Immunohistochemical evaluation of a trial of gantenerumab or solanezumab in dominantly inherited Alzheimer disease
Bibliographic record
Abstract
Clinical trials of anti-amyloid-β (Aβ) monoclonal antibodies in Alzheimer disease (AD) infer target engagement from Aβ positron emission tomography (PET) and/or fluid biomarkers such as cerebrospinal fluid (CSF) Aβ42/40. However, these biomarkers measure brain Aβ deposits indirectly and/or incompletely. In contrast, neuropathologic assessments allow direct investigation of treatment effects on brain Aβ deposits-and on potentially myriad 'downstream' pathologic features. From a clinical trial of anti-Aβ monoclonal antibodies in dominantly inherited AD (DIAD), in the largest study of its kind, we measured immunohistochemistry area fractions (AFs) for Aβ deposits (10D5), tauopathy (PHF1), microgliosis (IBA1), and astrocytosis (GFAP) in 10 brain regions from 10 trial cases-gantenerumab (n = 4), solanezumab (n = 4), placebo/no treatment (n = 2)-and 10 DIAD observational study cases. Strikingly, in proportion to total drug received, Aβ deposit AFs were significantly lower in the gantenerumab arm versus controls in almost all areas examined, including frontal, temporal, parietal, and occipital cortices, anterior cingulate, hippocampus, caudate, putamen, thalamus, and cerebellar gray matter; only posterior cingulate and cerebellar white matter comparisons were non-significant. In contrast, AFs of tauopathy, microgliosis, and astrocytosis showed no differences across groups. Our results demonstrate with direct histologic evidence that gantenerumab treatment in DIAD can reduce parenchymal Aβ deposits throughout the brain in a dose-dependent manner, suggesting that more complete removal may be possible with earlier and more aggressive treatment regimens. Although AFs of tauopathy, microgliosis, and astrocytosis showed no clear response to partial Aβ removal in this limited autopsy cohort, future examination of these cases with more sensitive techniques (e.g., mass spectrometry) may reveal more subtle 'downstream' effects.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".