[HTML][HTML] Transcriptomes define distinct subgroups of salivary gland adenoid cystic carcinoma with different driver mutations and outcomes

CA Frerich, KJ Brayer, BM Painter, H Kang, Y Mitani… - Oncotarget, 2018 - ncbi.nlm.nih.gov
CA Frerich, KJ Brayer, BM Painter, H Kang, Y Mitani, AK El-Naggar, SA Ness
Oncotarget, 2018ncbi.nlm.nih.gov
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 …
Abstract
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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