Intratumoral heterogeneity and hypoxia gene expression signatures: Is a single biopsy adequate?
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Gene expression signatures are often used to identify hypoxic tumors. However, intratumoral heterogeneity raises concern that multiple biopsies may be necessary to assess global hypoxia status. The objective of this study was to compare the impact of heterogeneity on the discriminative capacity of several previously described hypoxia gene signatures and determine if a single biopsy is sufficient to obtain a reliable estimate of hypoxia in cervical cancer. MATERIALS AND METHODS: ). The mean standardized error in the signature scores was estimated by comparing the score using one biopsy randomly selected from each tumor to the 'global' score using all available biopsies. RESULTS: ) decreased further. The mean error in the signature scores varied from 0.27 to 0.40 of one standard deviation, suggesting high capacity to discriminate among tumors with different global hypoxia scores. CONCLUSIONS: Compared with individual probes, hypoxia gene expression signatures are generally more consistent across multiple biopsies from different regions of a tumor and more tolerant of intratumoral heterogeneity.
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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 | 0.000 | 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) | 0.000 | 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".