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Journal Article Ppublish 1 citation

PubMed: 38817124 DOI: 10.1210/clinem/dgae380

Developing a Predictive Model for Metastatic Potential in Pancreatic Neuroendocrine Tumor.

J Clin Endocrinol Metab 110(1):263-274

Authors: Jacques A Greenberg, Yajas Shah, Nikolay A Ivanov, Teagan Marshall, Scott Kulm, Jelani Williams, Catherine Tran, Theresa Scognamiglio, Jonas J Heymann, Yeon J Lee-Saxton, Caitlin Egan, Sonali Majumdar, Irene M Min, Rasa Zarnegar, James Howe, Xavier M Keutgen, Thomas J Fahey, Olivier Elemento, Brendan M Finnerty

Affiliations

  • Department of Surgery, Weill Cornell Medicine, New York, NY 10065, USA.
  • Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, Cornell University, New York, NY, 10065, USA.
  • Department of Surgery, University of Chicago Medicine, Chicago, IL 60637, USA.
  • Department of Surgery, University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA.
  • Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10065, USA.
  • Genomics Facility, The Wistar Institute, Philadelphia, PA 19104, USA.

Abstract

CONTEXT

Pancreatic neuroendocrine tumors (PNETs) exhibit a wide range of behavior from localized disease to aggressive metastasis. A comprehensive transcriptomic profile capable of differentiating between these phenotypes remains elusive.

OBJECTIVE

Use machine learning to develop predictive models of PNET metastatic potential dependent upon transcriptomic signature.

METHODS

RNA-sequencing data were analyzed from 95 surgically resected primary PNETs in an international cohort. Two cohorts were generated with equally balanced metastatic PNET composition. Machine learning was used to create predictive models distinguishing between localized and metastatic tumors. Models were validated on an independent cohort of 29 formalin-fixed, paraffin-embedded samples using NanoString nCounter®, a clinically available mRNA quantification platform.

RESULTS

Gene expression analysis identified concordant differentially expressed genes between the 2 cohorts. Gene set enrichment analysis identified additional genes that contributed to enriched biologic pathways in metastatic PNETs. Expression values for these genes were combined with an additional 7 genes known to contribute to PNET oncogenesis and prognosis, including ARX and PDX1. Eight specific genes (AURKA, CDCA8, CPB2, MYT1L, NDC80, PAPPA2, SFMBT1, ZPLD1) were identified as sufficient to classify the metastatic status with high sensitivity (87.5-93.8%) and specificity (78.1-96.9%). These models remained predictive of the metastatic phenotype using NanoString nCounter® on the independent validation cohort, achieving a median area under the receiving operating characteristic curve of 0.886.

CONCLUSION

We identified and validated an 8-gene panel predictive of the metastatic phenotype in PNETs, which can be detected using the clinically available NanoString nCounter® system. This panel should be studied prospectively to determine its utility in guiding operative vs nonoperative management.

MeSH

Genes, Neoplasms, Phenotype, Machine Learning, Neuroendocrine Tumors, Neuroectodermal Tumors, Primitive, Learning

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