Radiosensitivity index emerges as a potential biomarker for combined radiotherapy and immunotherapy

Abstract

In the era of immunotherapy, there lacks of a reliable genomic predictor to identify optimal patient populations in combined radiotherapy and immunotherapy (CRI). The purpose of this study is to investigate whether genomic scores defining radiosensitivity are associated with immune response. Genomic data from Merged Microarray-Acquired dataset (MMD) were established and the Cancer Genome Atlas (TCGA) were obtained. Based on rank-based regression model including 10 genes, radiosensitivity index (RSI) was calculated. A total of 12832 primary tumours across 11 major cancer types were analysed for the association with DNA repair, cellular stemness, macrophage polarisation, and immune subtypes. Additional 585 metastatic tissues were extracted from MET500. RSI was stratified into RSI-Low and RSI-High by a cutpoint of 0.46. Proteomic differential analysis was used to identify significant proteins according to RSI categories. Gene Set Variance Analysis (GSVA) was applied to measure the genomic pathway activity (18 genes for T-cell inflamed activity). Kaplan-Meier analysis was performed for survival analysis. RSI was significantly associated with homologous DNA repair, cancer stemness and immune-related molecular features. Lower RSI was associated with higher fraction of M1 macrophage. Differential proteomic analysis identified significantly higher TAP2 expression in RSI-Low colorectal tumours. In the TCGA cohort, dominant interferon-γ (IFN-γ) response was characterised by low RSI and predicted better response to programmed cell death 1 (PD-1) blockade. In conclusion, in addition to radiation response, our study identified RSI to be associated with various immune-related features and predicted response to PD-1 blockade, thus, highlighting its potential as a candidate biomarker for CRI.

Relationship between Macrophage and Radiosensitivity in Human Primary and Recurrent Glioblastoma: In Silico Analysis with Publicly Available Datasets

“…However, this cutoff only classified 1.7% of brain glioma cases as RSI-low, whereas 98.3% were classified as RSI-high. Similarly, Dai et al [ 21 ] used the cutoff RSI value of 0.46 for stratification into RSI-low and RSI-high groups. A total of 8386 samples from the Merged Microarray-Acquired dataset (MMD) and a total of 6116 primary tumor samples from TCGA were analyzed.…”

“…The 12-chemokine signature was developed as a surrogate marker of immune activation for intratumoral lymphoid cell aggregates; however, the study population characterized by Strom et al did not include glioma patients. Dai et al [ 21 ] revealed that lower RSI scores were associated with higher proportions of M1 macrophages in two large transcriptome databases. These findings verify the relationship between radiosensitivity and immune status, particularly with respect to the proportions of M1 and M2 macrophages in the glioblastoma microenvironment.…”

“…The RSI comprises 10 genes: AR , JUN , STAT1 , PRKCB , RELA , ABL1 , SUMO1 , CDK1 , HDAC1 , and IRF1 . We calculated the RSI score using a linear equation described in a previous study [ 21 ]. Using a rank-based linear regression model, we ranked these 10 genes according to their expression levels, with 10 being the highest and 1 being the lowest expression levels.…”

“…The RSI was also integrated into a genomics-adjusted radiation dose model [ 20 ] to predict the time to first recurrence and overall survival in cancer patients who receive radiation therapy. With advances in immunotherapy, RSI has emerged as a potential biomarker for both radiation therapy and immunotherapy [ 21 ]. We hypothesized that the macrophage profile and RSI score of the immune microenvironment might determine treatment response and prognosis in glioblastoma.…”

Tumor Heterogeneity Research and Innovation in Biologically Based Radiation Therapy From the National Cancer Institute Radiation Research Program Portfolio

“…The broader utility remains a work in progress. [61][62][63] For many common cancers where RT is a crucial component of the first-line, curative-intent treatment, local failures (eg, in glioblastoma) or the lack of pathological complete response (eg, in rectal cancer) remain frustratingly common. 64 Our understanding of why the tumor is controlled in some patients but not in others remains limited.…”