Abstract
Purpose :
Lacrimal gland adenoid cystic carcinoma is a rare but very lethal cancer originating in the cells of secretory glands. With the 10-year survival rate of 20%, the only life extending technique is to remove the eye and surrounding socket contents entirely. ACC is a very slow growing but aggressive cancer, even with radical treatment, most patients will present with metastatic disease to the brain, lung, liver, or bones several years out from initial diagnosis and before symptoms are noticeable. Due to the rarity of LGACC, it is not well understood which leads to diagnosing, treating and monitoring disease progression difficult. Previously, genome analysis of ACC of the head and neck has uncovered a high mutation rate of MYB and NOTCH in patient samples, yet these two genes remain untreatable as there are no successful therapeutics for these targets. We aim to further understand the molecular drivers of LGACC and discover novel targets for specifically treating LGACC.
Methods :
We have RNA sequencing data for 7 primary LGACC tumors, mass spectrometry data for 12 primary tumors and RNA sequencing for 15 cell lines derived from tumors to further understand the molecular signatures of LGACC. We used trimgalore and star to prepare and align the RNA sequencing data. We completed extensive statistical analysis using edgeR and limma packages in R for RNA and proteomics, respectively.
Results :
The RNA sequencing of the tumor samples revealed the most significantly differentially expressed gene ontology group is the extracellular matrix, including HAPLN1, FABP7, and VCAN. The mass spectrometry data also showed the significant role of extracellular matrix proteins and metabolic regulation. We compared the differentially expressed genes in the mass spectrometry and RNA sequencing data and discovered 194 genes that were differentially expressed in both analysis types. The complete analysis revealed that genes and proteins in the extracellular matrix are significantly differentially expressed between cancer and normal tissue.
Conclusions :
With this extensive analysis of the molecular signatures, we now have a deeper understanding of the drivers of this slow growing, lethal cancer. This analysis lays the groundwork for the development of reliable molecular diagnostic, prognostic, and surveillance assays as well as therapies specifically targeting differential expression signatures.
This is a 2021 ARVO Annual Meeting abstract.