Cancer Res Treat.  2022 Apr;54(2):383-395. 10.4143/crt.2021.759.

Radiation Response Prediction Model Based on Integrated Clinical and Genomic Data Analysis

• Jang BS
• Chang JH
• Jeon SH
• Song MG
• Lee KH
• Im SA
• Kim JI
• Kim TY
• Chie EK

• KMID: 2528208
• DOI: http://doi.org/10.4143/crt.2021.759

Abstract

Purpose
The value of the genomic profiling by targeted gene-sequencing on radiation therapy response prediction was evaluated through integrated analysis including clinical information. Radiation response prediction model was constructed based on the analyzed findings.
Materials and Methods
Patients who had the tumor sequenced using institutional cancer panel after informed consent and received radiotherapy for the measurable disease served as the target cohort. Patients with irradiated tumor locally controlled for more than 6 months after radiotherapy were defined as the durable local control (DLC) group, otherwise, non-durable local control (NDLC) group. Significant genomic factors and domain knowledge were used to develop the Bayesian Network model to predict radiotherapy response.
Results
Altogether, 88 patients were collected for analysis. Of those, 41 (43.6%) and 47 (54.4%) patients were classified as the NDLC and DLC group, respectively. Somatic mutations of NOTCH2 and BCL were enriched in the NDLC group, whereas, mutations of CHEK2, MSH2, and NOTCH1 were more frequently found in the DLC group. Altered DNA repair pathway was associated with better local failure–free survival (hazard ratio, 0.40; 95% confidence interval, 0.19 to 0.86; p=0.014). Smoking somatic signature was found more frequently in the DLC group. Area under the receiver operating characteristic curve of the Bayesian network model predicting probability of 6-month local control was 0.83.
Conclusion
Durable radiation response was associated with alterations of DNA repair pathway and smoking somatic signature. Bayesian network model could provide helpful insights for high precision radiotherapy. However, these findings should be verified in prospective cohort for further individualization.

Keyword

Radiation therapy; Response; Targeted gene; Sequencing; Bayesian network

Figure

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  Fig. 1 (A) Distribution of primary cancer in the study cohort. (B) Dot plot representing radiation therapy (RT) site. (C) Dot plot showing the intent of RT. A dot represent one percent. CW, chest wall; MUO, metastasis of unknown origin.

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  Fig. 2 Oncoplot showing the gene alterations found in Catalogue of Somatic Mutations in Cancer (COSMIC) database ver. 91. Amp, amplification; CNV, copy number variation; Del, deletion; DLC, durable local control group; ECOG, Eastern Cooperative Oncology Group performance status; LFS, local failure–free survival; NDLC, non-durable local control group; RT, radiation therapy.

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  Fig. 3 (A) Bar plot showing the enrichment of gene mutation. The number of altered patient/a total number of patients are presented within bar plot. Bar plots representing percentage of patients having altered somatic signature (B) and pathway (C). p-value was estimated by Fisher exact test. (D) Kaplan-Meier curve depicting local failure–free survival between the altered and the non-altered DNA repair pathway. p-value was computed by log-rank test. AR, androgen receptor; DLC, durable local control group; MMR, mismatch repair; NDLC, non-durable local control group; PI3K, phosphoinositide 3-kinase; TMZ, temozolomide; UV, ultraviolet.

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  Fig. 4 (A) Cumulative bar plots showing the structural variation. Patients are sorted in ascending order in x-axis. (B) Bar plot comparing the number of structural variation events between the NDLC and the DLC groups. Circos plots depicting structural variation and copy number variations in the NDLC (C) and the DLC (D) groups. Heat map showing the number of patients having kinase fusion event in the NDLC (E) and the DLC (F) groups. Amp, amplification; CNV, copy number variation; Del or DEL, deletion; DLC, durable local control group; Dup or DUP, duplication; INS, insertion; INV, inversion; NDLC, non-durable local control group; SV, structural variation; TRA, translocation.

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  Fig. 5 (A) The Bayesian network model integrating genomic information and clinical domain knowledge. The final prediction is the probability of local control at 6 months after local RT (yellow circle). A receiver operating characteristic curve (B) and precision-recall curve (C) comparing clinico-genomic, clinical, and genomic Bayesian network models. AUC values are also presented in the plots. (D) Regarding probability prediction, contribution of genomic and clinical information in each cancer are represented. AUC, area under the curve; BED10, biologically effective dose with α/β=10; CNV, copy number variation; MUO, metastasis of unknown origin; PPV, positive predictive value; RT, radiation therapy; SV, structural variation.

Reference

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