Functional Copy-Number Alterations as Diagnostic and Prognostic Biomarkers in Neuroendocrine Tumors.
Hayley VaughnHeather MajorEvangeline KaderaKendall KeckTimothy DunhamQining QianBartley BrownAaron ScottAndrew M BellizziTerry BraunPatrick J BrehenyDawn E QuelleJames R HoweBenjamin DarbroPublished in: International journal of molecular sciences (2024)
Functional copy-number alterations (fCNAs) are DNA copy-number changes with concordant differential gene expression. These are less likely to be bystander genetic lesions and could serve as robust and reproducible tumor biomarkers. To identify candidate fCNAs in neuroendocrine tumors (NETs), we integrated chromosomal microarray (CMA) and RNA-seq differential gene-expression data from 31 pancreatic (pNETs) and 33 small-bowel neuroendocrine tumors (sbNETs). Tumors were resected from 47 early-disease-progression (<24 months) and 17 late-disease-progression (>24 months) patients. Candidate fCNAs that accurately differentiated these groups in this discovery cohort were then replicated using fluorescence in situ hybridization (FISH) on formalin-fixed, paraffin-embedded (FFPE) tissues in a larger validation cohort of 60 pNETs and 82 sbNETs (52 early- and 65 late-disease-progression samples). Logistic regression analysis revealed the predictive ability of these biomarkers, as well as the assay-performance metrics of sensitivity, specificity, and area under the curve. Our results indicate that copy-number changes at chromosomal loci 4p16.3, 7q31.2, 9p21.3, 17q12, 18q21.2, and 19q12 may be used as diagnostic and prognostic NET biomarkers. This involves a rapid, cost-effective approach to determine the primary tumor site for patients with metastatic liver NETs and to guide risk-stratified therapeutic decisions.
Keyphrases
- copy number
- neuroendocrine tumors
- gene expression
- mitochondrial dna
- genome wide
- dna methylation
- rna seq
- single cell
- small bowel
- end stage renal disease
- single molecule
- prognostic factors
- chronic kidney disease
- ejection fraction
- small molecule
- newly diagnosed
- lymph node
- cell free
- patient reported outcomes
- patient reported
- artificial intelligence
- structural basis
- deep learning