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Neuroinflammatory and Neurodegenerative Markers in Non-Hodgkin’s Lymphoma and Hematologic Malignancies with Central Nervous System Involvement

2008· article· en· W2582582530 on OpenAlexaff
Weihoa Tang, Kevin H.M. Kuo, Alejandro Lazo‐Langner, Stephen Pasternak, Anargyros Xenocostas

Bibliographic record

VenueBlood · 2008
Typearticle
Languageen
FieldMedicine
TopicBrain Metastases and Treatment
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoWestern University
Fundersnot available
KeywordsMedicinePathologyLymphomaLeukemiaChemotherapyInternal medicineGastroenterologyOncology

Abstract

fetched live from OpenAlex

Abstract Introduction: Central nervous system involvement (CNSI) in hematologic malignancies confers poor prognosis and is difficult to diagnose requiring clinical, radiological and cytological correlates. Neuroimaging studies generally lack specificity and sensitivity, and although cerebral spinal fluid (CSF) cytology is considered the gold standard, it has a very low sensitivity. Several CSF proteins have been studied as possible biomarkers for CNSI to no avail. CSF levels of beta-amyloid peptides (Ab42) and the proteins S100B, Tau, 14-3-3 and Hexosaminidase B are known to be altered by neurodegenerative disease or neuronal injury. Tau levels have also been shown to increase after intrathecal chemotherapy (ITC) in children with lymphoid malignancies. Because of the absence of a reliable biomarker for CNSI, we initiated a study of patients’ CSF to screen for candidate markers of neuronal damage or inflammation. Materials and Methods: Fifty-eight adult patients with hematological malignancies (31 high-grade non-Hodgkin’s lymphoma, 22 acute lymphoblastic leukemia, 1 post-transplant lymphoproliferative disorder, 2 chronic myelogenous leukemia in accelerated phase, and 2 acute myelogenous leukemia) and 6 normal control patients undergoing spinal anaesthesia were included. CSF samples were obtained and frozen at −80°C until analysis. For patients receiving ITC, the CSF sample was withdrawn prior to ITC administration. Clinical information collected included: age, sex, diagnosis, CNS irradiation, ITC/systemic chemotherapy, and presence or absence of CNS disease. Suspected CNS disease was defined as the presence of focal neurological signs or symptoms consistent with leptomeningeal or parenchymal disease, or radiographic evidence of CNSI. Proven CNS disease was defined by the finding of malignant cells in the CSF by cytology or flow cytometry. The control population consisted of patients undergoing spinal anaesthesia with no clinical or pathologic evidence of hematological malignancy or CNS disease. Tau, S100B and Ab42 were quantified by ELISA. 14-3-3 was assayed by Western blotting. Hexosaminidase B was assayed using a fluorogenic substrate. Variables potentially influencing the levels or presence of markers were explored using Mann-Whitney U tests, Student’s t-test, simple linear regression, ANOVA or Pearson ?2 tests, as appropriate. To test correlation with CNSI multiple logistic regression models were constructed. Receiver operating characteristic (ROC) curves and test performance statistics were generated when feasible. Results: One hundred and twenty-eight samples were analyzed from 58 patients and 6 controls. Whereas no difference was observed for S100B or Ab42, increased levels of Tau or positivity for 14-3-3 were associated with diagnosis, CNSI and probably radiotherapy or ITC (Table 1). Multivariate analysis showed that Tau level or 14-3-3 positivity were associated with CNSI after adjusting for confounders (Table 2). For Tau the area under the ROC curve was 0.791. Sensitivity and specificity were 90 and 33% for a cut-off of 200 pg/mL, and 63% and 90% for a cut-off of 500 pg/ml, respectively. Conclusions: Our results suggest that elevated CSF Tau levels and 14-3-3 positivity may correlate with CNSI in patients with hematologic malignancies and might be useful for diagnosis in suspected cases. These findings warrant further prospective studies. Table 1: Univariate analysis exploring variables potentially influencing Tau levels or presence of 14-3-3 protein P value (2 tailed) for differences in Tau levels P value (2-tailed) for differences in expression of 14-3-3 protein Age 0.992 0.792 Sex 0.372 0.715 Systemic chemotherapy 0.485 0.588 Intrathecal Chemotherapy 0.084 0.191 CNS Radiotherapy 0.163 0.022 Diagnosis <0.001 <0.001 Suspected/confirmed CNS infiltration <0.001 <0.001 Table 2. Logistic regression models exploring CNSI with CSF markers Tau level (Per decile increase) 14-3-3 (Positivity) OR (95% CI) P OR (95% CI) P Unadjusted 1.51 (1.29–1.78) <0.001 7.23 (3.12,–16.76) <0.001 Adjusted for CNS radiotherapy and diagnosis 1.40 (1.17–1.68) <0.001 3.85 (1.48–9.99) 0.006

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.011
GPT teacher head0.195
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2008
Admission routes1
Has abstractyes

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