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Gershon R, Polevikov A, Karepov Y, Shenkar A, Ben-Horin I, Alter Regev T, Dror-Levinsky M, Lipczyc K, Gasri-Plotnitsky L, Diamant G, Shapira N, Bensimhon B, Hagai A, Shahar T, Grossman R, Ram Z, Volovitz I. Frequencies of 4 tumor-infiltrating lymphocytes potently predict survival in glioblastoma, an immune desert. Neuro Oncol 2024; 26:473-487. [PMID: 37870293 PMCID: PMC10912003 DOI: 10.1093/neuonc/noad204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Indexed: 10/24/2023] Open
Abstract
BACKGROUND GBM is an aggressive grade 4 primary brain tumor (BT), with a 5%-13% 5-year survival. Most human GBMs manifest as immunologically "cold" tumors or "immune deserts," yet the promoting or suppressive roles of specific lymphocytes within the GBM tumor microenvironment (TME) is of considerable debate. METHODS We used meticulous multiparametric flow cytometry (FC) to determine the lymphocytic frequencies in 102 GBMs, lower-grade gliomas, brain metastases, and nontumorous brain specimen. FC-attained frequencies were compared with frequencies estimated by "digital cytometry." The FC-derived data were combined with the patients' demographic, clinical, molecular, histopathological, radiological, and survival data. RESULTS Comparison of FC-derived data to CIBERSORT-estimated data revealed the poor capacity of digital cytometry to estimate cell frequencies below 0.2%, the frequency range of most immune cells in BTs. Isocitrate dehydrogenase (IDH) mutation status was found to affect TME composition more than the gliomas' pathological grade. Combining FC and survival data disclosed that unlike other cancer types, the frequency of helper T cells (Th) and cytotoxic T lymphocytes (CTL) correlated negatively with glioma survival. In contrast, the frequencies of γδ-T cells and CD56bright natural killer cells correlated positively with survival. A composite parameter combining the frequencies of these 4 tumoral lymphocytes separated the survival curves of GBM patients with a median difference of 10 months (FC-derived data; P < .0001, discovery cohort), or 4.1 months (CIBERSORT-estimated data; P = .01, validation cohort). CONCLUSIONS The frequencies of 4 TME lymphocytes strongly correlate with the survival of patients with GBM, a tumor considered an immune desert.
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Affiliation(s)
- Rotem Gershon
- The Cancer Immunotherapy Laboratory, The Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
| | - Antonina Polevikov
- The Cancer Immunotherapy Laboratory, The Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
| | - Yevgeny Karepov
- Neurosurgery Department, The Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
| | - Anatoly Shenkar
- The Cancer Immunotherapy Laboratory, The Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
| | - Idan Ben-Horin
- The Cancer Immunotherapy Laboratory, The Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
- Oncology Department, The Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
| | - Tal Alter Regev
- The Cancer Immunotherapy Laboratory, The Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
| | - Meytal Dror-Levinsky
- The Cancer Immunotherapy Laboratory, The Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
| | - Kelly Lipczyc
- The Cancer Immunotherapy Laboratory, The Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
| | - Lital Gasri-Plotnitsky
- The Cancer Immunotherapy Laboratory, The Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
| | - Gil Diamant
- The Cancer Immunotherapy Laboratory, The Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
- Neurosurgery Department, The Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
| | - Nati Shapira
- The Cancer Immunotherapy Laboratory, The Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
- Neurosurgery Department, The Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
| | - Barak Bensimhon
- The Cancer Immunotherapy Laboratory, The Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
| | - Aharon Hagai
- The Cancer Immunotherapy Laboratory, The Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
| | - Tal Shahar
- Neurosurgery Department, The Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
| | - Rachel Grossman
- Neurosurgery Department, The Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
| | - Zvi Ram
- Neurosurgery Department, The Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
| | - Ilan Volovitz
- The Cancer Immunotherapy Laboratory, The Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
- Neurosurgery Department, The Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
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2
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Dogan E, Yildirim Z, Akalin T, Ozgiray E, Akinturk N, Aktan C, Solmaz AE, Biceroglu H, Caliskan KE, Ertan Y, Yurtseven T, Kosova B, Bozok V. Investigating the effects of PTEN mutations on cGAS-STING pathway in glioblastoma tumours. J Neurooncol 2024; 166:283-292. [PMID: 38214828 PMCID: PMC10834568 DOI: 10.1007/s11060-023-04556-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 12/27/2023] [Indexed: 01/13/2024]
Abstract
BACKGROUND PTEN is a tumour suppressor gene and well-known for being frequently mutated in several cancer types. Loss of immunogenicity can also be attributed to PTEN loss, because of its role in establishing the tumour microenvironment. Therefore, this study aimed to represent the link between PTEN and cGAS-STING activity, a key mediator of inflammation, in tumour samples of glioblastoma patients. METHODS Tumour samples of 36 glioblastoma patients were collected. After DNA isolation, all coding regions of PTEN were sequenced and analysed. PTEN expression status was also evaluated by qRT-PCR, western blot, and immunohistochemical methods. Interferon-stimulated gene expressions, cGAMP activity, CD8 infiltration, and Granzyme B expression levels were determined especially for the evaluation of cGAS-STING activity and immunogenicity. RESULTS Mutant PTEN patients had significantly lower PTEN expression, both at mRNA and protein levels. Decreased STING, IRF3, NF-KB1, and RELA mRNA expressions were also found in patients with mutant PTEN. Immunohistochemistry staining of PTEN displayed expressional loss in 38.1% of the patients. Besides, patients with PTEN loss had considerably lower amounts of IFNB and IFIT2 mRNA expressions. Furthermore, CD8 infiltration, cGAMP, and Granzyme B levels were reduced in the PTEN loss group. CONCLUSION This study reveals the immunosuppressive effects of PTEN loss in glioblastoma tumours via the cGAS-STING pathway. Therefore, determining the PTEN status in tumours is of great importance, like in situations when considering the treatment of glioblastoma patients with immunotherapeutic agents.
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Affiliation(s)
- Eda Dogan
- Department of Medical Biology, Ege University Faculty of Medicine, Izmir, Türkiye
| | - Zafer Yildirim
- Department of Medical Biology, Ege University Faculty of Medicine, Izmir, Türkiye
| | - Taner Akalin
- Department of Pathology, Ege University Faculty of Medicine, Izmir, Türkiye
| | - Erkin Ozgiray
- Department of Neurosurgery, Ege University Faculty of Medicine, Izmir, Türkiye
| | - Nevhis Akinturk
- Department of Neurosurgery, Ege University Faculty of Medicine, Izmir, Türkiye
| | - Cagdas Aktan
- Department of Medical Biology, Beykent University School of Medicine, İstanbul, Türkiye
| | - Asli Ece Solmaz
- Department of Medical Genetics, Ege University Faculty of Medicine, Izmir, Türkiye
| | - Huseyin Biceroglu
- Department of Neurosurgery, Ege University Faculty of Medicine, Izmir, Türkiye
| | - Kadri Emre Caliskan
- Department of Neurosurgery, Ege University Faculty of Medicine, Izmir, Türkiye
| | - Yesim Ertan
- Department of Pathology, Ege University Faculty of Medicine, Izmir, Türkiye
| | - Taskin Yurtseven
- Department of Neurosurgery, Ege University Faculty of Medicine, Izmir, Türkiye
| | - Buket Kosova
- Department of Medical Biology, Ege University Faculty of Medicine, Izmir, Türkiye
| | - Vildan Bozok
- Department of Medical Biology, Ege University Faculty of Medicine, Izmir, Türkiye.
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Ohmura K, Daimon T, Ikegame Y, Yano H, Yokoyama K, Kumagai M, Shinoda J, Iwama T. Resection of positive tissue on methionine-PET is associated with improved survival in glioblastomas. Brain Behav 2023; 13:e3291. [PMID: 37846176 PMCID: PMC10726771 DOI: 10.1002/brb3.3291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 10/06/2023] [Accepted: 10/10/2023] [Indexed: 10/18/2023] Open
Abstract
BACKGROUND AND PURPOSE The volume of excised tumor in contrast-enhanced areas evaluated via magnetic resonance imaging is known to have a strong influence on the survival of patients with glioblastoma (GBM). In this study, we investigated the effect of tumor resection on the survival of patients with GBM in the 11 C-methionine (MET) accumulation area using MET-positron emission tomography (MET-PET). METHODS A total of 26 patients (median age, 69 years; 15 males) who had undergone tumor resection and MET-PET before and after surgery, after being newly diagnosed with GBM, were included in the study. MET-PET before and after tumor resection were compared. The association between the decrease in the maximum standardized uptake value (SUV) of the tumor divided by the normal cortical mean SUV (%; ΔT/N), the MET extent of resection (MET-EOR) from the % reduction in the MET accumulation area (%), and residual MET accumulation area (in cm3 ; MET-residual tumor volume [RTV]), as well as the survival time of patients with GBM, were evaluated via univariate analysis. RESULTS ΔT/N were positively associated with survival (hazard ratio [HR], 0.98 [95% confidence interval (CI), 0.97-0.99], p = .02). MET-RTV revealed a negative association with survival (HR, 1.02 [95% CI, 1.01-1.04], p = .04). Additionally, MET-EOR showed a strong trend with survival (HR, 0.99 [95% CI, 0.97-1.01], p = .06). CONCLUSIONS Surgical resection of MET-accumulated areas in GBM significantly prolongs the survival of patients with GBM. However, a prospective large-scale multicenter study is needed to confirm our findings.
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Affiliation(s)
- Kazufumi Ohmura
- Chubu Medical Center for Prolonged Traumatic Brain DysfunctionMinokamoGifuJapan
- Department of NeurosurgeryGifu University Graduate School of MedicineGifuJapan
| | - Takashi Daimon
- Department of BiostatisticsHyogo College of MedicineNishinomiyaHyogoJapan
| | - Yuka Ikegame
- Chubu Medical Center for Prolonged Traumatic Brain DysfunctionMinokamoGifuJapan
- Chubu Neurorehabilitation HospitalMinokamoGifuJapan
- Department of Clinical Brain SciencesGifu University Graduate School of MedicineMinokamoGifuJapan
| | - Hirohito Yano
- Chubu Medical Center for Prolonged Traumatic Brain DysfunctionMinokamoGifuJapan
- Chubu Neurorehabilitation HospitalMinokamoGifuJapan
- Department of Clinical Brain SciencesGifu University Graduate School of MedicineMinokamoGifuJapan
| | - Kazutoshi Yokoyama
- Department of NeurosurgeryChubu International Medical CenterMinokamoGifuJapan
| | | | - Jun Shinoda
- Chubu Medical Center for Prolonged Traumatic Brain DysfunctionMinokamoGifuJapan
- Chubu Neurorehabilitation HospitalMinokamoGifuJapan
- Department of Clinical Brain SciencesGifu University Graduate School of MedicineMinokamoGifuJapan
| | - Toru Iwama
- Department of NeurosurgeryGifu University Graduate School of MedicineGifuJapan
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4
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Zhang D, Luan J, Liu B, Yang A, Lv K, Hu P, Han X, Yu H, Shmuel A, Ma G, Zhang C. Comparison of MRI radiomics-based machine learning survival models in predicting prognosis of glioblastoma multiforme. Front Med (Lausanne) 2023; 10:1271687. [PMID: 38098850 PMCID: PMC10720716 DOI: 10.3389/fmed.2023.1271687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 11/15/2023] [Indexed: 12/17/2023] Open
Abstract
Objective To compare the performance of radiomics-based machine learning survival models in predicting the prognosis of glioblastoma multiforme (GBM) patients. Methods 131 GBM patients were included in our study. The traditional Cox proportional-hazards (CoxPH) model and four machine learning models (SurvivalTree, Random survival forest (RSF), DeepSurv, DeepHit) were constructed, and the performance of the five models was evaluated using the C-index. Results After the screening, 1792 radiomics features were obtained. Seven radiomics features with the strongest relationship with prognosis were obtained following the application of the least absolute shrinkage and selection operator (LASSO) regression. The CoxPH model demonstrated that age (HR = 1.576, p = 0.037), Karnofsky performance status (KPS) score (HR = 1.890, p = 0.006), radiomics risk score (HR = 3.497, p = 0.001), and radiomics risk level (HR = 1.572, p = 0.043) were associated with poorer prognosis. The DeepSurv model performed the best among the five models, obtaining C-index of 0.882 and 0.732 for the training and test set, respectively. The performances of the other four models were lower: CoxPH (0.663 training set / 0.635 test set), SurvivalTree (0.702/0.655), RSF (0.735/0.667), DeepHit (0.608/0.560). Conclusion This study confirmed the superior performance of deep learning algorithms based on radiomics relative to the traditional method in predicting the overall survival of GBM patients; specifically, the DeepSurv model showed the best predictive ability.
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Affiliation(s)
- Di Zhang
- Department of Radiology, Liaocheng People’s Hospital, Shandong First Medical University & Shandong Academy of Medical Sciences, Liaocheng, Shandong, China
| | - Jixin Luan
- China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Bing Liu
- China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Aocai Yang
- China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Kuan Lv
- Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Pianpian Hu
- Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Xiaowei Han
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Hongwei Yu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Amir Shmuel
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Chuanchen Zhang
- Department of Radiology, Liaocheng People’s Hospital, Shandong First Medical University & Shandong Academy of Medical Sciences, Liaocheng, Shandong, China
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5
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Long JP, Shen Y. Detection method has independent prognostic significance in the PLCO lung screening trial. Sci Rep 2023; 13:13382. [PMID: 37591907 PMCID: PMC10435538 DOI: 10.1038/s41598-023-40415-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 08/09/2023] [Indexed: 08/19/2023] Open
Abstract
Prognostic models in cancer use patient demographic and tumor characteristics to predict survival and dynamic disease prognosis. Past work in breast cancer has shown that cancer detection method, screen-detected or symptom-detected, has prognostic significance. We investigate this phenomenon in the lung component of the Prostate, Lung, Colorectal, and Ovarian (PLCO) screening trial. Patients were randomized to intervention, receiving four annual chest x-rays (CXRs), or to control, receiving usual care. Patients were followed for a total of approximately 13 years. In PLCO, lung cancer detection method has independent prognostic value exceeding that of variables commonly used in lung cancer prognostic models, including sex, histology, and age. Results are robust to cohort selection and type of predictive model. These results imply that detection method should be considered when developing prognostic models in lung cancer studies, and cancer registries should routinely collect cancer detection method.
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Affiliation(s)
- James P Long
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Yu Shen
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, USA.
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6
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Decraene B, Vanmechelen M, Clement P, Daisne JF, Vanden Bempt I, Sciot R, Garg AD, Agostinis P, De Smet F, De Vleeschouwer S. Cellular and molecular features related to exceptional therapy response and extreme long-term survival in glioblastoma. Cancer Med 2023. [PMID: 36776000 DOI: 10.1002/cam4.5681] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 01/17/2023] [Accepted: 01/31/2023] [Indexed: 02/14/2023] Open
Abstract
Glioblastoma Multiforme (GBM) remains the most common malignant primary brain tumor with a dismal prognosis that rarely exceeds beyond 2 years despite extensive therapy, which consists of maximal safe surgical resection, radiotherapy, and/or chemotherapy. Recently, it has become clear that GBM is not one homogeneous entity and that both intra-and intertumoral heterogeneity contributes significantly to differences in tumoral behavior which may consequently be responsible for differences in survival. Strikingly and in spite of its dismal prognosis, small fractions of GBM patients seem to display extremely long survival, defined as surviving over 10 years after diagnosis, compared to the large majority of patients. Although the underlying mechanisms for this peculiarity remain largely unknown, emerging data suggest that still poorly characterized both cellular and molecular factors of the tumor microenvironment and their interplay probably play an important role. We hereby give an extensive overview of what is yet known about these cellular and molecular features shaping extreme long survival in GBM.
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Affiliation(s)
- B Decraene
- KU Leuven, Laboratory for Precision Cancer Medicine, Translational Cell and Tissue Research Unit, Leuven, Belgium.,KU Leuven Department of Neurosciences, Experimental Neurosurgery and Neuroanatomy Research Group, Leuven, Belgium.,Department of Neurosurgery, University Hospitals Leuven, Leuven, Belgium
| | - M Vanmechelen
- KU Leuven, Laboratory for Precision Cancer Medicine, Translational Cell and Tissue Research Unit, Leuven, Belgium.,Department of General Medical Oncology, University Hospitals Leuven, Leuven, Belgium
| | - P Clement
- Department of General Medical Oncology, University Hospitals Leuven, Leuven, Belgium
| | - J F Daisne
- Radiation Oncology Department, University Hospitals Leuven, Leuven, Belgium
| | - I Vanden Bempt
- Department of Human Genetics, University Hospitals Leuven, Leuven, Belgium
| | - R Sciot
- Department of Pathology, University Hospitals Leuven, Leuven, Belgium
| | - A D Garg
- KU Leuven, VIB Center for Cancer Biology Research, Leuven, Belgium
| | - P Agostinis
- KU Leuven, Laboratory of Cell Stress & Immunity (CSI), Department of Cellular & Molecular Medicine, Leuven, Belgium
| | - F De Smet
- KU Leuven, Laboratory for Precision Cancer Medicine, Translational Cell and Tissue Research Unit, Leuven, Belgium
| | - S De Vleeschouwer
- KU Leuven Department of Neurosciences, Experimental Neurosurgery and Neuroanatomy Research Group, Leuven, Belgium.,Department of Neurosurgery, University Hospitals Leuven, Leuven, Belgium.,KU Leuven, Leuven Brain Institute (LBI), Leuven, Belgium
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7
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Cetintas VB, Duzgun Z, Akalin T, Ozgiray E, Dogan E, Yildirim Z, Akinturk N, Biceroglu H, Ertan Y, Kosova B. Molecular dynamic simulation and functional analysis of pathogenic PTEN mutations in glioblastoma. J Biomol Struct Dyn 2023; 41:11471-11483. [PMID: 36591942 DOI: 10.1080/07391102.2022.2162582] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 12/20/2022] [Indexed: 01/03/2023]
Abstract
PTEN, a dual-phosphatase and scaffold protein, is one of the most commonly mutated tumour suppressor gene across various cancer types in human. The aim of this study therefore was to investigate the stability, structural and functional effects, and pathogenicity of 12 missense PTEN mutations (R15S, E18G, G36R, N49I, Y68H, I101T, C105F, D109N, V133I, C136Y, R173C and N276S) found by next generation sequencing of the PTEN gene in tissue samples obtained from glioblastoma patients. Computational tools and molecular dynamic simulation programs were used to identify the deleterious effects of these mutations. Furthermore, PTEN mRNA and protein expression levels were evaluated by qRT-PCR, Western Blot, and immunohistochemistry staining methods. Various computational tools predicted strong deleterious effects for the G36R, C105F, C136Y and N276S mutations. Molecular dynamic simulation revealed a significant decrease in protein stability for the Y68H and N276S mutations when compared with the wild type protein; whereas, C105F, D109N, V133I and R173C showed partial stability reduction. Significant residual fluctuations were observed in the R15S, N49I and C136Y mutations and radius of gyration graphs revealed the most compact structure for D109N and least for C136Y. In summary, our study is the first one to show the presence of PTEN E18G, N49I, D109N and N276S mutations in glioblastoma patients; where, D109N is neutral and N276S is a damaging and disease-associated mutation.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
| | - Zekeriya Duzgun
- Department of Medical Biology, Giresun University Faculty of Medicine, Giresun, Turkey
| | - Taner Akalin
- Department of Pathology, Ege University Faculty of Medicine, Izmir, Turkey
| | - Erkin Ozgiray
- Department of Neurosurgery, Ege University Faculty of Medicine, Izmir, Turkey
| | - Eda Dogan
- Department of Medical Biology, Ege University Faculty of Medicine, Izmir, Turkey
| | - Zafer Yildirim
- Department of Medical Biology, Ege University Faculty of Medicine, Izmir, Turkey
| | - Nevhis Akinturk
- Department of Neurosurgery, Ege University Faculty of Medicine, Izmir, Turkey
| | - Huseyin Biceroglu
- Department of Neurosurgery, Ege University Faculty of Medicine, Izmir, Turkey
| | - Yesim Ertan
- Department of Pathology, Ege University Faculty of Medicine, Izmir, Turkey
| | - Buket Kosova
- Department of Medical Biology, Ege University Faculty of Medicine, Izmir, Turkey
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8
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Muacevic A, Adler JR, Romero-Luna G, Ramírez-Stubbe V, Morales-Ramírez JJ, Alfaro-López C, Rembao-Bojórquez JD, Moreno-Jiménez S. Estimation of Survival in Patients with Glioblastoma Using an Online Calculator at a Tertiary-Level Hospital in Mexico. Cureus 2022; 14:e32693. [PMID: 36686121 PMCID: PMC9848716 DOI: 10.7759/cureus.32693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/19/2022] [Indexed: 12/23/2022] Open
Abstract
Background The mean survival duration of patients with glioblastoma after diagnosis is 15 months (14-21 months), while progression-free survival is 10 months (+/- one month). Although there are well-defined overall survival statistics for glioblastoma, individual survival prediction remains a challenge. Therefore, there is a need to validate an accessible and cost-effective prognostic tool to provide valuable data for decision-making. This study aims to calculate the mean survival of patients with glioblastoma at a tertiary-level hospital in Mexico using the online glioblastoma survival calculator developed by researchers at Harvard Medical School & Brigham and Women's Hospital and compare it with the actual mean survival. Methodology We conducted a retrospective observational study of patients who received a histopathological diagnosis of glioblastoma from the National Institute of Neurology and Neurosurgery "Manuel Velasco Suárez" between 2015 and 2021. We included 50 patients aged 20-83 years, with a tumor size of 15-79 mm, and who had died 30 days after surgery. Patient survival was estimated using the online calculator developed at Harvard Medical School & Brigham and Women's Hospital. The estimated mean survival was then compared with the actual mean survival of the patient. A two-tailed equivalence test for paired samples was performed to conduct this comparison. A value of p < 0.05 was considered significant. Results The mean age of the sample was 55.5 years (confidence interval (CI) 95%, 52.61-58.71). The mean tumor size in our sample was 49.12 mm (±14.9mm). We identified a difference between the mean estimated survival and the mean actual survival of -1.37 months (CI 95%; range of -3.7 to +0.9). After setting the inferior (IL) and superior limits (SL) at -3.8 and +3.8 months, respectively, we found that the difference between the mean estimated survival and the actual mean survival is within the equivalence interval (IL: p = 0.0453; SL: p = 0.0002). Conclusions The actual survival of patients diagnosed with glioblastoma at the National Institute of Neurology and Neurosurgery was equivalent to the estimated survival calculated by the online prediction calculator developed at Harvard Medical School & Brigham and Women's Hospital. This study validates a practical, cost-effective, and accessible tool for predicting patient survival, contributing to significant support for medical and personal decision-making for glioblastoma management.
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Radiomic and Volumetric Measurements as Clinical Trial Endpoints—A Comprehensive Review. Cancers (Basel) 2022; 14:cancers14205076. [PMID: 36291865 PMCID: PMC9599928 DOI: 10.3390/cancers14205076] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 10/12/2022] [Accepted: 10/14/2022] [Indexed: 11/23/2022] Open
Abstract
Simple Summary The extraction of quantitative data from standard-of-care imaging modalities offers opportunities to improve the relevance and salience of imaging biomarkers used in drug development. This review aims to identify the challenges and opportunities for discovering new imaging-based biomarkers based on radiomic and volumetric assessment in the single-site solid tumor sites: breast cancer, rectal cancer, lung cancer and glioblastoma. Developing approaches to harmonize three essential areas: segmentation, validation and data sharing may expedite regulatory approval and adoption of novel cancer imaging biomarkers. Abstract Clinical trials for oncology drug development have long relied on surrogate outcome biomarkers that assess changes in tumor burden to accelerate drug registration (i.e., Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST v1.1) criteria). Drug-induced reduction in tumor size represents an imperfect surrogate marker for drug activity and yet a radiologically determined objective response rate is a widely used endpoint for Phase 2 trials. With the addition of therapies targeting complex biological systems such as immune system and DNA damage repair pathways, incorporation of integrative response and outcome biomarkers may add more predictive value. We performed a review of the relevant literature in four representative tumor types (breast cancer, rectal cancer, lung cancer and glioblastoma) to assess the preparedness of volumetric and radiomics metrics as clinical trial endpoints. We identified three key areas—segmentation, validation and data sharing strategies—where concerted efforts are required to enable progress of volumetric- and radiomics-based clinical trial endpoints for wider clinical implementation.
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10
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Moradmand H, Aghamiri SMR, Ghaderi R, Emami H. The role of deep learning-based survival model in improving survival prediction of patients with glioblastoma. Cancer Med 2021; 10:7048-7059. [PMID: 34453413 PMCID: PMC8525162 DOI: 10.1002/cam4.4230] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 07/30/2021] [Accepted: 08/01/2021] [Indexed: 11/25/2022] Open
Abstract
This retrospective study has been conducted to validate the performance of deep learning‐based survival models in glioblastoma (GBM) patients alongside the Cox proportional hazards model (CoxPH) and the random survival forest (RSF). Furthermore, the effect of hyperparameters optimization methods on improving the prediction accuracy of deep learning‐based survival models was investigated. Of the 305 cases, 260 GBM patients were included in our analysis based on the following criteria: demographic information (i.e., age, Karnofsky performance score, gender, and race), tumor characteristic (i.e., laterality and location), details of post‐surgical treatment (i.e., time to initiate concurrent chemoradiation therapy, standard treatment, and radiotherapy techniques), and last follow‐up time as well as the molecular markers (i.e., O‐6‐methylguanine methyltransferase and isocitrate dehydrogenase 1 status). Experimental results have demonstrated that age (Elderly > 65: hazard ratio [HR] = 1.63; 95% confidence interval [CI]: 1.213–2.18; p value = 0.001) and tumors located at multiple lobes ([HR] = 1.75; 95% [CI]: 1.177–2.61; p value = 0.006) were associated with poorer prognosis. In contrast, age (young < 40: [HR] = 0.57; 95% [CI]: 0.343–0.96; p value = 0.034) and type of radiotherapy (others include stereotactic and brachytherapy: [HR] = 0.5; 95%[CI]: 0.266–0.95; p value = 0.035) were significantly related to better prognosis. Furthermore, the proposed deep learning‐based survival model (concordance index [c‐index] = 0.823 configured by Bayesian hyperparameter optimization), outperformed the RSF (c‐index = 0.728), and the CoxPH model (c‐index = 0.713) in the training dataset. Our results show the ability of deep learning in learning a complex association of risk factors. Moreover, the remarkable performance of the deep‐learning‐based survival model could be promising to support decision‐making systems in personalized medicine for patients with GBM.
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Affiliation(s)
- Hajar Moradmand
- Medical Radiation Engineering, Shahid Beheshti University, Tehran, Iran
| | | | - Reza Ghaderi
- Electrical Engineering, Shahid Beheshti University, Tehran, Iran
| | - Hamid Emami
- Department of Radiation Oncology, Isfahan University of Medical Sciences, Seyed Al-Shohada Charity Hospital, Isfahan, Iran
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