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 Multiple biopsies (33) were obtained from 11 locally advanced (FIGO IB to IVB) cervical cancers prior to treatment. Ten hypoxia gene signatures were analyzed. Variance component analysis was used to determine the ratio of within-tumor variability to total-tumor variability when one to five biopsies are available for analysis (W/T 1–5 ). 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 The ten hypoxia signatures were comprised of 6–99 genes each. The W/T 1 ratios for individual genes commonly found in the signatures ranged from 0.17 to 0.73. W/T 1 ratios for the signatures were generally lower (0.21–0.45), implying greater capacity to discriminate among tumors. With additional biopsies, the signature W/T ratios (ie W/T 2-5 ) 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 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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".