Prediction of the differences in tumor mutation burden between primary and metastatic lesions by radiogenomics

Abstract

Tumor mutational burden (TMB) is gaining attention as a biomarker for responses to immune checkpoint inhibitors in cancer patients. In this study, we evaluated the status of TMB in primary and liver metastatic lesions in patients with colorectal cancer (CRC). In addition, the status of TMB in primary and liver metastatic lesions was inferred by radiogenomics on the basis of computed tomography (CT) images. The study population included 24 CRC patients with liver metastases. DNA was extracted from primary and liver metastatic lesions obtained from the patients and TMB values were evaluated by next-generation sequencing. The TMB value was considered high when it equaled to or exceeded 10/100 Mb. Radiogenomic analysis of TMB was performed by machine learning using CT images and the construction of prediction models. In 7 out of 24 patients (29.2%), the TMB status differed between the primary and liver metastatic lesions. Radiogenomic analysis was performed to predict whether TMB status was high or low. The maximum values for the area under the receiver operating characteristic curve were 0.732 and 0.812 for primary CRC and CRC with liver metastasis, respectively. The sensitivity, specificity, and accuracy of the constructed models for TMB status discordance were 0.857, 0.600, and 0.682, respectively. Our results suggested that accurate inference of the TMB status is possible using radiogenomics. Therefore, radiogenomics could facilitate the diagnosis, treatment, and prognosis of patients with CRC in the clinical setting.

Keywords: colorectal cancer; heterogeneity; metastasis; radiogenomics; tumor mutational burden.

FIGURE 1

A summary of the steps involved in sample processing

FIGURE 2

Overall survival curves of patients with positive and negative tumor mutational burden status discordance between primary and metastatic lesions

FIGURE 3

Mutations in genes associated with the primary lesion and metastatic lesion

FIGURE 4

Receiver operating characteristic plots for machine learning prediction for high or low tumor mutational burden in the primary (A) and metastatic (B) lesions. ColA and ColV, computed tomography (CT) of arterial and venous phases for the primary colon lesion. LivN and LivP, CT of non‐contrast enhancement and portal phase for the metastatic liver lesion. Area under the curve is shown in parentheses