Radiogenomic method combining DNA methylation profiles and magnetic resonance imaging radiomics predicts patient prognosis in skull base chordoma.

in Clinical epigenetics by Xiaoyu Deng, Peiran Li, Kaibing Tian, Fan Zhang, Yumeng Yan, Yanghua Fan, Zhen Wu, Junting Zhang, Jiang Du, Wei Chen, Liang Wang

TLDR

  • The study developed a radiogenomic signature that combines DNA methylation profiles and MRI images to predict prognosis in patients with skull base chordoma.
  • The signature successfully distinguished between high- and low-risk patients, demonstrating its potential as a non-invasive and convenient method for stratification.

Abstract

Chordoma is a rare malignant bone tumor exhibiting poor survival and prognosis. Hence, it is crucial to develop a convenient and effective prognostic classification method for the rehabilitation and management of patients with chordoma. In this study, we combined DNA methylation profiles and magnetic resonance imaging (MRI) images to generate a radiogenomic signature to assess its effectiveness for prognosis classification in patients with skull base chordoma. DNA methylation profiles from chordoma tissue samples of 40 patients were factorized into eight DNA methylation signatures. Among them, Signature 4 was identified as the prognosis-specific signature. Based on the Signature 4 loading values, the patients were categorized into low-signature (LLG) and high-signature (HLG) loading groups. HLG patients had higher progression-free survival times than LLG patients. Combined analysis with external single-cell RNA-seq data revealed higher tumor cell proportions and lower natural killer cell proportions in the HLG than in the LLG. Additionally, 2,553 radiomic features were extracted from T1, T2, and enhanced T1 MRI images of the patients, and a radiogenomic signature comprising 14 radiomic features was developed. In a validation cohort of 122 patients, the radiogenomic signature successfully distinguished between the two groups (P = 0.027). Furthermore, the existence of Signature 4 was confirmed in an additional dataset of 14 patients. We developed a prognostic radiogenomic signature using a radiogenomic classification method, which leverages MRI images to extract features that reflect the DNA methylation signature associated with prognosis, enabling the stratification of patients based on their prognostic risk. This method offers the advantages of being noninvasive and convenient.

Overview

  • The study aimed to develop a convenient and effective prognostic classification method for patients with skull base chordoma by combining DNA methylation profiles and magnetic resonance imaging (MRI) images.
  • The researchers analyzed DNA methylation profiles from 40 patients and identified a prognosis-specific signature (Signature 4) that categorized patients into low-signature and high-signature groups.
  • The primary objective of the study was to develop a radiogenomic signature using MRI images that reflect the DNA methylation signature associated with prognosis, allowing for non-invasive stratification of patients based on their prognostic risk.

Comparative Analysis & Findings

  • The study found that patients with high-signature loading (HLG) had higher progression-free survival times than those with low-signature loading (LLG).
  • Combined analysis with external single-cell RNA-seq data revealed higher tumor cell proportions and lower natural killer cell proportions in the HLG compared to the LLG.
  • The radiogenomic signature developed using 14 radiomic features successfully distinguished between the two groups (P = 0.027) in a validation cohort of 122 patients.

Implications and Future Directions

  • This study demonstrates the potential of radiogenomic analysis to provide a non-invasive and convenient method for stratifying patients with chordoma based on their prognostic risk.
  • Future studies can further validate the radiogenomic signature in larger and more diverse patient cohorts, and explore its application in other types of cancer.
  • The study highlights the importance of integrating genetic and imaging data to improve the accuracy and efficiency of cancer diagnosis and treatment.
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