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Halbert M, Golbourn B, Halligan K, Varadharajan S, Krug B, Mbah N, Kabir N, Stanton AC, Locke A, Casillo S, Zhao Y, Sanders L, Cheney A, Mullett S, Chen A, Wassell M, Andren A, Perez J, Jane E, Premkumar D, Koncar R, Mirhadi S, McCarl L, Chang YF, Wu Y, Gatesman T, Cruz A, Zapotocky M, Hu B, Kohanbash G, Wang X, Vartanian A, Moran M, Lieberman F, Amankulor N, Wendell S, Vaske OM, Panigraphy A, Felker J, Bertrand KC, Kleinman C, Rich JN, Friedlander RM, Broniscer A, Lyssiotis C, Jabado N, Pollack IF, Mack SC, Agnihotri S. TMET-09. LOSS OF MAT2A COMPROMISES METHIONINE METABOLISM AND REPRESENTS A VULNERABILITY IN H3K27M MUTANT GLIOMAS. Neuro Oncol 2022. [DOI: 10.1093/neuonc/noac209.1014] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Abstract
H3K27-mutant diffuse midline gliomas (DMGs) are defined as grade IV tumors by the World Health Organization. DMGs are inoperable and resistant to chemo/radio therapies. Median survival ranges from 8-11 months, with 2% of patients surviving beyond 5 years. H3K27M mutations lead to global epigenetic and transcriptional reprogramming driven by global loss of negative transcriptional regulator H3K27 trimethylation (H3K27me3). Loss of H3K27me3 is an initiating event in gliomagenesis. This disease lacks appropriate models to predict disease biology and response to treatment. Therefore, we developed a novel syngeneic H3K27M mouse model. An unbiased integrated systems biology approach identified that H3K27M but not isogenic controls relied on the amino acid methionine and the enzyme Methionine Adenosyltransferase 2A (MAT2A). MAT2A is a central regulator of one-carbon metabolism by converting methionine to S-adenosylmethionine (SAM), the universal methyl-donor for protein and nucleotide methylation reactions. In complementary genetic approaches, we applied these findings to patient-derived cell lines with the H3K27M mutation. We hypothesize that MAT2A abrogation, genetic/pharmacological, would alter DMG viability by disrupting the methylome. The current MAT2A sensitivity paradigm is based on Methylthioadenosine Phosphorylase (MTAP) deletion through a synthetic lethal mechanism. We provide a novel mechanism whereby H3K27M cells are sensitive to MAT2A loss, independent of MTAP and through Adenosylmethionine Decarboxylase 1 (AMD1) overexpression disrupting MAT2A regulation. This results in H3K27M cells having lower MAT2A protein levels, conferring a sensitivity by inhibiting residual MAT2A. Genetic/pharmacological aberrations to MAT2A resulted in reduced proliferation. Parallel H3K36me3 ChIP and RNA-sequencing identified loss of oncogenic and developmental transcriptional programs associated with MAT2A loss. In vivo syngeneic and patient-derived xenograft models with both inducible MAT2A knockdown or methionine restricted diets showed extended survival. These results suggest novel interactions between methionine metabolism and the epigenome of H3K27M gliomas and provide evidence that MAT2A, presents exploitable therapeutic vulnerabilities in histone mutant gliomas.
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Affiliation(s)
- Matthew Halbert
- University of Pittsburgh School of Medicine , Pittsburgh, PA , USA
| | | | | | | | | | - Nneka Mbah
- University of Michigan , Ann Arbor , USA
| | | | | | | | | | | | - Lauren Sanders
- University of California, Santa Cruz , Santa Cruz, CA , USA
| | | | | | - Apeng Chen
- University of Pittsburgh School of Medicine , Pittsburgh , USA
| | | | | | - Jennifer Perez
- University of Pittsburgh School of Medicine , Pittsburgh , USA
| | - Esther Jane
- University of Pittsburgh School of Medicine , Pittsburgh , USA
| | - Daniel Premkumar
- University of Pittsburgh School of Medicine , Pittsburgh, PA , USA
| | - Robert Koncar
- University of Pittsburgh School of Medicine , Pittsburgh , USA
| | | | | | | | - Yigen Wu
- University of Pittsburgh , Pittsburgh , USA
| | - Taylor Gatesman
- University of Pittsburgh School of Medicine , Pittsburgh, PA , USA
| | - Andrea Cruz
- University of Pittsburgh School of Medicine , Pittsburgh , USA
| | | | - Baoli Hu
- University of Pittsburgh School of Medicine , Pittsburgh , USA
| | - Gary Kohanbash
- University of Pittsburgh School of Medicine , Pittsburgh , USA
| | - Xiuxing Wang
- Nanjing Medical University , Nanjing , China (People's Republic)
| | | | | | | | | | - Stacy Wendell
- University of Pittsburgh School of Medicine , Pittsburgh, PA , USA
| | - Olena M Vaske
- University of California, Santa Cruz , Santa Cruz, CA , USA
| | | | | | | | | | - Jeremy N Rich
- University of Pittsburgh School of Medicine , Pittsburgh , USA
| | | | | | | | - Nada Jabado
- The Research Institute of the McGill University Health Center, Montréal, Canada
| | - Ian F Pollack
- Children's Hospital of Pittsburgh , Pittsburgh , USA
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Pease M, Arefan D, Barber J, Yuh E, Puccio A, Hochberger K, Nwachuku E, Roy S, Casillo S, Temkin N, Okonkwo DO, Wu S. Outcome Prediction in Patients with Severe Traumatic Brain Injury Using Deep Learning from Head CT Scans. Radiology 2022; 304:385-394. [PMID: 35471108 PMCID: PMC9340242 DOI: 10.1148/radiol.212181] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Background After severe traumatic brain injury (sTBI), physicians use long-term prognostication to guide acute clinical care yet struggle to predict outcomes in comatose patients. Purpose To develop and evaluate a prognostic model combining deep learning of head CT scans and clinical information to predict long-term outcomes after sTBI. Materials and Methods This was a retrospective analysis of two prospectively collected databases. The model-building set included 537 patients (mean age, 40 years ± 17 [SD]; 422 men) from one institution from November 2002 to December 2018. Transfer learning and curriculum learning were applied to a convolutional neural network using admission head CT to predict mortality and unfavorable outcomes (Glasgow Outcomes Scale scores 1-3) at 6 months. This was combined with clinical input for a holistic fusion model. The models were evaluated using an independent internal test set and an external cohort of 220 patients with sTBI (mean age, 39 years ± 17; 166 men) from 18 institutions in the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study from February 2014 to April 2018. The models were compared with the International Mission on Prognosis and Analysis of Clinical Trials in TBI (IMPACT) model and the predictions of three neurosurgeons. Area under the receiver operating characteristic curve (AUC) was used as the main model performance metric. Results The fusion model had higher AUCs than did the IMPACT model in the prediction of mortality (AUC, 0.92 [95% CI: 0.86, 0.97] vs 0.80 [95% CI: 0.71, 0.88]; P < .001) and unfavorable outcomes (AUC, 0.88 [95% CI: 0.82, 0.94] vs 0.82 [95% CI: 0.75, 0.90]; P = .04) on the internal data set. For external TRACK-TBI testing, there was no evidence of a significant difference in the performance of any models compared with the IMPACT model (AUC, 0.83; 95% CI: 0.77, 0.90) in the prediction of mortality. The Imaging model (AUC, 0.73; 95% CI: 0.66-0.81; P = .02) and the fusion model (AUC, 0.68; 95% CI: 0.60, 0.76; P = .02) underperformed as compared with the IMPACT model (AUC, 0.83; 95% CI: 0.77, 0.89) in the prediction of unfavorable outcomes. The fusion model outperformed the predictions of the neurosurgeons. Conclusion A deep learning model of head CT and clinical information can be used to predict 6-month outcomes after severe traumatic brain injury. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Haller in this issue.
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Affiliation(s)
- Matthew Pease
- From the Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, Pa (M.P., A.P., K.H., E.N., S.R., S.C., D.O.O.); Departments of Radiology (D.A., S.W.), Biomedical Informatics (S.W.), and Bioengineering (S.W.), and Intelligent Systems Program (S.W.), University of Pittsburgh, 3240 Craft Pl, Room 322, Pittsburgh, PA 15213; Department of Neurosurgery, University of Washington, Seattle, Wash (J.B., N.T.); Department of Radiology, University of California San Francisco, San Francisco, Calif (E.Y.)
| | - Dooman Arefan
- From the Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, Pa (M.P., A.P., K.H., E.N., S.R., S.C., D.O.O.); Departments of Radiology (D.A., S.W.), Biomedical Informatics (S.W.), and Bioengineering (S.W.), and Intelligent Systems Program (S.W.), University of Pittsburgh, 3240 Craft Pl, Room 322, Pittsburgh, PA 15213; Department of Neurosurgery, University of Washington, Seattle, Wash (J.B., N.T.); Department of Radiology, University of California San Francisco, San Francisco, Calif (E.Y.)
| | - Jason Barber
- From the Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, Pa (M.P., A.P., K.H., E.N., S.R., S.C., D.O.O.); Departments of Radiology (D.A., S.W.), Biomedical Informatics (S.W.), and Bioengineering (S.W.), and Intelligent Systems Program (S.W.), University of Pittsburgh, 3240 Craft Pl, Room 322, Pittsburgh, PA 15213; Department of Neurosurgery, University of Washington, Seattle, Wash (J.B., N.T.); Department of Radiology, University of California San Francisco, San Francisco, Calif (E.Y.)
| | - Esther Yuh
- From the Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, Pa (M.P., A.P., K.H., E.N., S.R., S.C., D.O.O.); Departments of Radiology (D.A., S.W.), Biomedical Informatics (S.W.), and Bioengineering (S.W.), and Intelligent Systems Program (S.W.), University of Pittsburgh, 3240 Craft Pl, Room 322, Pittsburgh, PA 15213; Department of Neurosurgery, University of Washington, Seattle, Wash (J.B., N.T.); Department of Radiology, University of California San Francisco, San Francisco, Calif (E.Y.)
| | - Ava Puccio
- From the Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, Pa (M.P., A.P., K.H., E.N., S.R., S.C., D.O.O.); Departments of Radiology (D.A., S.W.), Biomedical Informatics (S.W.), and Bioengineering (S.W.), and Intelligent Systems Program (S.W.), University of Pittsburgh, 3240 Craft Pl, Room 322, Pittsburgh, PA 15213; Department of Neurosurgery, University of Washington, Seattle, Wash (J.B., N.T.); Department of Radiology, University of California San Francisco, San Francisco, Calif (E.Y.)
| | - Kerri Hochberger
- From the Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, Pa (M.P., A.P., K.H., E.N., S.R., S.C., D.O.O.); Departments of Radiology (D.A., S.W.), Biomedical Informatics (S.W.), and Bioengineering (S.W.), and Intelligent Systems Program (S.W.), University of Pittsburgh, 3240 Craft Pl, Room 322, Pittsburgh, PA 15213; Department of Neurosurgery, University of Washington, Seattle, Wash (J.B., N.T.); Department of Radiology, University of California San Francisco, San Francisco, Calif (E.Y.)
| | - Enyinna Nwachuku
- From the Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, Pa (M.P., A.P., K.H., E.N., S.R., S.C., D.O.O.); Departments of Radiology (D.A., S.W.), Biomedical Informatics (S.W.), and Bioengineering (S.W.), and Intelligent Systems Program (S.W.), University of Pittsburgh, 3240 Craft Pl, Room 322, Pittsburgh, PA 15213; Department of Neurosurgery, University of Washington, Seattle, Wash (J.B., N.T.); Department of Radiology, University of California San Francisco, San Francisco, Calif (E.Y.)
| | - Souvik Roy
- From the Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, Pa (M.P., A.P., K.H., E.N., S.R., S.C., D.O.O.); Departments of Radiology (D.A., S.W.), Biomedical Informatics (S.W.), and Bioengineering (S.W.), and Intelligent Systems Program (S.W.), University of Pittsburgh, 3240 Craft Pl, Room 322, Pittsburgh, PA 15213; Department of Neurosurgery, University of Washington, Seattle, Wash (J.B., N.T.); Department of Radiology, University of California San Francisco, San Francisco, Calif (E.Y.)
| | - Stephanie Casillo
- From the Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, Pa (M.P., A.P., K.H., E.N., S.R., S.C., D.O.O.); Departments of Radiology (D.A., S.W.), Biomedical Informatics (S.W.), and Bioengineering (S.W.), and Intelligent Systems Program (S.W.), University of Pittsburgh, 3240 Craft Pl, Room 322, Pittsburgh, PA 15213; Department of Neurosurgery, University of Washington, Seattle, Wash (J.B., N.T.); Department of Radiology, University of California San Francisco, San Francisco, Calif (E.Y.)
| | - Nancy Temkin
- From the Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, Pa (M.P., A.P., K.H., E.N., S.R., S.C., D.O.O.); Departments of Radiology (D.A., S.W.), Biomedical Informatics (S.W.), and Bioengineering (S.W.), and Intelligent Systems Program (S.W.), University of Pittsburgh, 3240 Craft Pl, Room 322, Pittsburgh, PA 15213; Department of Neurosurgery, University of Washington, Seattle, Wash (J.B., N.T.); Department of Radiology, University of California San Francisco, San Francisco, Calif (E.Y.)
| | - David O Okonkwo
- From the Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, Pa (M.P., A.P., K.H., E.N., S.R., S.C., D.O.O.); Departments of Radiology (D.A., S.W.), Biomedical Informatics (S.W.), and Bioengineering (S.W.), and Intelligent Systems Program (S.W.), University of Pittsburgh, 3240 Craft Pl, Room 322, Pittsburgh, PA 15213; Department of Neurosurgery, University of Washington, Seattle, Wash (J.B., N.T.); Department of Radiology, University of California San Francisco, San Francisco, Calif (E.Y.)
| | - Shandong Wu
- From the Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, Pa (M.P., A.P., K.H., E.N., S.R., S.C., D.O.O.); Departments of Radiology (D.A., S.W.), Biomedical Informatics (S.W.), and Bioengineering (S.W.), and Intelligent Systems Program (S.W.), University of Pittsburgh, 3240 Craft Pl, Room 322, Pittsburgh, PA 15213; Department of Neurosurgery, University of Washington, Seattle, Wash (J.B., N.T.); Department of Radiology, University of California San Francisco, San Francisco, Calif (E.Y.)
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- From the Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, Pa (M.P., A.P., K.H., E.N., S.R., S.C., D.O.O.); Departments of Radiology (D.A., S.W.), Biomedical Informatics (S.W.), and Bioengineering (S.W.), and Intelligent Systems Program (S.W.), University of Pittsburgh, 3240 Craft Pl, Room 322, Pittsburgh, PA 15213; Department of Neurosurgery, University of Washington, Seattle, Wash (J.B., N.T.); Department of Radiology, University of California San Francisco, San Francisco, Calif (E.Y.)
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Tonetti DA, Desai SM, Casillo S, Zussman BM, Brown MW, Jadhav AP, Jankowitz BT, Jovin TG, Gross BA. Large-bore aspiration catheter selection does not influence reperfusion or outcome after manual aspiration thrombectomy. J Neurointerv Surg 2019; 11:637-640. [PMID: 30733300 DOI: 10.1136/neurintsurg-2018-014633] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Revised: 01/04/2019] [Accepted: 01/07/2019] [Indexed: 11/03/2022]
Abstract
INTRODUCTION Various large-bore catheters can be employed for manual aspiration thrombectomy (MAT); clinical differences are rarely explored. METHODS Prospectively collected demographic, angiographic, and clinical data for patients with acute internal carotid artery, middle cerebral artery M1, or basilar occlusions undergoing MAT over 23 months at a comprehensive stroke center were reviewed. We excluded patients in stentriever-based randomized trials/registries. The four most commonly utilized aspiration catheters were analyzed, and multivariate logistic regression analyses were performed to determine the effect of primary aspiration catheter choice on first-pass success, final reperfusion, and modified Rankin Scale (mRS) score at 90 days. RESULTS Of 464 large vessel thrombectomies, 180 were performed via MAT on the first pass with one of four catheters. First-pass success was achieved in 42% of cases overall; this rate did not differ significantly between catheters: 50% for Sofia, 45% for CAT6, 40% for 0.072 inch Navien, and 36% for ACE68, p=0.67. Final Thrombolysis in Cerebral Infarction 2b or 3 reperfusion was achieved in 94% of cases overall: 97% of cases with CAT6, 95% with Sofia, 92% with Navien, and 92% with ACE68, p=0.70. Mean number of passes for index thrombus (2.0 overall), median procedure time (32 min overall), 90-day good outcome (mRS 0-2, mean 36%), and 90-day mortality (mean 27%) did not differ significantly between patients treated with different initial catheters. CONCLUSION Among large-bore aspiration catheters, catheter selection is not an independent predictor of first-pass success, final reperfusion, or clinical outcome.
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Affiliation(s)
- Daniel A Tonetti
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,The Stroke Institute, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA
| | - Shashvat M Desai
- The Stroke Institute, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA.,Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Stephanie Casillo
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Benjamin M Zussman
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,The Stroke Institute, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA
| | - Merritt W Brown
- The Stroke Institute, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA.,Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Ashutosh P Jadhav
- The Stroke Institute, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA.,Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Brian Thomas Jankowitz
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,The Stroke Institute, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA
| | - Tudor G Jovin
- The Stroke Institute, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA.,Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Bradley A Gross
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,The Stroke Institute, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA
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