Abstract B041: Biomarkers of neurocognitive functioning: molecular signatures for cognitive outcomes in a cohort of primary brain tumor patients
Bibliographic record
Abstract
Abstract Cognitive impairment is common in primary brain tumor (PBT) patients, with approximately 60% of diffuse glioma patients suffering from cognitive impairments in at least one domain. Gliomas exist within complex neuronal networks leading to hypotheses that associations exist between isocitrate dehydrogenase (IDH) mutation in gliomas and cognition, but the effects of tumor biology on these intrinsic networks are poorly understood and the contribution of specific mechanisms to cognitive impairment is unclear. Higher levels of specific proteins linked to neurodegeneration have been associated with worse cognitive function in neurological diseases (e.g., Traumatic Brain Injury (TBI), Alzheimer's disease, and amyotrophic lateral sclerosis), and these may also have a prognostic value in gliomas. We postulate that pathological changes in the brain might result in elevated peripheral blood levels of proteins associated with central nervous system (CNS) damage and neurodegeneration including neurofilament light chain (NfL), tau, and glial fibrillary acidic protein (GFAP). NfL is a marker of axonal damage, GFAP is an astrocytic protein, and tau is a microtubule-associated protein that can form neurofibrillary tangles as seen in certain neurodegenerative disorders. In this proposed study, serum levels of these protein markers of CNS damage will be compared between glioma patients with and without cognitive impairment. We hypothesize that higher levels of NfL, GFAP, and tau will be associated with more severe cognitive impairment. Our cohort (n=560) is comprised of adult glioma patients already enrolled and with banked serum samples collected in the National Cancer Institute (NCI)/Neuro-Oncology Branch (NOB) Natural History Study (NHS, NCT02851706: PI T. Armstrong), in which biological, clinical, imaging, patient-reported symptoms and objective measures of function were collected. Cognitive function was evaluated using the Montreal Cognitive Assessment (MoCA) with perceived cognitive function using the Quality of Life in Neurological Disorders Item Bank v2.0- Cognition Function-Short Form (Neuro-QOL), collected at the time of imaging analysis of tumor status. Serum protein levels will be measured in duplicates using an ultrasensitive platform (Meso Scale Discovery, MSD). We will also examine relationships between levels of these proteins, demographic and clinical information, including tumor type, grade and IDH status, and self-reported cognitive symptoms. Descriptive statistics, group comparisons, and multivariate analysis will be performed using IBM SPSS Statistics software. Independent t-tests and one-way ANOVAs followed by the Bonferroni method to adjust for multiple comparisons will be used to evaluate group differences. Blood-based biomarkers have the potential to shed light on the mechanisms underlying cognitive deficits that may inform novel therapeutic interventions to reduce cognitive symptom burden and improve quality of life in glioma patients. They also offer a non-invasive alternative for monitoring CNS damage and patient outcomes. Citation Format: Kaitlynn Slattery, McKenzie C. Kauss, Dhaivat Raval, Elizabeth Vera, Kimberly Reinhart, Hope Miller, Jaime Garcia, Morgan Johnson, Madhura V. Managoli, Maeve Pascoe, Alex R. Wollet, Mark R. Gilbert, Alvina Acquaye-Mallory, Terri S. Armstrong, Vivian A. Guedes. Biomarkers of neurocognitive functioning: molecular signatures for cognitive outcomes in a cohort of primary brain tumor patients [abstract]. In: Proceedings of the AACR Special Conference on Brain Cancer; 2023 Oct 19-22; Minneapolis, Minnesota. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_1):Abstract nr B041.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".