Molecular and Clinical Determinants of Response to Immunotherapy in High-Grade Glioma

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Ayala, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6649860/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Jun, 2025 Read the published version in Journal of Neuro-Oncology → Version 1 posted 8 You are reading this latest preprint version Abstract Purpose While immunotherapy has transformed treatment in multiple solid tumors, its efficacy in high-grade glioma remains limited. Understanding the molecular and clinical factors that influence glioma’s response to immunotherapy is essential to improving outcomes. Methods We identified patients with recurrent glioblastoma or astrocytoma, IDH-mutant grade 4, who had been treated with checkpoint inhibitor (CPI), virus therapy, or cell therapy and determined the association between their molecular, clinical, and demographic characteristics and survival outcomes. Results We identified 66 patients, 57 glioblastoma and 9 astrocytoma, IDH-mutant grade 4; 38 were treated with CPI, 22 with virus therapy, and 6 with cell therapy. PIK3CA mutation was associated with shorter PFS and OS (p = 0.022, 0.073, respectively) among all patients and a shorter OS among CPI-treated patients (p = 0.004). Tumor tissue without the mutation had less PD-1 expression in CD3+/CD8 + T cells. In CPI-treated patients, IDH1/2 mutation was associated with a shorter OS (p = 0.002), and mutations in RB1 and TERT promoter were associated with a shorter PFS (p-value = 0.00056, 0.022, respectively). Length of CPI therapy of more than 6 months was associated with increased PFS and OS (p = 0.048, 0.062, respectively), while steroid use at baseline was associated with a shorter OS (p = 0.00023). Multifocal disease was associated with shorter PFS and OS durations (p = 0.0017, 0.0013, respectively) among all patients. Conclusions In high-grade glioma, PIK3CA , IDH1/2 , RB1 , and TERT promoter mutations may be associated with a poor response to immunotherapy. Our results may provide the rationale for clinical trials combining PI3K and IDH inhibitors with CPI in high-grade glioma. immunotherapy high-grade glioma glioblastoma checkpoint inhibitor cell therapy virus therapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Despite the current standard of care, glioblastoma (GBM) has a median overall survival (OS) duration of 14.6 months and a 2-year survival rate of 26.5% 1 . There is a tremendous need for new, effective therapies. Immunotherapy has proven to be an effective form of therapy for many kinds of cancer. Specifically, checkpoint inhibitor immunotherapy (CPI) has transformed treatment for several previously challenging-to-treat solid cancers, leading to an increase in their regulatory approvals for various malignancies 2 , 3 . CPIs are monoclonal antibodies that bind to T cell inhibitory signals on T cells, antigen-presenting cells, and tumor cells, stimulating immune responses against tumors. The most established targets are CTLA-4, PD-1, and PD-L1 4, 5 . Given the success of CPIs in the treatment of cancer and the highly immunosuppressive GBM tumor microenvironment, CPIs have been heavily investigated for the treatment of gliomas. However, despite promising pre-clinical data and clinical data on brain metastases, clinical data on CPI in GBM have been disappointing. Anti-PD-1/PD-L1 blockade in addition to standard of care in newly diagnosed GBM failed to show a benefit 6 . CheckMate 498 (NCT02617589), an open-label phase III trial in unmethylated MGMT patients comparing the standard-of-care regimen versus nivolumab + RT did not meet its primary endpoint of improved OS 7 . CheckMate 548 (NCT02667587), another trial of nivolumab in patients with MGMT-methylated GBM, failed to meet its PFS and OS co-primary endpoints in the intention-to-treat population 8 . The large prospective randomized open-label phase III Checkmate 143 study (NCT02017717), which compared nivolumab monotherapy and bevacizumab in GBM patients at first recurrence after standard of care, demonstrated no statistically significant differences in survival, with an overall response rate in favor of bevacizumab 9 . Despite the clinical failure of CPI in high-grade astrocytoma, its potential may not yet be fully realized. Across trials, some patients experience a response, but we do not know the molecular characteristics or clinically unique aspects of their tumors. To understand the true potential of CPI in GBM and high-grade astrocytoma, it is crucial to understand the molecular and clinical determinants of patient outcomes that may reveal unique opportunities for response in subsets of these patients. Another form of immunotherapy is cell therapy, which utilizes the active transfer of immune cells to the patient, employing their anti-tumor functions 10 . One such cell therapy used autologous polyclonal CMV pp65-specifc T cells expanded ex vivo and was administered to patients after temozolomide-induced lymphodepletion in a phase I window-of-opportunity clinical trial 11 . Although it was well-tolerated, CMV seropositivity did not guarantee tumor susceptibility to CMV-specific T cells 11 . Immunotherapy oncolytic viruses have been an active focus of research in high-grade astrocytoma. A phase I trial of DNX-2401, an oncolytic adenovirus that was administered via intratumoral injection in recurrent malignant gliomas, demonstrated that 20% of patients were alive over 3 years after treatment of their recurrent GBM 12 . In addition to its direct oncolytic effects, it can elicit an immune-mediated antiglioma response. In this retrospective study, we identified patients with GBM or astrocytoma, IDH-mutant grade 4, who were treated with immunotherapy (CPI, virus therapy, or cell therapy). We determined the associations between molecular, clinical, and demographic data and survival outcomes for these patients. Methods Patients We identified adult patients with recurrent GBM or astrocytoma, IDH-mutant grade 4, who had received treatment with anti-PD-1 (nivolumab or pembrolizumab) and/or anti-CTLA-4 (ipilimumab) therapy at The University of Texas MD Anderson Cancer Center from January 1, 2015 to August 31, 2019. Molecular, clinical, and demographic data were extracted by manual chart review. In addition, we identified 6 patients who received polyclonal CMV pp65-specific T cells and 22 patients who received DNX-2401 (Delta-24-RGD; tasadenoturev, tumor-selective, replication-competent oncolytic adenovirus) at UT MD Anderson Cancer Center. This study was approved under our PROATIVE Protocol 2012 − 0441, which allows for retrospective and prospective clinical data reviews of information in the electronic medical records or elsewhere and retrospective analyses of stored samples that do not require further therapeutic or diagnostic intervention. Patients in this study are those who had consented to PROATIVE Protocol 2012 − 0441. In addition they were consented to various immunotherapy trials which included: NCT02667587, NCT02337686, NCT02337491, NCT02017717, NCT02526017, NCT02327078, NCT02798406, NCT02311920, NCT02852655, NCT00805376, NCT02661282. Some patients in the study received CPI as part of compassionate use. Statistical analysis Descriptive statistics [frequency distribution, mean (± s.d.), and median (range)] were used to summarize patients’ characteristics. Chi-square test or Fisher exact test was used to test differences in category variables, and Wilcoxon rank-sum test or Kruskal-Wallis test was used to detect differences in continuous variables between groups 13 . The distributions of progression-free survival (PFS) and OS durations were estimated by the Kaplan-Meier method 14 . PFS duration was defined as the time from immunotherapy initiation to the time of progression or death, whichever occurred first. OS duration was defined as the time from immunotherapy initiation to death. For events that had not occurred by the time of data analysis, times were censored at the last contact at which the patient was known to be progression-free for PFS or the last time the patient was known to be alive for OS. The log-rank test 15 was performed to test the difference in survival between groups. Regression analyses of survival data based on the Cox proportional hazards model 16 were conducted on PFS and OS durations in the multivariate setting. Surgery after immunotherapy initiation was analyzed as a time-dependent covariate in the analysis of PFS and OS duration. Multiplex immunofluorescence Sequential 4-µm-thick sections from 13 metastatic cervix primary squamous cell carcinoma and human tonsil control FFPE tissues were prepared for conventional multiplex IF staining using the Opal 9 multiplexed assay. We applied primary antibodies to tonsil specimens as controls at optimized concentrations that had been previously determined on uniplex control tissues following previous studies [Edwin Reference here]. Staining was performed consecutively using the same steps as those used in previous studies, applying each antibody in order one after the order at specified concentrations: for panel 10A, PD1 (clone EPR4877, ABCAM, 1:250, Opal 620 1:100), CD8 (clone C8/144B, ABCAM, 1:100, Opal 540 1:100), pan cytokeratin (AE1/AE3, Thermo Fisher Scientific, Opal 650 1:150), FoxP3 (D2W8E, Cell Signaling Technology, 1:50, Opal 570 1:100), Ki67 (MIB-1, Agilent Technologies, 1:200, Opal 480 1:100), CD68 (PG-M1, DAKO, 1:75, Opal 520 1:100), PD-L1 (E1L3N, Cell Signaling Technology, 1:1000, Opal 690 1:150), CD3 epsilon (D7A6E, Cell Signaling Technology, 1:100, Opal 780D 1:100), and DAPI, and for panel 3A, pan cytokeratin, B7-H3, B7-H4,CD68, IDO1, PD-L1, and CD3 (to confirm biomarker brands and concentrations of antibodies and dyes). After staining, slides are scanned in the PhenoImager HT2.0 (Akoya Bsc) using a low-band filter to acquire low-magnification images for region of interest selection, where a minimum of five areas of 0.65 mm 2 were captured per case by visualization using phenoChart 1.1.0 software (Akoya Bsc). Afterwards, medium-power (20x) magnification images were obtained for each ROI selected with the MSI workflow from the PhenoImager HT2.0 using the LCTF filter. Utilizing a previously standardized library of acquired intensities for the fluorescent dyes in the assay, each set of images belonging to one slide was loaded in the inform Software (Akoya Bsc) and manually analyzed by an experienced pathologist (MMA). A histologic assessment was performed to ensure that each area of analysis contained at least 200 tumoral cells, avoiding areas of necrosis as pertinent for the tissue present and excluding them from the image analysis. The remaining tissue that was present for each case underwent manual cell segmentation for each slide, and phenotyping was performed sequentially. Four mutually exclusive groups of cells were classified first: CK+, CD3e+, CD68+, and all other cells. Afterwards, each remaining marker was classified between positive and negative cells, and upon completion, exported data were consolidated and combined to create the phenotypes of interest for the study, consisting of CK+, CK+/PD-L1+, CK+/KI67+, CD3+, CD3+/CD8+, CD3+/PD-1+, CD3+/CD8+/KI67+, CD3+/PD-1+/PD-L1+, CD3+/KI67+,CD3+/FOXP3+/CD8-, CD68+, CD68+/PD-1+, CD68+/PD-L1 + for panel 10A and CK+, CK+/IDO1+, CK+/IDO1+/PD-L1+, CK+/IDO1+/B7-H3+, CK+/IDO1+/B7-H4+, CD68+, CD68 + IDO1+, CD68 + IDO1 + PD-L1+, CD68+/IDO1+/B7-H3+, and CD68+/IDO1+/B7-H4 + for panel 3A utilizing the Phenoptr Reports (Akoya Bsc) R script library. The multiplexed immunofluorescence cell segmentation data were used to identify the cell types at single-cell resolution using the spatial xy coordinates and phenotypic markers. The panel of antibodies used for this study were GFAP, CD3, CD68, CD8, PD-1, and PD-L1. The spatial coordinates were used to generate planar point pattern representations of the immunofluorescence images obtained from different tissue regions for each patient. Bioinformatic analysis for multiplex immunofluorescence The spatial point patterns were generated using spatstat library in R, which is a geospatial toolbox 17 , 18 . The antibody panel was used to identify different cell types, such as tumor cells, T helper cells, macrophages, and cytotoxic cells. The cell types designated by the phenotypic markers were macrophages (CD68+, either PDL1 + or PDL1-), Th cells (CD3 + CD8-, which can be PD1+/PDL1-, PD1-/PDL1+, PD1+/PDL1+, or PD1-/PDL1-), cytotoxic cells (CD3 + CD8+, which can be PD1+/PDL1-, PD1-/PDL1+, PD1+/PDL1+, or PD1-/PDL1-), and tumor cells (GFAP+, either PDL1 + or PDL1-). To account for differences in the tissue dimensions, the boundary coordinates of the region were used to define the spatial window of the point pattern with the owin function (spatstat package). This was used to calculate the area of the tissue region, calculated in mm 3 , by dividing the spatial window area (in microns) by 106. The density of each cell type for each region was calculated by dividing the number of cells by the area of the spatial window. These measures were averaged for each patient, designated by treatment (pre- or post- pembro) and status (mutant or wild-type). The minimum cell-to-cell distances were calculated using the SPIAT toolbox 19 , which is an R package for data analysis and visualization of spatial data. The function measures the closest cell of a specific cell type (e.g. cytotoxic PD-1+) from a reference cell-type (e.g. tumor PD-L1+), which was calculated between different T cell populations and tumor cells. Tissue region heterogeneity contributes to lower cell density when averaged across regions per patient. Hence, a summary table for each patient by region with cell density and abundance (cell count by type) measures was generated. All of the subtypes of Th cells, cytotoxic cells, tumor cells, and macrophages are merged in the summary table. Results Patient characteristics We identified 66 patients with recurrent GBM or astrocytoma, IDH-mutant grade 4. The characteristics of these patients are described in Table 1 . Of these, 38 received anti-PD-1 (nivolumab or pembrolizumab) and/or anti-CTLA-4 (ipilimumab) therapy, 22 received DNX-2401, and 6 received polyclonal CMV pp65-specific T cells. The variables in this analysis are listed in Table 1 . Table 1 Patient Characteristics (n = 66) Variable category Frequency count Percent of total frequency Sex Female 30 45 Male 36 55 Genomic testing availability No 26 39 Yes 40 61 Type of immunotherapy Immune checkpoint therapy 38 58 Cell therapy 6 9 Viral therapy 22 33 KPS immunotherapy start date Missing 1 2 60 1 2 70 7 11 80 7 11 90 32 48 100 18 27 Recurrent diseases at immunotherapy 66 100 Disease focality Multifocal 5 8 Unifocal 59 89 Unknown 2 3 Total number of surgeries 1 20 30 2 29 44 3 16 24 4 1 2 Steroid use at baseline No 39 59 Unknown 11 17 Yes 16 24 Steroid use during immunotherapy No 22 33 Unknown 2 3 Yes 42 64 IDH1/2 status Mutant 9 14 Wild-type 36 55 Unknown 21 32 MGMT promoter methylation status Methylated 9 14 Unmethylated 12 18 Unknown 45 68 ATRX status Mutant 12 18 Wild-type 31 47 Unknown 23 35 EGFR status Mutant 16 24 Wild-type 29 44 Unknown 21 32 TP53 status Mutant 23 35 Wild-type 26 39 Unknown 17 26 PIK3CA status Mutant 11 17 Wild-type 30 45 Unknown 25 38 PTEN status Mutant 7 11 Wild-type 33 50 Unknown 26 39 RB1 status Mutant 3 5 Wild-type 36 55 Unknown 27 41 TERT promoter status Mutant 9 14 Wild-type 31 47 Unknown 26 39 MAPK pathway gene status Mutant 1 2 Wild-type 38 58 Unknown 27 41 Hypermutation status Mutant 1 2 Wild-type 39 59 Unknown 26 39 Molecular determinants of response Eleven molecular alterations, comprising some of the most common seen in next-generation sequencing, were correlated with outcome and are listed in Table 1 . Among these patients, the presence of phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha ( PIK3CA ) mutation was found to be associated with shorter PFS and OS durations, as defined from the start of immunotherapy, with a statistically significant difference in PFS duration compared to patients with wild-type PIK3CA status (p-value = 0.022, 0.073, respectively) (Fig. 1 ). In the patients treated with CPI, mutations in PIK3CA were associated with a statistically significantly shorter OS duration, calculated from CPI initiation (p-value = 0.004) (Fig. 2 A). The presence of an isocitrate dehydrogenase ( IDH1/2 ) mutations in patients treated with CPI was associated with a statistically significantly shorter OS duration, as calculated from the time of diagnosis (p-value = 0.002) (Fig. 2 B). Retinoblastoma ( RB1 ) and telomerase reverse transcriptase ( TERT ) promoter mutations were associated with shorter PFS durations in patients treated with CPI. This association was statistically significant for both mutations; PFS (p-value = 0.00056, 0.022, respectively) (Fig. 2 C, 2 D). There were no statistically significant differences in PFS or OS for the other genetic alterations listed in Table 1 . Clinical determinants of response Among the 38 patients treated with CPI, length of treatment over 6 months was associated with longer PFS and OS durations (p-value = 0.048, 0.062, respectively) (Fig. 3 A, 3 B). Previous studies have defined responders as patients whose tumor volumes, as seen on MRI, were either stable or shrinking continually over at least 6 months 20 . On the basis of this criteria, six of the 38 patients (16%) experienced a response to CPI. In the patients treated with CPI in our study, steroid administration prior to CPI initiation was associated with a statistically significantly shorter OS duration (p-value = 0.00023) (Fig. 3 C). In all 66 patients treated with immunotherapy, multifocal disease was associated with shorter PFS and OS durations than was unifocal disease (p-value = 0.0017, 0.0013, respectively) (Fig. 4 ). There was statistically significant shorter PFS and OS (p = 0.0017, 0.0013, respectively) among all patients for multifocal disease compared with unifocal disease. PIK3CA mutation may induce an immunosuppressive environment through increased PD-1 expression We hypothesized that PIK3CA mutation leads to immunosuppression, which is why patients in this cohort did worse compared to those without PIK3CA mutation after treatment with immunotherapy as described previously. Tumor tissue from four patients from this cohort were obtained; two harbored mutations in PIK3CA , while two were wild-type. To investigate changes in the tumor immune microenvironment, we conducted multiplex immunofluorescence. The PIK3CA mutant cases had higher PD-1 expression in the cytotoxic CD3+/CD8 + cells (Fig. 5 ). Notably, the wild-type case with no PD-1 expression had a robust response to CPI in terms of PFS and OS duration (Fig. 5 A). Discussion In this study, we correlated molecular and clinical features with survival outcomes in high-grade glioma patients treated with immunotherapy. Our data suggests that there are molecular and clinical determinants to the outcomes of patients with high-grade glioma treated with immunotherapy. PIK3CA encodes for the p110α catalytic subunit of phosphoinositide 3-kinase (PI3K) 21 . It is intriguing that this study demonstrated a negative association with the presence of PIK3CA mutation and PFS and OS duration which could indicate that PIK3CA mutation has a role in immunosuppression, or at a minimum, an inhibitory role for immunotherapy. Recently, a preclinical study found that PI3K activation allows immune evasion by promoting an inhibitory myeloid tumor microenvironment in mouse models 22 . So far, to our knowledge, such a role for mutant PIK3CA in high-grade astrocytoma is not known. Likewise, our multiplex immunofluorescence data support the notion that PIK3CA mutation may induce an immunosuppressive environment through increased PD-1 expression. Increased PD-1 expression can indicate immunosuppression 23 . It was also fitting that the patient in our study with the lowest tumor PD-1 expression had a robust response to CPI. Our data suggest that mutant PIK3CA has an immunosuppressive role in this context, which could provide a rationale for the testing of immunotherapy in conjunction with PI3K inhibitors. Based on our data the presence of an isocitrate dehydrogenase ( IDH1/2 ) mutations in patients treated with CPI was associated with a statistically significantly shorter OS duration, as calculated from the time of diagnosis. Interestingly, although IDH mutation is a favorable prognostic indicator in gliomas 24 , in the setting of CPI treatment, this may not hold on the basis of these data, consistent with previously described increased suppression of T cell-mediated antitumor activity in IDH-mutant vs. wild-type gliomas 25 . Furthermore, use of an IDH inhibitor that is specific for mutant IDH in a pre-clinical mouse model led to improved antitumor immunity and enhanced the efficacy of a peptide vaccine 26 . However, it is again important to note that the small sample size here calls for larger-scale studies to further investigate this relationship. Taken together, the results of this study could support a rationale for combining IDH inhibitor therapy with CPI in patients with IDH-mutant high-grade glioma. There is currently an ongoing clinical trial: study of vorasidenib and pembrolizumab combination in recurrent or progressive IDH-1 mutant glioma, NCT05484622 exploring such concept. In our cohort multifocal disease was associated with shorter PFS and OS. Most trials exclude multifocal disease so it is helpful to note such clinical differences. In regard to the patients treated with CPI, it was noteworthy that the duration of CPI treatment was positively associated with PFS and OS durations. It is difficult to ascertain whether this was a function of the actual length of CPI treatment or simply a consequence of the fact that patients who can tolerate CPI longer may take longer to experience tumor progression. The finding that steroid administration prior to CPI initiation was associated with a shorter OS duration from time of diagnosis seems rational on the basis of the function of steroids in suppressing the immune system and possibly blunting the effect of CPI. This finding was consistent with the effects of steroids seen in a systematic review and meta-analysis evaluating patients with melanoma and non-small cell lung cancer who were treated with CPI 27 . Taken together, this analysis supports the notion that there are molecular and clinical determinants to the outcomes of patients with high-grade glioma treated with immunotherapy. It is important to note that the small sample size and retrospective nature of the study carries selection bias and/or confounding effects. However, we believe that this analysis provides valuable insights into the molecular and clinical factors affecting outcomes in patients with high-grade astrocytoma treated with various forms of immunotherapy. Declarations Competing interests None Ethics approval This study was approved by MD Anderson’s institutional review board. Consent to participate Not required Consent to publish Not required Funding MD Anderson GBM Moonshots program Author Contribution PD was involved with conception, data collection, data interpretation and analysis, wrote the manuscript, and edited and revised the final manuscript for submission. HL provided statistical analysis. GK and KPB provided bioinformatic analysis of multiplex immunofluorescence data. ZS helped with data collection. MMA and ERP conducted the multiplex immunofluorescence. PD, SPW, FFL, KPB, JDG, and NKM provided crucial guidance. JDG and NKM was involved with conception, data interpretation and, editing of the manuscript. All authors reviewed the manuscript. Acknowledgement This work used the Biostatistics Resource Group. Editorial assistance was provided by Editing Services, Research Medical Library, MD Anderson. This work was also supported by the MD Anderson GBM Moonshots program. This work was supported by the Prospective Assessment of Correlative and Tissue Biomarkers in Glioma Patients (The PROACTIVE-Glioma Program team through consents, biospecimens and data collections under the MD Anderon IRB-approved protocol 2012-0441. We extend our deepest gratitude to the patients and their families, whose willingness to donate biological samples made this research possible. We also thank the outstanding staff of the Translational Molecular Pathology Department at The University of Texas MD Anderson Cancer Center for their expert work on the multiplex immunofluorescence staining, with special appreciation to Heladio Ibarguen and Auriole Tamegnon for their invaluable help and technical guidance throughout the project. Data Availability The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. References Stupp R, Mason WP, van den Bent MJ et al (2005) Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 352(10):987–996. 10.1056/NEJMoa043330 Robert C (2020) A decade of immune-checkpoint inhibitors in cancer therapy. Nat Commun 11(1):3801. 10.1038/s41467-020-17670-y Soffietti R, Ahluwalia M, Lin N, Rudà R (2020) Management of brain metastases according to molecular subtypes. 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Cancers (Basel) Feb 27(3). 10.3390/cancers12030546 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 25 Jun, 2025 Read the published version in Journal of Neuro-Oncology → Version 1 posted Editorial decision: Revision requested 28 May, 2025 Reviews received at journal 24 May, 2025 Reviewers agreed at journal 17 May, 2025 Reviewers agreed at journal 16 May, 2025 Reviewers invited by journal 13 May, 2025 Editor assigned by journal 13 May, 2025 Submission checks completed at journal 13 May, 2025 First submitted to journal 12 May, 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6649860","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Case Report","associatedPublications":[],"authors":[{"id":456587160,"identity":"2f0dc31a-3cfe-4c94-be1d-c2af21bfd978","order_by":0,"name":"Pushan Dasgupta","email":"data:image/png;base64,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","orcid":"","institution":"The University of Texas MD Anderson Cancer Center","correspondingAuthor":true,"prefix":"","firstName":"Pushan","middleName":"","lastName":"Dasgupta","suffix":""},{"id":456587162,"identity":"6960d942-9831-4f1c-bab6-f6f4842532ad","order_by":1,"name":"Heather Lin","email":"","orcid":"","institution":"The University of Texas MD Anderson Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Heather","middleName":"","lastName":"Lin","suffix":""},{"id":456587163,"identity":"5f378a5b-573f-4bf2-9de5-4e6ff3ef7650","order_by":2,"name":"Gayatri Kumar","email":"","orcid":"","institution":"Mayo Clinic Phoenix","correspondingAuthor":false,"prefix":"","firstName":"Gayatri","middleName":"","lastName":"Kumar","suffix":""},{"id":456587164,"identity":"f9c84222-f802-4e48-9c94-c36e724a7eea","order_by":3,"name":"Zaid Soomro","email":"","orcid":"","institution":"The University of Texas MD Anderson Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Zaid","middleName":"","lastName":"Soomro","suffix":""},{"id":456587165,"identity":"29fe9285-35e2-4bc7-9b2e-ddaee5ab4e54","order_by":4,"name":"Max M. Ayala","email":"","orcid":"","institution":"The University of Texas MD Anderson Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Max","middleName":"M.","lastName":"Ayala","suffix":""},{"id":456587166,"identity":"d79a7294-0ee2-4cc7-8a70-61126332f9fe","order_by":5,"name":"Edwin R. Parra","email":"","orcid":"","institution":"The University of Texas MD Anderson Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Edwin","middleName":"R.","lastName":"Parra","suffix":""},{"id":456587167,"identity":"f8c7694b-5988-45dd-b0db-28d33dcf579c","order_by":6,"name":"Shiao-Pei Weathers","email":"","orcid":"","institution":"The University of Texas MD Anderson Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Shiao-Pei","middleName":"","lastName":"Weathers","suffix":""},{"id":456587168,"identity":"8131ff3c-8f17-4bee-a113-e85f3c2d86ba","order_by":7,"name":"Frederick F. Lang","email":"","orcid":"","institution":"The University of Texas MD Anderson Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Frederick","middleName":"F.","lastName":"Lang","suffix":""},{"id":456587169,"identity":"9bc6afc0-a190-4b3c-9ada-b71e2d10f5f9","order_by":8,"name":"Krishna P. Bhat","email":"","orcid":"","institution":"Mayo Clinic Phoenix","correspondingAuthor":false,"prefix":"","firstName":"Krishna","middleName":"P.","lastName":"Bhat","suffix":""},{"id":456587170,"identity":"7d0acd9b-f20a-45f0-8ea0-11f69065ad7a","order_by":9,"name":"John Groot","email":"","orcid":"","institution":"University of California-San Francisco","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"","lastName":"Groot","suffix":""},{"id":456587171,"identity":"6014cf63-d530-4ced-9504-effad3ae1f80","order_by":10,"name":"Nazanin K. Majd","email":"","orcid":"","institution":"The University of Texas MD Anderson Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Nazanin","middleName":"K.","lastName":"Majd","suffix":""}],"badges":[],"createdAt":"2025-05-12 22:23:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6649860/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6649860/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11060-025-05131-9","type":"published","date":"2025-06-25T15:57:37+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82888735,"identity":"c0645698-de17-4eda-bea1-34f193494d82","added_by":"auto","created_at":"2025-05-16 12:03:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":37404,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePIK3CA\u003c/em\u003e mutations are associated with shorter PFS and OS durations in patients treated with immunotherapy. \u003cstrong\u003eA\u003c/strong\u003e Kaplan-Meier curve of PFS duration by \u003cem\u003ePIK3CA\u003c/em\u003e mutation status. \u003cstrong\u003eB\u003c/strong\u003e Kaplan-Meier curve of OS duration by \u003cem\u003ePIK3CA\u003c/em\u003e mutation status.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6649860/v1/23bea44d792ab65f050366d6.png"},{"id":82891150,"identity":"e4ac69f4-e517-4390-82c5-01de98f72c14","added_by":"auto","created_at":"2025-05-16 12:11:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":66134,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePIK3CA\u003c/em\u003e and \u003cem\u003eIDH1/2\u003c/em\u003e mutations are associated with a shorter OS duration in patients treated with CPI, while \u003cem\u003eRB1\u003c/em\u003e and \u003cem\u003eTERT\u003c/em\u003epromoter mutations are associated with shorter PFS duration. \u003cstrong\u003eA\u003c/strong\u003e Kaplan-Meier curve of OS from CPI initiation by \u003cem\u003ePIK3CA\u003c/em\u003emutation status. \u003cstrong\u003eB\u003c/strong\u003e Kaplan-Meier curve of OS duration from diagnosis by \u003cem\u003eIDH1/2\u003c/em\u003emutation status. \u003cstrong\u003eC\u003c/strong\u003e Kaplan-Meier curve of PFS duration by \u003cem\u003eRB1\u003c/em\u003e status. \u003cstrong\u003eD\u003c/strong\u003e Kaplan-Meier curve of PFS duration by \u003cem\u003eTERT\u003c/em\u003e promoter status.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6649860/v1/70105732a3cb21fe3ea72ed4.png"},{"id":82888738,"identity":"1a053aaa-55e0-4981-8bd9-ecca6cb1f55a","added_by":"auto","created_at":"2025-05-16 12:03:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":47458,"visible":true,"origin":"","legend":"\u003cp\u003eIncreased length of CPI treatment is associated with longer PFS and OS durations, while steroid use at baseline was associated with a shorter OS duration. \u003cstrong\u003eA\u003c/strong\u003e Kaplan-Meier curve of PFS duration by length of CPI treatment. \u003cstrong\u003eB\u003c/strong\u003e Kaplan-Meier curve of OS duration from CPI initiation by length of CPI treatment. \u003cstrong\u003eC\u003c/strong\u003e Kaplan-Meier curve of OS duration on the basis of steroid use at baseline before the start of CPI.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6649860/v1/5988aa2980530ab1adeea603.png"},{"id":82888743,"identity":"5e7ca0b8-3a2b-4f8a-baa8-f4dd0d38e649","added_by":"auto","created_at":"2025-05-16 12:03:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":29021,"visible":true,"origin":"","legend":"\u003cp\u003eMultifocal disease is associated with shorter PFS and OS durations in patients treated with immunotherapy. \u003cstrong\u003eA\u003c/strong\u003e Kaplan-Meier curve of PFS duration by disease focality. \u003cstrong\u003eB\u003c/strong\u003e Kaplan-Meier curve of OS duration from immunotherapy initiation by disease focality.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6649860/v1/9392c4f4a97739b429040c71.png"},{"id":82891152,"identity":"5d82ce96-d50b-4ba8-8e01-33c71ed8a9af","added_by":"auto","created_at":"2025-05-16 12:11:46","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":22946,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePIK3CA \u003c/em\u003emutations may induce an immunosuppressive environment through increased PD-1 expression. \u003cstrong\u003eA\u003c/strong\u003e Table of patients showing \u003cem\u003ePIK3CA\u003c/em\u003e mutation status and PFS and OS duration after CPI. \u003cstrong\u003eB\u003c/strong\u003e Multiplex immunofluorescence cell density of CD3\u003csup\u003e+\u003c/sup\u003e/CD8\u003csup\u003e+\u003c/sup\u003e/PD-1\u003csup\u003e+\u003c/sup\u003e cell populations from patient tissue. \u003cstrong\u003eC\u003c/strong\u003e Multiplex immunofluorescence cell density of CD3\u003csup\u003e+\u003c/sup\u003e/CD8\u003csup\u003e+\u003c/sup\u003ecell populations from patient tissue.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6649860/v1/ded9345df99649b98a6777d8.png"},{"id":85686210,"identity":"2dda1699-a6cf-4bf0-8701-7bf0bf803404","added_by":"auto","created_at":"2025-06-30 16:04:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1034108,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6649860/v1/e2f634e1-c84d-4aa1-a275-d5e7cbfbc813.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Molecular and Clinical Determinants of Response to Immunotherapy in High-Grade Glioma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDespite the current standard of care, glioblastoma (GBM) has a median overall survival (OS) duration of 14.6 months and a 2-year survival rate of 26.5%\u003csup\u003e1\u003c/sup\u003e. There is a tremendous need for new, effective therapies. Immunotherapy has proven to be an effective form of therapy for many kinds of cancer. Specifically, checkpoint inhibitor immunotherapy (CPI) has transformed treatment for several previously challenging-to-treat solid cancers, leading to an increase in their regulatory approvals for various malignancies\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. CPIs are monoclonal antibodies that bind to T cell inhibitory signals on T cells, antigen-presenting cells, and tumor cells, stimulating immune responses against tumors. The most established targets are CTLA-4, PD-1, and PD-L1\u003csup\u003e4, 5\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGiven the success of CPIs in the treatment of cancer and the highly immunosuppressive GBM tumor microenvironment, CPIs have been heavily investigated for the treatment of gliomas. However, despite promising pre-clinical data and clinical data on brain metastases, clinical data on CPI in GBM have been disappointing. Anti-PD-1/PD-L1 blockade in addition to standard of care in newly diagnosed GBM failed to show a benefit\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. CheckMate 498 (NCT02617589), an open-label phase III trial in unmethylated MGMT patients comparing the standard-of-care regimen versus nivolumab\u0026thinsp;+\u0026thinsp;RT did not meet its primary endpoint of improved OS\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. CheckMate 548 (NCT02667587), another trial of nivolumab in patients with MGMT-methylated GBM, failed to meet its PFS and OS co-primary endpoints in the intention-to-treat population\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. The large prospective randomized open-label phase III Checkmate 143 study (NCT02017717), which compared nivolumab monotherapy and bevacizumab in GBM patients at first recurrence after standard of care, demonstrated no statistically significant differences in survival, with an overall response rate in favor of bevacizumab\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite the clinical failure of CPI in high-grade astrocytoma, its potential may not yet be fully realized. Across trials, some patients experience a response, but we do not know the molecular characteristics or clinically unique aspects of their tumors. To understand the true potential of CPI in GBM and high-grade astrocytoma, it is crucial to understand the molecular and clinical determinants of patient outcomes that may reveal unique opportunities for response in subsets of these patients.\u003c/p\u003e \u003cp\u003eAnother form of immunotherapy is cell therapy, which utilizes the active transfer of immune cells to the patient, employing their anti-tumor functions\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. One such cell therapy used autologous polyclonal CMV pp65-specifc T cells expanded ex \u003cem\u003evivo\u003c/em\u003e and was administered to patients after temozolomide-induced lymphodepletion in a phase I window-of-opportunity clinical trial\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Although it was well-tolerated, CMV seropositivity did not guarantee tumor susceptibility to CMV-specific T cells\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eImmunotherapy oncolytic viruses have been an active focus of research in high-grade astrocytoma. A phase I trial of DNX-2401, an oncolytic adenovirus that was administered via intratumoral injection in recurrent malignant gliomas, demonstrated that 20% of patients were alive over 3 years after treatment of their recurrent GBM\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. In addition to its direct oncolytic effects, it can elicit an immune-mediated antiglioma response.\u003c/p\u003e \u003cp\u003eIn this retrospective study, we identified patients with GBM or astrocytoma, IDH-mutant grade 4, who were treated with immunotherapy (CPI, virus therapy, or cell therapy). We determined the associations between molecular, clinical, and demographic data and survival outcomes for these patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eWe identified adult patients with recurrent GBM or astrocytoma, IDH-mutant grade 4, who had received treatment with anti-PD-1 (nivolumab or pembrolizumab) and/or anti-CTLA-4 (ipilimumab) therapy at The University of Texas MD Anderson Cancer Center from January 1, 2015 to August 31, 2019. Molecular, clinical, and demographic data were extracted by manual chart review. In addition, we identified 6 patients who received polyclonal CMV pp65-specific T cells and 22 patients who received DNX-2401 (Delta-24-RGD; tasadenoturev, tumor-selective, replication-competent oncolytic adenovirus) at UT MD Anderson Cancer Center. This study was approved under our PROATIVE Protocol 2012\u0026thinsp;\u0026minus;\u0026thinsp;0441, which allows for retrospective and prospective clinical data reviews of information in the electronic medical records or elsewhere and retrospective analyses of stored samples that do not require further therapeutic or diagnostic intervention. Patients in this study are those who had consented to PROATIVE Protocol 2012\u0026thinsp;\u0026minus;\u0026thinsp;0441. In addition they were consented to various immunotherapy trials which included: NCT02667587, NCT02337686, NCT02337491, NCT02017717, NCT02526017, NCT02327078, NCT02798406, NCT02311920, NCT02852655, NCT00805376, NCT02661282. Some patients in the study received CPI as part of compassionate use.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics [frequency distribution, mean (\u0026plusmn;\u0026thinsp;s.d.), and median (range)] were used to summarize patients\u0026rsquo; characteristics. Chi-square test or Fisher exact test was used to test differences in category variables, and Wilcoxon rank-sum test or Kruskal-Wallis test was used to detect differences in continuous variables between groups\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. The distributions of progression-free survival (PFS) and OS durations were estimated by the Kaplan-Meier method\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. PFS duration was defined as the time from immunotherapy initiation to the time of progression or death, whichever occurred first. OS duration was defined as the time from immunotherapy initiation to death. For events that had not occurred by the time of data analysis, times were censored at the last contact at which the patient was known to be progression-free for PFS or the last time the patient was known to be alive for OS. The log-rank test\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e was performed to test the difference in survival between groups. Regression analyses of survival data based on the Cox proportional hazards model\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e were conducted on PFS and OS durations in the multivariate setting. Surgery after immunotherapy initiation was analyzed as a time-dependent covariate in the analysis of PFS and OS duration.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMultiplex immunofluorescence\u003c/h3\u003e\n\u003cp\u003eSequential 4-\u0026micro;m-thick sections from 13 metastatic cervix primary squamous cell carcinoma and human tonsil control FFPE tissues were prepared for conventional multiplex IF staining using the Opal 9 multiplexed assay. We applied primary antibodies to tonsil specimens as controls at optimized concentrations that had been previously determined on uniplex control tissues following previous studies [Edwin Reference here]. Staining was performed consecutively using the same steps as those used in previous studies, applying each antibody in order one after the order at specified concentrations: for panel 10A, PD1 (clone EPR4877, ABCAM, 1:250, Opal 620 1:100), CD8 (clone C8/144B, ABCAM, 1:100, Opal 540 1:100), pan cytokeratin (AE1/AE3, Thermo Fisher Scientific, Opal 650 1:150), FoxP3 (D2W8E, Cell Signaling Technology, 1:50, Opal 570 1:100), Ki67 (MIB-1, Agilent Technologies, 1:200, Opal 480 1:100), CD68 (PG-M1, DAKO, 1:75, Opal 520 1:100), PD-L1 (E1L3N, Cell Signaling Technology, 1:1000, Opal 690 1:150), CD3 epsilon (D7A6E, Cell Signaling Technology, 1:100, Opal 780D 1:100), and DAPI, and for panel 3A, pan cytokeratin, B7-H3, B7-H4,CD68, IDO1, PD-L1, and CD3 (to confirm biomarker brands and concentrations of antibodies and dyes).\u003c/p\u003e \u003cp\u003eAfter staining, slides are scanned in the PhenoImager HT2.0 (Akoya Bsc) using a low-band filter to acquire low-magnification images for region of interest selection, where a minimum of five areas of 0.65 mm\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e were captured per case by visualization using phenoChart 1.1.0 software (Akoya Bsc). Afterwards, medium-power (20x) magnification images were obtained for each ROI selected with the MSI workflow from the PhenoImager HT2.0 using the LCTF filter. Utilizing a previously standardized library of acquired intensities for the fluorescent dyes in the assay, each set of images belonging to one slide was loaded in the inform Software (Akoya Bsc) and manually analyzed by an experienced pathologist (MMA). A histologic assessment was performed to ensure that each area of analysis contained at least 200 tumoral cells, avoiding areas of necrosis as pertinent for the tissue present and excluding them from the image analysis. The remaining tissue that was present for each case underwent manual cell segmentation for each slide, and phenotyping was performed sequentially. Four mutually exclusive groups of cells were classified first: CK+, CD3e+, CD68+, and all other cells. Afterwards, each remaining marker was classified between positive and negative cells, and upon completion, exported data were consolidated and combined to create the phenotypes of interest for the study, consisting of CK+, CK+/PD-L1+, CK+/KI67+, CD3+, CD3+/CD8+, CD3+/PD-1+, CD3+/CD8+/KI67+, CD3+/PD-1+/PD-L1+, CD3+/KI67+,CD3+/FOXP3+/CD8-, CD68+, CD68+/PD-1+, CD68+/PD-L1\u0026thinsp;+\u0026thinsp;for panel 10A and CK+, CK+/IDO1+, CK+/IDO1+/PD-L1+, CK+/IDO1+/B7-H3+, CK+/IDO1+/B7-H4+, CD68+, CD68\u0026thinsp;+\u0026thinsp;IDO1+, CD68\u0026thinsp;+\u0026thinsp;IDO1\u0026thinsp;+\u0026thinsp;PD-L1+, CD68+/IDO1+/B7-H3+, and CD68+/IDO1+/B7-H4\u0026thinsp;+\u0026thinsp;for panel 3A utilizing the Phenoptr Reports (Akoya Bsc) R script library.\u003c/p\u003e \u003cp\u003eThe multiplexed immunofluorescence cell segmentation data were used to identify the cell types at single-cell resolution using the spatial xy coordinates and phenotypic markers. The panel of antibodies used for this study were GFAP, CD3, CD68, CD8, PD-1, and PD-L1. The spatial coordinates were used to generate planar point pattern representations of the immunofluorescence images obtained from different tissue regions for each patient.\u003c/p\u003e\n\u003ch3\u003eBioinformatic analysis for multiplex immunofluorescence\u003c/h3\u003e\n\u003cp\u003eThe spatial point patterns were generated using spatstat library in R, which is a geospatial toolbox\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. The antibody panel was used to identify different cell types, such as tumor cells, T helper cells, macrophages, and cytotoxic cells. The cell types designated by the phenotypic markers were macrophages (CD68+, either PDL1\u0026thinsp;+\u0026thinsp;or PDL1-), Th cells (CD3\u0026thinsp;+\u0026thinsp;CD8-, which can be PD1+/PDL1-, PD1-/PDL1+, PD1+/PDL1+, or PD1-/PDL1-), cytotoxic cells (CD3\u0026thinsp;+\u0026thinsp;CD8+, which can be PD1+/PDL1-, PD1-/PDL1+, PD1+/PDL1+, or PD1-/PDL1-), and tumor cells (GFAP+, either PDL1\u0026thinsp;+\u0026thinsp;or PDL1-).\u003c/p\u003e \u003cp\u003eTo account for differences in the tissue dimensions, the boundary coordinates of the region were used to define the spatial window of the point pattern with the owin function (spatstat package). This was used to calculate the area of the tissue region, calculated in mm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, by dividing the spatial window area (in microns) by 106. The density of each cell type for each region was calculated by dividing the number of cells by the area of the spatial window. These measures were averaged for each patient, designated by treatment (pre- or post- pembro) and status (mutant or wild-type). The minimum cell-to-cell distances were calculated using the SPIAT toolbox\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, which is an R package for data analysis and visualization of spatial data. The function measures the closest cell of a specific cell type (e.g. cytotoxic PD-1+) from a reference cell-type (e.g. tumor PD-L1+), which was calculated between different T cell populations and tumor cells. Tissue region heterogeneity contributes to lower cell density when averaged across regions per patient. Hence, a summary table for each patient by region with cell density and abundance (cell count by type) measures was generated. All of the subtypes of Th cells, cytotoxic cells, tumor cells, and macrophages are merged in the summary table.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePatient characteristics\u003c/h2\u003e \u003cp\u003eWe identified 66 patients with recurrent GBM or astrocytoma, IDH-mutant grade 4. The characteristics of these patients are described in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Of these, 38 received anti-PD-1 (nivolumab or pembrolizumab) and/or anti-CTLA-4 (ipilimumab) therapy, 22 received DNX-2401, and 6 received polyclonal CMV pp65-specific T cells. The variables in this analysis are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient Characteristics (n\u0026thinsp;=\u0026thinsp;66)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency count\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercent of total frequency\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenomic testing availability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of immunotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmune checkpoint therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCell therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eViral therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKPS immunotherapy start date\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecurrent diseases at immunotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease focality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultifocal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnifocal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of surgeries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSteroid use at baseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSteroid use during immunotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eIDH1/2\u003c/em\u003e status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMutant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWild-type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eMGMT promoter methylation status\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethylated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmethylated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eATRX\u003c/em\u003e status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMutant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWild-type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEGFR\u003c/em\u003e status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMutant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWild-type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTP53\u003c/em\u003e status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMutant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWild-type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePIK3CA\u003c/em\u003e status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMutant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWild-type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePTEN\u003c/em\u003e status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMutant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWild-type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRB1\u003c/em\u003e status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMutant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWild-type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTERT\u003c/em\u003e promoter status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMutant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWild-type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAPK pathway gene status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMutant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWild-type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypermutation status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMutant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWild-type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMolecular determinants of response\u003c/h3\u003e\n\u003cp\u003eEleven molecular alterations, comprising some of the most common seen in next-generation sequencing, were correlated with outcome and are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Among these patients, the presence of phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (\u003cem\u003ePIK3CA\u003c/em\u003e) mutation was found to be associated with shorter PFS and OS durations, as defined from the start of immunotherapy, with a statistically significant difference in PFS duration compared to patients with wild-type \u003cem\u003ePIK3CA\u003c/em\u003e status (p-value\u0026thinsp;=\u0026thinsp;0.022, 0.073, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In the patients treated with CPI, mutations in \u003cem\u003ePIK3CA\u003c/em\u003e were associated with a statistically significantly shorter OS duration, calculated from CPI initiation (p-value\u0026thinsp;=\u0026thinsp;0.004) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The presence of an isocitrate dehydrogenase (\u003cem\u003eIDH1/2\u003c/em\u003e) mutations in patients treated with CPI was associated with a statistically significantly shorter OS duration, as calculated from the time of diagnosis (p-value\u0026thinsp;=\u0026thinsp;0.002) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Retinoblastoma (\u003cem\u003eRB1\u003c/em\u003e) and telomerase reverse transcriptase (\u003cem\u003eTERT\u003c/em\u003e) promoter mutations were associated with shorter PFS durations in patients treated with CPI. This association was statistically significant for both mutations; PFS (p-value\u0026thinsp;=\u0026thinsp;0.00056, 0.022, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eC,\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). There were no statistically significant differences in PFS or OS for the other genetic alterations listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \n\u003ch3\u003eClinical determinants of response\u003c/h3\u003e\n\u003cp\u003eAmong the 38 patients treated with CPI, length of treatment over 6 months was associated with longer PFS and OS durations (p-value\u0026thinsp;=\u0026thinsp;0.048, 0.062, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePrevious studies have defined responders as patients whose tumor volumes, as seen on MRI, were either stable or shrinking continually over at least 6 months\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. On the basis of this criteria, six of the 38 patients (16%) experienced a response to CPI. In the patients treated with CPI in our study, steroid administration prior to CPI initiation was associated with a statistically significantly shorter OS duration (p-value\u0026thinsp;=\u0026thinsp;0.00023) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). In all 66 patients treated with immunotherapy, multifocal disease was associated with shorter PFS and OS durations than was unifocal disease (p-value\u0026thinsp;=\u0026thinsp;0.0017, 0.0013, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). There was statistically significant shorter PFS and OS (p\u0026thinsp;=\u0026thinsp;0.0017, 0.0013, respectively) among all patients for multifocal disease compared with unifocal disease.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePIK3CA mutation may induce an immunosuppressive environment through increased PD-1 expression\u003c/h2\u003e \u003cp\u003eWe hypothesized that \u003cem\u003ePIK3CA\u003c/em\u003e mutation leads to immunosuppression, which is why patients in this cohort did worse compared to those without \u003cem\u003ePIK3CA\u003c/em\u003e mutation after treatment with immunotherapy as described previously. Tumor tissue from four patients from this cohort were obtained; two harbored mutations in \u003cem\u003ePIK3CA\u003c/em\u003e, while two were wild-type. To investigate changes in the tumor immune microenvironment, we conducted multiplex immunofluorescence. The \u003cem\u003ePIK3CA\u003c/em\u003e mutant cases had higher PD-1 expression in the cytotoxic CD3+/CD8\u0026thinsp;+\u0026thinsp;cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Notably, the wild-type case with no PD-1 expression had a robust response to CPI in terms of PFS and OS duration (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we correlated molecular and clinical features with survival outcomes in high-grade glioma patients treated with immunotherapy. Our data suggests that there are molecular and clinical determinants to the outcomes of patients with high-grade glioma treated with immunotherapy. \u003cem\u003ePIK3CA\u003c/em\u003e encodes for the p110α catalytic subunit of phosphoinositide 3-kinase (PI3K)\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. It is intriguing that this study demonstrated a negative association with the presence of \u003cem\u003ePIK3CA\u003c/em\u003e mutation and PFS and OS duration which could indicate that \u003cem\u003ePIK3CA\u003c/em\u003e mutation has a role in immunosuppression, or at a minimum, an inhibitory role for immunotherapy. Recently, a preclinical study found that PI3K activation allows immune evasion by promoting an inhibitory myeloid tumor microenvironment in mouse models\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. So far, to our knowledge, such a role for mutant \u003cem\u003ePIK3CA\u003c/em\u003e in high-grade astrocytoma is not known. Likewise, our multiplex immunofluorescence data support the notion that \u003cem\u003ePIK3CA\u003c/em\u003e mutation may induce an immunosuppressive environment through increased PD-1 expression. Increased PD-1 expression can indicate immunosuppression\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. It was also fitting that the patient in our study with the lowest tumor PD-1 expression had a robust response to CPI. Our data suggest that mutant \u003cem\u003ePIK3CA\u003c/em\u003e has an immunosuppressive role in this context, which could provide a rationale for the testing of immunotherapy in conjunction with PI3K inhibitors.\u003c/p\u003e \u003cp\u003eBased on our data the presence of an isocitrate dehydrogenase (\u003cem\u003eIDH1/2\u003c/em\u003e) mutations in patients treated with CPI was associated with a statistically significantly shorter OS duration, as calculated from the time of diagnosis. Interestingly, although IDH mutation is a favorable prognostic indicator in gliomas\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, in the setting of CPI treatment, this may not hold on the basis of these data, consistent with previously described increased suppression of T cell-mediated antitumor activity in IDH-mutant vs. wild-type gliomas\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Furthermore, use of an IDH inhibitor that is specific for mutant IDH in a pre-clinical mouse model led to improved antitumor immunity and enhanced the efficacy of a peptide vaccine\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. However, it is again important to note that the small sample size here calls for larger-scale studies to further investigate this relationship. Taken together, the results of this study could support a rationale for combining IDH inhibitor therapy with CPI in patients with IDH-mutant high-grade glioma. There is currently an ongoing clinical trial: study of vorasidenib and pembrolizumab combination in recurrent or progressive IDH-1 mutant glioma, NCT05484622 exploring such concept.\u003c/p\u003e \u003cp\u003eIn our cohort multifocal disease was associated with shorter PFS and OS. Most trials exclude multifocal disease so it is helpful to note such clinical differences. In regard to the patients treated with CPI, it was noteworthy that the duration of CPI treatment was positively associated with PFS and OS durations. It is difficult to ascertain whether this was a function of the actual length of CPI treatment or simply a consequence of the fact that patients who can tolerate CPI longer may take longer to experience tumor progression. The finding that steroid administration prior to CPI initiation was associated with a shorter OS duration from time of diagnosis seems rational on the basis of the function of steroids in suppressing the immune system and possibly blunting the effect of CPI. This finding was consistent with the effects of steroids seen in a systematic review and meta-analysis evaluating patients with melanoma and non-small cell lung cancer who were treated with CPI\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTaken together, this analysis supports the notion that there are molecular and clinical determinants to the outcomes of patients with high-grade glioma treated with immunotherapy. It is important to note that the small sample size and retrospective nature of the study carries selection bias and/or confounding effects. However, we believe that this analysis provides valuable insights into the molecular and clinical factors affecting outcomes in patients with high-grade astrocytoma treated with various forms of immunotherapy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by MD Anderson\u0026rsquo;s institutional review board.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot required\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot required\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eMD Anderson GBM Moonshots program\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003ePD was involved with conception, data collection, data interpretation and analysis, wrote the manuscript, and edited and revised the final manuscript for submission. HL provided statistical analysis. GK and KPB provided bioinformatic analysis of multiplex immunofluorescence data. ZS helped with data collection. MMA and ERP conducted the multiplex immunofluorescence. PD, SPW, FFL, KPB, JDG, and NKM provided crucial guidance. JDG and NKM was involved with conception, data interpretation and, editing of the manuscript. All authors reviewed the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThis work used the Biostatistics Resource Group. Editorial assistance was provided by Editing Services, Research Medical Library, MD Anderson. This work was also supported by the MD Anderson GBM Moonshots program. This work was supported by the Prospective Assessment of Correlative and Tissue Biomarkers in Glioma Patients (The PROACTIVE-Glioma Program team through consents, biospecimens and data collections under the MD Anderon IRB-approved protocol 2012-0441. We extend our deepest gratitude to the patients and their families, whose willingness to donate biological samples made this research possible. We also thank the outstanding staff of the Translational Molecular Pathology Department at The University of Texas MD Anderson Cancer Center for their expert work on the multiplex immunofluorescence staining, with special appreciation to Heladio Ibarguen and Auriole Tamegnon for their invaluable help and technical guidance throughout the project.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eStupp R, Mason WP, van den Bent MJ et al (2005) Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. 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Cancers (Basel) Feb 27(3). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/cancers12030546\u003c/span\u003e\u003cspan address=\"10.3390/cancers12030546\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\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":"immunotherapy, high-grade glioma, glioblastoma, checkpoint inhibitor, cell therapy, virus therapy","lastPublishedDoi":"10.21203/rs.3.rs-6649860/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6649860/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eWhile immunotherapy has transformed treatment in multiple solid tumors, its efficacy in high-grade glioma remains limited. Understanding the molecular and clinical factors that influence glioma\u0026rsquo;s response to immunotherapy is essential to improving outcomes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe identified patients with recurrent glioblastoma or astrocytoma, IDH-mutant grade 4, who had been treated with checkpoint inhibitor (CPI), virus therapy, or cell therapy and determined the association between their molecular, clinical, and demographic characteristics and survival outcomes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe identified 66 patients, 57 glioblastoma and 9 astrocytoma, IDH-mutant grade 4; 38 were treated with CPI, 22 with virus therapy, and 6 with cell therapy. \u003cem\u003ePIK3CA\u003c/em\u003e mutation was associated with shorter PFS and OS (p\u0026thinsp;=\u0026thinsp;0.022, 0.073, respectively) among all patients and a shorter OS among CPI-treated patients (p\u0026thinsp;=\u0026thinsp;0.004). Tumor tissue without the mutation had less PD-1 expression in CD3+/CD8\u0026thinsp;+\u0026thinsp;T cells. In CPI-treated patients, \u003cem\u003eIDH1/2\u003c/em\u003e mutation was associated with a shorter OS (p\u0026thinsp;=\u0026thinsp;0.002), and mutations in \u003cem\u003eRB1\u003c/em\u003e and \u003cem\u003eTERT\u003c/em\u003e promoter were associated with a shorter PFS (p-value\u0026thinsp;=\u0026thinsp;0.00056, 0.022, respectively). Length of CPI therapy of more than 6 months was associated with increased PFS and OS (p\u0026thinsp;=\u0026thinsp;0.048, 0.062, respectively), while steroid use at baseline was associated with a shorter OS (p\u0026thinsp;=\u0026thinsp;0.00023). Multifocal disease was associated with shorter PFS and OS durations (p\u0026thinsp;=\u0026thinsp;0.0017, 0.0013, respectively) among all patients.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIn high-grade glioma, \u003cem\u003ePIK3CA\u003c/em\u003e, \u003cem\u003eIDH1/2\u003c/em\u003e, \u003cem\u003eRB1\u003c/em\u003e, and \u003cem\u003eTERT\u003c/em\u003e promoter mutations may be associated with a poor response to immunotherapy. Our results may provide the rationale for clinical trials combining PI3K and IDH inhibitors with CPI in high-grade glioma.\u003c/p\u003e","manuscriptTitle":"Molecular and Clinical Determinants of Response to Immunotherapy in High-Grade Glioma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-16 12:03:41","doi":"10.21203/rs.3.rs-6649860/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-29T00:23:28+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-24T22:54:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"255370405895830711701001025996360846250","date":"2025-05-17T11:43:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"265533817609613688529573535839852261131","date":"2025-05-16T15:29:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-13T11:56:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-13T11:14:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-13T11:10:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Neuro-Oncology","date":"2025-05-12T22:20:57+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":"99c978c0-d513-4a82-aae3-2bc5e1125e55","owner":[],"postedDate":"May 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-30T16:02:40+00:00","versionOfRecord":{"articleIdentity":"rs-6649860","link":"https://doi.org/10.1007/s11060-025-05131-9","journal":{"identity":"journal-of-neuro-oncology","isVorOnly":false,"title":"Journal of Neuro-Oncology"},"publishedOn":"2025-06-25 15:57:37","publishedOnDateReadable":"June 25th, 2025"},"versionCreatedAt":"2025-05-16 12:03:41","video":"","vorDoi":"10.1007/s11060-025-05131-9","vorDoiUrl":"https://doi.org/10.1007/s11060-025-05131-9","workflowStages":[]},"version":"v1","identity":"rs-6649860","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6649860","identity":"rs-6649860","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}