting for stage and age.
- Protein–protein interaction analysis revealed interactions not previously implicated in ACC.
- Several genes in the signature have therapeutic potential as targets or markers.
Background
Adrenocortical carcinoma (ACC) is one of the most lethal endocrine malignancies and there is a lack of clinically useful markers for prognosis and patient stratification. Therefore our aim was to identify clinical and genetic markers that predict outcome in patients with ACC.
Methods
Clinical and genetic data from a total of 162 patients with ACC were analyzed by combining an independent cohort consisting of tumors from Yale School of Medicine, Karolinska Institutet, and Düsseldorf University (YKD) with two public databases [The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO)]. We used a novel bioinformatical pipeline combining differential expression and messenger RNA (mRNA)- and DNA-dependent survival. Data included reanalysis of previously conducted whole-exome sequencing (WES) for the YKD cohort, WES and RNA data for the TCGA cohort, and RNA data for the GEO cohort.
Results
We identified 3903 significant differentially expressed genes when comparing ACC and adrenocortical adenoma, and the mRNA expression levels of 461/3903 genes significantly impacted survival. Subsequent analysis revealed 45 of these genes to be mutated in patients with significantly worse survival. The relationship was significant even after adjusting for stage and age. Protein–protein interaction showed previously unexplored interactions among many of the 45 proteins, including the cancer-related proteins DNA polymerase delta 1 (POLD1), aurora kinase A (AURKA), and kinesin family member 23 (KIF23). Furthermore 14 of the proteins had significant interactions with TP53 which is the most frequently mutated gene in the germline of patients with ACC.
Conclusions
Using a multiparameter approach, we identified 45 genes that significantly influenced survival. Notably, many of these genes have protein interactions not previously implicated in ACC. These findings may lay the foundation for improved prognostication and future targeted therapies.
