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

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.

Developing a Prediction Model for Radiation Pneumonitis in Lung Cancer Patients Treated With Stereotactic Body Radiation Therapy Combined With Clinical, Dosimetric Factors, and Laboratory Biomarkers

BACKGROUND

The study aims to identify the risk factors and develop a model for predicting grade ≥2 radiation pneumonitis (RP) for lung cancer patients treated with stereotactic body radiation therapy (SBRT).

MATERIALS AND METHODS

Clinical data, dosimetric data, and laboratory biomarkers from 186 patients treated with lung SBRT were collected. Univariate and multivariate logistic regression were performed to determine the predictive factors for grade ≥2 RP. Three models were developed by using the clinical, dosimetric, and combined factors, respectively.

RESULTS

With a median follow-up of 36 months, grade ≥2 RP was recorded in 13.4% of patients. On univariate logistic regression analysis, clinical factors of age and lung volume, dosimetric factors of treatment durations, fractional dose and V10, and laboratory biomarkers of neutrophil, PLT, PLR, and Hb levels were significantly associated with grade ≥2 RP. However, on multivariate analysis, only age, lung volume, fractional dose, V10, and Hb levels were independent factors. AUC values for the clinical, dosimetric, and combined models were 0.730 (95% CI, 0.660-0.793), 0.711 (95% CI, 0.641-0.775) and 0.830 (95% CI, 0.768-0.881), respectively. The combined model provided superior discriminative ability than the clinical and dosimetric models (P < .05).

CONCLUSION

Age, lung volume, fractional dose, V10, and Hb levels were demonstrated to be significant factors associated with grade ≥2 RP for lung cancer patients after SBRT. A novel model combining clinical, dosimetric factors, and laboratory biomarkers improved predictive performance compared with the clinical and dosimetric model alone.

Establishment and Verification of a Prediction Model for Symptomatic Radiation Pneumonitis in Patients with Esophageal Cancer Receiving Radiotherapy

BACKGROUND This study aimed to determine the value of the significant index in predicting symptomatic radiation pneumonitis (RP) in esophageal cancer patients, establish a nomogram prediction model, and verify the model. MATERIAL AND METHODS The patients enrolled were divided into 2 groups: a model group and a validation group. According to the logistic regression analysis, the independent predictors for symptomatic RP were obtained, and the nomogram prediction model was established according to these independent predictors. The consistency index (C-index) and calibration curve were used to evaluate the accuracy of the model, and the prediction ability of the model was verified in the validation group. Recursive partitioning analysis (RPA) was used for the risk stratification analysis. RESULTS The ratio of change regarding the pre-albumin at the end of treatment (P=0.001), platelet-to-lymphocyte ratio during treatment (P=0.027), and neutrophil-to-lymphocyte ratio at the end of treatment (P=0.001) were the independent predictors for symptomatic RP. The C-index of the nomogram model was 0.811. According to the risk stratification of RPA, the whole group was divided into 3 groups: a low-risk group, a medium-risk group, and a high-risk group. The incidence of symptomatic RP was 0%, 16.9%, and 57.6%, respectively. The receiver operating characteristic curve also revealed that the nomogram model has good accuracy in the validation group. CONCLUSIONS The developed nomogram and corresponding risk classification system have superior prediction ability for symptomatic RP and can predict the occurrence of RP in the early stage.

Identification of Gene Signatures for Diagnosis and Prognosis of Hepatocellular Carcinomas Patients at Early Stage

The onset of liver cancer is insidious. Currently, there is no effective method for the early detection of hepatocellular carcinoma (HCC). Transcriptomic profiles of 826 tissue samples from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), Genotype tissue expression (GTEx), and International Cancer Genome Consortium (ICGC) databases were utilized to establish models for early detection and surveillance of HCC. The overlapping differentially expressed genes (DEGs) were screened by elastic net and robust rank aggregation (RRA) analyses to construct the diagnostic prediction model for early HCC (DP.eHCC). Prognostic prediction genes were screened by univariate cox regression and lasso cox regression analyses to construct the survival risk prediction model for early HCC (SP.eHCC). The relationship between the variation of transcriptome profile and the oncogenic risk-score of early HCC was analyzed by combining Weighted Correlation Network Analysis (WGCNA), Gene Set Enrichment Analysis (GSEA), and genome networks (GeNets). The results showed that the AUC of DP.eHCC model for the diagnosis of early HCC was 0.956 (95% CI: 0.941–0.972; p < 0.001) with a sensitivity of 90.91%, a specificity of 92.97%. The SP.eHCC model performed well for predicting the overall survival risk of HCC patients (HR = 10.79; 95% CI: 6.16–18.89; p < 0.001). The oncogenesis of early HCC was revealed mainly involving in pathways associated with cell proliferation and tumor microenvironment. And the transcription factors including EZH2, EGR1, and SOX17 were screened in the genome networks as the promising targets used for precise treatment in patients with HCC. Our findings provide robust models for the early diagnosis and prognosis of HCC, and are crucial for the development of novel targets applied in the precision therapy of HCC.

Markers Useful in Monitoring Radiation-Induced Lung Injury in Lung Cancer Patients: A Review

In 2018, lung cancer was the most common cancer and the most common cause of cancer death, accounting for a 1.76 million deaths. Radiotherapy (RT) is a widely used and effective non-surgical cancer treatment that induces remission in, and even cures, patients with lung cancer. However, RT faces some restrictions linked to the radioresistance and treatment toxicity, manifesting in radiation-induced lung injury (RILI). About 30–40% of lung cancer patients will develop RILI, which next to the local recurrence and distant metastasis is a substantial challenge to the successful management of lung cancer treatment. These data indicate an urgent need of looking for novel, precise biomarkers of individual response and risk of side effects in the course of RT. The aim of this review was to summarize both preclinical and clinical approaches in RILI monitoring that could be brought into clinical practice. Next to transforming growth factor-β1 (TGFβ1) that was reported as one of the most important growth factors expressed in the tissues after ionizing radiation (IR), there is a group of novel, potential biomarkers—microRNAs—that may be used as predictive biomarkers in therapy response and disease prognosis.