High-content imaging of primary chronic lymphocytic leukemia cells predicts patient cohorts with distinct cellular drug responses
Bibliographic record
Abstract
Cancer precision medicine benefits from identifying biomarkers that can predict therapy response. However, within a population of chronic lymphocytic leukemia (CLL) patients, there is heterogeneity that is inherent to the disease and also between patients. This heterogeneity, usually explained at the level of genetic and epigenetic abnormalities, obscures conventional potential biomarkers. As an alternative, confocal microscopy of live primary CLL patient samples in a microenvironment model that mimics proliferation centers was used to identify morphological features that define cellular phenotypes that can be used as alternative biomarkers. Applying machine learning to micrographs of 133 patient samples revealed five stable patient clusters, not discernible by standard clinical methods. Within clusters, CLL patient samples responded similarly to drugs, suggesting that live cell imaging could be used to stratify patients and predict drug responses for rational treatment design.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".