GSTP1 Ile105Val polymorphism might be associated with the risk of radiation pneumonitis among lung cancer patients in Chinese population: A prospective study

Synergizing the interaction of single nucleotide polymorphisms with dosiomics features to build a dual-omics model for the prediction of radiation pneumonitis

OBJECTIVE

Radiation pneumonitis (RP) is the major dose-limiting toxicity of thoracic radiotherapy. This study aimed to developed a dual-omics (single nucleotide polymorphisms, SNP and dosiomics) prediction model for symptomatic RP.

MATERIALS AND METHODS

The potential SNPs, which are of significant difference between the RP grade ≥ 3 group and the RP grade ≤ 1 group, were selected from the whole exome sequencing SNPs using the Fisher's exact test. Patients with lung cancer who received thoracic radiotherapy at our institution from 2009 to 2016 were enrolled for SNP selection and model construction. The factorization machine (FM) method was used to model the SNP epistasis effect, and to construct the RP prediction model (SNP-FM). The dosiomics features were extracted, and further selected using the minimum redundancy maximum relevance (mRMR) method. The selected dosiomics features were added to the SNP-FM model to construct the dual-omics model.

RESULTS

For SNP screening, peripheral blood samples of 28 patients with RP grade ≥ 3 and the matched 28 patients with RP grade ≤ 1 were sequenced. 81 SNPs were of significant difference (P < 0.015) and considered as potential SNPs. In addition, 21 radiation toxicity related SNPs were also included. For model construction, 400 eligible patients (including 108 RP grade ≥ 2) were enrolled. Single SNP showed no strong correlation with RP. On the other hand, the SNP-SNP interaction (epistasis effect) of 19 SNPs were modeled by the FM method, and achieved an area under the curve (AUC) of 0.76 in the testing group. In addition, 4 dosiomics features were selected and added to the model, and increased the AUC to 0.81.

CONCLUSIONS

A novel dual-omics model by synergizing the SNP epistasis effect with dosiomics features was developed. The enhanced the RP prediction suggested its promising clinical utility in identifying the patients with severe RP during thoracic radiotherapy.

Predicting Radiation-Induced Lung Injury in Patients With Lung Cancer: Challenges and Opportunities

Radiation-induced lung injury (RILI) is one of the main dose-limiting toxicities in radiation therapy (RT) for lung cancer. Approximately 10% to 20% of patients show signs of RILI of variable severity. The reason for the wide range of RILI severity and the mechanisms underlying its development are only partially understood. A number of clinical risk factors have been identified that can aid in clinical decision making. Technological advancements in RT and the use of strict organ-at-risk dose constraints have helped to reduce RILI. Predicting patients at risk for RILI may be further improved with a combination of cytokine assessments, γH2AX-assays in leukocytes, or epigenetic markers. A complicating factor is the lack of an objective definition of RILI. Tools such as computed tomography densitometry, fluorodeoxyglucose-positron emission tomography uptake, changes in lung function measurements, and exhaled breath analysis can be implemented to better define and quantify RILI. This can aid in the search for new biomarkers, which can be accelerated by omics techniques, single-cell RNA sequencing, mass cytometry, and advances in patient-specific in vitro cell culture models. An objective quantification of RILI combined with these novel techniques can aid in the development of biomarkers to better predict patients at risk and allow personalized treatment decisions.

The essential role of GSTP1 I105V polymorphism in the prediction of CDNB metabolism and toxicity: In silico and in vitro insights

Humans are continuously exposed to toxic chemicals such as nitro-chlorobenzene (CDNB) through occupation, water, and even the air we breathe. Due to the severe toxicity caused by the high electrophilicity of CDNB, occupational and environmental exposure to CDNB can produce toxic effects that ultimately lead to cell damage. CDNB can be eliminated from organisms by binding to GSH, the catalytic product of glutathione S-transferase P1 (GSTP1). Therefore, GSTP1 plays an important role in the detoxification of CDNB. However, subtle variations in GSTP1 can result in single nucleotide polymorphisms (SNPs). Indeed, the correlation between the clinical outcome of the disease and certain genotypes of GSTP1 has been extensively studied, however, their impact on the metabolic detoxification of toxicants such as CDNB remains to be elucidated. Among the various SNPs of GSTP1, I105V has a significant effect on the catalytic activity of GSTP1. In this paper, a GSTP1 I105V polymorphism model was successfully established, and its effect on CDNB metabolism and toxicity was studied by computer analysis including molecular docking and molecular dynamics simulation. The result demonstrated that the binding capacity of CDNB decreases with the I105V mutation of GSTP1(p < 0.001), indicating the changes in its detoxification efficacy in CDNB-induced cell damage. Organisms expressing GSTP1 V105 are more susceptible to cell damage caused by CDNB than individuals expressing GSTP1 I105 (p < 0.001). In sum, the data in this study provide prospective insights into the mechanism and capacity of CDNB detoxification in the GSTP1 allele, extending the CDNB-mediated toxicological profile. In addition, the heterogeneity of the GSTP1 allele should be included in toxicological studies of individuals exposed to CDNB.

Radiogenomics in lung cancer: Where are we?

Huge technological and biomedical advances have improved the survival and quality of life of lung cancer patients treated with radiotherapy. However, during treatment planning, a probability that the patient will experience adverse effects is assumed. Radiotoxicity is a complex entity that is largely dose-dependent but also has important intrinsic factors. One of the most studied is the genetic variants that may be associated with susceptibility to the development of adverse effects of radiotherapy. This review aims to present the current status of radiogenomics in lung cancer, integrating results obtained in association studies of SNPs (single nucleotide polymorphisms) related to radiotherapy toxicities. We conclude that despite numerous publications in this field, methodologies and endpoints vary greatly, making comparisons between studies difficult. Analyzing SNPs from the candidate gene approach, together with the study in cohorts limited by the sample size, has complicated the possibility of having validated results. All this delays the incorporation of genetic biomarkers in predictive models for clinical application. Thus, from all analysed SNPs, only 12 have great potential as esophagitis genetic risk factors and deserve further exploration. This review highlights the efforts that have been made to date in the radiogenomic study of radiotoxicity in lung cancer.

Promising Biomarkers of Radiation-Induced Lung Injury: A Review

Radiation-induced lung injury (RILI) is one of the main dose-limiting side effects in patients with thoracic cancer during radiotherapy. No reliable predictors or accurate risk models are currently available in clinical practice. Severe radiation pneumonitis (RP) or pulmonary fibrosis (PF) will reduce the quality of life, even when the anti-tumor treatment is effective for patients. Thus, precise prediction and early diagnosis of lung toxicity are critical to overcome this longstanding problem. This review summarizes the primary mechanisms and preclinical animal models of RILI reported in recent decades, and analyzes the most promising biomarkers for the early detection of lung complications. In general, ideal integrated models considering individual genetic susceptibility, clinical background parameters, and biological variations are encouraged to be built up, and more prospective investigations are still required to disclose the molecular mechanisms of RILI as well as to discover valuable intervention strategies.

GSTP1 as a novel target in radiation induced lung injury

The glutathione S-transferase P1(GSTP1) is an isoenzyme in the glutathione-S transferases (GSTs) enzyme system, which is the most abundant GSTs expressed in adult lungs. Recent research shows that GSTP1 is closely related to the regulation of cell oxidative stress, inhibition of cell apoptosis and promotion of cytotoxic metabolism. Interestingly, there is evidence that GSTP1 single nucleotide polymorphisms (SNP) 105Ile/Val related to the risk of radiation induced lung injury (RILI) development, which strongly suggests that GSTP1 is closely associated with the occurrence and development of RILI. In this review, we discuss our understanding of the role of GSTP1 in RILI and its possible mechanism.

Significant Association Between XRCC1 Expression and Its rs25487 Polymorphism and Radiotherapy-Related Cancer Prognosis

Background/Aims

XRCC1 (X-ray repair cross-complementing protein 1) expression and its single nucleotide polymorphism XRCC1 rs25487 (G>A) may be related to radiotherapy-related cancer prognosis or radiation-induced side effects. However, this association is controversial. We performed a bioinformatic analysis and a meta-analysis to obtain comprehensive results.

Results

Sixty nine articles with 10232 patients and 17 TCGA data sets with 2705 patients were included in the analysis. We observed that high XRCC1 expression was associated with an increased risk of minor treatment response and poor overall survival, XRCC1 rs25487 was associated with reduced risk of minor treatment response in esophageal cancer and an increased risk of high-grade side effects in head and neck cancer.

Conclusion

The results suggest that XRCC1 expression and rs25487 polymorphism are prognostic factors for patients receiving radiotherapy-related treatment. Considering the insufficient treatment parameters provided and the various sample sizes in most of the studies, we suggest that genetic association studies related to radiation-based treatment should include more cancer types with sufficient statistical power and more detailed clinical parameters.

Low dose ionizing radiation effects on the immune system

Ionizing radiation interacts with the immune system in many ways with a multiplicity that mirrors the complexity of the immune system itself: namely the need to maintain a delicate balance between different compartments, cells and soluble factors that work collectively to protect, maintain, and restore tissue function in the face of severe challenges including radiation damage. The cytotoxic effects of high dose radiation are less relevant after low dose exposure, where subtle quantitative and functional effects predominate that may go unnoticed until late after exposure or after a second challenge reveals or exacerbates the effects. For example, low doses may permanently alter immune fitness and therefore accelerate immune senescence and pave the way for a wide spectrum of possible pathophysiological events, including early-onset of age-related degenerative disorders and cancer. By contrast, the so called low dose radiation therapy displays beneficial, anti-inflammatory and pain relieving properties in chronic inflammatory and degenerative diseases. In this review, epidemiological, clinical and experimental data regarding the effects of low-dose radiation on the homeostasis and functional integrity of immune cells will be discussed, as will be the role of immune-mediated mechanisms in the systemic manifestation of localized exposures such as inflammatory reactions. The central conclusion is that ionizing radiation fundamentally and durably reshapes the immune system. Further, the importance of discovery of immunological pathways for modifying radiation resilience amongst other research directions in this field is implied.

Association between NF-κB Activation in Peripheral Blood Mononuclear Cells and Late Skin and Subcutaneous Fibrosis following Radiotherapy

Background

This study aimed at evaluating the association between the speed of nuclear factor-kappa B (NF-κB) activation in peripheral blood mononuclear cells (PBMCs) and late skin and subcutaneous fibrosis in patients with head and neck squamous cell carcinoma (HNSCC) after radiotherapy.

Methods

The speed of NF-κB activation was represented by the nuclear p65 expression ratio before and after irradiation. The optimal time point to measure the ratio was determined by Western blot in the PBMCs from healthy outpatients ranging from 0 to 12 hours after ex vivo irradiation. We recruited patients with HNSCC who had received ratiotherapy and who were under regular follow-up care. We assessed the association between the risk of developing ≥grade 2 late fibrosis and the nuclear p65 expression ratio in the PBMCs after ex vivo irradiation in these patients.

Results

The maximum nuclear p65 ratio was observed at 1 hour after ex vivo irradiation in the PBMCs from the healthy outpatients. The speed of NF-κB activation was then represented by the nuclear p65 ratio in the PBMCs before and 1 hour after ex vivo irradiation. A total of 200 patients with HNSCC were recruited, 32.50% (n = 65) of which presented with ≥grade 2 late fibrosis. There was a significant association between the speed of NF-κB activation in the PBMCs and an increased risk of developing ≥grade 2 late fibrosis in these patients (P = 0.004). Subgroup analysis suggested that this finding was independent of the known clinical characteristics.

Conclusions

The speed of NF-κB activation might be a potential predictor of late toxicity in cancer patients after radiotherapy. Prospective studies are needed for validation.

Precise prediction of the radiation pneumonitis in lung cancer: an explorative preliminary mathematical model using genotype information

Purpose: Radiation pneumonitis (RP) is the most significant dose-limiting toxicity and is one major obstacle for lung cancer radiotherapy. Grade ≥2 RP usually needs clinical interventions and serve RP could be life threatening. Clinically, tissue response could be strikingly different even two similar patients after identical radiotherapy. Previous methods for the RP prediction can hardly distinguish substantial variations among individuals. Reliable predictive factors or methods emphasizing the individual differences are strongly desired by clinical radiation oncologists. The purpose of this study is to develop an approach for the personalized RP risk prediction.

Experimental Design: One hundred eighteen lung cancer patients who received radiotherapy were enrolled. Seven hundred thousand single-nucleotide polymorphism (SNP) sites were assessed via Generalized Linear Models via Lasso and Elastic-Net Regularization (GLMNET) to determine their synergistic effects on the RP risk prediction. Non-genetic factors including patient's phenotypes and clinical interventional parameters were separately assessed by statistic test. Based on the results of the aforementioned analysis, a multiple linear regression model named Radiation Pneumonitis Index (RPI) was built, for the assessment of Grade ≥2RP risk.

Results: Only previous surgery and fractional dose were discovered statistical significantly associated with grade ≥2RP. Thirty-nine effective SNPs for predicting the Grade ≥2RP risk were discovered and their coefficients of the synergistic effect were determined. The RPI score can successfully distinguish the RP≥2 population with 92.0% sensitivity and 100% specificity.

Conclusions: Individual radiation sensitivity can be determined with genotype information and personalized radiotherapy could be achieved based on mathematical model result.