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T2-FLAIR mismatch sign predicts DNA methylation subclass and CDKN2A/B status in IDH-mutant astrocytomas

Lee, Matthew D; Jain, Rajan; Galbraith, Kristyn; Chen, Anna; Lieberman, Evan; Patel, Sohil H; Placantonakis, Dimitris G; Zagzag, David; Barbaro, Marissa; Guillermo Prieto Eibl, Maria Del Pilar; Golfinos, John G; Orringer, Daniel A; Snuderl, Matija
PURPOSE/OBJECTIVE:DNA methylation profiling stratifies isocitrate dehydrogenase (IDH)-mutant astrocytomas into methylation low-grade and high-grade groups. We investigated the utility of the T2-FLAIR mismatch sign for predicting DNA methylation grade and cyclin-dependent kinase inhibitor 2A/B (CDKN2A/B) homozygous deletion, a molecular biomarker for grade 4 IDH-mutant astrocytomas, according to the 2021 World Health Organization (WHO) classification. EXPERIMENTAL DESIGN/METHODS:Preoperative MRI scans of IDH-mutant astrocytomas subclassified by DNA methylation profiling (n=71) were independently evaluated by two radiologists for the T2-FLAIR mismatch sign. The diagnostic utility of T2-FLAIR mismatch in predicting methylation grade, CDKN2A/B status, copy number variation, and survival was analyzed. RESULTS:The T2-FLAIR mismatch sign was present in 21 of 45 (46.7%) methylation low-grade and 1 of 26 (3.9%) methylation high-grade cases (p<0.001), resulting in 96.2% specificity, 95.5% positive predictive value, and 51.0% negative predictive value for predicting low methylation grade. The T2-FLAIR mismatch sign was also significantly associated with intact CDKN2A/B status (p=0.028) with 87.5% specificity, 86.4% positive predictive value, and 42.9% negative predictive value. Overall multivariable Cox analysis showed that retained CDKN2A/B status remained significant for PFS (p=0.01). Multivariable Cox analysis of the histologic grade 3 subset, which was nearly evenly divided by CDKN2A/B status, CNV, and methylation grade, showed trends toward significance for DNA methylation grade with OS (p=0.045) and CDKN2A/B status with PFS (p=0.052). CONCLUSIONS:The T2-FLAIR mismatch sign is highly specific for low methylation grade and intact CDKN2A/B in IDH-mutant astrocytomas.
PMID: 38829583
ISSN: 1557-3265
CID: 5664982

Prognostic value of DNA methylation subclassification, aneuploidy, and CDKN2A/B homozygous deletion in predicting clinical outcome of IDH mutant astrocytomas

Galbraith, Kristyn; Garcia, Mekka; Wei, Siyu; Chen, Anna; Schroff, Chanel; Serrano, Jonathan; Pacione, Donato; Placantonakis, Dimitris G; William, Christopher M; Faustin, Arline; Zagzag, David; Barbaro, Marissa; Eibl, Maria Del Pilar Guillermo Prieto; Shirahata, Mitsuaki; Reuss, David; Tran, Quynh T; Alom, Zahangir; von Deimling, Andreas; Orr, Brent A; Sulman, Erik P; Golfinos, John G; Orringer, Daniel A; Jain, Rajan; Lieberman, Evan; Feng, Yang; Snuderl, Matija
BACKGROUND:Isocitrate dehydrogenase (IDH) mutant astrocytoma grading, until recently, has been entirely based on morphology. The 5th edition of the Central Nervous System World Health Organization (WHO) introduces CDKN2A/B homozygous deletion as a biomarker of grade 4. We sought to investigate the prognostic impact of DNA methylation-derived molecular biomarkers for IDH mutant astrocytoma. METHODS:We analyzed 98 IDH mutant astrocytomas diagnosed at NYU Langone Health between 2014 and 2022. We reviewed DNA methylation subclass, CDKN2A/B homozygous deletion, and ploidy and correlated molecular biomarkers with histological grade, progression free (PFS), and overall (OS) survival. Findings were confirmed using 2 independent validation cohorts. RESULTS:There was no significant difference in OS or PFS when stratified by histologic WHO grade alone, copy number complexity, or extent of resection. OS was significantly different when patients were stratified either by CDKN2A/B homozygous deletion or by DNA methylation subclass (P value = .0286 and .0016, respectively). None of the molecular biomarkers were associated with significantly better PFS, although DNA methylation classification showed a trend (P value = .0534). CONCLUSIONS:The current WHO recognized grading criteria for IDH mutant astrocytomas show limited prognostic value. Stratification based on DNA methylation shows superior prognostic value for OS.
PMID: 38243818
ISSN: 1523-5866
CID: 5664582

Ultra-Rapid Droplet Digital PCR Enables Intraoperative Tumor Quantification

Murphy, Zachary R; Bianchini, Emilia C; Smith, Andrew; Körner, Lisa I; Russell, Teresa; Reinecke, David; Wang, Yuxiu; Snuderl, Matija; Orringer, Daniel A; Evrony, Gilad D
The diagnosis and treatment of tumors often depends on molecular-genetic data. However, rapid and iterative access to molecular data is not currently feasible during surgery, complicating intraoperative diagnosis and precluding measurement of tumor cell burdens at surgical margins to guide resections. To address this gap, we developed Ultra-Rapid droplet digital PCR (UR-ddPCR), which can be completed in 15 minutes from tissue to result with an accuracy comparable to standard ddPCR. We demonstrate UR-ddPCR assays for the IDH1 R132H and BRAF V600E clonal mutations that are present in many low-grade gliomas and melanomas, respectively. We illustrate the clinical feasibility of UR-ddPCR by performing it intraoperatively for 13 glioma cases. We further combine UR-ddPCR measurements with UR-stimulated Raman histology intraoperatively to estimate tumor cell densities in addition to tumor cell percentages. We anticipate that UR-ddPCR, along with future refinements in assay instrumentation, will enable novel point-of-care diagnostics and the development of molecularly-guided surgeries that improve clinical outcomes.
PMID: 38854127
CID: 5668772

Impact of Rare and Multiple Concurrent Gene Fusions on Diagnostic DNA Methylation Classifier in Brain Tumors

Galbraith, Kristyn; Serrano, Jonathan; Shen, Guomiao; Tran, Ivy; Slocum, Cheyanne C; Ketchum, Courtney; Abdullaev, Zied; Turakulov, Rust; Bale, Tejus; Ladanyi, Marc; Sukhadia, Purvil; Zaidinski, Michael; Mullaney, Kerry; DiNapoli, Sara; Liechty, Benjamin L; Barbaro, Marissa; Allen, Jeffrey C; Gardner, Sharon L; Wisoff, Jeffrey; Harter, David; Hidalgo, Eveline Teresa; Golfinos, John G; Orringer, Daniel A; Aldape, Kenneth; Benhamida, Jamal; Wrzeszczynski, Kazimierz O; Jour, George; Snuderl, Matija
UNLABELLED:DNA methylation is an essential molecular assay for central nervous system (CNS) tumor diagnostics. While some fusions define specific brain tumors, others occur across many different diagnoses. We performed a retrospective analysis of 219 primary CNS tumors with whole genome DNA methylation and RNA next-generation sequencing. DNA methylation profiling results were compared with RNAseq detected gene fusions. We detected 105 rare fusions involving 31 driver genes, including 23 fusions previously not implicated in brain tumors. In addition, we identified 6 multi-fusion tumors. Rare fusions and multi-fusion events can impact the diagnostic accuracy of DNA methylation by decreasing confidence in the result, such as BRAF, RAF, or FGFR1 fusions, or result in a complete mismatch, such as NTRK, EWSR1, FGFR, and ALK fusions. IMPLICATIONS/UNASSIGNED:DNA methylation signatures need to be interpreted in the context of pathology and discordant results warrant testing for novel and rare gene fusions.
PMID: 37870438
ISSN: 1557-3125
CID: 5625782

Stimulated Raman Histology Interpretation by Artificial Intelligence Provides Near-Real-Time Pathologic Feedback for Unprocessed Prostate Biopsies

Mannas, M P; Deng, F M; Ion-Margineanu, A; Jones, D; Hoskoppal, D; Melamed, J; Pastore, S; Freudiger, C; Orringer, D A; Taneja, S S
PURPOSE/UNASSIGNED:Stimulated Raman histology is an innovative technology that generates real-time, high-resolution microscopic images of unprocessed tissue, significantly reducing prostate biopsy interpretation time. This study aims to evaluate the ability for an artificial intelligence convolutional neural network to interpretate prostate biopsy histologic images created with stimulated Raman histology. MATERIALS AND METHODS/UNASSIGNED:Unprocessed, unlabeled prostate biopsies were prospectively imaged using a stimulated Raman histology microscope. Following stimulated Raman histology creation, the cores underwent standard pathological processing and interpretation by at least 2 genitourinary pathologists to establish a ground truth assessment. A network, trained on 303 prostate biopsies from 100 participants, was used to measure the accuracy, sensitivity, and specificity of detecting prostate cancer on stimulated Raman histology relative to conventional pathology. The performance of the artificial intelligence was evaluated on an independent 113-biopsy test set. RESULTS/UNASSIGNED:Prostate biopsy images obtained through stimulated Raman histology can be generated within a time frame of 2 to 2.75 minutes. The artificial intelligence system achieved a rapid classification of prostate biopsies with cancer, with a potential identification time of approximately 1 minute. The artificial intelligence demonstrated an impressive accuracy of 96.5% in detecting prostate cancer. Moreover, the artificial intelligence exhibited a sensitivity of 96.3% and a specificity of 96.6%. CONCLUSIONS/UNASSIGNED:Stimulated Raman histology generates microscopic images capable of accurately identifying prostate cancer in real time, without the need for sectioning or tissue processing. These images can be interpreted by artificial intelligence, providing physicians with near-real-time pathological feedback during the diagnosis or treatment of prostate cancer.
PMID: 38100831
ISSN: 1527-3792
CID: 5589002

Metabolomic Profiles of Human Glioma Inform Patient Survival

Scott, Andrew J; Correa, Luis O; Edwards, Donna M; Sun, Yilun; Ravikumar, Visweswaran; Andren, Anthony C; Zhang, Li; Srinivasan, Sudharsan; Jairath, Neil; Verbal, Kait; Muraszko, Karin; Sagher, Oren; Carty, Shannon A; Hervey-Jumper, Shawn; Orringer, Daniel; Kim, Michelle M; Junck, Larry; Umemura, Yoshie; Leung, Denise; Venneti, Sriram; Camelo-Piragua, Sandra; Lawrence, Theodore S; Ippolito, Joseph E; Al-Holou, Wajd N; Chinnaiyan, Prakash; Heth, Jason; Rao, Arvind; Lyssiotis, Costas A; Wahl, Daniel R
PMID: 36852494
ISSN: 1557-7716
CID: 5608972

A spectroscopic liquid biopsy for the earlier detection of multiple cancer types

Cameron, James M; Sala, Alexandra; Antoniou, Georgios; Brennan, Paul M; Butler, Holly J; Conn, Justin J A; Connal, Siobhan; Curran, Tom; Hegarty, Mark G; McHardy, Rose G; Orringer, Daniel; Palmer, David S; Smith, Benjamin R; Baker, Matthew J
BACKGROUND:A rapid, low-cost blood test that can be applied to reliably detect multiple different cancer types would be transformational. METHODS:In this large-scale discovery study (n = 2092 patients) we applied the Dxcover® Cancer Liquid Biopsy to examine eight different cancers. The test uses Fourier transform infrared (FTIR) spectroscopy and machine-learning algorithms to detect cancer. RESULTS:Area under the receiver operating characteristic curve (ROC) values were calculated for eight cancer types versus symptomatic non-cancer controls: brain (0.90), breast (0.76), colorectal (0.91), kidney (0.91), lung (0.91), ovarian (0.86), pancreatic (0.84) and prostate (0.86). We assessed the test performance when all eight cancer types were pooled to classify 'any cancer' against non-cancer patients. The cancer versus asymptomatic non-cancer classification detected 64% of Stage I cancers when specificity was 99% (overall sensitivity 57%). When tuned for higher sensitivity, this model identified 99% of Stage I cancers (with specificity 59%). CONCLUSIONS:This spectroscopic blood test can effectively detect early-stage disease and can be fine-tuned to maximise either sensitivity or specificity depending on the requirements from different healthcare systems and cancer diagnostic pathways. This low-cost strategy could facilitate the requisite earlier diagnosis, when cancer treatment can be more effective, or less toxic. STATEMENT OF TRANSLATIONAL RELEVANCE:The earlier diagnosis of cancer is of paramount importance to improve patient survival. Current liquid biopsies are mainly focused on single tumour-derived biomarkers, which limits test sensitivity, especially for early-stage cancers that do not shed enough genetic material. This pan-omic liquid biopsy analyses the full complement of tumour and immune-derived markers present within blood derivatives and could facilitate the earlier detection of multiple cancer types. There is a low barrier to integrating this blood test into existing diagnostic pathways since the technology is rapid, simple to use, only minute sample volumes are required, and sample preparation is minimal. In addition, the spectroscopic liquid biopsy described in this study has the potential to be combined with other orthogonal tests, such as cell-free DNA, which could provide an efficient route to diagnosis. Cancer treatment can be more effective when given earlier, and this low-cost strategy has the potential to improve patient prognosis.
PMID: 37717120
ISSN: 1532-1827
CID: 5593452

Association of partial T2-FLAIR mismatch sign and isocitrate dehydrogenase mutation in WHO grade 4 gliomas: results from the ReSPOND consortium

Lee, Matthew D; Patel, Sohil H; Mohan, Suyash; Akbari, Hamed; Bakas, Spyridon; Nasrallah, MacLean P; Calabrese, Evan; Rudie, Jeffrey; Villanueva-Meyer, Javier; LaMontagne, Pamela; Marcus, Daniel S; Colen, Rivka R; Balana, Carmen; Choi, Yoon Seong; Badve, Chaitra; Barnholtz-Sloan, Jill S; Sloan, Andrew E; Booth, Thomas C; Palmer, Joshua D; Dicker, Adam P; Flanders, Adam E; Shi, Wenyin; Griffith, Brent; Poisson, Laila M; Chakravarti, Arnab; Mahajan, Abhishek; Chang, Susan; Orringer, Daniel; Davatzikos, Christos; Jain, Rajan
PURPOSE/OBJECTIVE:While the T2-FLAIR mismatch sign is highly specific for isocitrate dehydrogenase (IDH)-mutant, 1p/19q-noncodeleted astrocytomas among lower-grade gliomas, its utility in WHO grade 4 gliomas is not well-studied. We derived the partial T2-FLAIR mismatch sign as an imaging biomarker for IDH mutation in WHO grade 4 gliomas. METHODS:Preoperative MRI scans of adult WHO grade 4 glioma patients (n = 2165) from the multi-institutional ReSPOND (Radiomics Signatures for PrecisiON Diagnostics) consortium were analyzed. Diagnostic performance of the partial T2-FLAIR mismatch sign was evaluated. Subset analyses were performed to assess associations of imaging markers with overall survival (OS). RESULTS:One hundred twenty-one (5.6%) of 2165 grade 4 gliomas were IDH-mutant. Partial T2-FLAIR mismatch was present in 40 (1.8%) cases, 32 of which were IDH-mutant, yielding 26.4% sensitivity, 99.6% specificity, 80.0% positive predictive value, and 95.8% negative predictive value. Multivariate logistic regression demonstrated IDH mutation was significantly associated with partial T2-FLAIR mismatch (odds ratio [OR] 5.715, 95% CI [1.896, 17.221], p = 0.002), younger age (OR 0.911 [0.895, 0.927], p < 0.001), tumor centered in frontal lobe (OR 3.842, [2.361, 6.251], p < 0.001), absence of multicentricity (OR 0.173, [0.049, 0.612], p = 0.007), and presence of cystic (OR 6.596, [3.023, 14.391], p < 0.001) or non-enhancing solid components (OR 6.069, [3.371, 10.928], p < 0.001). Multivariate Cox analysis demonstrated cystic components (p = 0.024) and non-enhancing solid components (p = 0.003) were associated with longer OS, while older age (p < 0.001), frontal lobe center (p = 0.008), multifocality (p < 0.001), and multicentricity (p < 0.001) were associated with shorter OS. CONCLUSION/CONCLUSIONS:Partial T2-FLAIR mismatch sign is highly specific for IDH mutation in WHO grade 4 gliomas.
PMID: 37468750
ISSN: 1432-1920
CID: 5535892

Long-Term Follow-up of Multinodular and Vacuolating Neuronal Tumors and Implications for Surveillance Imaging

Dogra, S; Zagzag, D; Young, M; Golfinos, J; Orringer, D; Jain, R
BACKGROUND AND PURPOSE:Most multinodular and vacuolating neuronal tumors (MVNTs) are diagnosed and followed radiologically without any change across time. There are no surveillance guidelines or quantitative volumetric assessments of these tumors. We evaluated MVNT volumes during long follow-up periods using segmentation tools with the aim of quantitative assessment. MATERIALS AND METHODS:All patients with MVNTs in a brain MR imaging report in our system were reviewed. Patients with only 1 brain MR imaging or in whom MVNT was not clearly the most likely diagnosis were excluded. All MVNTs were manually segmented. For all follow-up examinations, absolute and percentage volume change from immediately prior and initial examinations were calculated. RESULTS:= .67), respectively. CONCLUSIONS:MVNT segmentation across follow-up brain MR imaging examinations did not demonstrate significant volume differences, suggesting that these tumors do not enlarge with time. Hence, frequent surveillance imaging of newly diagnosed MVNTs may not be necessary.
PMID: 37500290
ISSN: 1936-959x
CID: 5593842

Unlocking glioma genetics with deep learning [Comment]

Orringer, Daniel A; Hollon, Todd C
The AI era in medicine has ushered in new opportunities to improve the diagnosis and treatment of human disease. CHARM, an AI algorithm described in this issue,1 has the potential to streamline molecular classification, intraoperative diagnosis, surgical decision making, and trial enrollment for glioma patients.
PMID: 37572648
ISSN: 2666-6340
CID: 5595442