Transcriptomes define distinct subgroups of salivary gland adenoid cystic carcinoma with different driver mutations and outcomes
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Candace A. Frerich1, Kathryn J. Brayer1,2, Brandon M. Painter1, Huining Kang1, Yoshitsugu Mitani3, Adel K. El-Naggar3 and Scott A. Ness1,2
1Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM, USA
2University of New Mexico Comprehensive Cancer Center, Albuquerque, NM, USA
3Head and Neck Pathology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
Scott A. Ness, email: email@example.com
Keywords: personalized medicine; precision medicine; biomarker; emt; bioinformatics
Abbreviations: ACC: Adenoid cystic carcinoma; FFPE: Formalin-fixed, paraffin-embedded; RNA-seq: Ribonucleic acid sequencing; FISH: Fluorescence In Situ Hybridization
Received: October 05, 2017 Accepted: December 08, 2017 Published: December 23, 2017
The relative rarity of salivary gland adenoid cystic carcinoma (ACC) and its slow growing yet aggressive nature has complicated the development of molecular markers for patient stratification. To analyze molecular differences linked to the protracted disease course of ACC and metastases that form 5 or more years after diagnosis, detailed RNA-sequencing (RNA-seq) analysis was performed on 68 ACC tumor samples, starting with archived, formalin-fixed paraffin-embedded (FFPE) samples up to 25 years old, so that clinical outcomes were available. A statistical peak-finding approach was used to classify the tumors that expressed MYB or MYBL1, which had overlapping gene expression signatures, from a group that expressed neither oncogene and displayed a unique phenotype. Expression of MYB or MYBL1 was closely correlated to the expression of the SOX4 and EN1 genes, suggesting that they are direct targets of Myb proteins in ACC tumors. Unsupervised hierarchical clustering identified a subgroup of approximately 20% of patients with exceptionally poor overall survival (median less than 30 months) and a unique gene expression signature resembling embryonic stem cells. The results provide a strategy for stratifying ACC patients and identifying the high-risk, poor-outcome group that are candidates for personalized therapies.
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