Correction: Tasci et al. RadWise: A Rank-Based Hybrid Feature Weighting and Selection Method for Proteomic Categorization of Chemoirradiation in Patients with Glioblastoma. Cancers 2023, 15, 2672.

In the original publication [...].

Serum CD133-Associated Proteins Identified by Machine Learning Are Connected to Neural Development, Cancer Pathways, and 12-Month Survival in Glioblastoma.

Glioma is the most prevalent type of primary central nervous system cancer, while glioblastoma (GBM) is its most aggressive variant, with a median survival of only 15 months when treated with maximal surgical resection followed by chemoradiation therapy (CRT). CD133 is a potentially significant GBM biomarker. However, current clinical biomarker studies rely on invasive tissue samples. These make prolonged data acquisition impossible, resulting in increased interest in the use of liquid biopsies. Our study, analyzed 7289 serum proteins from 109 patients with pathology-proven GBM obtained prior to CRT using the aptamer-based SOMAScan® proteomic assay technology. We developed a novel methodology that identified 24 proteins linked to both serum CD133 and 12-month overall survival (OS) through a multi-step machine learning (ML) analysis. These identified proteins were subsequently subjected to survival and clustering evaluations, categorizing patients into five risk groups that accurately predicted 12-month OS based on their protein profiles. Most of these proteins are involved in brain function, neural development, and/or cancer biology signaling, highlighting their significance and potential predictive value. Identifying these proteins provides a valuable foundation for future serum investigations as validation of clinically applicable GBM biomarkers can unlock immense potential for diagnostics and treatment monitoring.

MGMT ProFWise: Unlocking a New Application for Combined Feature Selection and the Rank-Based Weighting Method to Link MGMT Methylation Status to Serum Protein Expression in Patients with Glioblastoma.

Glioblastoma (GBM) is a fatal brain tumor with limited treatment options. O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation status is the central molecular biomarker linked to both the response to temozolomide, the standard chemotherapy drug employed for GBM, and to patient survival. However, MGMT status is captured on tumor tissue which, given the difficulty in acquisition, limits the use of this molecular feature for treatment monitoring. MGMT protein expression levels may offer additional insights into the mechanistic understanding of MGMT but, currently, they correlate poorly to promoter methylation. The difficulty of acquiring tumor tissue for MGMT testing drives the need for non-invasive methods to predict MGMT status. Feature selection aims to identify the most informative features to build accurate and interpretable prediction models. This study explores the new application of a combined feature selection (i.e., LASSO and mRMR) and the rank-based weighting method (i.e., MGMT ProFWise) to non-invasively link MGMT promoter methylation status and serum protein expression in patients with GBM. Our method provides promising results, reducing dimensionality (by more than 95%) when employed on two large-scale proteomic datasets (7k SomaScan® panel and CPTAC) for all our analyses. The computational results indicate that the proposed approach provides 14 shared serum biomarkers that may be helpful for diagnostic, prognostic, and/or predictive operations for GBM-related processes, given further validation.

Corrigendum: Glioblastoma survival is associated with distinct proteomic alteration signatures post chemoirradiation in a large-scale proteomic panel.

[This corrects the article DOI: 10.3389/fonc.2023.1127645.].

Revisiting Concurrent Radiation Therapy, Temozolomide, and the Histone Deacetylase Inhibitor Valproic Acid for Patients with Glioblastoma-Proteomic Alteration and Comparison Analysis with the Standard-of-Care Chemoirradiation.

BACKGROUND: Glioblastoma (GBM) is the most common brain tumor with an overall survival (OS) of less than 30% at two years. Valproic acid (VPA) demonstrated survival benefits documented in retrospective and prospective trials, when used in combination with chemo-radiotherapy (CRT). PURPOSE: The primary goal of this study was to examine if the differential alteration in proteomic expression pre vs. post-completion of concurrent chemoirradiation (CRT) is present with the addition of VPA as compared to standard-of-care CRT. The second goal was to explore the associations between the proteomic alterations in response to VPA/RT/TMZ correlated to patient outcomes. The third goal was to use the proteomic profile to determine the mechanism of action of VPA in this setting. MATERIALS AND METHODS: Serum obtained pre- and post-CRT was analyzed using an aptamer-based SOMAScan® proteomic assay. Twenty-nine patients received CRT plus VPA, and 53 patients received CRT alone. Clinical data were obtained via a database and chart review. Tests for differences in protein expression changes between radiation therapy (RT) with or without VPA were conducted for individual proteins using two-sided t-tests, considering p-values of <0.05 as significant. Adjustment for age, sex, and other clinical covariates and hierarchical clustering of significant differentially expressed proteins was carried out, and Gene Set Enrichment analyses were performed using the Hallmark gene sets. Univariate Cox proportional hazards models were used to test the individual protein expression changes for an association with survival. The lasso Cox regression method and 10-fold cross-validation were employed to test the combinations of expression changes of proteins that could predict survival. Predictiveness curves were plotted for significant proteins for VPA response (p-value < 0.005) to show the survival probability vs. the protein expression percentiles. RESULTS: A total of 124 proteins were identified pre- vs. post-CRT that were differentially expressed between the cohorts who received CRT plus VPA and those who received CRT alone. Clinical factors did not confound the results, and distinct proteomic clustering in the VPA-treated population was identified. Time-dependent ROC curves for OS and PFS for landmark times of 20 months and 6 months, respectively, revealed AUC of 0.531, 0.756, 0.774 for OS and 0.535, 0.723, 0.806 for PFS for protein expression, clinical factors, and the combination of protein expression and clinical factors, respectively, indicating that the proteome can provide additional survival risk discrimination to that already provided by the standard clinical factors with a greater impact on PFS. Several proteins of interest were identified. Alterations in GALNT14 (increased) and CCL17 (decreased) (p = 0.003 and 0.003, respectively, FDR 0.198 for both) were associated with an improvement in both OS and PFS. The pre-CRT protein expression revealed 480 proteins predictive for OS and 212 for PFS (p < 0.05), of which 112 overlapped between OS and PFS. However, FDR-adjusted p values were high, with OS (the smallest p value of 0.586) and PFS (the smallest p value of 0.998). The protein PLCD3 had the lowest p-value (p = 0.002 and 0.0004 for OS and PFS, respectively), and its elevation prior to CRT predicted superior OS and PFS with VPA administration. Cancer hallmark genesets associated with proteomic alteration observed with the administration of VPA aligned with known signal transduction pathways of this agent in malignancy and non-malignancy settings, and GBM signaling, and included epithelial-mesenchymal transition, hedgehog signaling, Il6/JAK/STAT3, coagulation, NOTCH, apical junction, xenobiotic metabolism, and complement signaling. CONCLUSIONS: Differential alteration in proteomic expression pre- vs. post-completion of concurrent chemoirradiation (CRT) is present with the addition of VPA. Using pre- vs. post-data, prognostic proteins emerged in the analysis. Using pre-CRT data, potentially predictive proteins were identified. The protein signals and hallmark gene sets associated with the alteration in the proteome identified between patients who received VPA and those who did not, align with known biological mechanisms of action of VPA and may allow for the identification of novel biomarkers associated with outcomes that can help advance the study of VPA in future prospective trials.

Glioblastoma survival is associated with distinct proteomic alteration signatures post chemoirradiation in a large-scale proteomic panel.

Background: Glioblastomas (GBM) are rapidly progressive, nearly uniformly fatal brain tumors. Proteomic analysis represents an opportunity for noninvasive GBM classification and biological understanding of treatment response. Purpose: We analyzed differential proteomic expression pre vs. post completion of concurrent chemoirradiation (CRT) in patient serum samples to explore proteomic alterations and classify GBM by integrating clinical and proteomic parameters. Materials and methods: 82 patients with GBM were clinically annotated and serum samples obtained pre- and post-CRT. Serum samples were then screened using the aptamer-based SOMAScan® proteomic assay. Significant traits from uni- and multivariate Cox models for overall survival (OS) were designated independent prognostic factors and principal component analysis (PCA) was carried out. Differential expression of protein signals was calculated using paired t-tests, with KOBAS used to identify associated KEGG pathways. GSEA pre-ranked analysis was employed on the overall list of differentially expressed proteins (DEPs) against the MSigDB Hallmark, GO Biological Process, and Reactome databases with weighted gene correlation network analysis (WGCNA) and Enrichr used to validate pathway hits internally. Results: 3 clinical clusters of patients with differential survival were identified. 389 significantly DEPs pre vs. post-treatment were identified, including 284 upregulated and 105 downregulated, representing several pathways relevant to cancer metabolism and progression. The lowest survival group (median OS 13.2 months) was associated with DEPs affiliated with proliferative pathways and exhibiting distinct oppositional response including with respect to radiation therapy related pathways, as compared to better-performing groups (intermediate, median OS 22.4 months; highest, median OS 28.7 months). Opposite signaling patterns across multiple analyses in several pathways (notably fatty acid metabolism, NOTCH, TNFα via NF-κB, Myc target V1 signaling, UV response, unfolded protein response, peroxisome, and interferon response) were distinct between clinical survival groups and supported by WGCNA. 23 proteins were statistically signficant for OS with 5 (NETO2, CST7, SEMA6D, CBLN4, NPS) supported by KM. Conclusion: Distinct proteomic alterations with hallmarks of cancer, including progression, resistance, stemness, and invasion, were identified in serum samples obtained from GBM patients pre vs. post CRT and corresponded with clinical survival. The proteome can potentially be employed for glioma classification and biological interrogation of cancer pathways.

RadWise: A Rank-Based Hybrid Feature Weighting and Selection Method for Proteomic Categorization of Chemoirradiation in Patients with Glioblastoma.

Glioblastomas (GBM) are rapidly growing, aggressive, nearly uniformly fatal, and the most common primary type of brain cancer. They exhibit significant heterogeneity and resistance to treatment, limiting the ability to analyze dynamic biological behavior that drives response and resistance, which are central to advancing outcomes in glioblastoma. Analysis of the proteome aimed at signal change over time provides a potential opportunity for non-invasive classification and examination of the response to treatment by identifying protein biomarkers associated with interventions. However, data acquired using large proteomic panels must be more intuitively interpretable, requiring computational analysis to identify trends. Machine learning is increasingly employed, however, it requires feature selection which has a critical and considerable effect on machine learning problems when applied to large-scale data to reduce the number of parameters, improve generalization, and find essential predictors. In this study, using 7k proteomic data generated from the analysis of serum obtained from 82 patients with GBM pre- and post-completion of concurrent chemoirradiation (CRT), we aimed to select the most discriminative proteomic features that define proteomic alteration that is the result of administering CRT. Thus, we present a novel rank-based feature weighting method (RadWise) to identify relevant proteomic parameters using two popular feature selection methods, least absolute shrinkage and selection operator (LASSO) and the minimum redundancy maximum relevance (mRMR). The computational results show that the proposed method yields outstanding results with very few selected proteomic features, with higher accuracy rate performance than methods that do not employ a feature selection process. While the computational method identified several proteomic signals identical to the clinical intuitive (heuristic approach), several heuristically identified proteomic signals were not selected while other novel proteomic biomarkers not selected with the heuristic approach that carry biological prognostic relevance in GBM only emerged with the novel method. The computational results show that the proposed method yields promising results, reducing 7k proteomic data to 7 selected proteomic features with a performance value of 93.921%, comparing favorably with techniques that do not employ feature selection.

Microarray analysis of hub genes, non-coding RNAs and pathways in lung after whole body irradiation in a mouse model.

PURPOSE: Previous research has highlighted the impact of radiation damage, with cancer patients developing acute disorders including radiation induced pneumonitis or chronic disorders including pulmonary fibrosis months after radiation therapy ends. We sought to discover biomarkers that predict these injuries and develop treatments that mitigate this damage and improve quality of life. MATERIALS AND METHODS: Six- to eight-week-old female C57BL/6 mice received 1, 2, 4, 8, 12 Gy or sham whole body irradiation. Animals were euthanized 48 h post exposure and lungs removed, snap frozen and underwent RNA isolation. Microarray analysis was performed to determine dysregulation of messenger RNA (mRNA), microRNA (miRNA), and long non-coding RNA (lncRNA) after radiation injury. RESULTS: We observed sustained dysregulation of specific RNA markers including: mRNAs, lncRNAs, and miRNAs across all doses. We also identified significantly upregulated genes that can indicate high dose exposure, including Cpt1c, Pdk4, Gdf15, and Eda2r, which are markers of senescence and fibrosis. Only three miRNAs were significantly dysregulated across all radiation doses: miRNA-142-3p and miRNA-142-5p were downregulated and miRNA-34a-5p was upregulated. IPA analysis predicted inhibition of several molecular pathways with increasing doses of radiation, including: T cell development, Quantity of leukocytes, Quantity of lymphocytes, and Cell viability. CONCLUSIONS: These RNA biomarkers might be highly relevant in the development of treatments and in predicting normal tissue injury in patients undergoing radiation treatment. We are conducting further experiments in our laboratory, which includes a human lung-on-a-chip model, to develop a decision tree model using RNA biomarkers.

Microarray analysis identifies coding and non-coding RNA markers of liver injury in whole body irradiated mice.

Radiation injury from medical, accidental, or intentional sources can induce acute and long-term hepatic dysregulation, fibrosis, and cancer. This long-term hepatic dysregulation decreases quality of life and may lead to death. Our goal in this study is to determine acute changes in biological pathways and discover potential RNA biomarkers predictive of radiation injury. We performed whole transcriptome microarray analysis of mouse liver tissue (C57BL/6 J) 48 h after whole-body irradiation with 1, 2, 4, 8, and 12 Gray to identify significant expression changes in mRNAs, lncRNAs, and miRNAs, We also validated changes in specific RNAs through qRT-PCR. We used Ingenuity Pathway Analysis (IPA) to identify pathways associated with gene expression changes. We observed significant dysregulation of multiple mRNAs across all doses. In contrast, miRNA dysregulation was observed upwards of 2 Gray. The most significantly upregulated mRNAs function as tumor suppressors: Cdkn1a, Phlda3, and Eda2r. The most significantly downregulated mRNAs were involved in hemoglobin synthesis, inflammation, and mitochondrial function including multiple members of Hbb and Hba. The most significantly upregulated miRNA included: miR-34a-5p, miR-3102-5p, and miR-3960, while miR-342-3p, miR-142a-3p, and miR-223-3p were most significantly downregulated. IPA predicted activation of cell cycle checkpoint control pathways and inhibition of pathways relevant to inflammation and erythropoietin. Clarifying expression of mRNA, miRNA and lncRNA at a short time point (48 h) offers insight into potential biomarkers, including radiation markers shared across organs and animal models. This information, once validated in human models, can aid in development of bio-dosimetry biomarkers, and furthers our understanding of acute pathway dysregulation.

Computational modeling of first responders' willingness to accept radiation exposure during radiological or nuclear events.

Since the events of 9/11, a concerted interagency effort has been undertaken to create comprehensive emergency planning and preparedness strategies for the management of a radiological or nuclear event in the US. These planning guides include protective action guidelines, medical countermeasure recommendations, and systems for diagnosing and triaging radiation injury. Yet, key areas such as perception of risk from radiation exposure by first responders have not been addressed. In this study, we identify the need to model and develop new strategies for medical management of large-scale population exposures to radiation and examine the phenomena of radiation dread and its role in emergency response using an agent-based modeling approach. Using the computational modeling platform NetLogo, we developed a series of models examining factors affecting first responders' willingness to work (WTW) in the context of entering areas where radioactive contamination is present or triaging individuals potentially contaminated with radioactive materials. In these models, the presence of radiation subject matter experts (SMEs) was found to increase WTW. Degree of communication was found to be a dynamic variable with either positive or negative effects on WTW dependent on the initial WTW demographics of the test population. Our findings illustrate that radiation dread is a significant confounder for emergency response to radiological or nuclear events and that increasing the presence of radiation SME in the field and communication among first responders when such radiation SMEs are present will help mitigate the effect of radiation dread and improve first responder WTW during future radiological or nuclear events.

The Next Frontier in Health Disparities-A Closer Look at Exploring Sex Differences in Glioma Data and Omics Analysis, from Bench to Bedside and Back.

Sex differences are increasingly being explored and reported in oncology, and glioma is no exception. As potentially meaningful sex differences are uncovered, existing gender-derived disparities mirror data generated in retrospective and prospective trials, real-world large-scale data sets, and bench work involving animals and cell lines. The resulting disparities at the data level are wide-ranging, potentially resulting in both adverse outcomes and failure to identify and exploit therapeutic benefits. We set out to analyze the literature on women's data disparities in glioma by exploring the origins of data in this area to understand the representation of women in study samples and omics analyses. Given the current emphasis on inclusive study design and research, we wanted to explore if sex bias continues to exist in present-day data sets and how sex differences in data may impact conclusions derived from large-scale data sets, omics, biospecimen analysis, novel interventions, and standard of care management.

Analytical Considerations of Large-Scale Aptamer-Based Datasets for Translational Applications.

The development and advancement of aptamer technology has opened a new realm of possibilities for unlocking the biocomplexity available within proteomics. With ultra-high-throughput and multiplexing, alongside remarkable specificity and sensitivity, aptamers could represent a powerful tool in disease-specific research, such as supporting the discovery and validation of clinically relevant biomarkers. One of the fundamental challenges underlying past and current proteomic technology has been the difficulty of translating proteomic datasets into standards of practice. Aptamers provide the capacity to generate single panels that span over 7000 different proteins from a singular sample. However, as a recent technology, they also present unique challenges, as the field of translational aptamer-based proteomics still lacks a standardizing methodology for analyzing these large datasets and the novel considerations that must be made in response to the differentiation amongst current proteomic platforms and aptamers. We address these analytical considerations with respect to surveying initial data, deploying proper statistical methodologies to identify differential protein expressions, and applying datasets to discover multimarker and pathway-level findings. Additionally, we present aptamer datasets within the multi-omics landscape by exploring the intersectionality of aptamer-based proteomics amongst genomics, transcriptomics, and metabolomics, alongside pre-existing proteomic platforms. Understanding the broader applications of aptamer datasets will substantially enhance current efforts to generate translatable findings for the clinic.

Comparison of Proteomic Expression Profiles after Radiation Exposure across Four Different Species.

There is a need to identify biomarkers of radiation exposure for use in development of circulating biodosimeters for radiation exposure and for clinical use as markers of radiation injury. Most research approaches for biomarker discovery rely on a single animal model. The current study sought to take advantage of a novel aptamer-based proteomic assay which has been validated for use in many species to characterize changes to the blood proteome after total-body irradiation (TBI) across four different mammalian species including humans. Plasma was collected from C57BL6 mice, Sinclair minipigs, and Rhesus non-human primates (NHPs) receiving a single dose of TBI at a range of 3.3 Gy to 4.22 Gy at 24 h postirradiation. NHP and minipig models were irradiated using a 60Co source at a dose rate of 0.6 Gy/min, the C57BL6 mouse model using an X-ray source at a dose rate of 2.28 Gy/min and clinical samples from a photon source at 10 cGy/min. Plasma was collected from human patients receiving a single dose of 2 Gy TBI collected 6 h postirradiation. Plasma was screened using the aptamer-based SomaLogic SomaScan® proteomic assay technology to evaluate changes in the expression of 1,310 protein analytes. Confirmatory analysis of protein expression of biomarker HIST1H1C, was completed using plasma from C57BL6 mice receiving a 2, 3.5 or 8 Gy TBI collected at days 1, 3, and 7 postirradiation by singleplex ELISA. Summary of key pathways with altered expression after radiation exposure across all four mammalian species was determined using Ingenuity Pathway Analysis (IPA). Detectable values were obtained for all 1,310 proteins in all samples included in the SomaScan assay. A subset panel of protein biomarkers which demonstrated significant (p < 0.05) changes in expression of at least 1.3-fold after radiation exposure were characterized for each species. IPA of significantly altered proteins yielded a variety of top disease and biofunction pathways across species with the organismal injury and abnormalities pathway held in common for all four species. The HIST1H1C protein was shown to be radiation responsive within the human, NHP and murine species within the SomaScan dataset and was shown to demonstrate dose dependent upregulation at 2, 3.5 and 8 Gy at 24 h postirradiation in a separate murine cohort by ELISA. The SomaScan proteomics platform is a useful screening tool to evaluate changes in biomarker expression across multiple mammalian species. In our study, we were able to identify a novel biomarker of radiation exposure, HIST1H1C, and characterize panels of radiation responsive proteins and functional proteomic pathways altered by radiation exposure across murine, minipig, NHP and human species. Our study demonstrates the efficacy of using a multispecies approach for biomarker discovery.

Proteomic Biomarker Analysis of Serum from Japanese Field Mice (Apodemus Speciosus) Collected within the Fukushima Difficult-to-return Zone.

ABSTRACT: The environmental impact of the Fukushima Daiichi nuclear power plant accident is a source of ongoing concern as there is uncertainty regarding the effects of chronic radiation exposure on local plant and animal life from Fukushima-derived radionuclides. In the current study, changes in proteomic biomarker expression due to chronic environmentally-derived radiation exposures was examined in wild field mice. Serum from 10 wild field mice (Apodemus speciosus) native to the Fukushima difficult-to-return zone and from eight wild field mice native to the Soma area (control) were collected. External dose estimations were completed using measurements of ambient radiation levels and calculating 137Cs concentrations in soil. Internal dose was estimated by counting whole mice using an HPGe detector. Age of the mice was estimated using molar wear. Serum was screened using the aptamer-based SOMAscan proteomic assay technology for changes in expression of 1,310 protein analytes. A subset panel of protein biomarkers that demonstrated significant changes in expression between control and exposed mice was determined and analyzed using Ingenuity Pathway Analysis (IPA). Control animals had a calculated lifetime dose range from 0.001 to 0.007 Gy, and exposed animals had a calculated lifetime dose range from 0.01 to 0.64 Gy. No discernable effect of dose rate was seen as relative dose rate correlated with dose for all samples. Detectable values were obtained for all 1,310 proteins included in the SOMAscan assay. Subset panels of proteins demonstrating significant (p < 0.05) changes in expression with either an upregulated or downregulated 1.5-fold change over control were identified for both the sample cohort inclusive of all exposed samples and the sample cohort restricted to samples from animals receiving "low" dose exposures. These panels of proteins from exposed animals were analyzed using IPA, which highlighted changes in key biological pathways related to injury, respiratory, renal, urological, and gastrointestinal disease, and cancer. Significant changes in expression of proteomic biomarkers were seen in the serum of wild field mice that received environmental exposure to Fukushima-derived radionuclides. Our findings demonstrate novel biomarkers of radiation exposure in wildlife within the Fukushima difficult-to-return zone.

The impact of radiation dread on mass casualty medical management during a radiological or nuclear event.

Since the events of 9/11, a concerted interagency effort has been undertaken to create comprehensive emergency planning and preparedness strategies for management of a radiological or nuclear event in the US. These planning guides include protective action guidelines, medical countermeasure recommendations, and systems for diagnosing and triaging radiation injury. Yet, key areas such as perception of risk from radiation exposure by first responders have not been addressed. In this article, we identify the need to model and develop new strategies for the medical manage-ment of large-scale population exposures to radiation, examine the phenomena of radiation dread and its role in emergency response, and review recent findings on the willingness to work of first responders and other personnel involved in mass casualty medical management during a radiological or nuclear event.

Novel Murine Biomarkers of Radiation Exposure Using An Aptamer-Based Proteomic Technology.

Purpose: There is a need to identify new biomarkers of radiation exposure both for use in the development of biodosimetry blood diagnostics for radiation exposure and for clinical use as markers of radiation injury. In the current study, a novel high-throughput proteomics screening approach was used to identify proteomic markers of radiation exposure in the plasma of total body irradiated mice. A subset panel of significantly altered proteins was selected to build predictive models of radiation exposure and received radiation dose useful for population screening in a future radiological or nuclear event. Methods: Female C57BL6 Mice of 8-14 weeks of age received a single total body irradiation (TBI) dose of 2, 3.5, 8 Gy or sham radiation and plasma was collected by cardiac puncture at days 1, 3, and 7 post-exposure. Plasma was then screened using the aptamer-based SOMAscan proteomic assay technology, for changes in expression of 1,310 protein analytes. A subset panel of protein biomarkers which demonstrated significant changes (p < 0.05) in expression following radiation exposure were used to build predictive models of radiation exposure and radiation dose. Results: Detectable values were obtained for all 1,310 proteins included in the SOMAscan assay. For the Control vs. Radiation model, the top predictive proteins were immunoglobulin heavy constant mu (IGHM), mitogen-activated protein kinase 14 (MAPK14), ectodysplasin A2 receptor (EDA2R) and solute carrier family 25 member 18 (SLC25A18). For the Control vs. Dose model, the top predictive proteins were cyclin dependent kinase 2/cyclin A2 (CDK2. CCNA2), E-selectin (SELE), BCL2 associated agonist of cell death (BAD) and SLC25A18. Following model validation with a training set of samples, both models tested with a new sample cohort had overall predictive accuracies of 85% and 73% for the Control vs. Radiation and Control vs. Dose models respectively. Conclusion: The SOMAscan proteomics platform is a useful screening tool to evaluate changes in biomarker expression. In our study we were able to identify a novel panel of radiation responsive proteins useful for predicting whether an animal had received a radiation exposure and to what dose they had received. Such diagnostic tools are needed for future medical management of radiation exposures.

A Serum Proteomic Signature Predicting Survival in Patients with Glioblastoma.

PURPOSE: Glioblastoma (GBM) is the most common form of brain tumor and has a uniformly poor prognosis. Development of prognostic biomarkers in easily accessible serum samples have the potential to improve the outcomes of patients with GBM through personalized therapy planning. MATERIAL/METHODS: In this study pre-treatment serum samples from 30 patients newly diagnosed with GBM were evaluated using a 40-protein multiplex ELISA platform. Analysis of potentially relevant gene targets using The Cancer Genome Atlas database was done using the Glioblastoma Bio Discovery Portal (GBM-BioDP). A ten-biomarker subgroup of clinically relevant molecules was selected using a functional grouping analysis of the 40 plex genes with two genes selected from each group on the basis of degree of variance, lack of co-linearity with other biomarkers and clinical interest. A Multivariate Cox proportional hazard approach was used to analyze the relationship between overall survival (OS), gene expression, and resection status as covariates. RESULTS: Thirty of 40 of the MSD molecules mapped to known genes within TCGA and separated the patient cohort into two main clusters centered predominantly around a grouping of classical and proneural versus the mesenchymal subtype as classified by Verhaak. Using the values for the 30 proteins in a prognostic index (PI) demonstrated that patients in the entire cohort with a PI below the median lived longer than those patients with a PI above the median (HR 1.8, p=0.001) even when stratified by both age and MGMT status. This finding was also consistent within each Verhaak subclass and highly significant (range p=0.0001-0.011). Additionally, a subset of ten proteins including, CRP, SAA, VCAM1, VEGF, MDC, TNFA, IL7, IL8, IL10, IL16 were found to have prognostic value within the TCGA database and a positive correlation with overall survival in GBM patients who had received gross tumor resection followed by conventional radiation therapy and temozolomide treatment concurrent with the addition of valproic acid. CONCLUSION: These findings demonstrate that proteomic approaches to the development of prognostic assays for treatment of GBM may hold potential clinical value.

Multivariate Analysis of Radiation Responsive Proteins to Predict Radiation Exposure in Total-Body Irradiation and Partial-Body Irradiation Models.

In the event of a radiological or nuclear attack, advanced clinical countermeasures are needed for screening and medical management of the exposed population. In such a scenario, minimally invasive biomarkers that can accurately quantify radiation exposure would be useful for triage management by first responders. In this murine study, we evaluated the efficacy of a novel combination of radiation responsive proteins, Flt3 ligand (FL), serum amyloid A (SAA), matrix metalloproteinase 9 (MMP9), fibrinogen beta (FGB) and pentraxin 3 (PTX3) to predict the received dose after whole- or partial-body irradiation. Ten-week-old female C57BL6 mice received a single whole-body or partial-body dose of 18 Gy from a Pantak X-ray source at a dose rate of 2.28 Gy/min. Plasma was collected by cardiac puncture at 24, 48, 72 h and 1 week postirradiation. Plasma protein levels were determined via commercially available ELISA assay. A multivariate discriminant analysis was utilized to generate best-fit dose prediction models for whole-body exposures using the selected biomarker panel and its potential application to partial-body exposures was examined. The combination of values from FL, SAA, MMP9, FGB and PTX3 between 24 h and 1 week postirradiation yielded novel dose-response relationships. For day 1 postirradiation, the best-fit model yielded a predictive accuracy of 81% utilizing FL alone. The use of additional proteins did not enhance the model accuracy whereas, at day 2 postirradiation, the addition of PTX3 and FGB to FL increased the accuracy to 100%. At day 3 the use of FL and PTX3 yielded a predictive accuracy of 93% and at day 7 use of FL and SAA had an accuracy of 90%. Dose prediction of partial-body exposures based on the TBI model had a higher predictive accuracy when the percentage of the body exposed to radiation increased. Our findings indicate that this novel combination of radiation responsive biomarker proteins are an efficient method for predicting radiation exposure and are more accurate when used in concert compared to using any single biomarker protein alone.

State-of-the-Art Advances in Radiation Biodosimetry for Mass Casualty Events Involving Radiation Exposure.

With the possibility of large-scale terrorist attacks around the world, the need for modeling and development of new medical countermeasures for potential future chemical, biological, radiological and nuclear (CBRN) has been well established. Project Bioshield, initiated in 2004, provided a framework to develop and expedite research in the field of CBRN exposures. To respond to large-scale population exposures from a nuclear event or radiation dispersal device (RDD), new methods for determining received dose using biological modeling became necessary. The field of biodosimetry has advanced significantly beyond this original initiative, with expansion into the fields of genomics, proteomics, metabolomics and transcriptomics. Studies are ongoing to evaluate the use of lymphocyte kinetics for dose assessment, as well as the development of field-deployable EPR technology. In addition, expansion of traditional cytogenetic assessment methods through the use of automated platforms and the development of laboratory surge capacity networks have helped to advance our biodefense preparedness. In this review of the latest advances in the field of biodosimetry we evaluate our progress and identify areas that still need to be addressed to achieve true field-deployment readiness.

Serum Amyloid A as a Biomarker for Radiation Exposure.

There is a need for minimally invasive biomarkers that can accurately and quickly quantify radiation exposure. Radiation-responsive proteins have applications in clinical medicine and for mass population screenings after a nuclear or radiological incident where the level of radiation exposure and exposure pattern complicate medical triage for first responders. In this study, we evaluated the efficacy of the acute phase protein serum amyloid A (SAA) as a biomarker for radiation exposure using plasma from irradiated mice. Ten-week-old female C57BL6 mice received a 1-8 Gy single whole-body or partial-body dose from a Pantak X-ray source at a dose rate of 2.28 Gy/min. Plasma was collected by mandibular or cardiac puncture at 6, 24, 48 and 72 h or 1-3 weeks postirradiation. SAA levels were determined using a commercially available ELISA assay. Data was pooled to generate SAA µg/ml threshold values correlating plasma SAA levels with radiation dose. SAA levels were statistically significant over control at all exposures between 2 and 8 Gy at 24 h postirradiation but not at 6, 48 and 72 h or 1-3 weeks postirradiation. SAA levels at 1 Gy were not significantly elevated over control at all time points. Total-body-irradiated (TBI) SAA levels at 24 h were used to generate a dose prediction model that successfully differentiated TBI mice into dose received cohorts of control/1 Gy and ≥ 2 Gy groups with a high degree of accuracy in a blind study. Dose prediction of partial-body exposures based on the TBI model correlated increasing predictive accuracy with percentage of body exposure to radiation. Our findings indicate that plasma SAA levels might be a useful biomarker for radiation exposure in a variety of total- and partial-body irradiation settings.