Multi-center multi-omics integration predicts individualized prognosis in medullary thyroid carcinoma

Area of Science:

• Oncology
• Genomics
• Proteomics

Background:

• Medullary thyroid carcinoma (MTC) is a rare, aggressive neuroendocrine tumor with limited treatment options and high recurrence rates.
• Comprehensive risk stratification for MTC recurrence is currently lacking, hindering personalized management.

Purpose of the Study:

• To comprehensively profile MTC tumors using multi-omics data.
• To identify novel clinical, genomic, and proteomic factors associated with MTC recurrence.
• To develop and validate an integrative machine learning model for MTC recurrence risk stratification.

Main Methods:

• Multi-center, multi-omics profiling of 482 MTC samples from 452 patients.
• Identification of proteins and mutations using advanced proteomic and genomic techniques.
• Development and validation of a machine learning model integrating clinical, genomic, and proteomic data.

Main Results:

• Identified significant clinical recurrence risk factors including MTC grading, concurrent papillary thyroid carcinoma, and lymph node metastasis.
• Correlated specific RET mutations (M918T, S891A) with high recurrence risk in sporadic and hereditary MTC.
• Discovered downregulated E3 ligases (CUL4B, TRIM32) associated with structural recurrence and defined three distinct molecular subtypes.

Conclusions:

• This multi-center, multi-omics study provides a deeper understanding of MTC heterogeneity.
• An integrative machine learning model combining clinical, genomic, and proteomic features demonstrates strong predictive power for recurrence.
• Findings facilitate enhanced personalized patient management and risk stratification for medullary thyroid carcinoma.