Prediction model for pheochromocytoma/paraganglioma using nCounter assay

Young Lyun Oh ¹, Sun-Ju Byeon ², Yong Joon Suh ³

⁰Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.

Journal of Surgical Oncology +

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April 18, 2024

View abstract on PubMed

Summary

Predicting pheochromocytoma/paraganglioma (PPGL) metastasis is vital. A new model using gene expression and tumor size achieved high accuracy, aiding treatment strategies for these rare tumors.

Area Of Science

Endocrinology
Oncology
Genomics

Background

The World Health Organization reclassified pheochromocytomas/paragangliomas (PPGL) as malignant in 2017 due to limitations in predicting aggressive behavior.
Accurate prediction of PPGL metastasis is critical for effective treatment planning.

Purpose Of The Study

To develop a predictive model for PPGL metastasis.
To identify key molecular and clinical factors associated with PPGL metastasis.

Main Methods

Analysis of 97 PPGL cases from two hospitals, including FFPE tissue samples.
nCounter assay to identify differentially expressed genes between metastatic and non-metastatic PPGL.
Development and performance evaluation of a Lasso regression-based prediction model.

Main Results

CDK1, TYMS, and TOP2A gene expressions were significantly different between metastatic and non-metastatic PPGL.
Tumor size showed a positive correlation with CDK1 expression.
The Lasso regression model achieved 91.7% sensitivity and 95.5% specificity in predicting metastasis.

Conclusions

Machine learning-based multi-modal classifiers, utilizing nCounter assay data, can predict PPGL metastasis with high accuracy.
CDK1 inhibitors represent a potential therapeutic avenue for PPGL treatment.

Keywords:

malignancy nCounter assay paraganglioma pheochromocytoma prediction model