bluuebunny/crossref_metadata_2025_split huggingface dataset:
BIOM-21. THE EFFECTIVENESS OF CIRCULATING MICRO RNAS DERIVED FROM GLIOBLASTOMA AS A BIOMARKER FOR EARLY DIAGNOSIS AND PROGNOSIS
Authors: Joo Park Hyun , Hwang Kihwan , Kwon Ji-Eyon , Han Ji Yun , Park Kyoung Un , Hong Yong-Kil , Kim Chae-Yong 
Published: November 2022

Abstract

BACKGROUND
Glioblastoma is the most common primary intracranial malignancy in adults. In cases where biopsy is not feasible due to tumors in inoperable locations including the brainstem, biomarkers such as micro RNA-21 (miR-21) extracted from blood samples could be utilized for diagnostic or prognostic purposes. In this study, we examined the role of miR-21 as a diagnostic factor in patients with glioblastoma.

METHODS
Inclusion criteria included patients with age greater than 20 years and with histologic confirmation of diagnosis. The blood samples were collected prior to surgery, after surgical resection, after concurrent chemoradiotherapy, and after 3 cycles of adjuvant temozolomide therapy. Using TaqMan assay, we extracted miR-21 from blood samples and compared its level with tumor volume on brain magnetic resonance imaging (MRI) scans. miR-21 levels before and after treatments were compared within each subject.

RESULTS
Eleven newly diagnosed glioblastoma patients were enrolled in this study, and final analyses were performed for eight patients with complete follow up serum samples. Two patients with stable disease after treatment displayed a decrease in miR-21 levels. Three out of four subjects with increase in miR-21 showed progressive disease on MRI scans. One patient with stable disease and an increased miR-21 level experienced postoperative intracranial hemorrhage and ischemic event. The two patients with unchanged miR-21 levels showed stable disease.

CONCLUSION
miR-21 levels were associated with progressive disease in glioblastoma patients. miR-21 has potential be utilized as a diagnostic marker in patients with glioblastoma, and further studies are needed to elucidate its role as a prognostic marker.
BIOM-25. DETERMINATION OF LEVELS OF SMALL MOLECULAR WEIGHT THIOLS AND DISULFIDES IN MOUSE AND HUMAN NORMAL BRAIN AND GLIOBLASTOMA TISSUE
Authors: Shaik Kamal , Margaryan Tigran , Sanai Nader , Tovmasyan Artak 
Published: November 2022

Abstract

BACKGROUND
Small molecular weight (SMW) thiols play an essential role in many biochemical processes, particularly in maintaining redox homeostasis. Herein, we report on the development and qualification of a sensitive and rapid LC-MS/MS method for the determination of oxidative stress biomarkers glutathione (GSH), cysteine (Cys), glutathione disulfide (GSSG), cystine (CySS), as well as the interrelated precursors and products methionine (Met), homocysteine (Hcy), cystathionine (Cysth), gamma glutamyl-cysteine (Glu-Cys), and cysteinylglycine (Cys-Gly) in both mouse and human normal brain and glioblastoma tissue.

METHODS
Mouse and human tissue samples were homogenized with N-ethylmaleimide solution to prevent thiol oxidation. Analytes were then extracted from homogenate samples by protein precipitation with 2% formic acid in acetonitrile. Separate calibration curves were prepared for thiols and disulfides in analytical solutions using stable isotope-labeled internal standards (IS). Three levels of quality control samples were prepared in mouse and human brain homogenates. The detection was performed on Sciex QTRAP 6500+ mass spectrometer in positive electrospray ionization mode.

RESULTS
The concentration ranges of 0.04-40 µmol/L for Glu-Cys/Hcy, 4-400 µmol/L for Met/GSH/Cys/Cys-Gly and 0.05-20 µmol/L for GSSG/CySS/Cysth were established using a linear regression model. A gradient elution was applied on Intrada Amino Acid column to achieve the optimal chromatographic separation with a 5 minute total run time. At least three precision and accuracy runs were conducted on different days. The stability of analytes in biological matrix and in analytical solutions has been tested at room temperature, 4 °C, and -80 °C for appropriate sample preparation, analysis, and storage.

CONCLUSIONS
A bioanalytical method to quantify a panel of SMW thiols and disulfides has been successfully developed, qualified, and utilized to assess the levels of these biomolecules in mouse normal brain and glioblastoma tissue as well as in glioblastoma tissue of patients enrolled in the sonodynamic therapy clinical trial (NCT04559685).
BIOM-30. ANALYSIS OF GLIOMA-RELATED GENES USING A BREAKTHROUGH AMPLIFICATION TECHNIQUE FOR CELL-FREE DNA IN CEREBROSPINAL FLUID
Authors: Adachi Jun-ichi , Mishima Kazuhiko , Nishikawa Ryo 
Published: November 2022

Abstract

BACKGROUND
In the pathological classification of brain tumors, newly revised in 2021, molecular genetic analysis of gliomas has become more important for diagnosis and understanding of the pathogenesis of the disease. Gliomas are prone to recurrence and malignant progression, and collecting tumor tissue during every genetic analysis is highly invasive. Therefore, techniques have been developed to analyze tumor-derived circulating cell-free DNA (ccfDNA) in the cerebrospinal fluid as a substitute for tissue collection. However, the amount of ccfDNA derived from the cerebrospinal fluid is extremely small and the number of cases for whom genetic analysis is possible is currently limited. In this study, we attempted to establish an efficient method for the integrated analysis of glioma-related genes by in vitro amplification of cerebrospinal fluid-derived ccfDNA from glioma patients and a highly sensitive assay.

METHODS
Tumor-derived ccfDNA was extracted and purified from 1 ml of cerebrospinal fluid obtained by lumbar puncture using the Maxwell RSC instrument in five glioma cases. Hot spots of mutations in IDH1 and H3F3A genes were analyzed by high resolution melting (HRM), TERT mutation by droplet digital PCR, MGMT promoter methylation by methylation-specific HRM, and chromosome 1p/19q loss by multiplex ligation-dependent probe amplification. For samples in which the amount of ccfDNA was too small to analyze, DNA was amplified approximately 500-fold by DNA polymerase using the GenomiPhi kit and analyzed by the same assay method.

RESULTS AND CONCLUSIONS
Although two of the five cases required amplification with the GenomiPhi kit, it was possible to analyze all cases for glioma-related genes as described above. Although the amount of ccfDNA obtained from low-grade gliomas and gliomas that are not in contact with the ventricles or subarachnoid space is small, this amplification method may increase the possibility of liquid biopsy using cerebrospinal fluid.
BIOM-37. EVALUATION OF TEMPORALIS MUSCLE THICKNESS WITH TOXICITY AND SURVIVAL IN GLIOBLASTOMA PATIENTS RECEIVING CHEMORADIATION
Authors: Saraf Anurag , Hill Cameron , Youssef Gilbert , Christ Sebastian , Tanguturi Shyam , McFaline-Figueroa J Ricardo , Chukwueke Ugonma , Lee Eudocia , Reardon David A , Arnaout Omar , Bi Wenya Linda , Haas-Kogan Daphne , Ligon Keith , Alexander Brian , Wen Patrick Y , Rahman Rifaquat 
Published: November 2022

Abstract

BACKGROUND
Treatment-related toxicity is common in patients with glioblastoma (GBM) receiving chemotherapy and radiotherapy (RT). Temporalis muscle thickness (TMT) is a biomarker associated with sarcopenia and worse clinical outcomes in GBM, however its relation to treatment toxicity is less studied. We hypothesize that TMT may predict toxicity and survival in GBM patients.

METHODS
We reviewed consecutive patients with IDH-wildtype GBM treated from 2014-2019 at a single academic center. TMT was retrospectively assessed on T1-weighted MRI scans and dichotomized based upon previously validated sex-specific cutoff values. TMT was measured on baseline MRI scan at time of diagnosis. Cox regression multivariable analysis (MVA) was used to assess survival.

RESULTS
We evaluated 351 patients with median age of 60y (range 20-94) and median follow-up of 14mo. Most patients were male (59%), baseline KPS &gt;70 (95%), and MGMT unmethylated (55%). After maximal safe resection, most patients received standard (90%) or hypofractionated (10%) RT with concurrent systemic therapy (89%). On MVA, baseline low TMT (HR 1.93, p=0.01), age &gt;65y, baseline KPS, and MGMT-unmethylated status were associated with worse OS. On MVA, baseline low TMT (HR 1.95, p=0.01), age &gt;65y, MGMT-unmethylated status, and discontinuing systemic therapy were associated with worse profession-free survival (PFS). 21 patients did not complete anticipated treatment course of chemoradiation and adjuvant systemic therapy due to toxicity, primarily thrombocytopenia, associated with worse OS on MVA (HR 1.99, p&lt; 0.01). Low TMT was associated with higher risk of stopping treatment due to adverse events (OR 5.25, p&lt; 0.01) independent of age, sex, extent of resection, RT dose on MVA.

CONCLUSION
Baseline low TMT was associated with worse PFS and OS, and it was associated with treatment interruption due to treatment toxicity in GBM patients. While further validation is needed, TMT may help identify patients who will benefit from aggressive symptom management or treatment deintensification.
BIOM-42. A DEEP LEARNING MODEL FOR AUTOMATED DETECTION AND COUNTING OF TUNNELING NANOTUBES AND CANCER CELLS IN MICROSCOPY IMAGES
Authors: Ceran Yasin , Ergüder Hamza , Ladner Katherine , Korenfeld Sophie , Deniz Karina , Padmanabhan Sanyukta , Baday Murat , Pengo Thomas , Lou Emil , Patel Chirag 
Published: November 2022

Abstract

BACKGROUND
Tunneling nanotubes (TNTs) are cellular structures connecting cell membranes and mediating intercellular communication. TNTs are manually identified and counted by a trained investigator; however, this process is time-intensive. We therefore sought to develop an automated approach for quantitative analysis of TNTs.

METHODS
We used the convolutional neural network (U-Net) deep learning model to segment phase contrast microscopy images of both cancer and non-cancer cells. Our method was composed of preprocessing and model development. We developed a new preprocessing method to label TNTs on a pixel-wise basis. Two sequential models were employed to detect TNTs. First, we identified the regions of images with TNTs by implementing a classification algorithm. Second, we fed parts of the image classified as TNT-containing into a modified U-Net model to estimate TNTs on a pixel-wise basis.

RESULTS
The U-Net model detected 73.3% of human expert-identified TNTs, counted TNTs and cells, and calculated the TNT-to-cell ratio (TCR). We obtained a precision of 0.88, recall of 0.67, and f-1 score of 0.76 on a test data set. The predicted and true TCRs were not significantly different between the training and test data sets.

CONCLUSIONS
In summary, we report application of an automated model generated by deep learning and trained to accurately label and detect TNTs and cells imaged in culture. Continued application and refinement of this process will provide a new approach to the analysis of TNTs, which form to connect cancer and other cells. This approach has the potential to enhance the drug screens intended to assess therapeutic efficacy of experimental agents, and to reproducibly assess TNTs as a potential biomarker of response to therapy in cancer.
BIOM-47. PREDICTORS OF SEIZURE AT ONSET USING A FUNCTIONAL VARIANT ANALYSIS OF TARGETED NEXT GENERATION SEQUENCING IN GBM
Authors: Lim-Fat Mary Jane , Rahman Rifaquat , Iorgulescu Bryan , Bhave Varun , Youssef Gilbert , Allen Marie , Chukwueke Ugonma , McFaline-Figueroa J Ricardo , Nayak Lakshmi , Lee Eudocia , Reardon David A , Batchelor Tracy , Beroukhim Rameen , Huang Raymond , Bi Wenya Linda , Ligon Keith , Wen Patrick Y 
Published: November 2022

Abstract

BACKGROUND
Adverse events (AE) including seizures cause significant morbidity in patients with GBM. We propose a novel method for assessing genomic predictors of AEs using results from a clinical targeted sequencing platform with variant function analysis.

METHODS
We identified 1,011 consecutive adult patients with newly diagnosed, histologically confirmed IDH-wildtype GBM with targeted exome NGS (Oncopanel) at Dana-Farber Cancer Institute from 2013-2019. Seizure at presentation was retrospectively identified as an AE. Biologic function (high loss, low loss, neutral, low gain and high gain) was assigned to variants using a three-tiered approach leveraging a genetic variant database (OncoKB), followed by analysis using protein prediction tools (Sift, Polyphen2 and Provean). Univariate logistic regression was performed for each relevant altered gene against the outcome of interest with false-discovery rate correction. Genes associated with seizure at presentation were included iteratively in a multivariate logistic model including other predictors of the outcome.

RESULTS
Our analysis included 470 GBM patients with 107 genes and 12 whole chromosome or arm level candidate variants covered by all versions of Oncopanel and with &gt;10% alteration. Seizure at presentation occurred in 143/463 patients (31%) and was associated with EGFR amplification (high gain) (OR: 2.76, 95% CI: 1.4-5.3, p = 0.04). In a multivariate analysis (including age, sex, and preoperative tumor volume), EGFR amplification remained statistically significant (OR: 1.5, 95% CI: 1.0-2.2, p = 0.03).

CONCLUSION
Genomic biomarkers based on functional variant analysis of a routine clinical panel may predict adverse events in GBM. Seizure at presentation was independently associated with EGFR amplification. Our ongoing analysis will look at predictors of myelosuppression, thromboembolism, pseudoprogression and early progression using a similar approach. Identifying molecular risk factors could improve the management of patients through supportive care and consideration of prophylactic therapies.
BIOM-51. ELUCIDATION OF THE TRANSCRIPTOMIC CONSEQUENCES OF KLF4 AND TRAF7 MUTATIONS IN SECRETORY MENINGIOMAS
Authors: Michaelsen Signe Regner , Cloos Paul , Broholm Helle , Johansen Jens , Ahmadov Ulvi , Mathiesen Tiit , Kristensen Lasse , Kristensen Bjarne Winther , Hansen Klaus 
Published: November 2022

Abstract
Secretory meningioma (SM) is a benign histological meningioma subtype frequently located in the central skull-base, complicating surgical removal, highlighting the need of gentle targeting treatment alternatives. SM tumors arise from the arachnoid layer in the meninges and are associated with a hotspot p.K409Q mutation in KLF4 combined with different mutations in TRAF7. The objective of this study was to identify how KLF4 and TRAF7 mutations alter signaling in SM tumors.The study includes 16 FFPE samples from SM tumors and three arachnoid cysts as normal-tissue reference. All tissues were histologically confirmed, followed by genomic sequencing of KLF4, TRAF7, NF2 and TERT-promoter (C220T/C228T) using a customized NGS panel. Gene expression profiles were analyzed using NanoString Pan-cancer Progression and Pan-cancer Pathways panels, which resulted in measurement of 1,202 unique transcripts over background level.All 16 SM tumors presented the KLF4p.K409Q mutation, of which 12 had mutation in TRAF7 (four being p.N520S), while no cysts had mutations in these genes. Neither tumors nor cysts had NF2 or TERT-promoter mutations. PCA analysis showed different RNA expression profiles for SM tumors and cysts, while TRAF7 mutation was without influence on the overall gene expression patterns in the tumors. Sub-analysis found increased KLF4 expression in TRAF7-mutated SM tumors versus non-mutated tumors and cysts at the RNA level, while TRAF7 expression was stable in all samples. In total 98 genes were found differentially expressed (54 up- and 44 downregulated) with a log2-fold change over 1 and an adjusted p-value under 0.05 in tumors versus cysts. Further analyses are planned to explore and validate these results. In conclusion, we find that the KLF4p.K409Q mutation correlates more strongly with SM histology, than the TRAF7 mutation, and that SM tumors have gene expressions profiles that are distinct from normal arachnoid tissue for a number of disease relevant genes.
BIOM-53. SPECIFIC MICRORNA EXPRESSION PATTERN PREDICTS RECURRENCE IN ATYPICAL MENINGIOMA
Authors: Fadrus Pavel , Tukmachi Dagmar , Duba Milos , Naar Ondrej , Vybihal Vaclav , Sana Jiri , Smrcka Martin , Slaby Ondrej 
Published: November 2022

Abstract
Meningiomas are the most common primary tumors of the central nervous system (CNS). Atypical meningioma (AM) recurs in 40% of patients despite total resection and radiotherapy (RT). No consensus on optimal adjuvant management was found, and it is difficult to identify patients insensitive to RT in real-life clinical practice. A promising group of biomarkers represent microRNAs (miRNAs), short non-coding RNAs that regulate most biological processes, including cell proliferation, differentiation, and apoptosis. The study aims to identify tissue miRNAs capable of predicting patients with AM who could benefit from the indicated adjuvant RT. The study includes 80 patients with AM in the exploratory phase and 400 patients with meningioma in the validation phase. Total RNA enriched with miRNAs was isolated from FFPE tissue using the mirVana miRNA Isolation Kit (TF Scientific). Subsequently, RNA quantity and quality controls were measured using NanoDrop 2000 (TF Scientific) and Qubit 2.0 (TF Scientific) instruments. A global miRNA expression profile was generated using the TaqMan Array Human MicroRNA Cards (TF Scientific), which allow the detection of up to 754 miRNAs simultaneously. Obtained data were processed and integrated through bioinformatics algorithms with clinicopathological data of patients with AM.The study identified significantly dysregulated miRNAs among AM patients with and without recurrence (p &lt; 0.05). Results also suggest dysregulated miRNA expression profile in AM patients with indicated RT who did/did not develop a recurrence (p &lt; 0.05). Lists of individual miRNAs and detailed graphical analyses will be included in the conference presentation.The results will help predict the prognosis of surgically intervened patients more accurately and can help determine which patients will benefit from the adjuvant RT. This research is supported by the AZV grant from the Ministry of Health of the Czech Republic (reg. No. NV19-03-00559) and Conceptual development of research organization (FNBr, 65269705). All rights reserved.
BIOM-59. NOVEL BIOMARKER DISCOVERY AND DIAGNOSTICS FOR INTRA-AXIAL BRAIN TUMORS, USING CSF PROTEOMICS
Authors: Mansouri Alireza , Mikolajewicz Nicholas , Khan Shahbaz , Trifoi Mara , Zacharia Brad , Glantz Michael , Zadeh Gelareh , Kislinger Thomas 
Published: November 2022

Abstract

BACKGROUND
Invasive brain sampling is typically necessary for reliable diagnosis and prognostication of intra-axial brain tumors but carries risk of morbidity. Liquid biopsy of proximal fluids may mitigate this risk. Through direct contact with the tumor microenvironment and, as an ultra-filtrate of plasma, the cerebrospinal fluid may be the ideal matrix. Reflecting the tumor phenotype, proteomic analyses are critical. Here we identified diagnostic CSF proteomic signatures and putatively novel biomarkers for glioblastoma (GBM), brain metastases (BM), and central nervous system lymphoma (CNSL).

METHODS
CSF samples were retrospectively retrieved from the Penn State Neuroscience Biorepository and profiled using shotgun proteomics and the MStern approach. Proteomic signatures were identified using machine learning classifiers and survival analyses.

RESULTS
With as little as 30 µL of CSF, 755 unique proteins were recovered across 73 samples (22 GBM, 17 BM, 14 CNSL, 20 NPH). Proteomic-based classifiers identified malignancy with area under the receiver operating characteristic (AUROC) of 0.94 and distinguished between tumor entities with AUROC ≥0.95. More clinically relevant triplex classifiers, comprised of just 3 proteins, distinguished between tumor entities with AUROC of 0.75-0.89. Novel biomarkers were identified, including GAP43, TFF3 and CACNA2D2, and characterized using single-cell RNA sequencing.

DISCUSSION
Reliable classification of intra-axial malignancies using low CSF volumes is feasible, allowing for longitudinal tumor surveillance. Based on emerging evidence, upfront implantation of CSF reservoirs in brain tumor patients warrants consideration.
BIOM-60. APOLLO: RAMAN-BASED PATHOLOGY OF MALIGNANT GLIOMA
Authors: Lita Adrian , Sjöberg Joel , Filipescu Stefan , Celiku Orieta , Petre Luigia , Gilbert Mark , Noushmehr Houtan , Petre Ion , Larion Mioara 
Published: November 2022

Abstract
Methylation classification is an essential component for integrative diagnosis in glioma, however, the DNA methylation classification is not always available for all the samples. We hypothesized that Raman spectroscopy might be suitable to predict the glioma methylome, based upon its ability to create a molecular fingerprint of the tumor and would provide biological insights into the discriminatory features. Raman Spectroscopy was used for molecular fingerprinting of the regions of interest using 1mm2 FFPE tissue spots from 45 patient samples with LGm1 to LGm6 methylation subtypes. Spectral information was then used to train a convolutional neural network (CNN), capable of detecting the glioma methylation subtypes. 70 % of the dataset was used for model training while the remaining 30% for validation. We demonstrate that Raman spectroscopy can accurately and rapidly classify gliomas according to their methylation subtype from achieved FFPE samples, as a novel way to obtain classification. For each sample we ran Ward linkage clustering with a variable number of clusters (from 2 to 7), with the majority cluster corresponding to tumor spots and the others corresponding to (various types of) non-tumor spots. The average accuracy over all samples was 90:3%, the average precision was 99:6% and the average recall was 90:2%. We show that Raman spectroscopy together with artificial intelligence can predict the methylome of glioma samples and augment the ability to classify these tumors retrospectively. The non-destructive nature of this method and the ability to be applied on FFPE samples directly, allows the histopathologist to reuse of the same slide for subsequent staining and downstream analyses.