Gene signatures based on therapy responsiveness provide guidance for combined radiotherapy and chemotherapy for lower grade glioma

Abstract

For a long time, the guidance for adjuvant chemoradiotherapy for lower grade glioma (LGG) lacks instructions on the application timing and order of radiotherapy (RT) and chemotherapy. We, therefore, aimed to develop indicators to distinguish between the different beneficiaries of RT and chemotherapy, which would provide more accurate guidance for combined chemoradiotherapy. By analysing 942 primary LGG samples from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) databases, we trained and validated two gene signatures (Rscore and Cscore) that independently predicted the responsiveness to RT and chemotherapy (Rscore AUC = 0.84, Cscore AUC = 0.79) and performed better than a previous signature. When the two scores were combined, we divided patients into four groups with different prognosis after adjuvant chemoradiotherapy: RSCS (RT‐sensitive and chemotherapy‐sensitive), RSCR (RT‐sensitive and chemotherapy‐resistant), RRCS (RT‐resistant and chemotherapy‐sensitive) and RRCR (RT‐resistant and chemotherapy‐resistant). The order and dose of RT and chemotherapy can be adjusted more precisely based on this patient stratification. We further found that the RRCR group exhibited a microenvironment with significantly increased T cell inflammation. In silico analyses predicted that patients in the RRCR group would show a stronger response to checkpoint blockade immunotherapy than other patients.

Promoting Prognostic Model Application: A Review Based on Gliomas

“…In the high-quality group, nine [20][21][22][23][24][25][26][27][28] and one [29] confer high and acceptable accuracy, respectively. Common advantages and additional luminous points in the high-quality group are shown.…”

Machine Learning Based on a Multiparametric and Multiregional Radiomics Signature Predicts Radiotherapeutic Response in Patients with Glioblastoma

“…By clustering four different microarray experiments, Kim et al built a radiotherapeutic response prediction signature [ 7 ] containing 31 genes. The 31-gene signature has been validated in independent clinical datasets of different cancer types, including glioblastoma [ 8 ], low-grade gliomas [ 9 ], head and neck tumours [ 10 ], and oesophageal cancer [ 11 ]. Eschrich et al constructed the tumour radiosensitivity index (RSI) [ 12 ], which has also been verified in a number of different types of tumour datasets [ 13 ].…”

Development and validation of an immune-related gene signature for predicting the radiosensitivity of lower-grade gliomas

“…We compared two existing gene signatures for predicting radiotherapy response, an Rscore 25 and the radiation sensitivity index (RSI) 26 , in the TCGA and CGGA datasets. For the RSI, the 25th percentile of RSI in patients receiving radiotherapy was used as the cutoff value for dividing patients into RS and RR groups, as in previous studies 22 , 27 .…”