Radiomics features for assessing tumor-infiltrating lymphocytes correlate with molecular traits of triple-negative breast cancer

肿瘤浸润淋巴细胞 肿瘤微环境 医学 乳腺癌 无线电技术 队列 肿瘤科 免疫疗法 CD8型 免疫系统 三阴性乳腺癌 放射基因组学 内科学 癌症 癌症研究 免疫学 放射科
作者
Guan-Hua Su,Yi Xiao,Lin Jiang,Rencheng Zheng,He Wang,Yan Chen,Yajia Gu,Chao You,Zhi-Ming Shao
出处
期刊:Journal of Translational Medicine [Springer Nature]
卷期号:20 (1) 被引量:35
标识
DOI:10.1186/s12967-022-03688-x
摘要

Abstract Background Tumor-infiltrating lymphocytes (TILs) have become a promising biomarker for assessing tumor immune microenvironment and predicting immunotherapy response. However, the assessment of TILs relies on invasive pathological slides. Methods We retrospectively extracted radiomics features from magnetic resonance imaging (MRI) to develop a radiomic cohort of triple-negative breast cancer (TNBC) (n = 139), among which 116 patients underwent transcriptomic sequencing. This radiomic cohort was randomly divided into the training cohort (n = 98) and validation cohort (n = 41) to develop radiomic signatures to predict the level of TILs through a non-invasive method. Pathologically evaluated TILs in the H&E sections were set as the gold standard. Elastic net and logistic regression were utilized to perform radiomics feature selection and model training, respectively. Transcriptomics was utilized to infer the detailed composition of the tumor microenvironment and to validate the radiomic signatures. Results We selected three radiomics features to develop a TILs-predicting radiomics model, which performed well in the validation cohort (AUC 0.790, 95% confidence interval (CI) 0.638–0.943). Further investigation with transcriptomics verified that tumors with high TILs predicted by radiomics (Rad-TILs) presented activated immune-related pathways, such as antigen processing and presentation, and immune checkpoints pathways. In addition, a hot immune microenvironment, including upregulated T cell infiltration gene signatures, cytokines, costimulators and major histocompatibility complexes (MHCs), as well as more CD8 + T cells, follicular helper T cells and memory B cells, was found in high Rad-TILs tumors. Conclusions Our study demonstrated the feasibility of radiomics model in predicting TILs status and provided a method to make the features interpretable, which will pave the way toward precision medicine for TNBC.

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