A bioinformatics approach for biomarker identification in radiation-induced lung inflammation from limited proteomics data

Biomarkers associated with pulmonary exacerbations in a randomized trial of nintedanib for radiation pneumonitis

BACKGROUND AND PURPOSE

Radiation pneumonitis (RP) is a common side effect of thoracic radiotherapy and often has a long course characterized by acute exacerbations and progression to permanent lung fibrosis. There are no validated biomarkers of prognosis in patients diagnosed with RP.

MATERIALS AND METHODS

We analyzed a time course of serum chemokines, cytokines, and other proteins from patients with grade 2+ RP in a randomized clinical trial of a steroid taper plus nintedanib, a multiple tyrosine kinase inhibitor, versus placebo plus a steroid taper for the treatment of RP. Weighted gene correlation network analysis (WGCNA) and univariable zero inflated Poisson models were used to identify groups of correlated analytes and their associations with clinical outcomes.

RESULTS

Thirty enrolled patients had biomarker data available, and 17 patients had enough analytes tested for network analysis. WGNCA identified ten analytes, including transforming growth factor beta-1 (TGF-β1), monocyte chemoattractant protein-1 (MCP-1), and platelet-derived growth factor (PDGF), that in aggregate were correlated with the occurrence of pulmonary exacerbations (p = 0.008), the total number of acute pulmonary exacerbations (p = 0.002), and treatment arm (p = 0.036). By univariable analysis, an increase in rate of change of two components of the RP module were associated with an increased incidence rate of pulmonary exacerbations: interleukin 5 (IL-5, incidence rate ratio (IRR) 1.02, 95% CI 1.01-1.04, p = 0.002), and tumor necrosis factor superfamily 12 (TNFSF12, IRR 1.06, CI 1-1.11, p = 0.036). An increased slope of epidermal growth factor (EGF) was associated with a decreased incidence rate of exacerbations (IRR 0.94, CI 0.89-1, p = 0.036).

CONCLUSION

We identified a panel of serum biomarkers that showed association with nintedanib treatment and acute pulmonary exacerbations in patients with RP. A confirmatory study will be needed to validate this panel for use as a prognostic tool in patients with RP.

The Predictive Value of MLR for Radiation Pneumonia During Radiotherapy of Thoracic Tumor Patients

Purpose

To evaluate the predictive value of blood lymphocyte, monocyte to lymphocyte ratio (MLR), and neutrophil to lymphocyte ratio (NLR) for radiation pneumonia (RP) in patients with thoracic tumors receiving radiotherapy.

Patients and Methods

The clinical data of 65 patients with thoracic tumor (esophageal cancer, lung cancer) treated by radiotherapy in our hospital were retrospectively analyzed. Patients were divided into the RP group and the non-RP group according to the Common Terminology Criteria for Adverse Events (CTCAE 5.0). Data on blood cell counts, including lymphocytes, monocytes, and neutrophils, were collected before (0 weeks) and after 1, 2, and 4 weeks of radiotherapy.

Results

Of the 65 patients enrolled, 27 developed radiation pneumonia and 38 did not. Patients’ clinical factors, including age, TNM stage, tumor type, underlying lung disease, and history of smoking, had no correlation with RP. ANOVA of repeated measurement data showed that the changes of MLR in the group with RP during radiotherapy were significantly different from those in the non-RP group (P<0.05). The RP prediction model based on the identified risk factors was established using receiver operator characteristic curves. The results showed that the area under the curve for the monocyte to lymphocyte ratio was 0.755 (95% CI, 0.63–0.87, P=0.000), and the best cutoff point for MLR was 0.426.

Conclusion

MLR could predict radiation pneumonia in patients with thoracic tumor radiotherapy and achieve early monitoring, early prevention, and treatment.

Predictive Modeling of Thoracic Radiotherapy Toxicity and the Potential Role of Serum Alpha-2-Macroglobulin

Background: To investigate the impact of alpha-2-macroglobulin (A2M), a suspected intrinsic radioprotectant, on radiation pneumonitis and esophagitis using multifactorial predictive models. Materials and Methods: Baseline A2M levels were obtained for 258 patients prior to thoracic radiotherapy (RT). Dose-volume characteristics were extracted from treatment plans. Spearman's correlation (Rs) test was used to correlate clinical and dosimetric variables with toxicities. Toxicity prediction models were built using least absolute shrinkage and selection operator (LASSO) logistic regression on 1,000 bootstrapped datasets. Results: Grade ≥2 esophagitis and pneumonitis developed in 61 (23.6%) and 36 (14.0%) patients, respectively. The median A2M level was 191 mg/dL (range: 94-511). Never/former/current smoker status was 47 (18.2%)/179 (69.4%)/32 (12.4%). We found a significant negative univariate correlation between baseline A2M levels and esophagitis (Rs = -0.18/p = 0.003) and between A2M and smoking status (Rs = 0.13/p = 0.04). Further significant parameters for grade ≥2 esophagitis included age (Rs = -0.32/p < 0.0001), chemotherapy use (Rs = 0.56/p < 0.0001), dose per fraction (Rs = -0.57/p < 0.0001), total dose (Rs = 0.35/p < 0.0001), and several other dosimetric variables with Rs > 0.5 (p < 0.0001). The only significant non-dosimetric parameter for grade ≥2 pneumonitis was sex (Rs = -0.32/p = 0.037) with higher risk for women. For pneumonitis D15 (lung) (Rs = 0.19/p = 0.006) and D45 (heart) (Rs = 0.16/p = 0.016) had the highest correlation. LASSO models applied on the validation data were statistically significant and resulted in areas under the receiver operating characteristic curve of 0.84 (esophagitis) and 0.78 (pneumonitis). Multivariate predictive models did not require A2M to reach maximum predictive power. Conclusion: This is the first study showing a likely association of higher baseline A2M values with lower risk of radiation esophagitis and with smoking status. However, the baseline A2M level was not a significant risk factor for radiation pneumonitis.

Precision radiotherapy for non-small cell lung cancer

Precision medicine is becoming the standard of care in anti-cancer treatment. The personalized precision management of cancer patients highly relies on the improvement of new technology in next generation sequencing and high-throughput big data processing for biological and radiographic information.

Systemic precision cancer therapy has been developed for years. However, the role of precision medicine in radiotherapy has not yet been fully implemented. Emerging evidence has shown that precision radiotherapy for cancer patients is possible with recent advances in new radiotherapy technologies, panomics, radiomics and dosiomics.

This review focused on the role of precision radiotherapy in non-small cell lung cancer and demonstrated the current landscape.

The Possibility of Using Genotoxicity, Oxidative Stress and Inflammation Blood Biomarkers to Predict the Occurrence of Late Cutaneous Side Effects after Radiotherapy

Despite the progresses performed in the field of radiotherapy, toxicity to the healthy tissues remains a major limiting factor. The aim of this work was to highlight blood biomarkers whose variations could predict the occurrence of late cutaneous side effects. Two groups of nine patients treated for Merkel Cell Carcinoma (MCC) were established according to the grade of late skin toxicity after adjuvant irradiation for MCC: grade 0, 1 or 2 and grade 3 or 4 of RTOG (Radiation Therapy Oncology Group)/EORTC (European Organization for Research and Treatment of Cancer). To try to discriminate these 2 groups, biomarkers of interest were measured on the different blood compartments after ex vivo irradiation. In lymphocytes, cell cycle, apoptosis and genotoxicity were studied. Oxidative stress was evaluated by the determination of the erythrocyte antioxidant capacity (superoxide dismutase, catalase, glutathione peroxidase, reduced and oxidized glutathione) as well as degradation products (protein carbonylation, lipid peroxidation). Inflammation was assessed in the plasma by the measurement of 14 cytokines. The most radiosensitive patients presented a decrease in apoptosis, micronucleus frequency, antioxidant enzyme activities, glutathione and carbonyls; and an increase in TNF-a (Tumor Necrosis Factor a), IL-8 (Interleukin 8) and TGF-β1 (Transforming Growth Factor β1) levels. These findings have to be confirmed on a higher number of patients and before radiotherapy and could allow to predict the occurrence of late skin side effects after radiotherapy.

[Predictive assays for responses of tumors and normal tissues in radiation oncology]

The impact of curative radiotherapy depends mainly on the total dose delivered homogenously in the target volume. Tumor sensitivity to radiotherapy may be particularly inconstant depending on location, histology, somatic genetic parameters and the capacity of the immune system to infiltrate the tumor. In addition, the dose delivered to the surrounding healthy tissues may reduce the therapeutic ratio of many radiation treatments. In a same population treated in one center with the same technique, it appears that individual radiosensitivity clearly exists, namely in terms of late side effects that are in principle non-reversible. This review details the different radiobiological approaches that have been developed to better predict the tumor response but also the radiation-induced late effects.

Quantitative proteomic analysis reveals AK2 as potential biomarker for late normal tissue radiotoxicity

Background

Biomarkers for predicting late normal tissue toxicity to radiotherapy are necessary to personalize treatments and to optimize clinical benefit. Many radiogenomic studies have been published on this topic. Conversely, proteomics approaches are not much developed, despite their advantages.

Methods

We used the isobaric tags for relative and absolute quantitation (iTRAQ) proteomic approach to analyze differences in protein expression levels in ex-vivo irradiated (8 Gy) T lymphocytes from patients with grade ≥ 2 radiation-induced breast fibrosis (grade ≥ 2 bf+) and patients with grade < 2 bf + after curative intent radiotherapy. Patients were selected from two prospective clinical trials (COHORT and PHRC 2005) and were used as discovery and confirmation cohorts.

Results

Among the 1979 quantified proteins, 23 fulfilled our stringent biological criteria. Immunoblotting analysis of four of these candidate proteins (adenylate kinase 2, AK2; annexin A1; heat shock cognate 71 kDa protein; and isocitrate dehydrogenase 2) confirmed AK2 overexpression in 8 Gy-irradiated T lymphocytes from patients with grade ≥ 2 bf + compared with patients with grade < 2 bf+. As these candidate proteins are involved in oxidative stress regulation, we also evaluated radiation-induced reactive oxygen species (ROS) production in peripheral blood mononuclear cells from patients with grade ≥ 2 bf + and grade < 2 bf+. Total ROS level, and especially superoxide anion level, increased upon ex-vivo 8 Gy-irradiation in all patients. Analysis of NADPH oxidases (NOXs), a major source of superoxide ion in the cell, showed a significant increase of NOX4 mRNA and protein levels after irradiation in both patient groups. Conversely, only NOX4 mRNA level was significantly different between groups (grade ≥ 2 bf + and grade < 2 bf+).

Conclusion

These findings identify AK2 as a potential radiosensitivity candidate biomarker. Overall, our proteomic approach highlights the important role of oxidative stress in late radiation-induced toxicity, and paves the way for additional studies on NOXs and superoxide ion metabolism.

Electronic supplementary material

The online version of this article (10.1186/s13014-019-1351-8) contains supplementary material, which is available to authorized users.

Acute Proteomic Changes in the Lung After WTLI in a Mouse Model: Identification of Potential Initiating Events for Delayed Effects of Acute Radiation Exposure

Radiation-induced lung injury is a delayed effect of acute radiation exposure resulting in pulmonary pneumonitis and fibrosis. Molecular mechanisms that lead to radiation-induced lung injury remain incompletely understood. Using a murine model of whole-thorax lung irradiation, C57BL/6J mice were irradiated at 8, 10, 12, and 14 Gy and assayed at day 1, 3, and 6 postexposure and compared to nonirradiated (sham) controls. Tryptic digests of lung tissues were analyzed by liquid chromatography-tandem mass spectrometry on a Waters nanoLC instrument coupled to a Thermo Scientific Q Exactive hybrid quadrupole-orbitrap mass spectrometer. Pathway and gene ontology analysis were performed with Qiagen Ingenuity, Panther GO, and DAVID databases. A number of trends were identified in the proteomic data, including protein changes greater than 10 fold, protein changes that were consistently up regulated or down regulated at all time points and dose levels interrogated, time and dose dependency of protein changes, canonical pathways affected by irradiation, changes in proteins that serve as upstream regulators, and proteins involved in key processes including inflammation, radiation, and retinoic acid signaling. The proteomic profiling conducted here represents an untargeted systems biology approach to identify acute molecular events that could potentially be initiating events for radiation-induced lung injury.

Effects of Acanthopanax senticosus on Brain Injury Induced by Simulated Spatial Radiation in Mouse Model Based on Pharmacokinetics and Comparative Proteomics

The active compounds in Acanthopanax senticosus (AS) have different pharmacokinetic characteristics in mouse models. Cmax and AUC of Acanthopanax senticosus polysaccharides (ASPS) were significantly reduced in radiation-injured mice, suggesting that the blood flow of mouse was blocked or slowed, due to the pathological state of ischemia and hypoxia, which are caused by radiation. In contrast, the ability of various metabolizing enzymes to inactivate, capacity of biofilm transport decrease, and lessening of renal blood flow accounts for radiation, resulting in the accumulation of syringin and eleutheroside E in the irradiated mouse. Therefore, there were higher pharmacokinetic parameters-AUC, MRT, and t1/2 of the two compounds in radiation-injured mouse, when compared with normal mouse. In order to investigate the intrinsic mechanism of AS on radiation injury, AS extract's protective effects on brain, the main part of mouse that suffered from radiation, were explored. The function of AS extract in repressing expression changes of radiation response proteins in prefrontal cortex (PFC) of mouse brain included tubulin protein family (α-, β-tubulin subunits), dihydropyrimidinase-related protein 2 (CRMP2), γ-actin, 14-3-3 protein family (14-3-3ζ, ε), heat shock protein 90β (HSP90β), and enolase 2. The results demonstrated the AS extract had positive effects on nerve cells' structure, adhesion, locomotion, fission, and phagocytosis, through regulating various action pathways, such as Hippo, phagosome, PI3K/Akt (phosphatidylinositol 3 kinase/protein kinase B), Neurotrophin, Rap1 (Ras-related protein RAP-1A), gap junction glycolysis/gluconeogenesis, and HIF-1 (Hypoxia-inducible factor 1) signaling pathways to maintain normal mouse neurological activity. All of the results indicated that AS may be a promising alternative medicine for the treatment of radiation injury in mouse brain. It would be tested that whether the bioactive ingredients of AS could be effective through the blood-brain barrier in the future.

Blood-based biomarkers for precision medicine in lung cancer: precision radiation therapy

Both tumors and patients are complex and models that determine survival and toxicity of radiotherapy or any other treatment ideally must take into account this variability as well as its dynamic state. The genetic features of the tumor and the host, and increasingly also the epi-genetic and proteomic characteristics, are being unraveled. Multiple techniques, including histological examination, blood sampling, measurement of circulating tumor cells (CTCs), and functional and molecular imaging, can be used for this purpose. However, the effects of radiation on the tumor and on organs at risk (OARs) are also influenced by the applied dose and volume of irradiated tissues. Combining all these biological, clinical, imaging, and dosimetric parameters in a validated prognostic or predictive model poses a major challenge. Here we aimed to provide an objective review of the potential of blood markers to guide high precision radiation therapy. A combined biological-mathematical approach opens new doors beyond prognostication of patients, as it allows truly precise oncological treatment. Indeed, the core for individualized and precision medicine is not only selection of patients, but even more the optimization of the therapeutic window on an individual basis. A holistic model will allow for determination of an individual dose-response relationship for each organ at risk for each tumor in each individual patient for the complete oncological treatment package. This includes, but is not limited to, radiotherapy alone. Individualized dose-response curves will allow for consideration of different doses of radiation and combinations with other drugs to plan for both optimal toxicity and complete response. Insights into the interactions between a multitude of parameters will lead to the discovery of new pathways and networks that will fuel new biological research on target discovery.

Radiogenomics and radiotherapy response modeling

Advances in patient-specific information and biotechnology have contributed to a new era of computational medicine. Radiogenomics has emerged as a new field that investigates the role of genetics in treatment response to radiation therapy. Radiation oncology is currently attempting to embrace these recent advances and add to its rich history by maintaining its prominent role as a quantitative leader in oncologic response modeling. Here, we provide an overview of radiogenomics starting with genotyping, data aggregation, and application of different modeling approaches based on modifying traditional radiobiological methods or application of advanced machine learning techniques. We highlight the current status and potential for this new field to reshape the landscape of outcome modeling in radiotherapy and drive future advances in computational oncology.

Incorporation of pre-therapy 18 F-FDG uptake data with CT texture features into a radiomics model for radiation pneumonitis diagnosis

PURPOSE

To determine whether the addition of standardized uptake value (SUV) from PET scans to CT lung texture features could improve a radiomics-based model of radiation pneumonitis (RP) diagnosis in patients undergoing radiotherapy.

METHODS AND MATERIALS

Anonymized data from 96 esophageal cancer patients (18 RP-positive cases of Grade ≥ 2) were collected including pre-therapy PET/CT scans, pre-/post-therapy diagnostic CT scans and RP status. Twenty texture features (first-order, fractal, Laws' filter and gray-level co-occurrence matrix) were calculated from diagnostic CT scans and compared in anatomically matched regions of the lung. Classifier performance (texture, SUV, or combination) was assessed by calculating the area under the receiver operating characteristic curve (AUC). For each texture feature, logistic regression classifiers consisting of the average change in texture feature value and the pre-therapy SUV standard deviation (SUV_SD ) were created and compared with the texture feature as a lone classifier using ANOVA with correction for multiple comparisons (P < 0.0025).

RESULTS

While clinical parameters (mean lung dose, smoking history, tumor location) were not significantly different among patients with and without symptomatic RP, SUV and texture parameters were significantly associated with RP status. AUC for single-texture feature classifiers alone ranged from 0.58 to 0.81 and 0.53 to 0.71 in high-dose (≥ 30 Gy) and low-dose (< 10 Gy) regions of the lungs, respectively. AUC for SUV_SD alone was 0.69 (95% confidence interval: 0.54-0.83). Adding SUV_SD into a logistic regression model significantly improved model fit for 18, 14 and 11 texture features and increased the mean AUC across features by 0.08, 0.06, and 0.04 in the low-, medium-, and high-dose regions, respectively.

CONCLUSIONS

Addition of SUV_SD to a single-texture feature improves classifier performance on average, but the improvement is smaller in magnitude when SUV_SD is added to an already effective classifier using texture alone. These findings demonstrate the potential for more accurate assessment of RP using information from multiple imaging modalities.

Systemic effects of ionizing radiation at the proteome and metabolome levels in the blood of cancer patients treated with radiotherapy: the influence of inflammation and radiation toxicity

PURPOSE

Blood is the most common replacement tissue used to study systemic responses of organisms to different types of pathological conditions and environmental insults. Local irradiation during cancer radiotherapy induces whole body responses that can be observed at the blood proteome and metabolome levels. Hence, comparative blood proteomics and metabolomics are emerging approaches used in the discovery of radiation biomarkers. These techniques enable the simultaneous measurement of hundreds of molecules and the identification of sets of components that can discriminate different physiological states of the human body. Radiation-induced changes are affected by the dose and volume of irradiated tissues; hence, the molecular composition of blood is a hypothetical source of biomarkers for dose assessment and the prediction and monitoring of systemic responses to radiation. This review aims to provide a comprehensive overview on the available evidence regarding molecular responses to ionizing radiation detected at the level of the human blood proteome and metabolome. It focuses on patients exposed to radiation during cancer radiotherapy and emphasizes effects related to radiation-induced toxicity and inflammation.

CONCLUSIONS

Systemic responses to radiation detected at the blood proteome and metabolome levels are primarily related to the intensity of radiation-induced toxicity, including inflammatory responses. Thus, several inflammation-associated molecules can be used to monitor or even predict radiation-induced toxicity. However, these abundant molecular features have a rather limited applicability as universal biomarkers for dose assessment, reflecting the individual predisposition of the immune system and tissue-specific mechanisms involved in radiation-induced damage.

Computational methods using genome-wide association studies to predict radiotherapy complications and to identify correlative molecular processes

The biological cause of clinically observed variability of normal tissue damage following radiotherapy is poorly understood. We hypothesized that machine/statistical learning methods using single nucleotide polymorphism (SNP)-based genome-wide association studies (GWAS) would identify groups of patients of differing complication risk, and furthermore could be used to identify key biological sources of variability. We developed a novel learning algorithm, called pre-conditioned random forest regression (PRFR), to construct polygenic risk models using hundreds of SNPs, thereby capturing genomic features that confer small differential risk. Predictive models were trained and validated on a cohort of 368 prostate cancer patients for two post-radiotherapy clinical endpoints: late rectal bleeding and erectile dysfunction. The proposed method results in better predictive performance compared with existing computational methods. Gene ontology enrichment analysis and protein-protein interaction network analysis are used to identify key biological processes and proteins that were plausible based on other published studies. In conclusion, we confirm that novel machine learning methods can produce large predictive models (hundreds of SNPs), yielding clinically useful risk stratification models, as well as identifying important underlying biological processes in the radiation damage and tissue repair process. The methods are generally applicable to GWAS data and are not specific to radiotherapy endpoints.

Gene expression profiles among murine strains segregate with distinct differences in the progression of radiation-induced lung disease

Molecular mechanisms underlying development of acute pneumonitis and/or late fibrosis following thoracic irradiation remain poorly understood. Here, we hypothesize that heterogeneity in disease progression and phenotypic expression of radiation-induced lung disease (RILD) across murine strains presents an opportunity to better elucidate mechanisms driving tissue response toward pneumonitis and/or fibrosis. Distinct differences in disease progression were observed in age- and sex-matched CBA/J, C57L/J and C57BL/6J mice over 1 year after graded doses of whole-thorax lung irradiation (WTLI). Separately, comparison of gene expression profiles in lung tissue 24 h post-exposure demonstrated >5000 genes to be differentially expressed (P<0.01; >twofold change) between strains with early versus late onset of disease. An immediate divergence in early tissue response between radiation-sensitive and -resistant strains was observed. In pneumonitis-prone C57L/J mice, differentially expressed genes were enriched in proinflammatory pathways, whereas in fibrosis-prone C57BL/6J mice, genes were enriched in pathways involved in purine and pyrimidine synthesis, DNA replication and cell division. At 24 h post-WTLI, different patterns of cellular damage were observed at the ultrastructural level among strains but microscopic damage was not yet evident under light microscopy. These data point toward a fundamental difference in patterns of early pulmonary tissue response to WTLI, consistent with the macroscopic expression of injury manifesting weeks to months after exposure. Understanding the mechanisms underlying development of RILD might lead to more rational selection of therapeutic interventions to mitigate healthy tissue damage.

Summary: Rational mouse model selection is crucial for identifying new therapeutic targets and screening medical interventions in acute pneumonitis and/or late fibrosis following thoracic irradiation.

Radiogenomics: A systems biology approach to understanding genetic risk factors for radiotherapy toxicity?

Adverse reactions in normal tissue after radiotherapy (RT) limit the dose that can be given to tumour cells. Since 80% of individual variation in clinical response is estimated to be caused by patient-related factors, identifying these factors might allow prediction of patients with increased risk of developing severe reactions. While inactivation of cell renewal is considered a major cause of toxicity in early-reacting normal tissues, complex interactions involving multiple cell types, cytokines, and hypoxia seem important for late reactions. Here, we review 'omics' approaches such as screening of genetic polymorphisms or gene expression analysis, and assess the potential of epigenetic factors, posttranslational modification, signal transduction, and metabolism. Furthermore, functional assays have suggested possible associations with clinical risk of adverse reaction. Pathway analysis incorporating different 'omics' approaches may be more efficient in identifying critical pathways than pathway analysis based on single 'omics' data sets. Integrating these pathways with functional assays may be powerful in identifying multiple subgroups of RT patients characterised by different mechanisms. Thus 'omics' and functional approaches may synergise if they are integrated into radiogenomics 'systems biology' to facilitate the goal of individualised radiotherapy.

Prognostic value of blood-biomarkers related to hypoxia, inflammation, immune response and tumour load in non-small cell lung cancer - A survival model with external validation

AIM

Improve the prognostic prediction of clinical variables for non-small cell lung cancer (NSCLC), by selecting from blood-biomarkers, non-invasively describing hypoxia, inflammation and tumour load.

METHODS

Model development and validation included 182 and 181 inoperable stage I-IIIB NSCLC patients treated radically with radiotherapy (55.2%) or chemo-radiotherapy (44.8%). Least absolute shrinkage and selection operator (LASSO), selected from blood-biomarkers related to hypoxia [osteopontin (OPN) and carbonic anhydrase IX (CA-IX)], inflammation [interleukin-6 (IL-6), IL-8, and C-reactive protein (CRP)], and tumour load [carcinoembryonic antigen (CEA), and cytokeratin fragment 21-1 (Cyfra 21-1)]. Sequent model extension selected from alpha-2-macroglobulin (α2M), serum interleukin-2 receptor (sIL2r), toll-like receptor 4 (TLR4), and vascular endothelial growth factor (VEGF). Discrimination was reported by concordance-index.

RESULTS

OPN and Cyfra 21-1 (hazard ratios of 3.3 and 1.7) significantly improved a clinical model comprising gender, World Health Organization performance-status, forced expiratory volume in 1s, number of positive lymph node stations, and gross tumour volume, from a concordance-index of 0.66 to 0.70 (validation=0.62 and 0.66). Extension of the validated model yielded a concordance-index of 0.67, including α2M, sIL2r and VEGF (hazard ratios of 4.6, 3.1, and 1.4).

CONCLUSION

Improvement of a clinical model including hypoxia and tumour load blood-biomarkers was validated. New immunological markers were associated with overall survival. Data and models can be found at www.cancerdata.org (http://dx.doi.org/10.17195/candat.2016.04.1) and www.predictcancer.org.

Bayesian network ensemble as a multivariate strategy to predict radiation pneumonitis risk

PURPOSE

Prediction of radiation pneumonitis (RP) has been shown to be challenging due to the involvement of a variety of factors including dose-volume metrics and radiosensitivity biomarkers. Some of these factors are highly correlated and might affect prediction results when combined. Bayesian network (BN) provides a probabilistic framework to represent variable dependencies in a directed acyclic graph. The aim of this study is to integrate the BN framework and a systems' biology approach to detect possible interactions among RP risk factors and exploit these relationships to enhance both the understanding and prediction of RP.

METHODS

The authors studied 54 nonsmall-cell lung cancer patients who received curative 3D-conformal radiotherapy. Nineteen RP events were observed (common toxicity criteria for adverse events grade 2 or higher). Serum concentration of the following four candidate biomarkers were measured at baseline and midtreatment: alpha-2-macroglobulin, angiotensin converting enzyme (ACE), transforming growth factor, interleukin-6. Dose-volumetric and clinical parameters were also included as covariates. Feature selection was performed using a Markov blanket approach based on the Koller-Sahami filter. The Markov chain Monte Carlo technique estimated the posterior distribution of BN graphs built from the observed data of the selected variables and causality constraints. RP probability was estimated using a limited number of high posterior graphs (ensemble) and was averaged for the final RP estimate using Bayes' rule. A resampling method based on bootstrapping was applied to model training and validation in order to control under- and overfit pitfalls.

RESULTS

RP prediction power of the BN ensemble approach reached its optimum at a size of 200. The optimized performance of the BN model recorded an area under the receiver operating characteristic curve (AUC) of 0.83, which was significantly higher than multivariate logistic regression (0.77), mean heart dose (0.69), and a pre-to-midtreatment change in ACE (0.66). When RP prediction was made only with pretreatment information, the AUC ranged from 0.76 to 0.81 depending on the ensemble size. Bootstrap validation of graph features in the ensemble quantified confidence of association between variables in the graphs where ten interactions were statistically significant.

CONCLUSIONS

The presented BN methodology provides the flexibility to model hierarchical interactions between RP covariates, which is applied to probabilistic inference on RP. The authors' preliminary results demonstrate that such framework combined with an ensemble method can possibly improve prediction of RP under real-life clinical circumstances such as missing data or treatment plan adaptation.

Discovery and Validation of Predictive Biomarkers of Survival for Non-small Cell Lung Cancer Patients Undergoing Radical Radiotherapy: Two Proteins With Predictive Value

Lung cancer is the most frequent cause of cancer-related death world-wide. Radiotherapy alone or in conjunction with chemotherapy is the standard treatment for locally advanced non-small cell lung cancer (NSCLC). Currently there is no predictive marker with clinical utility to guide treatment decisions in NSCLC patients undergoing radiotherapy. Identification of such markers would allow treatment options to be considered for more effective therapy. To enable the identification of appropriate protein biomarkers, plasma samples were collected from patients with non-small cell lung cancer before and during radiotherapy for longitudinal comparison following a protocol that carries sufficient power for effective discovery proteomics. Plasma samples from patients pre- and during radiotherapy who had survived > 18 mo were compared to the same time points from patients who survived < 14 mo using an 8 channel isobaric tagging tandem mass spectrometry discovery proteomics platform. Over 650 proteins were detected and relatively quantified. Proteins which showed a change during radiotherapy were selected for validation using an orthogonal antibody-based approach. Two of these proteins were verified in a separate patient cohort: values of CRP and LRG1 combined gave a highly significant indication of extended survival post one week of radiotherapy treatment.

Long intergenic non-coding RNA induced by X-ray irradiation regulates DNA damage response signaling in the human bronchial epithelial BEAS-2B cell line

Long non-coding RNAs (lncRNAs) have been regarded as the primary genetic regulators of several important biological processes. However, the biological functions of lncRNAs in radiation-induced lung damage remain largely unknown. The present study aimed to investigate the potential effects of lncRNAs on radiation-induced lung injury (RILI). Female C57BL/6 mice were exposed to 12 Gy single doses of total body irradiation (TBI). LncRNA microarray screening was conducted at 24 h post-irradiation (IR) to investigate the differentially-expressed lncRNAs during RILI. Following the subsequent bioinformatics analysis and reverse transcription-polymerase chain reaction (RT-PCR) validation, one of the verified differentially-expressed long intergenic radiation-responsive ncRNAs (LIRRs), LIRR1, was selected for further functional study. The normal human bronchial epithelial BEAS-2B cell line was used as the cell model. The recombinant eukaryotic expression vector for the lncRNA was designed, constructed and transfected using lipofectamine. RT-PCR, clonogenic and flow cytometry assays, immunofluorescence detection and western blot analysis were performed to reveal the role of the lncRNA in the radiosensitivity regulation of the RILI target cells. In lung tissues 24 h after 12 Gy TBI, six of the identified differentially-expressed LIRRs near the coding genes were validated using quantitative (q)PCR. The upregulation of two LIRRs was observed and confirmed using qPCR. LIRR1 was chosen for further functional study. Following the stable transfection of LIRR1, identified through G418 screening, increased radiosensitivity, evident cell cycle G1 phase arrest and increased γ-H2AX foci formation were observed in the bronchial epithelial BEAS-2B cell line subsequent to IR. LIRR1 overexpression also led to decreased expression of the KU70, KU80 and RAD50 DNA repair proteins, marked activation of p53, decreased mouse double minute 2 homolog (MDM2) expression, and substantially induced p21 and suppressed cyclin-dependent kinase 2 in BEAS-2B following IR. Subsequent to the use of Pifithrin-α, a specific inhibitor of p53 activation, increased MDM2 expression was observed in the LIRR1-overexpressing cells, suggesting that LIRR1 could mediate the DNA damage response (DDR) signaling in a p53-dependent manner. The present study provides a novel mechanism for RILI, using the concept of lncRNAs.

Proteomics in radiation research: present status and future perspectives

Rapidly developing postgenome research has made proteins an attractive target for biological analysis. The well-established term of proteome is defined as the complete set of proteins expressed in a given cell, tissue or organism. Unlike the genome, a proteome is rapidly changing as it tends to adapt to microenvironmental signals. The systematic analysis of the proteome at a given time and state is referred to as proteomics. This technique provides information on the molecular and cellular mechanisms that regulate physiology and pathophysiology of the cell. Applications of proteome profiling in radiation research are increasing. However, the large-scale proteomics data sets generated need to be integrated into other fields of radiation biology to facilitate the interpretation of radiation-induced cellular and tissue effects. The aim of this review is to introduce the most recent developments in the field of radiation proteomics.

RASSF1A promotes apoptosis and suppresses the proliferation of ovarian cancer cells

As the most lethal gynecological malignancy, ovarian cancer has attracted much attention over the past few decades; however, the early detection of this malignancy has been largely unsuccessful. The aim of this study was to determine the effects of Ras-association domain family 1, isoform A (RASSF1A) on ovarian cancer and to elucidate the molecular mechanisms responsible for these effects. The expression of RASSF1A in different ovarian cancer cells was detected by quantitative reverse transcription-polymerase chain reaction (qRT-PCR). The morphology, structure, apoptosis and proliferation of differently treated SKOV-3 cells were then analyzed using a fluorescence microscope, transmission electron microscope, flow cytometer and by western blot analysis, respectively. Moreover, the GSE14407 affymetrix microarray data were downloaded from the Gene Expression Omnibus database and the expression of RASSF1A was quantified by Spotfire DecisionSite software. A RASSF1A related protein-protein interaction (PPI) network was then constructed using STRING and Cytoscape software. Finally, DAVID was utilized to perform KEGG pathway enrichment analysis of the network. RASSF1A was expressed in the HO8910, HO8910PM cells and the SKOV-3 cells transfected with RASSF1A, whereas it was absent in the other SKOV-3 cells and OVCAR-3 cells. Additionally, compared with the other SKOV-3 cells, the nucleus of SKOV-3 cells transfected with RASSF1A was vacuolated, apoptosis was increased, and the expression of cyclin D1 and survivin was decreased (P<0.05), and that of p27 and caspase-3 was increased (P<0.01). Additionally, 10 genes, including serine/threonine kinase (STK)3, STK4, Harvey rat sarcoma viral oncogene homolog (HRAS) and cell division cycle 20 (CDC20), were found to have close interactions with RASSF1A in the PPI network. Finally, a total of 8 enriched pathways, such as bladder cancer, non-small cell lung cancer and pathways in cancer were identified. To our knowledge, this is the first study to explore the biological functions and the underlying mechanisms of action of RASSF1A in the development of ovarian cancer. Our findings may provide a novel therapeutic target for ovarian cancer.

Respiratory Proteomics Today: Are Technological Advances for the Identification of Biomarker Signatures Catching up with Their Promise? A Critical Review of the Literature in the Decade 2004-2013

To improve the knowledge on a variety of severe disorders, research has moved from the analysis of individual proteins to the investigation of all proteins expressed by a tissue/organism. This global proteomic approach could prove very useful: (i) for investigating the biochemical pathways involved in disease; (ii) for generating hypotheses; or (iii) as a tool for the identification of proteins differentially expressed in response to the disease state. Proteomics has not been used yet in the field of respiratory research as extensively as in other fields, only a few reproducible and clinically applicable molecular markers, which can assist in diagnosis, having been currently identified. The continuous advances in both instrumentation and methodology, which enable sensitive and quantitative proteomic analyses in much smaller amounts of biological material than before, will hopefully promote the identification of new candidate biomarkers in this area. The aim of this report is to critically review the application over the decade 2004-2013 of very sophisticated technologies to the study of respiratory disorders. The observed changes in protein expression profiles from tissues/fluids of patients affected by pulmonary disorders opens the route for the identification of novel pathological mediators of these disorders.

[Intrinsic radiosensitivity: predictive assays that will change daily practice]

The impact of curative radiotherapy depends mainly on the total dose delivered homogenously in the targeted volume. Nevertheless, the dose delivered to the surrounding healthy tissues may reduce the therapeutic ratio of many radiation treatments. In a same population treated in one center with the same technique, it appears that individual radiosensitivity clearly exists, namely in terms of late side effects that are in principle non-reversible. This review details the different radiobiological approaches that have been developed to better understand the mechanisms of radiation-induced late effects. We also present the possibilities of clinical use of predictive assays in the close future.

Serum and plasma proteomics and its possible use as detector and predictor of radiation diseases

All tissues can be damaged by ionizing radiation. Early biomarkers of radiation injury are critical for triage, treatment and follow-up of large numbers of people exposed to ionizing radiation after terrorist attacks or radiological accident, and for prediction of normal tissue toxicity before, during and after a treatment by radiotherapy. The comparative proteomic approach is a promising and powerful tool for the discovery of new radiation biomarkers. In association with multivariate statistics, proteomics enables measurement of the level of hundreds or thousands of proteins at the same time and identifies set of proteins that can discriminate between different groups of individuals. Human serum and plasma are the preferred samples for the study of normal and disease-associated proteins. Extreme complexity, extensive dynamic range, genetic and physiological variations, protein modifications and incompleteness of sampling by two-dimensional electrophoresis and mass spectrometry represent key challenges to reproducible, high-resolution, and high-throughput analyses of serum and plasma proteomes. The future of radiation research will possibly lie in molecular networks that link genome, transcriptome, proteome and metabolome variations to radiation pathophysiology and serve as sensors of radiation disease. This chapter reviews recent advances in proteome analysis of serum and plasma as well as its applications to radiation biology and radiation biomarker discovery for both radiation exposure and radiation tissue toxicity.

A bioinformatics filtering strategy for identifying radiation response biomarker candidates

The number of biomarker candidates is often much larger than the number of clinical patient data points available, which motivates the use of a rational candidate variable filtering methodology. The goal of this paper is to apply such a bioinformatics filtering process to isolate a modest number (<10) of key interacting genes and their associated single nucleotide polymorphisms involved in radiation response, and to ultimately serve as a basis for using clinical datasets to identify new biomarkers. In step 1, we surveyed the literature on genetic and protein correlates to radiation response, in vivo or in vitro, across cellular, animal, and human studies. In step 2, we analyzed two publicly available microarray datasets and identified genes in which mRNA expression changed in response to radiation. Combining results from Step 1 and Step 2, we identified 20 genes that were common to all three sources. As a final step, a curated database of protein interactions was used to generate the most statistically reliable protein interaction network among any subset of the 20 genes resulting from Steps 1 and 2, resulting in identification of a small, tightly interacting network with 7 out of 20 input genes. We further ranked the genes in terms of likely importance, based on their location within the network using a graph-based scoring function. The resulting core interacting network provides an attractive set of genes likely to be important to radiation response.

Improving Detection Accuracy of Lung Cancer Serum Proteomic Profiling via Two-Stage Training Process

Background

Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (SELDI-TOF-MS) is a frequently used technique for cancer biomarker research. The specificity of biomarkers detected by SELDI can be influenced by concomitant inflammation. This study aimed to increase detection accuracy using a two-stage analysis process.

Methods

Sera from 118 lung cancer patients, 72 healthy individuals, and 31 patients with inflammatory disease were randomly divided into training and testing groups by 3:2 ratio. In the training group, the traditional method of using SELDI profile analysis to directly distinguish lung cancer patients from sera was used. The two-stage analysis of distinguishing the healthy people and non-healthy patients (1^st-stage) and then differentiating cancer patients from inflammatory disease patients (2^nd-stage) to minimize the influence of inflammation was validated in the test group.

Results

In the test group, the one-stage method had 87.2% sensitivity, 37.5% specificity, and 64.4% accuracy. The two-stage method had lower sensitivity (> 70.1%) but statistically higher specificity (80%) and accuracy (74.7%). The predominantly expressed protein peak at 11480 Da was the primary splitter regardless of one- or two-stage analysis. This peak was suspected to be SAA (Serum Amyloid A) due to the similar m/z countered around this area. This hypothesis was further tested using an SAA ELISA assay.

Conclusions

Inflammatory disease can severely interfere with the detection accuracy of SELDI profiles for lung cancer. Using a two-stage training process will improve the specificity and accuracy of detecting lung cancer.