Radiation Immunodynamics: Longitudinal Cytokine Levels During Radiotherapy in Glioblastoma Predict Survival

Radiation Immunodynamics: Longitudinal Cytokine Levels During Radiotherapy in Glioblastoma Predict Survival | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Radiation Immunodynamics: Longitudinal Cytokine Levels During Radiotherapy in Glioblastoma Predict Survival Sushant Puri, Lindsey Sloan, Michele Doucet, Lisa Katulis, Kristin Redmond, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8118956/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Feb, 2026 Read the published version in Journal of Neuro-Oncology → Version 1 posted 14 You are reading this latest preprint version Abstract Background A critical challenge in therapies for glioblastoma is systemic immunosuppression. The immunosuppressed state is associated with poor response to therapies and thus correlated with worse outcomes. Immunosuppression is mediated by multiple factors including glioblastoma tumor and secreted cytokines, as well as radiation and chemotherapy. Methods Patients were enrolled in a single-institution, prospective, immune surveillance study. Peripheral blood was collected prior to initiating treatments and weekly during concurrent radiation and chemotherapy. Cytokine levels were measured from plasma samples isolated from peripheral blood. The cytokines were categorized as proinflammatory or anti-inflammatory. Baseline levels and dynamic changes in the levels of cytokines were analyzed for association with survival. Results 16 patients and 8 healthy controls were enrolled. A higher level of immunosuppressive cytokines at baseline (IL-10, IP-10, MCP-1, IL-34, M-CSF, PD-L1, LAG-3, PD-1, TIM-3, and CTLA-4) was inversely related with shorter survival (according to the ANOVA analysis (p < 0.05)). No association of baseline levels of proinflammatory cytokines with survival was observed. However, an increase in the levels of select proinflammatory cytokines at end of radiation (IL-34, IL-12 p70) was associated with poor survival as was increased MCP-1 in those with unmethylated tumor No association between outcomes and dynamic changes in remaining proinflammatory cytokines or any of the immunosuppressive cytokines was noted. Conclusions Our data suggests that in patients with glioblastoma, measurements of plasma cytokines at diagnosis may predict response to treatments and overall survival. In addition, the dynamic changes in the cytokine levels may similarly serve as a biomarker. glioblastoma radiation therapy chemoradiotherapy plasma cytokines immunosuppression Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Glioblastoma is an aggressive tumor that is incurable with current therapies; the standard of care includes maximal safe neurosurgical resection followed be concurrent radiation and chemotherapy with temozolomide and adjuvant temozolomide ( 1 , 2 ). The median overall survival (mOS) is in the range of 14.6 months, and the 5-year survival is around 11% ( 2 , 3 ). The emergence of immunotherapy has revolutionized treatments of several cancer types but clinical trials in glioblastoma have failed to improve prognosis ( 4 – 6 ). Barriers to successful development of treatments in glioblastoma include the marked local and systemic immunosuppression, complex regulation of the central nervous system (CNS) permeability by the blood brain barrier, inter and intratumoral heterogeneity, and limited understanding of tumor evolution during therapy ( 7 , 8 ). Radiation and temozolomide chemotherapy are the backbone of treatments after resection in newly diagnosed glioblastoma. The efficacy of radiation is mediated by direct tumor cell cytotoxicity and other indirect effects within the TiME. Moreover, radiation for glioblastoma is also thought to contribute to anti-tumor mechanisms that promote local immunogenicity ( 9 ). In contrast there is evidence that radiation for glioblastoma induces systemic immunosuppression by affecting peripheral immune cell populations, in particular circulating lymphocytes which are critical in the antineoplastic immune response. ( 10 – 13 ). This is characterized by lymphopenia, downregulation of major histocompatibility class I and II, overexpression of checkpoint inhibitor molecules, thus producing an exhausted phenotype ( 7 , 14 – 16 ). Consequently, lymphopenia and T cell exhaustion has been recognized as contributing to poor overall survival ( 17 – 21 ). There is evidence of the role of immunosuppressive regulatory T helper cells, and myeloid-derived suppressor cells (MDSCs) in mediating immunosuppression, with one critical mechanism involving the release of immune-active, soluble factors ( 22 – 25 ). Cytokines are small, soluble peptides or proteins that are that participate in an immune response including lymphoid cells, myeloid cells, fibroblasts, and stromal cells amongst others. Whereas lymphoid and myeloid cells are cellular components of the immune system, secreted cytokines also contribute to immunosuppression. While there is evidence that tumor-supportive cytokines predominate within the glioblastoma TiME ( 26 ), there are limited data on the systemic concentrations, and their relationship with clinical outcomes. In this pilot study, we analyzed relationship between concurrent chemoradiation in glioblastoma and dynamic changes in cytokine levels during treatment. Our primary hypothesis was that both the baseline and the changes in plasma cytokine levels can predict survival. Our cohort was unique in that all patients had multiple timepoints of plasma collection during concurrent chemoradiation and long term follow up, both of which provided an opportunity to study dynamic changes of cytokine levels and outcomes in glioblastoma. An understanding of the treatment related changes in plasma cytokines may guide development of biomarkers to predict outcome. 2. Materials and Methods 2.1 Human Subjects. Patients were enrolled in a prospective study, J1840, at Johns Hopkins Hospital. The protocol (IRB00293533) was reviewed by the ethics committee and approved by the Institutional Review Board. Between October 2018 and June 2019, patients with diagnosed glioblastoma per the CNS4 WHO Classification of CNS Tumors, and healthy normal controls were enrolled. Patients were older than 18 years of age and were recruited to participate after surgical resection of glioblastoma but before starting radiation treatments. An informed, written consent was obtained from all patients prior to starting with research study. Patients with recurrent glioblastoma were excluded. 2.2 Plasma Collection Blood samples were collected at serial time points: prior to starting radiation treatments for baseline (T0), followed by weekly labs during concurrent chemotherapy and radiation (T1, T2, T3, T4, T5, T6, and T7). The interval between each consecutive time point (T1 to T2, T2 to T3, etc.) was 1 week that coincide with the lab draw for monitoring of blood counts. A one-time blood sample was collected from 8 normal controls. Plasma was freshly processed 1800 rpm for 3 mins at 4° C and stored at -20 C for later analysis. (Fig. 1 ) . The procedures have been previously described ( 23 ) where we evaluated dynamic changes of immune cell populations including myeloid derived suppressor cells in these specimens. 2.3 Evaluation of Serum / Plasma Proteins The Bioplex 200 platform (Biorad, Hercules CA) was used to determine the concentration of multiple target proteins in the plasma specimens. Luminex bead-based immunoassays (Millipore, Billerica NY) were performed following validated test methods by the SKCCC Immune Monitoring Core at Johns Hopkins. Concentrations were determined using 5 parameter log curve fits (using Bioplex Manager 6.0) with vendor provided standards and quality controls. The HCKP1-11K, HCVD6MAG-67K, HCYTOMAG-60K panels (Millipore, Billerica NY) were used to detect G-CSF, GM-CSF, IL-10, IFNγ, IL-12 (p40), IL-12 (p70), IL-1β, IL-1, IL-4, IL-6, IL8, IP-10, MCP-1, TNF-a, TGF-b1, TGF-b2, TGF-b3, IL-34, M-CSF, PD-L1, LAG-3, BTLA, PD-1, TIM-3, CTLA-4, and sCD14. The 26-plex immune cytokine panel comprised of both immunostimulatory and immunoinhibitory cytokines. These are categorized as either pro-inflammatory (IFNγ, GM-CSF, IL-1β, IL-1α, IL-4, IL-6, IL-8, IL-12 p40, IL-12 p70, IL-34, MCP-1, TNF-α, and IP-10) and hence bolster humoral immunity, or immunosuppressive and hence contribute to poor tumor immune control (G-CSF, M-CSF, IL-10, TGF-β1, TGF- β2, TGF- β3, PD-L1, LAG-3, BTLA, PD-1, TIM-3, CTLA-4, and sCD14). 2.4 Statistical Analysis: For statistical analysis, values of cytokine levels lower than the lower limit of detection were replaced with the lower limit of the standard curve of the assay and values exceeding the upper limit of detection were replaced with the upper limit of the standard curve. On-treatment and baseline specimens were paired to account for batch effect and ensure accurate fold change calculations. Average concentrations of baseline samples were used for baseline analyses in patients with baseline samples that were run in multiple batches. Statistical analysis was performed in R. The analysis was performed in December 2024 to include long term outcomes for all participants in this study. Circulating cytokines in the control group were measured only at baseline. Patients who lived beyond 2 years were defined as survivors, and those who lived less than 2 years as non-survivors. Cytokines of patients in both groups were measured at seven time points. We assumed that biomarkers in the control group remained stable over time. At each visit, a hierarchical testing procedure was applied to control the overall type I error rate: initially, ANOVA was used to compare each biomarker among the three groups at an alpha significance level of 0.05. For biomarkers showing significant differences among the groups, group-pair comparisons using two-sample t-tests were subsequently conducted to compare long overall survival with control, and short overall survival with control, both at a two-sided significance level of 0.025. In survival analysis, the relationship between cytokine levels (measured at baseline, throughout their longitudinal trajectory, and at their final values) and overall survival were investigated. We assessed potential non-linear associations and interactions to uncover complex patterns. Top biomarkers, individually or in combination, were identified and integrated into a Cox proportional hazards model to evaluate differences in overall survival across subgroups defined by these top biomarkers. 3. Results 3.1 Cohort Description: Between October 2018 and June 2019, a total of 16 patients and 8 normal, healthy controls were enrolled. Following neurosurgical resection, these patients received adjuvant radiation and chemotherapy with temozolomide ( 23 ). As described previously, patients with OS beyond 2 years since diagnosis were categorized as survivors, and those with OS less than 2 years as non-survivors ( 23 ). The median age at diagnosis was 63 years (interquartile range 53 to 65) (Table 1 , Fig. 2a ). MGMT status was unmethylated in 11/16 patients. All but one patient was deceased at the time the analysis of these data were performed (Fig. 2b). Additional information regarding treatment characteristics is listed in Table 2 . Table 1 Patient demographics and tumor characteristics Patient Number Age at Diagnosis (years) WHO Grade IDH Status MGMT Olig2 P53 ATRX 1 46 IV WT 0.06 Unavailable Expressed Retained 2 64 IV WT 187.09 Unavailable Unavailable Unavailable 3 63 IV WT 0 Yes Expressed Retained 4 64 IV WT 31.29 Yes Expressed Retained 5 60 IV WT 0.05 Yes Expressed Retained 6 66 IV WT 0.67 Yes Expressed Retained 7 69 IV WT 30.65 Yes Expressed Retained 8 61 IV WT 0.91 Yes Not expressed Retained 9 46 IV Mutant 0 Yes Expressed Lost 10 47 IV WT 0.08 Yes Expressed Unavailable 11 70 IV WT 0 Yes Expressed Retained 12 55 IV WT 162.73 Yes Expressed Retained 13 63 IV WT 0.11 Yes Expressed Retained 14 73 IV WT 0.41 Yes Expressed Retained 15 64 IV WT 0.26 Yes Expressed Retained 16 47 IV WT 1.43 Yes Expressed Retained Table 2 Treatment and Survival Data Patient Number Resection Adjuvant Temozolomide Chemotherapy Prior History of Radiation Progression Free Survival (PFS) (days) Alive Overall Survival (OS) (days) Recurrence (Within our Outside RT field) 1 GTR Yes No 156 No 299 Inside 2 GTR Yes No 942 No 1136 Inside 3 GTR Yes No 163 No 415 Outside 4 STR No No Unavailable No 106 Unavailable 5 STR Yes No 306 No 341 Inside 6 GTR No No 437 No 483 Inside 7 STR Yes No 847 No 1050 Outside 8 STR Yes No 1028 Yes 2221 Unavailable 9 GTR Yes Yes 428 No 501 Inside 10 GTR Yes No 184 No 476 Inside 11 STR Yes No 321 No 627 Inside 12 GTR Yes No 716 No 914 Inside 13 GTR No No 187 No 278 Outside 14 STR No No Unavailable No 220 Unavailable 15 STR Yes No 302 No 911 Inside 16 GTR Yes No 153 No 617 Inside 3.2 Immunosuppressive Cytokine Levels are Elevated at Baseline and Associated with Poor Survival in Glioblastoma Compared to the control group, both non-survivors and survivors showed significant differences in a total of 12 cytokines. These were predominantly part of the immunosuppressive module: IL-10, IP-10, MCP-1, TGF-β2, TGF-β3, IL-34, M-CSF, PD-L1, LAG-3, PD-1, TIM-3, and CTLA-4, according to the ANOVA analysis (p < 0.05) ( Fig. 3 a, 3 b ) . In pairwise group comparisons, significant differences were observed between the nonsurvivors and the control group in the predominantly immunosuppressive cytokines IL-10, IP-10, TGF-β3, IL-34, M-CSF, PD-L1, LAG-3, PD-1, and CTLA-4. Similarly, significant differences were found between the survivors and the control group in the predominantly immunosuppressive cytokines TGF-β3, M-CSF, and TIM-3. Levels TGF-β2 and TGF-β3 were lower in glioblastoma patients which could suggest a pleiotropic function in behaving like a proinflammatory cytokine. No significant differences were found in the remaining 14 cytokines. These were predominantly part of the proinflammatory module (G-CSF, GM-CSF, IFNγ, IL-12 (p40), IL-12 (p70), IL-1, IL-1β, IL-4, IL-6, IL-8, TNF-α, TGF-β1, BTLA, sCD14) (Fig S1 a and b) (according to the ANOVA analysis (p < 0.05)). These results suggest that the systemic cytokine milieu is immunosuppressive and patients with shorter OS have higher levels of the inhibitory cytokines but proinflammatory cytokines generally cannot predict outcomes. 3.3 Dynamic Changes in Proinflammatory Cytokines during Radiotherapy are Associated with Overall Survival Next, cytokine kinetics i.e. dynamic changes in levels over time were analyzed and correlated with differences in OS ( Fig. 4 ) . First, an increase in the levels of IL-34 levels, a proinflammatory cytokine over time corresponded to a shorter OS. The three patients (patients 6,8,12) with decreasing IL-34 during treatment (green in Fig. 4 ) had the best overall survival, the five patients (red) with an increasing IL-34 had the worst overall survival, and the five patients with stable IL-34 (black) were in between ( Fig. 4 a ) . Using continuous scale, Cox proportional hazards model gives HR = 1.1331 (p = 0.0391) for the change in log2 (IL-34). That is, a one-fold increase in IL-34 corresponds to 13.31% increase in mortality rate (p = 0.0391). Second, the last measure of pro-inflammatory cytokine IL-12 p70, at time point T7, greater than 1, corresponded to an increased mortality rate (HR = 6.693, p = 0.0216). The 7 patients in black (patient ID = 2,3,7,8,11,12,14,15) had IL-12 p70 < 1 and had a favorable overall survival than the 7 patients in red (patient number = 1,5,6,9,10,13,16) who had last IL 12 p70 measures greater than 1 ( Fig. 4 b ) . Third, patients with unmethylated MGMT promoter (< 0.79) and a rapid increase in MCP-1 had a worse overall survival than other patients (HR = 23.69, p = 0.0071). This is seen from three patients ( 5 , 13 , 14 ) ( Fig. 4 c ) . Interestingly changes in any of the immunosuppressive cytokines did not correlate with outcomes. Thus, a dynamic change in select proinflammatory cytokine panel can also predict outcomes. Discussion Radiation therapy is central to the management of newly diagnosed glioblastoma. Pivotal trials have demonstrated unequivocally its efficacy in prolonging the PFS and OS in patients with newly diagnosed, as well as recurrent glioblastoma. While radiotherapy is cytotoxic to tumor cells it also has a direct effect on immune cells that may influence outcomes. ( 10 , 12 , 19 , 23 ). The systemic immune state in glioblastoma is notorious for being extremely suppressive. The immunosuppressed state including through lymphopenia and low CD4 + T cell count may be exacerbated during treatment due to radiation and is associated with worse survival outcome ( 10 , 12 , 15 ). In our previous work we demonstrated that dynamic changes in MDSCs during treatment also promote this immunosuppressed state ( 23 ), While there are studies that predict outcomes with baseline cytokine measurements at disease diagnosis, there is limited evidence exploring the utility of longitudinal cytokine measurements in predicting survival ( 27 – 31 ) which may be a predictive biomarker associated with treatment effectiveness for individual patients. Currently there are no imaging (including MRI) or blood biomarkers that are effective at assessing response during radiation treatment of glioblastoma. To our knowledge this is the first study investigating cytokine concentrations not only before treatment but also measuring changes during therapy. Our approach has the advantage of analyzing not only the baseline measurements but also the kinetics of cytokine levels, i.e. the rate of change in levels over time that may be associated with tumor response. We divided cytokines into proinflammatory and immunosuppressive subtypes. We found that patients with a high level of predominantly immunosuppressive cytokines at baseline had poor overall survival compared to patients with lower levels of these cytokines. Interestingly, within the immunosuppressive cytokines we found that soluble immune checkpoint molecules (PD-1, PD-L1, CTLA-4, LAG-3, TIM-3) consistently predicted survival. In contrast, baseline levels of proinflammatory cytokines did not influence survival. Surprisingly, we observed that patients with a rapid increase in select proinflammatory cytokines (IL-34, IL-12 p70, and MCP-1 in combination with unmethylated MGMT status) were associated with a poor outcome. An explanation for this finding may be that we are observing a compensatory increase in proinflammatory cytokines in these patients from the stroma of the TiME or from systemic release. This compensatory increase in proinflammatory cytokine may reflect a significantly immunosuppressed environment overall, which may not overcome the suppressed immune environment. Such an increase may not be observed in patients with comparatively better survival outcomes because of a relatively less systemic immunosuppression. Indeed, there is evidence of early release of local and systemic proinflammatory cytokines in patients undergoing radiation therapy ( 32 ). Tabatabaei et al detected cytokines from a microdialysis study and reported an increase in MCP-1, and other proinflammatory cytokines almost immediately with radiation treatment. Monocyte chemoattractant protein (MCP-1), also known as CCL2, has a vital role in inflammatory processes as a chemoattractant recruiting macrophage, and has been hypothesized to be a marker of severity of inflammatory diseases. As a corollary, studies have shown an increase in the proinflammatory cytokine level in circulation are associated with a higher risk for radiation induced injury ( 33 ) and in other inflammatory processes that are mediated by cytokine release ( 34 ). Although our study results support our hypothesis that changes in immune-active cytokines may be associated with tumor outcome after radiotherapy, limitations include a small sample size in this pilot study, and the cytokine diversity. These could be overcome by increasing the cohort size, and the cytokine panel to improve its validity in clinical practice. Additionally, challenges in understanding the source of cytokine production i.e. circulating lymphocytes versus the tumor microenvironment could be resolved with advanced single cell sequencing methodologies. Lastly, classification of cytokines into pro inflammatory and anti-inflammatory modules may be oversimplification since the orchestration of immune response is complex and dynamic, and depending on the context, some cytokines also have a pleiotropic effect. We can thus stratify patients not only on the baseline circulating cytokines but also on the dynamic changes during treatment which may ultimately support personalized modification of treatment. Based on our observations we propose a model: elevated levels of suppressive cytokines, secreted from either the TiME or immune cells in circulation are unable to abet tumor immunity and given this severe state of immunosuppression, the body’s homeostatic mechanisms release proinflammatory cytokines (Fig. 5 ) . Taken together, our results suggest a potential role as cytokines as a biomarker to inform clinical trial design especially with immunotherapy. Immunotherapy trials have shown no benefit but patients with a more permissive systemic immune status may be more responsive to immune based therapies. Our findings could also find a role in guiding treatment decisions and modifications of therapy in routine clinical care. Declarations Consent to participate: Participants provided their written informed consent to participate in this study. Ethics Declaration: Our research protocol was approved by our institutional review board and ethics committee at the Johns Hopkins University School of Medicine, and in accordance with the Declaration of Helsinki. Competing Interests Financial interests:L. S. has received research and travel support from GT Medical Technologies Inc. and serves as a consultant for GT Medical Technologies. K. R. has received grant funding from Accuray, Canon, Icotec, and GammaTile; honorarium for a speaking engagement from Accuray; and travel support from Brainlab and Icotec. She also has a patent under development with Canon and serves on the Data Safety Monitoring Board for BioMimetix. C. M.J. is a cofounder of Egret Therapeutics with equity interests and has received research support from Grifols and Biohaven. M. L. has received consulting fees from Biohaven, Global Coalition for Adaptive Research (GCAP), CraniUS, Hemispherian, Hoth, Insightec, MediFlix, Merck, Novocure, Noxxon, Sanianoia, Stryker, and VBI; grant funding from Arbor, Accuray, and Biohaven; honorarium from Insightec; and holds ownership interest in Egret Therapeutics.D. P. serves or has served as a scientific or strategic advisor or consultant for Amgen, Bristol-Myers Squibb, Compugen, Immunomic Therapeutics, Janssen Pharmaceuticals, Normunity, RAPT Therapeutics (formerly FLX Bio), Regeneron (consulting ended August 2025), Takeda Pharmaceuticals, and Tizona LLC, including short-term consulting for Bristol-Myers Squibb (ended June 2025). He is a founder or equity holder in Clasp Therapeutics (formerly ManaT Bio), Dracen Pharmaceuticals, Enara Bio (formerly Ervaxx Ltd.), RAPT Therapeutics, and Tizona LLC. He receives patent royalties through his institution from Bristol-Myers Squibb and Immunomic Therapeutics, and receives research support from Bristol-Myers Squibb, Compugen, and Immunomic Therapeutics. He also has an equity relationship with Catalio Capital Management and maintains a material transfer agreement with Tempest Therapeutics.L.R.K. has received research support from Bristol-Myers Squibb, Incyte, Novartis, and Novocure; serves on the Study Steering Committee for Novocure; and has received speaking honoraria from Accuray.H.Q., C. E., P. H., S. G., and S. P. declare they have no financial interests. Funding: This study was supported by funding from Nicholl Family Foundation (LRK). Author Contribution S.P., L.S., S.G. and L.R.K. conceived the study and developed the overall methodology. S.P., L.S., and L.R.K led data acquisition, data curation, and database management. S.P. and P.H. performed formal statistical analyses. S.P. generated all visualizations and figures. S.P., L.S. contributed to the literature review and drafted significant portions of the Introduction and Discussion sections. M.D., L.K., M.L., D.P., C.E. provided subject-matter expertise, clinical insights, and interpretation of the results in the context of current practice. S.P., L.R.K. wrote and revised the main manuscript text. K.J.R, H.Q., C.M.J., C.T. critically reviewed the manuscript for intellectual content and provided substantial revisions. All authors read and approved the final manuscript. Acknowledgement Figure 1 and Figure 5 were created with biorender.com. Data Availability Data will be available from the corresponding author upon reasonable request. References Hegi ME, Diserens AC, Gorlia T, Hamou MF, de Tribolet N, Weller M, et al. MGMT gene silencing and benefit from temozolomide in glioblastoma. N Engl J Med. 2005;352(10):997-1003. Stupp R, Mason WP, van den Bent MJ, Weller M, Fisher B, Taphoorn MJ, et al. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med. 2005;352(10):987-96. 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S. has received research and travel support from GT Medical Technologies Inc. and serves as a consultant for GT Medical Technologies. K. R. has received grant funding from Accuray, Canon, Icotec, and GammaTile; honorarium for a speaking engagement from Accuray; and travel support from Brainlab and Icotec. She also has a patent under development with Canon and serves on the Data Safety Monitoring Board for BioMimetix. C. M.J. is a cofounder of Egret Therapeutics with equity interests and has received research support from Grifols and Biohaven. M. L. has received consulting fees from Biohaven, Global Coalition for Adaptive Research (GCAP), CraniUS, Hemispherian, Hoth, Insightec, MediFlix, Merck, Novocure, Noxxon, Sanianoia, Stryker, and VBI; grant funding from Arbor, Accuray, and Biohaven; honorarium from Insightec; and holds ownership interest in Egret Therapeutics. D. P. serves or has served as a scientific or strategic advisor or consultant for Amgen, Bristol-Myers Squibb, Compugen, Immunomic Therapeutics, Janssen Pharmaceuticals, Normunity, RAPT Therapeutics (formerly FLX Bio), Regeneron (consulting ended August 2025), Takeda Pharmaceuticals, and Tizona LLC, including short-term consulting for Bristol-Myers Squibb (ended June 2025). He is a founder or equity holder in Clasp Therapeutics (formerly ManaT Bio), Dracen Pharmaceuticals, Enara Bio (formerly Ervaxx Ltd.), RAPT Therapeutics, and Tizona LLC. He receives patent royalties through his institution from Bristol-Myers Squibb and Immunomic Therapeutics, and receives research support from Bristol-Myers Squibb, Compugen, and Immunomic Therapeutics. He also has an equity relationship with Catalio Capital Management and maintains a material transfer agreement with Tempest Therapeutics. L.R.K. has received research support from Bristol-Myers Squibb, Incyte, Novartis, and Novocure; serves on the Study Steering Committee for Novocure; and has received speaking honoraria from Accuray. H.Q., C. E., P. H., S. G., and S. P. declare they have no financial interests. Supplementary Files SupplementaryFiguresandTables.docx Cite Share Download PDF Status: Published Journal Publication published 10 Feb, 2026 Read the published version in Journal of Neuro-Oncology → Version 1 posted Editorial decision: Revision requested 12 Dec, 2025 Reviews received at journal 10 Dec, 2025 Reviews received at journal 09 Dec, 2025 Reviews received at journal 05 Dec, 2025 Reviewers agreed at journal 01 Dec, 2025 Reviewers agreed at journal 30 Nov, 2025 Reviews received at journal 30 Nov, 2025 Reviewers agreed at journal 30 Nov, 2025 Reviewers agreed at journal 27 Nov, 2025 Reviewers agreed at journal 22 Nov, 2025 Reviewers invited by journal 20 Nov, 2025 Editor assigned by journal 20 Nov, 2025 Submission checks completed at journal 20 Nov, 2025 First submitted to journal 14 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Patients with a known pathologic diagnosis of glioblastoma after surgical resection were enrolled in this study and consented prior to starting standard of care with concurrent radiation and temozolomide chemotherapy. Blood samples were collected before starting treatment (T0) followed by weekly lab draw (T1 to T6) as part of routine care. Blood was centrifuged to separate plasma and analyzed for cytokine levels using Multiplex/Luminex assay. Patients were followed longitudinally.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8118956/v1/4ae95cc13efc7cf8a7eea659.png"},{"id":97115066,"identity":"744728bb-35be-48c7-a2e9-094b5e1c1435","added_by":"auto","created_at":"2025-12-01 07:02:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":70046,"visible":true,"origin":"","legend":"\u003cp\u003ea) Oncoplot showing patient demographics, tumor genomic landscape, treatment history, pattern of recurrence and survival outcomes and b) Swimmer’s plot showing Progression Free Survival (PFS) (represented by the star symbol) and Overall Survival (OS) in days. Patient 8 in green was alive at the time of the analysis.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8118956/v1/7708543670fdff50ddd1e0be.png"},{"id":97115070,"identity":"03f02877-5eae-424c-b454-0292801d9640","added_by":"auto","created_at":"2025-12-01 07:02:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":140065,"visible":true,"origin":"","legend":"\u003cp\u003e12 cytokines at baseline and during longitudinal measurements that are prognostic of OS. a) Box plot comparing the baseline cytokine levels between control (left), non-survivors (\u0026lt;2 years, center), and survivors (\u0026gt;2 years, right). Top row (left to right): PD-L1, PD-1, Lag-3, second row: TIM-3, CTLA-4, IL-10, third row: M-CSF, IL-34, IP-10, bottom row: MCP-1, TGF-β2, and TGF-β3 (left to right) b) dynamic changes in the above cytokines during the 6-week course of radiation treatment.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8118956/v1/66cc553ed7b705ec305b6c69.png"},{"id":97115081,"identity":"becf04f4-68e3-47d9-a1f7-b914090d7c50","added_by":"auto","created_at":"2025-12-01 07:02:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":55748,"visible":true,"origin":"","legend":"\u003cp\u003eCytokine kinetics associated with OS. Kaplan Meier curve showing (a) change from baseline in IL-34 (red, increasing levels with poor OS; black with no changes; green, decreasing levels and better OS), (b) last measure of Il12 p70 (red \u0026lt;1 with shorter OS, black \u0026gt;1 with better OS), (c) MGMT and MCP-1 (in red, patients with MGMT \u0026lt;0.79 and a rapid increase with poor survival, and in black, other patients).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8118956/v1/0587782c3d44fc3a52afde45.png"},{"id":97141168,"identity":"b39a6950-17d9-490d-bbed-c7cb5e978aa1","added_by":"auto","created_at":"2025-12-01 10:06:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":94907,"visible":true,"origin":"","legend":"\u003cp\u003eProposed model of cytokine kinetics: patients with immunosuppressive cytokines at baseline produced either locally within the TME or systemically have poor OS. Additionally, patients with a compensatory increase in proinflammatory cytokines have worse outcomes, reflecting extreme immunosuppressive state.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8118956/v1/fedc80afa476aac8a77da12e.png"},{"id":102786654,"identity":"60451aca-3fe6-42bc-94e3-c3d83b663331","added_by":"auto","created_at":"2026-02-16 16:14:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1363093,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8118956/v1/1310da87-1c3c-4f75-b191-8f8488646007.pdf"},{"id":97115071,"identity":"08206fe6-2d0c-4c95-9dd1-d8e72bedce66","added_by":"auto","created_at":"2025-12-01 07:02:05","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2638091,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFiguresandTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-8118956/v1/33857007232f7e6729958463.docx"}],"financialInterests":"Competing interest reported. Financial interests:\nL. S. has received research and travel support from GT Medical Technologies Inc. and serves as a consultant for GT Medical Technologies. K. R. has received grant funding from Accuray, Canon, Icotec, and GammaTile; honorarium for a speaking engagement from Accuray; and travel support from Brainlab and Icotec. She also has a patent under development with Canon and serves on the Data Safety Monitoring Board for BioMimetix. C. M.J. is a cofounder of Egret Therapeutics with equity interests and has received research support from Grifols and Biohaven. M. L. has received consulting fees from Biohaven, Global Coalition for Adaptive Research (GCAP), CraniUS, Hemispherian, Hoth, Insightec, MediFlix, Merck, Novocure, Noxxon, Sanianoia, Stryker, and VBI; grant funding from Arbor, Accuray, and Biohaven; honorarium from Insightec; and holds ownership interest in Egret Therapeutics.\nD. P. serves or has served as a scientific or strategic advisor or consultant for Amgen, Bristol-Myers Squibb, Compugen, Immunomic Therapeutics, Janssen Pharmaceuticals, Normunity, RAPT Therapeutics (formerly FLX Bio), Regeneron (consulting ended August 2025), Takeda Pharmaceuticals, and Tizona LLC, including short-term consulting for Bristol-Myers Squibb (ended June 2025). He is a founder or equity holder in Clasp Therapeutics (formerly ManaT Bio), Dracen Pharmaceuticals, Enara Bio (formerly Ervaxx Ltd.), RAPT Therapeutics, and Tizona LLC. He receives patent royalties through his institution from Bristol-Myers Squibb and Immunomic Therapeutics, and receives research support from Bristol-Myers Squibb, Compugen, and Immunomic Therapeutics. He also has an equity relationship with Catalio Capital Management and maintains a material transfer agreement with Tempest Therapeutics.\nL.R.K. has received research support from Bristol-Myers Squibb, Incyte, Novartis, and Novocure; serves on the Study Steering Committee for Novocure; and has received speaking honoraria from Accuray.\nH.Q., C. E., P. H., S. G., and S. P. declare they have no financial interests.","formattedTitle":"Radiation Immunodynamics: Longitudinal Cytokine Levels During Radiotherapy in Glioblastoma Predict Survival","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGlioblastoma is an aggressive tumor that is incurable with current therapies; the standard of care includes maximal safe neurosurgical resection followed be concurrent radiation and chemotherapy with temozolomide and adjuvant temozolomide (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The median overall survival (mOS) is in the range of 14.6 months, and the 5-year survival is around 11% (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The emergence of immunotherapy has revolutionized treatments of several cancer types but clinical trials in glioblastoma have failed to improve prognosis (\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Barriers to successful development of treatments in glioblastoma include the marked local and systemic immunosuppression, complex regulation of the central nervous system (CNS) permeability by the blood brain barrier, inter and intratumoral heterogeneity, and limited understanding of tumor evolution during therapy (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRadiation and temozolomide chemotherapy are the backbone of treatments after resection in newly diagnosed glioblastoma. The efficacy of radiation is mediated by direct tumor cell cytotoxicity and other indirect effects within the TiME. Moreover, radiation for glioblastoma is also thought to contribute to anti-tumor mechanisms that promote local immunogenicity (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). In contrast there is evidence that radiation for glioblastoma induces systemic immunosuppression by affecting peripheral immune cell populations, in particular circulating lymphocytes which are critical in the antineoplastic immune response. (\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). This is characterized by lymphopenia, downregulation of major histocompatibility class I and II, overexpression of checkpoint inhibitor molecules, thus producing an exhausted phenotype (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Consequently, lymphopenia and T cell exhaustion has been recognized as contributing to poor overall survival (\u003cspan additionalcitationids=\"CR18 CR19 CR20\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThere is evidence of the role of immunosuppressive regulatory T helper cells, and myeloid-derived suppressor cells (MDSCs) in mediating immunosuppression, with one critical mechanism involving the release of immune-active, soluble factors (\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Cytokines are small, soluble peptides or proteins that are that participate in an immune response including lymphoid cells, myeloid cells, fibroblasts, and stromal cells amongst others. Whereas lymphoid and myeloid cells are cellular components of the immune system, secreted cytokines also contribute to immunosuppression. While there is evidence that tumor-supportive cytokines predominate within the glioblastoma TiME (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), there are limited data on the systemic concentrations, and their relationship with clinical outcomes.\u003c/p\u003e\u003cp\u003eIn this pilot study, we analyzed relationship between concurrent chemoradiation in glioblastoma and dynamic changes in cytokine levels during treatment. Our primary hypothesis was that both the baseline and the changes in plasma cytokine levels can predict survival. Our cohort was unique in that all patients had multiple timepoints of plasma collection during concurrent chemoradiation and long term follow up, both of which provided an opportunity to study dynamic changes of cytokine levels and outcomes in glioblastoma. An understanding of the treatment related changes in plasma cytokines may guide development of biomarkers to predict outcome.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Human Subjects.\u003c/h2\u003e\u003cp\u003ePatients were enrolled in a prospective study, J1840, at Johns Hopkins Hospital. The protocol (IRB00293533) was reviewed by the ethics committee and approved by the Institutional Review Board. Between October 2018 and June 2019, patients with diagnosed glioblastoma per the CNS4 WHO Classification of CNS Tumors, and healthy normal controls were enrolled. Patients were older than 18 years of age and were recruited to participate after surgical resection of glioblastoma but before starting radiation treatments. An informed, written consent was obtained from all patients prior to starting with research study. Patients with recurrent glioblastoma were excluded.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Plasma Collection\u003c/h2\u003e\u003cp\u003eBlood samples were collected at serial time points: prior to starting radiation treatments for baseline (T0), followed by weekly labs during concurrent chemotherapy and radiation (T1, T2, T3, T4, T5, T6, and T7). The interval between each consecutive time point (T1 to T2, T2 to T3, etc.) was 1 week that coincide with the lab draw for monitoring of blood counts. A one-time blood sample was collected from 8 normal controls. Plasma was freshly processed 1800 rpm for 3 mins at 4\u0026deg; C and stored at -20 C for later analysis. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The procedures have been previously described (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) where we evaluated dynamic changes of immune cell populations including myeloid derived suppressor cells in these specimens.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Evaluation of Serum / Plasma Proteins\u003c/h2\u003e\u003cp\u003eThe Bioplex 200 platform (Biorad, Hercules CA) was used to determine the concentration of multiple target proteins in the plasma specimens. Luminex bead-based immunoassays (Millipore, Billerica NY) were performed following validated test methods by the SKCCC Immune Monitoring Core at Johns Hopkins. Concentrations were determined using 5 parameter log curve fits (using Bioplex Manager 6.0) with vendor provided standards and quality controls. The HCKP1-11K, HCVD6MAG-67K, HCYTOMAG-60K panels (Millipore, Billerica NY) were used to detect G-CSF, GM-CSF, IL-10, IFNγ, IL-12 (p40), IL-12 (p70), IL-1β, IL-1, IL-4, IL-6, IL8, IP-10, MCP-1, TNF-a, TGF-b1, TGF-b2, TGF-b3, IL-34, M-CSF, PD-L1, LAG-3, BTLA, PD-1, TIM-3, CTLA-4, and sCD14. The 26-plex immune cytokine panel comprised of both immunostimulatory and immunoinhibitory cytokines. These are categorized as either pro-inflammatory (IFNγ, GM-CSF, IL-1β, IL-1α, IL-4, IL-6, IL-8, IL-12 p40, IL-12 p70, IL-34, MCP-1, TNF-α, and IP-10) and hence bolster humoral immunity, or immunosuppressive and hence contribute to poor tumor immune control (G-CSF, M-CSF, IL-10, TGF-β1, TGF- β2, TGF- β3, PD-L1, LAG-3, BTLA, PD-1, TIM-3, CTLA-4, and sCD14).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Statistical Analysis:\u003c/h2\u003e\u003cp\u003eFor statistical analysis, values of cytokine levels lower than the lower limit of detection were replaced with the lower limit of the standard curve of the assay and values exceeding the upper limit of detection were replaced with the upper limit of the standard curve. On-treatment and baseline specimens were paired to account for batch effect and ensure accurate fold change calculations. Average concentrations of baseline samples were used for baseline analyses in patients with baseline samples that were run in multiple batches.\u003c/p\u003e\u003cp\u003eStatistical analysis was performed in R. The analysis was performed in December 2024 to include long term outcomes for all participants in this study. Circulating cytokines in the control group were measured only at baseline. Patients who lived beyond 2 years were defined as survivors, and those who lived less than 2 years as non-survivors. Cytokines of patients in both groups were measured at seven time points. We assumed that biomarkers in the control group remained stable over time. At each visit, a hierarchical testing procedure was applied to control the overall type I error rate: initially, ANOVA was used to compare each biomarker among the three groups at an alpha significance level of 0.05. For biomarkers showing significant differences among the groups, group-pair comparisons using two-sample t-tests were subsequently conducted to compare long overall survival with control, and short overall survival with control, both at a two-sided significance level of 0.025. In survival analysis, the relationship between cytokine levels (measured at baseline, throughout their longitudinal trajectory, and at their final values) and overall survival were investigated. We assessed potential non-linear associations and interactions to uncover complex patterns. Top biomarkers, individually or in combination, were identified and integrated into a Cox proportional hazards model to evaluate differences in overall survival across subgroups defined by these top biomarkers.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Cohort Description:\u003c/h2\u003e\n \u003cp\u003eBetween October 2018 and June 2019, a total of 16 patients and 8 normal, healthy controls were enrolled. Following neurosurgical resection, these patients received adjuvant radiation and chemotherapy with temozolomide (\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e). As described previously, patients with OS beyond 2 years since diagnosis were categorized as survivors, and those with OS less than 2 years as non-survivors (\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e). The median age at diagnosis was 63 years (interquartile range 53 to 65) (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cstrong\u003eFig.\u0026nbsp;2a\u003c/strong\u003e). MGMT status was unmethylated in 11/16 patients. All but one patient was deceased at the time the analysis of these data were performed \u003cstrong\u003e(Fig.\u0026nbsp;2b).\u003c/strong\u003e Additional information regarding treatment characteristics is listed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePatient demographics and tumor characteristics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePatient Number\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAge at Diagnosis (years)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWHO Grade\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIDH Status\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMGMT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOlig2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP53\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eATRX\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnavailable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExpressed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetained\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e187.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnavailable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnavailable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnavailable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExpressed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetained\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExpressed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetained\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExpressed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetained\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExpressed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetained\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExpressed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetained\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot expressed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetained\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMutant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExpressed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLost\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExpressed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnavailable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExpressed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetained\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e162.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExpressed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetained\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExpressed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetained\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExpressed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetained\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExpressed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetained\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExpressed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetained\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTreatment and Survival Data\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePatient Number\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eResection\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAdjuvant Temozolomide Chemotherapy\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePrior History of Radiation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProgression Free Survival (PFS) (days)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAlive\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOverall Survival (OS) (days)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRecurrence (Within our Outside RT field)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInside\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInside\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOutside\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnavailable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnavailable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInside\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInside\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOutside\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnavailable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInside\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInside\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInside\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInside\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOutside\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnavailable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnavailable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInside\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInside\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Immunosuppressive Cytokine Levels are Elevated at Baseline and Associated with Poor Survival in Glioblastoma\u003c/h2\u003e\n \u003cp\u003eCompared to the control group, both non-survivors and survivors showed significant differences in a total of 12 cytokines. These were predominantly part of the immunosuppressive module: IL-10, IP-10, MCP-1, TGF-\u0026beta;2, TGF-\u0026beta;3, IL-34, M-CSF, PD-L1, LAG-3, PD-1, TIM-3, and CTLA-4, according to the ANOVA analysis (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea, \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb\u003cstrong\u003e)\u003c/strong\u003e.\u003c/p\u003e\n \u003cp\u003eIn pairwise group comparisons, significant differences were observed between the nonsurvivors and the control group in the predominantly immunosuppressive cytokines IL-10, IP-10, TGF-\u0026beta;3, IL-34, M-CSF, PD-L1, LAG-3, PD-1, and CTLA-4. Similarly, significant differences were found between the survivors and the control group in the predominantly immunosuppressive cytokines TGF-\u0026beta;3, M-CSF, and TIM-3. Levels TGF-\u0026beta;2 and TGF-\u0026beta;3 were lower in glioblastoma patients which could suggest a pleiotropic function in behaving like a proinflammatory cytokine. No significant differences were found in the remaining 14 cytokines. These were predominantly part of the proinflammatory module (G-CSF, GM-CSF, IFN\u0026gamma;, IL-12 (p40), IL-12 (p70), IL-1, IL-1\u0026beta;, IL-4, IL-6, IL-8, TNF-\u0026alpha;, TGF-\u0026beta;1, BTLA, sCD14) \u003cstrong\u003e(Fig \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003ea and b)\u003c/strong\u003e (according to the ANOVA analysis (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)). These results suggest that the systemic cytokine milieu is immunosuppressive and patients with shorter OS have higher levels of the inhibitory cytokines but proinflammatory cytokines generally cannot predict outcomes.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Dynamic Changes in Proinflammatory Cytokines during Radiotherapy are Associated with Overall Survival\u003c/h2\u003e\n \u003cp\u003eNext, cytokine kinetics i.e. dynamic changes in levels over time were analyzed and correlated with differences in OS \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e. First, an increase in the levels of IL-34 levels, a proinflammatory cytokine over time corresponded to a shorter OS. The three patients (patients 6,8,12) with decreasing IL-34 during treatment (green in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) had the best overall survival, the five patients (red) with an increasing IL-34 had the worst overall survival, and the five patients with stable IL-34 (black) were in between \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea\u003cstrong\u003e)\u003c/strong\u003e. Using continuous scale, Cox proportional hazards model gives HR\u0026thinsp;=\u0026thinsp;1.1331 (p\u0026thinsp;=\u0026thinsp;0.0391) for the change in log2 (IL-34). That is, a one-fold increase in IL-34 corresponds to 13.31% increase in mortality rate (p\u0026thinsp;=\u0026thinsp;0.0391). Second, the last measure of pro-inflammatory cytokine IL-12 p70, at time point T7, greater than 1, corresponded to an increased mortality rate (HR\u0026thinsp;=\u0026thinsp;6.693, p\u0026thinsp;=\u0026thinsp;0.0216). The 7 patients in black (patient ID\u0026thinsp;=\u0026thinsp;2,3,7,8,11,12,14,15) had IL-12 p70\u0026thinsp;\u0026lt;\u0026thinsp;1 and had a favorable overall survival than the 7 patients in red (patient number\u0026thinsp;=\u0026thinsp;1,5,6,9,10,13,16) who had last IL 12 p70 measures greater than 1 \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb\u003cstrong\u003e)\u003c/strong\u003e. Third, patients with unmethylated MGMT promoter (\u0026lt;\u0026thinsp;0.79) and a rapid increase in MCP-1 had a worse overall survival than other patients (HR\u0026thinsp;=\u0026thinsp;23.69, p\u0026thinsp;=\u0026thinsp;0.0071). This is seen from three patients (\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e) \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ec\u003cstrong\u003e)\u003c/strong\u003e. Interestingly changes in any of the immunosuppressive cytokines did not correlate with outcomes. Thus, a dynamic change in select proinflammatory cytokine panel can also predict outcomes.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eRadiation therapy is central to the management of newly diagnosed glioblastoma. Pivotal trials have demonstrated unequivocally its efficacy in prolonging the PFS and OS in patients with newly diagnosed, as well as recurrent glioblastoma. While radiotherapy is cytotoxic to tumor cells it also has a direct effect on immune cells that may influence outcomes. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). The systemic immune state in glioblastoma is notorious for being extremely suppressive. The immunosuppressed state including through lymphopenia and low CD4\u0026thinsp;+\u0026thinsp;T cell count may be exacerbated during treatment due to radiation and is associated with worse survival outcome (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). In our previous work we demonstrated that dynamic changes in MDSCs during treatment also promote this immunosuppressed state (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e),\u003c/p\u003e\u003cp\u003eWhile there are studies that predict outcomes with baseline cytokine measurements at disease diagnosis, there is limited evidence exploring the utility of longitudinal cytokine measurements in predicting survival (\u003cspan additionalcitationids=\"CR28 CR29 CR30\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) which may be a predictive biomarker associated with treatment effectiveness for individual patients. Currently there are no imaging (including MRI) or blood biomarkers that are effective at assessing response during radiation treatment of glioblastoma. To our knowledge this is the first study investigating cytokine concentrations not only before treatment but also measuring changes during therapy. Our approach has the advantage of analyzing not only the baseline measurements but also the kinetics of cytokine levels, i.e. the rate of change in levels over time that may be associated with tumor response.\u003c/p\u003e\u003cp\u003eWe divided cytokines into proinflammatory and immunosuppressive subtypes. We found that patients with a high level of predominantly immunosuppressive cytokines at baseline had poor overall survival compared to patients with lower levels of these cytokines. Interestingly, within the immunosuppressive cytokines we found that soluble immune checkpoint molecules (PD-1, PD-L1, CTLA-4, LAG-3, TIM-3) consistently predicted survival. In contrast, baseline levels of proinflammatory cytokines did not influence survival. Surprisingly, we observed that patients with a rapid increase in select proinflammatory cytokines (IL-34, IL-12 p70, and MCP-1 in combination with unmethylated MGMT status) were associated with a poor outcome. An explanation for this finding may be that we are observing a compensatory increase in proinflammatory cytokines in these patients from the stroma of the TiME or from systemic release. This compensatory increase in proinflammatory cytokine may reflect a significantly immunosuppressed environment overall, which may not overcome the suppressed immune environment. Such an increase may not be observed in patients with comparatively better survival outcomes because of a relatively less systemic immunosuppression.\u003c/p\u003e\u003cp\u003eIndeed, there is evidence of early release of local and systemic proinflammatory cytokines in patients undergoing radiation therapy (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Tabatabaei et al detected cytokines from a microdialysis study and reported an increase in MCP-1, and other proinflammatory cytokines almost immediately with radiation treatment. Monocyte chemoattractant protein (MCP-1), also known as CCL2, has a vital role in inflammatory processes as a chemoattractant recruiting macrophage, and has been hypothesized to be a marker of severity of inflammatory diseases. As a corollary, studies have shown an increase in the proinflammatory cytokine level in circulation are associated with a higher risk for radiation induced injury (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) and in other inflammatory processes that are mediated by cytokine release (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough our study results support our hypothesis that changes in immune-active cytokines may be associated with tumor outcome after radiotherapy, limitations include a small sample size in this pilot study, and the cytokine diversity. These could be overcome by increasing the cohort size, and the cytokine panel to improve its validity in clinical practice. Additionally, challenges in understanding the source of cytokine production i.e. circulating lymphocytes versus the tumor microenvironment could be resolved with advanced single cell sequencing methodologies. Lastly, classification of cytokines into pro inflammatory and anti-inflammatory modules may be oversimplification since the orchestration of immune response is complex and dynamic, and depending on the context, some cytokines also have a pleiotropic effect.\u003c/p\u003e\u003cp\u003eWe can thus stratify patients not only on the baseline circulating cytokines but also on the dynamic changes during treatment which may ultimately support personalized modification of treatment. Based on our observations we propose a model: elevated levels of suppressive cytokines, secreted from either the TiME or immune cells in circulation are unable to abet tumor immunity and given this severe state of immunosuppression, the body\u0026rsquo;s homeostatic mechanisms release proinflammatory cytokines (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Taken together, our results suggest a potential role as cytokines as a biomarker to inform clinical trial design especially with immunotherapy. Immunotherapy trials have shown no benefit but patients with a more permissive systemic immune status may be more responsive to immune based therapies. Our findings could also find a role in guiding treatment decisions and modifications of therapy in routine clinical care.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConsent to participate:\u003c/h2\u003e\n\u003cp\u003eParticipants provided their written informed consent to participate in this study.\u003c/p\u003e\n\u003ch2\u003eEthics Declaration:\u003c/h2\u003e\n\u003cp\u003eOur research protocol was approved by our institutional review board and ethics committee at the Johns Hopkins University School of Medicine, and in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFinancial interests:L. S. has received research and travel support from GT Medical Technologies Inc. and serves as a consultant for GT Medical Technologies. K. R. has received grant funding from Accuray, Canon, Icotec, and GammaTile; honorarium for a speaking engagement from Accuray; and travel support from Brainlab and Icotec. She also has a patent under development with Canon and serves on the Data Safety Monitoring Board for BioMimetix. C. M.J. is a cofounder of Egret Therapeutics with equity interests and has received research support from Grifols and Biohaven. M. L. has received consulting fees from Biohaven, Global Coalition for Adaptive Research (GCAP), CraniUS, Hemispherian, Hoth, Insightec, MediFlix, Merck, Novocure, Noxxon, Sanianoia, Stryker, and VBI; grant funding from Arbor, Accuray, and Biohaven; honorarium from Insightec; and holds ownership interest in Egret Therapeutics.D. P. serves or has served as a scientific or strategic advisor or consultant for Amgen, Bristol-Myers Squibb, Compugen, Immunomic Therapeutics, Janssen Pharmaceuticals, Normunity, RAPT Therapeutics (formerly FLX Bio), Regeneron (consulting ended August 2025), Takeda Pharmaceuticals, and Tizona LLC, including short-term consulting for Bristol-Myers Squibb (ended June 2025). He is a founder or equity holder in Clasp Therapeutics (formerly ManaT Bio), Dracen Pharmaceuticals, Enara Bio (formerly Ervaxx Ltd.), RAPT Therapeutics, and Tizona LLC. He receives patent royalties through his institution from Bristol-Myers Squibb and Immunomic Therapeutics, and receives research support from Bristol-Myers Squibb, Compugen, and Immunomic Therapeutics. He also has an equity relationship with Catalio Capital Management and maintains a material transfer agreement with Tempest Therapeutics.L.R.K. has received research support from Bristol-Myers Squibb, Incyte, Novartis, and Novocure; serves on the Study Steering Committee for Novocure; and has received speaking honoraria from Accuray.H.Q., C. E., P. H., S. G., and S. P. declare they have no financial interests.\u003c/p\u003e\n\u003ch2\u003eFunding:\u003c/h2\u003e\n\u003cp\u003eThis study was supported by funding from Nicholl Family Foundation (LRK).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eS.P., L.S., S.G. and L.R.K. conceived the study and developed the overall methodology. S.P., L.S., and L.R.K led data acquisition, data curation, and database management. S.P. and P.H. performed formal statistical analyses. S.P. generated all visualizations and figures. S.P., L.S. contributed to the literature review and drafted significant portions of the Introduction and Discussion sections. M.D., L.K., M.L., D.P., C.E. provided subject-matter expertise, clinical insights, and interpretation of the results in the context of current practice. S.P., L.R.K. wrote and revised the main manuscript text. K.J.R, H.Q., C.M.J., C.T. critically reviewed the manuscript for intellectual content and provided substantial revisions. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eFigure 1 and Figure 5 were created with biorender.com.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eData will be available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHegi ME, Diserens AC, Gorlia T, Hamou MF, de Tribolet N, Weller M, et al. MGMT gene silencing and benefit from temozolomide in glioblastoma. N Engl J Med. 2005;352(10):997-1003.\u003c/li\u003e\n\u003cli\u003eStupp R, Mason WP, van den Bent MJ, Weller M, Fisher B, Taphoorn MJ, et al. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med. 2005;352(10):987-96.\u003c/li\u003e\n\u003cli\u003eStupp R, Hegi ME, Mason WP, van den Bent MJ, Taphoorn MJ, Janzer RC, et al. Effects of radiotherapy with concomitant and adjuvant temozolomide versus radiotherapy alone on survival in glioblastoma in a randomised phase III study: 5-year analysis of the EORTC-NCIC trial. Lancet Oncol. 2009;10(5):459-66.\u003c/li\u003e\n\u003cli\u003eReardon DA, Brandes AA, Omuro A, Mulholland P, Lim M, Wick A, et al. Effect of Nivolumab vs Bevacizumab in Patients With Recurrent Glioblastoma: The CheckMate 143 Phase 3 Randomized Clinical Trial. JAMA Oncol. 2020;6(7):1003-10.\u003c/li\u003e\n\u003cli\u003eLiau LM, Ashkan K, Brem S, Campian JL, Trusheim JE, Iwamoto FM, et al. Association of Autologous Tumor Lysate-Loaded Dendritic Cell Vaccination With Extension of Survival Among Patients With Newly Diagnosed and Recurrent Glioblastoma: A Phase 3 Prospective Externally Controlled Cohort Trial. JAMA Oncol. 2023;9(1):112-21.\u003c/li\u003e\n\u003cli\u003eOmuro A, Brandes AA, Carpentier AF, Idbaih A, Reardon DA, Cloughesy T, et al. Radiotherapy combined with nivolumab or temozolomide for newly diagnosed glioblastoma with unmethylated MGMT promoter: An international randomized phase III trial. Neuro Oncol. 2023;25(1):123-34.\u003c/li\u003e\n\u003cli\u003eLin H, Liu C, Hu A, Zhang D, Yang H, Mao Y. Understanding the immunosuppressive microenvironment of glioma: mechanistic insights and clinical perspectives. J Hematol Oncol. 2024;17(1):31.\u003c/li\u003e\n\u003cli\u003eKamson DO, Puri S, Sang Y, Shi MJ, Blair L, Blakeley JO, et al. Impact of Frontline Ivosidenib on Volumetric Growth Patterns in Isocitrate Dehydrogenase-mutant Astrocytic and Oligodendroglial Tumors. Clin Cancer Res. 2023;29(23):4863-9.\u003c/li\u003e\n\u003cli\u003eSharabi AB, Lim M, DeWeese TL, Drake CG. Radiation and checkpoint blockade immunotherapy: radiosensitisation and potential mechanisms of synergy. Lancet Oncol. 2015;16(13):e498-509.\u003c/li\u003e\n\u003cli\u003eYovino S, Kleinberg L, Grossman SA, Narayanan M, Ford E. The etiology of treatment-related lymphopenia in patients with malignant gliomas: modeling radiation dose to circulating lymphocytes explains clinical observations and suggests methods of modifying the impact of radiation on immune cells. Cancer Invest. 2013;31(2):140-4.\u003c/li\u003e\n\u003cli\u003eGreenlund L, Shanley R, Mulford K, Neil EC, Lawrence J, Arnold S, et al. Comparison of peripheral leukocyte parameters in patients receiving conventionally and hypofractionated radiotherapy schemes for the treatment of newly diagnosed glioblastoma. Front Immunol. 2023;14:1284118.\u003c/li\u003e\n\u003cli\u003eGrossman SA, Ye X, Lesser G, Sloan A, Carraway H, Desideri S, et al. Immunosuppression in patients with high-grade gliomas treated with radiation and temozolomide. Clin Cancer Res. 2011;17(16):5473-80.\u003c/li\u003e\n\u003cli\u003eAlban TJ, Alvarado AG, Sorensen MD, Bayik D, Volovetz J, Serbinowski E, et al. Global immune fingerprinting in glioblastoma patient peripheral blood reveals immune-suppression signatures associated with prognosis. JCI Insight. 2018;3(21).\u003c/li\u003e\n\u003cli\u003eAyasoufi K, Pfaller CK, Evgin L, Khadka RH, Tritz ZP, Goddery EN, et al. Brain cancer induces systemic immunosuppression through release of non-steroid soluble mediators. Brain. 2020;143(12):3629-52.\u003c/li\u003e\n\u003cli\u003eHughes MA, Parisi M, Grossman S, Kleinberg L. Primary brain tumors treated with steroids and radiotherapy: low CD4 counts and risk of infection. Int J Radiat Oncol Biol Phys. 2005;62(5):1423-6.\u003c/li\u003e\n\u003cli\u003eEllsworth S, Balmanoukian A, Kos F, Nirschl CJ, Nirschl TR, Grossman SA, et al. Sustained CD4(+) T cell-driven lymphopenia without a compensatory IL-7/IL-15 response among high-grade glioma patients treated with radiation and temozolomide. Oncoimmunology. 2014;3(1):e27357.\u003c/li\u003e\n\u003cli\u003eKut C, Kleinberg L. Radiotherapy, lymphopenia and improving the outcome for glioblastoma: a narrative review. Chin Clin Oncol. 2023;12(1):4.\u003c/li\u003e\n\u003cli\u003eCampian JL, Piotrowski AF, Ye X, Hakim FT, Rose J, Yan XY, et al. Serial changes in lymphocyte subsets in patients with newly diagnosed high grade astrocytomas treated with standard radiation and temozolomide. J Neurooncol. 2017;135(2):343-51.\u003c/li\u003e\n\u003cli\u003eGrossman SA, Ellsworth S, Campian J, Wild AT, Herman JM, Laheru D, et al. Survival in Patients With Severe Lymphopenia Following Treatment With Radiation and Chemotherapy for Newly Diagnosed Solid Tumors. J Natl Compr Canc Netw. 2015;13(10):1225-31.\u003c/li\u003e\n\u003cli\u003eKleinberg L, Sloan L, Grossman S, Lim M. Radiotherapy, Lymphopenia, and Host Immune Capacity in Glioblastoma: A Potentially Actionable Toxicity Associated With Reduced Efficacy of Radiotherapy. Neurosurgery. 2019;85(4):441-53.\u003c/li\u003e\n\u003cli\u003eMendez JS, Govindan A, Leong J, Gao F, Huang J, Campian JL. Association between treatment-related lymphopenia and overall survival in elderly patients with newly diagnosed glioblastoma. J Neurooncol. 2016;127(2):329-35.\u003c/li\u003e\n\u003cli\u003eGhosh S, Huang J, Inkman M, Zhang J, Thotala S, Tikhonova E, et al. Radiation-induced circulating myeloid-derived suppressor cells induce systemic lymphopenia after chemoradiotherapy in patients with glioblastoma. Sci Transl Med. 2023;15(680):eabn6758.\u003c/li\u003e\n\u003cli\u003eSloan L, Sen R, Liu C, Doucet M, Blosser L, Katulis L, et al. Radiation immunodynamics in patients with glioblastoma receiving chemoradiation. Front Immunol. 2024;15:1438044.\u003c/li\u003e\n\u003cli\u003eBergerud KMB, Berkseth M, Pardoll DM, Ganguly S, Kleinberg LR, Lawrence J, et al. Radiation Therapy and Myeloid-Derived Suppressor Cells: Breaking Down Their Cancerous Partnership. Int J Radiat Oncol Biol Phys. 2024;119(1):42-55.\u003c/li\u003e\n\u003cli\u003eFecci PE, Mitchell DA, Whitesides JF, Xie W, Friedman AH, Archer GE, et al. Increased regulatory T-cell fraction amidst a diminished CD4 compartment explains cellular immune defects in patients with malignant glioma. Cancer Res. 2006;66(6):3294-302.\u003c/li\u003e\n\u003cli\u003eCai J, Zhang W, Yang P, Wang Y, Li M, Zhang C, et al. Identification of a 6-cytokine prognostic signature in patients with primary glioblastoma harboring M2 microglia/macrophage phenotype relevance. PLoS One. 2015;10(5):e0126022.\u003c/li\u003e\n\u003cli\u003eLin Y, Zhang G, Zhang J, Gao G, Li M, Chen Y, et al. A panel of four cytokines predicts the prognosis of patients with malignant gliomas. J Neurooncol. 2013;114(2):199-208.\u003c/li\u003e\n\u003cli\u003eHolst CB, Christensen IJ, Vitting-Seerup K, Skj\u0026oslash;th-Rasmussen J, Hamerlik P, Poulsen HS, et al. Plasma IL-8 and ICOSLG as prognostic biomarkers in glioblastoma. Neurooncol Adv. 2021;3(1):vdab072.\u003c/li\u003e\n\u003cli\u003eZisakis A, Piperi C, Themistocleous MS, Korkolopoulou P, Boviatsis EI, Sakas DE, et al. Comparative analysis of peripheral and localised cytokine secretion in glioblastoma patients. Cytokine. 2007;39(2):99-105.\u003c/li\u003e\n\u003cli\u003eChang CY, Li MC, Liao SL, Huang YL, Shen CC, Pan HC. Prognostic and clinical implication of IL-6 expression in glioblastoma multiforme. J Clin Neurosci. 2005;12(8):930-3.\u003c/li\u003e\n\u003cli\u003eFontanilles M, Heisbourg JD, Daban A, Di Fiore F, P\u0026eacute;pin LF, Marguet F, et al. Metabolic remodeling in glioblastoma: a longitudinal multi-omics study. Acta Neuropathol Commun. 2024;12(1):162.\u003c/li\u003e\n\u003cli\u003eTabatabaei P, Visse E, Bergstr\u0026ouml;m P, Br\u0026auml;nnstr\u0026ouml;m T, Siesj\u0026ouml; P, Bergenheim AT. Radiotherapy induces an immediate inflammatory reaction in malignant glioma: a clinical microdialysis study. J Neurooncol. 2017;131(1):83-92.\u003c/li\u003e\n\u003cli\u003eArpin D, Perol D, Blay JY, Falchero L, Claude L, Vuillermoz-Blas S, et al. Early variations of circulating interleukin-6 and interleukin-10 levels during thoracic radiotherapy are predictive for radiation pneumonitis. J Clin Oncol. 2005;23(34):8748-56.\u003c/li\u003e\n\u003cli\u003eChen Y, Wang J, Liu C, Su L, Zhang D, Fan J, et al. IP-10 and MCP-1 as biomarkers associated with disease severity of COVID-19. Mol Med. 2020;26(1):97.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-neuro-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"neon","sideBox":"Learn more about [Journal of Neuro-Oncology](https://www.springer.com/journal/11060)","snPcode":"11060","submissionUrl":"https://submission.nature.com/new-submission/11060/3","title":"Journal of Neuro-Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"glioblastoma, radiation therapy, chemoradiotherapy, plasma cytokines, immunosuppression","lastPublishedDoi":"10.21203/rs.3.rs-8118956/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8118956/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eA critical challenge in therapies for glioblastoma is systemic immunosuppression. The immunosuppressed state is associated with poor response to therapies and thus correlated with worse outcomes. Immunosuppression is mediated by multiple factors including glioblastoma tumor and secreted cytokines, as well as radiation and chemotherapy.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003ePatients were enrolled in a single-institution, prospective, immune surveillance study. Peripheral blood was collected prior to initiating treatments and weekly during concurrent radiation and chemotherapy. Cytokine levels were measured from plasma samples isolated from peripheral blood. The cytokines were categorized as proinflammatory or anti-inflammatory. Baseline levels and dynamic changes in the levels of cytokines were analyzed for association with survival.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003e16 patients and 8 healthy controls were enrolled. A higher level of immunosuppressive cytokines at baseline (IL-10, IP-10, MCP-1, IL-34, M-CSF, PD-L1, LAG-3, PD-1, TIM-3, and CTLA-4) was inversely related with shorter survival (according to the ANOVA analysis (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)). No association of baseline levels of proinflammatory cytokines with survival was observed. However, an increase in the levels of select proinflammatory cytokines at end of radiation (IL-34, IL-12 p70) was associated with poor survival as was increased MCP-1 in those with unmethylated tumor No association between outcomes and dynamic changes in remaining proinflammatory cytokines or any of the immunosuppressive cytokines was noted.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eOur data suggests that in patients with glioblastoma, measurements of plasma cytokines at diagnosis may predict response to treatments and overall survival. In addition, the dynamic changes in the cytokine levels may similarly serve as a biomarker.\u003c/p\u003e","manuscriptTitle":"Radiation Immunodynamics: Longitudinal Cytokine Levels During Radiotherapy in Glioblastoma Predict Survival","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-01 07:02:01","doi":"10.21203/rs.3.rs-8118956/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-12T12:56:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-10T18:33:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-09T19:16:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-05T16:54:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"66127732409270353999483077361724945562","date":"2025-12-01T19:13:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"242531601239928545087813413846939800362","date":"2025-12-01T02:12:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-30T14:10:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"164920864262425207485415233530239931260","date":"2025-11-30T12:35:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"111352688925261101433291656474094183421","date":"2025-11-27T18:02:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"254803852125458187537974451453218410221","date":"2025-11-22T15:04:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-20T17:20:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-20T17:19:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-20T16:10:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Neuro-Oncology","date":"2025-11-15T02:51:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-neuro-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"neon","sideBox":"Learn more about [Journal of Neuro-Oncology](https://www.springer.com/journal/11060)","snPcode":"11060","submissionUrl":"https://submission.nature.com/new-submission/11060/3","title":"Journal of Neuro-Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"db2becdc-efd5-405c-8034-f76894d93858","owner":[],"postedDate":"December 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-16T16:11:48+00:00","versionOfRecord":{"articleIdentity":"rs-8118956","link":"https://doi.org/10.1007/s11060-025-05414-1","journal":{"identity":"journal-of-neuro-oncology","isVorOnly":false,"title":"Journal of Neuro-Oncology"},"publishedOn":"2026-02-10 15:57:28","publishedOnDateReadable":"February 10th, 2026"},"versionCreatedAt":"2025-12-01 07:02:01","video":"","vorDoi":"10.1007/s11060-025-05414-1","vorDoiUrl":"https://doi.org/10.1007/s11060-025-05414-1","workflowStages":[]},"version":"v1","identity":"rs-8118956","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8118956","identity":"rs-8118956","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}