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Hervey-Jumper SL, Zhang Y, Phillips JJ, Morshed RA, Young JS, McCoy L, Lafontaine M, Luks T, Ammanuel S, Kakaizada S, Egladyous A, Gogos A, Villanueva-Meyer J, Shai A, Warrier G, Rice T, Crane J, Wrensch M, Wiencke JK, Daras M, Oberheim Bush NA, Taylor JW, Butowski N, Clarke J, Chang S, Chang E, Aghi M, Theodosopoulos P, McDermott M, Jakola AS, Kavouridis VK, Nawabi N, Solheim O, Smith T, Berger MS, Molinaro AM. Interactive Effects of Molecular, Therapeutic, and Patient Factors on Outcome of Diffuse Low-Grade Glioma. J Clin Oncol 2023; 41:2029-2042. [PMID: 36599113 PMCID: PMC10082290 DOI: 10.1200/jco.21.02929] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 08/18/2022] [Accepted: 11/14/2022] [Indexed: 01/06/2023] Open
Abstract
PURPOSE In patients with diffuse low-grade glioma (LGG), the extent of surgical tumor resection (EOR) has a controversial role, in part because a randomized clinical trial with different levels of EOR is not feasible. METHODS In a 20-year retrospective cohort of 392 patients with IDH-mutant grade 2 glioma, we analyzed the combined effects of volumetric EOR and molecular and clinical factors on overall survival (OS) and progression-free survival by recursive partitioning analysis. The OS results were validated in two external cohorts (n = 365). Propensity score analysis of the combined cohorts (n = 757) was used to mimic a randomized clinical trial with varying levels of EOR. RESULTS Recursive partitioning analysis identified three survival risk groups. Median OS was shortest in two subsets of patients with astrocytoma: those with postoperative tumor volume (TV) > 4.6 mL and those with preoperative TV > 43.1 mL and postoperative TV ≤ 4.6 mL. Intermediate OS was seen in patients with astrocytoma who had chemotherapy with preoperative TV ≤ 43.1 mL and postoperative TV ≤ 4.6 mL in addition to oligodendroglioma patients with either preoperative TV > 43.1 mL and residual TV ≤ 4.6 mL or postoperative residual volume > 4.6 mL. Longest OS was seen in astrocytoma patients with preoperative TV ≤ 43.1 mL and postoperative TV ≤ 4.6 mL who received no chemotherapy and oligodendroglioma patients with preoperative TV ≤ 43.1 mL and postoperative TV ≤ 4.6 mL. EOR ≥ 75% improved survival outcomes, as shown by propensity score analysis. CONCLUSION Across both subtypes of LGG, EOR beginning at 75% improves OS while beginning at 80% improves progression-free survival. Nonetheless, maximal resection with preservation of neurological function remains the treatment goal. Our findings have implications for surgical strategies for LGGs, particularly oligodendroglioma.
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Affiliation(s)
- Shawn L. Hervey-Jumper
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
| | - Yalan Zhang
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
| | - Joanna J. Phillips
- Department of Pathology, University of California, San Francisco, San Francisco, CA
| | - Ramin A. Morshed
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
| | - Jacob S. Young
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
| | - Lucie McCoy
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
| | - Marisa Lafontaine
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA
| | - Tracy Luks
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA
| | - Simon Ammanuel
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
| | - Sofia Kakaizada
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
| | - Andrew Egladyous
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
| | - Andrew Gogos
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
| | - Javier Villanueva-Meyer
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA
| | - Anny Shai
- Department of Pathology, University of California, San Francisco, San Francisco, CA
| | - Gayathri Warrier
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
| | - Terri Rice
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
| | - Jason Crane
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA
| | - Margaret Wrensch
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
| | - John K. Wiencke
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
| | - Mariza Daras
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Nancy Ann Oberheim Bush
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Jennie W. Taylor
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Nicholas Butowski
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
| | - Jennifer Clarke
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Susan Chang
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Edward Chang
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
| | - Manish Aghi
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
| | - Philip Theodosopoulos
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
| | - Michael McDermott
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
| | - Asgeir S. Jakola
- Department of Neurological Surgery, St Olavs University Hospital, Trondheim, Norway
- Institute of Neuroscience and Physiology, Department of Clinical Neuroscience, University of Gothenburg, Sahlgrenska Academy, Gothenburg, Sweden
| | | | - Noah Nawabi
- Department of Neurological Surgery, Brigham and Women's Hospital, Boston, MA
| | - Ole Solheim
- Department of Neurological Surgery, St Olavs University Hospital, Trondheim, Norway
- Norwegian University of Science and Technology, Trondheim, Norway
| | - Timothy Smith
- Department of Neurological Surgery, Brigham and Women's Hospital, Boston, MA
| | - Mitchel S. Berger
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
| | - Annette M. Molinaro
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
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Abstract
Gliomas are the most common central nervous system tumors exhibiting poor clinical outcomes. The ability to estimate prognosis is crucial for both patients and providers in order to select the most appropriate treatment. Machine learning (ML) allows for sophisticated approaches to survival prediction using real world clinical parameters needed to achieve superior predictive accuracy. We employed Cox Proportional hazards (CPH) model, Support Vector Machine (SVM) model, Random Forest (RF) model in a large glioma dataset (3462 patients, diagnosed 2000-2018) to explore the most optimal approach to survival prediction. Features employed were age, sex, surgical resection status, tumor histology and tumor site, administration of radiation therapy (RT) and chemotherapy status. Concordance index (c-index) was employed to assess the accuracy of survival time prediction. All three models performed well with prediction accuracy (CI 0.767, 0.771, 0.57 for CPH, SVM, RF models respectively) with the best performance achieved when incorporating RT and chemotherapy administration status which emerged as key predictive features. Within the subset of glioblastoma patients, similar prediction accuracy was achieved. These findings should prompt stricter clinician oversight over registry data accuracy through quality assurance as we move towards meaningful predictive ability using ML approaches in glioma.
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Affiliation(s)
| | | | | | - Andra V Krauze
- 3421National Cancer Institute, NIH, USA; 184934BC Cancer Surrey, Canada
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Pérez IF, Valverde D, Valverde CF, Iglesias JB, Silva MJV, Quintela ML, Meléndez B. An analysis of prognostic factors in a cohort of low-grade gliomas and degree of consistency between RTOG and EORTC scores. Sci Rep 2022; 12:16433. [PMID: 36180501 PMCID: PMC9525658 DOI: 10.1038/s41598-022-20429-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 09/13/2022] [Indexed: 11/16/2022] Open
Abstract
Due to their rarity and heterogeneity and despite the introduction of molecular features in the current WHO classification, clinical criteria such as those from the European Organization for Research and Treatment of Cancer (EORTC) and the Radiation Therapy Oncology Group (RTOG) are still being used to make treatment decisions in low-grade gliomas (LGG). Patients with diffuse low-grade glioma treated at our institution between 2002 and 2018 were analyzed, retrieving and assessing the degree of consistency between the EORTC and RTOG criteria, as well as the isocitrate dehydrogenase 1 and 2 (IDH) gene mutational status. Likewise, multivariate analyses were performed to ascertain the superiority of any of the factors over the others. One hundred and two patients were included. The degree of consistency between the RTOG and EORTC criteria was 71.6% (K = 0.426; p = 0.0001). Notably, 51.7% of those assigned to low risk by the EORTC were classified as high risk according to the RTOG classification. In multivariate analysis, only complete resection, age > 40 years, size and IDH mutation status were independently correlated with OS. When the RTOG and EORTC scores were entered into the model, only the EORTC model was independently associated with mortality. The degree of consistency between the EORT and RTOG criteria is low. Therefore, there is a need to integrate clinical-molecular scores to improve treatment decisions in LGG.
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Affiliation(s)
- Isaura Fernández Pérez
- Department of Medical Oncology, University Hospital Complex of Vigo, Vigo, Spain. .,Translational Oncology Research Group, Galicia Sur Health Research Institute (IISGS), Vigo, Spain.
| | - Diana Valverde
- Rare Disease Research Group, Galicia Sur Health Research Institute (IISGS), Faculty of Biology, University of Vigo, Vigo, Spain
| | | | - Jenifer Brea Iglesias
- Translational Oncology Research Group, Galicia Sur Health Research Institute (IISGS), Vigo, Spain
| | - María José Villanueva Silva
- Department of Medical Oncology, University Hospital Complex of Vigo, Vigo, Spain.,Translational Oncology Research Group, Galicia Sur Health Research Institute (IISGS), Vigo, Spain
| | - Martín Lázaro Quintela
- Department of Medical Oncology, University Hospital Complex of Vigo, Vigo, Spain.,Translational Oncology Research Group, Galicia Sur Health Research Institute (IISGS), Vigo, Spain
| | - Bárbara Meléndez
- Molecular Pathology Research Unit, Department of Pathology, University Hospital of Toledo, Toledo, Spain
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Wu Y, Peng Z, Gu S, Wang H, Xiang W, Zhao T. A Risk Score Signature Consisting of Six Immune Genes Predicts Overall Survival in Patients with Lower-Grade Gliomas. Computational and Mathematical Methods in Medicine 2022; 2022:1-17. [PMID: 35186111 PMCID: PMC8856808 DOI: 10.1155/2022/2558548] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Accepted: 01/17/2022] [Indexed: 12/18/2022]
Abstract
Background. Lower-grade gliomas (LGGs) are less aggressive with a long overall survival (OS) time span. Because of individualized genomic features, a prognostic system incorporating molecular signatures can more accurately predict OS. Methods. Differential expression analysis between LGGs and normal tissues was performed using the Gene Expression Omnibus (GEO) datasets (GSE4290 and GSE12657). Immune-related differentially expressed genes (ImmPort-DEGs) were analyzed for functional enrichment. The least absolute shrinkage and selection operator (LASSO) analysis was performed to develop an immune risk score signature (IRSS). We extracted information from the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) to establish and validate the model. The relationship of model gene sets with immune infiltration was analyzed based on gene set variation analysis (GSVA) scores. Patients were divided into low- and high-risk groups based on the median score. The time-dependent receiver-operating characteristic (ROC) curve and the Kaplan-Meier curve were used to evaluate the model. Then, a precise prognostic nomogram was established, and its efficacy was verified. Results. A total of 18 related immune genes were identified, building a 6-gene IRSS (BMP2, F2R, FGF13, PCSK1, PRKCB, and PTGER3). DEGs were enriched in T cell and NK cell regulatory pathways. Immune infiltration analysis confirmed that the gene signature correlated with a decrease in innate immune cells. In terms of model evaluation, ROC curves at 1, 3, and 5 years showed moderate predictive ability of IRSS (
, 0.797, and 0.728). The Cox regression analysis revealed that IRSS was an independent prognostic factor, and the nomogram model had good predictive ability (
). Meanwhile, the predictive power of IRSS was also confirmed in the training cohort. The Kaplan-Meier results showed that the prognosis of the high-risk group was significantly worse in all cohorts. Conclusion. IRSS may serve as a novel survival prediction tool in the classification of LGG patients.
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Pan X, Wang Z, Liu F, Zou F, Xie Q, Guo Y, Shen L. A novel tailored immune gene pairs signature for overall survival prediction in lower-grade gliomas. Transl Oncol 2021; 14:101109. [PMID: 33946034 PMCID: PMC8111095 DOI: 10.1016/j.tranon.2021.101109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 03/26/2021] [Accepted: 04/16/2021] [Indexed: 12/04/2022] Open
Abstract
The Immune-related gene pairs (IRGPs) pronostic signature for LGG is correlated with immune cells infiltration. WGCNA presented a gene set correlating with immune cells infiltration and genes co-expression relationships were visualized. The nomogram constrcted by three IRGPs and clinical factors is a novel tailored tool for individual-level prediction.
Lower-grade gliomas (LGGs) have a good prognosis with a wide range of overall survival (OS) outcomes. An accurate prognostic system can better predict survival time. An RNA-Sequencing (RNA-seq) prognostic signature showed a better predictive power than clinical predictor models. A signature constructed using gene pairs can transcend changes from biological heterogeneity, technical biases, and different measurement platforms. RNA-seq coupled with corresponding clinical information were extracted from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). Immune-related gene pairs (IRGPs) were used to establish a prognostic signature through univariate and multivariate Cox proportional hazards regression. Weighted gene co-expression network analysis (WGCNA) was used to evaluate module eigengenes correlating with immune cell infiltration and to construct gene co-expression networks. Samples in the training and testing cohorts were dichotomized into high- and low-risk groups. Risk score was identified as an independent predictor, and exhibited a closed relationship with prognosis. WGCNA presented a gene set that was positively correlated with age, WHO grade, isocitrate dehydrogenase (IDH) mutation status, 1p/19 codeletion, risk score, and immune cell infiltrations (CD4 T cells, B cells, dendritic cells, and macrophages). A nomogram comprising of age, WHO grade, 1p/19q codeletion, and three gene pairs (BIRC5|SSTR2, BMP2|TNFRSF12A, and NRG3|TGFB2) was established as a tool for predicting OS. The IPGPs signature, which is associated with immune cell infiltration, is a novel tailored tool for individual-level prediction.
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Affiliation(s)
- Xuyan Pan
- Department of Neurosurgery, Huzhou Cent Hospital, Affiliated Cent Hospital Huzhou University, 1558 Third Ring North Road, Huzhou, Zhejiang 313000, China
| | - Zhaopeng Wang
- Department of Neurosurgery, The affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, 68 Gehu Road, Changzhou, Jiangsu 213000, China
| | - Fang Liu
- Department of Neurosurgery, The affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, 68 Gehu Road, Changzhou, Jiangsu 213000, China
| | - Feihui Zou
- Department of Neurosurgery, The affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, 68 Gehu Road, Changzhou, Jiangsu 213000, China
| | - Qijun Xie
- Department of Neurosurgery, The affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, 68 Gehu Road, Changzhou, Jiangsu 213000, China
| | - Yizhuo Guo
- Department of Neurosurgery, The affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, 68 Gehu Road, Changzhou, Jiangsu 213000, China
| | - Liang Shen
- Department of Neurosurgery, The affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, 68 Gehu Road, Changzhou, Jiangsu 213000, China.
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