Radiomic biomarkers of tumor immune biology and immunotherapy response

Abstract

Immunotherapies are leading to improved outcomes for many cancers, including those with devastating prognoses. As therapies like immune checkpoint inhibitors (ICI) become a mainstay in treatment regimens, many concurrent challenges have arisen - for instance, delineating clinical responders from non-responders. Predicting response has proven to be difficult given a lack of consistent and accurate biomarkers, heterogeneity of the tumor microenvironment (TME), and a poor understanding of resistance mechanisms. For the most part, imaging data have remained an untapped, yet abundant, resource to address these challenges. In recent years, quantitative image analyses have highlighted the utility of medical imaging in predicting tumor phenotypes, prognosis, and therapeutic response. These studies have been fueled by an explosion of resources in high-throughput mining of image features (i.e. radiomics) and artificial intelligence. In this review, we highlight current progress in radiomics to understand tumor immune biology and predict clinical responses to immunotherapies. We also discuss limitations in these studies and future directions for the field, particularly if high-dimensional imaging data are to play a larger role in precision medicine.

Keywords: Biomarkers; Imaging genomics; Immunotherapy; Precision medicine; Radiogenomics; Radiomics; Tumor immunology.

Fig. 1

Search criteria for studies reporting radiogenomic associations or associations between imaging features and response to immunotherapy.

Fig. 2

Heatmap depicting associations between lower order radiomic features on CT/MRI and either immune phenotypes or response to immunotherapy. Included features were reported to have significant associations in > 1 study. Immune: NK: natural killer, TIL: tumor infiltrating lymphocyte, TLR: toll-like receptor, CTLA4: cytotoxic T-lymphocyte-associated protein 4, IL: interleukin, MDSC: myeloid derived suppressor cell, CD: cluster of differentiation, NFKB: necrosis factor kappa B, PD-L1: programmed death-ligand 1, ICI: immune checkpoint inhibitor; Features: ADC: apparent diffusion coefficient, SD: standard deviation.

Fig. 3

Heatmap depicting associations between higher order radiomic features on CT/MRI and either immune phenotypes or response to immunotherapy. Included features were reported to have significant associations in > 1 study. Immune: TNF: tumor necrosis factor, NK: natural killer, TIL: tumor infiltrating lymphocyte, TLR: toll-like receptor, TGFB: transforming growth factor beta, CTLA4: cytotoxic T-lymphocyte-associated protein 4, CD: cluster of differentiation, NFKB: necrosis factor kappa B, PD-L1: programmed death-ligand 1, PD1: programmed cell death protein 1, ICI: immune checkpoint inhibitor; Features: CoLIAGe: co-occurrence of local anisotropic gradient orientations, SD: standard deviation, GLCM: gray level co-occurrence matrix, IMC: information measure of correlation, GLRLM: gray level run length matrix, HGRE: high gray level run emphasis, SRHGE: short run high gray level emphasis, GLSZM: gray level size zone matrix, NGTDM: neighborhood gray tone difference matrix.

Fig. 4

Heatmap depicting associations between imaging features on PET and either immune phenotypes or response to immunotherapy. Included features were reported to have significant associations in > 1 study. Immune: PD-L1: programmed death-ligand 1, PD1: programmed cell death protein 1, PFS: progression free survival, OS: overall survival, CD: cluster of differentiation, ICI: immune checkpoint inhibitor; Features: MTV: metabolic tumor volume, SUV: standardized uptake value, TLG: total lesion glycolysis.

Fig. 5

Size distributions of primary and validation cohorts for studies reporting radiogenomic associations and associations between imaging features and immunotherapy response.