γH2AX Expression in Tumors Exposed to Cisplatin and Fractionated Irradiation
Bibliographic record
Abstract
PURPOSE: Is retention of gammaH2AX foci useful as a biomarker for predicting the response of xenograft tumors to cisplatin with X-ray? Is a similar approach feasible using biopsies from patients with cervical cancer? EXPERIMENTAL DESIGN: Mice bearing SiHa, WiDr, or HCT116 xenograft tumors were exposed to cisplatin and/or three daily doses of 2 Gy. Tumors were excised 24 h after treatment and single cells were analyzed for clonogenic fraction and retention of gammaH2AX foci. Tumor biopsies were examined using 47 paraffin-embedded sections from untreated tumors and 24 sections from 8 patients undergoing radiochemotherapy for advanced cancer of the cervix. RESULTS: Residual gammaH2AX measured 24 h after cisplatin injection accurately predicted surviving fraction in SiHa and WiDr xenografts. When a clinically equivalent protocol using cisplatin and fractionated irradiation was employed, the fraction of xenograft cells lacking gammaH2AX ranked survival accurately but underestimated tumor cell kill. Residual gammaH2AX foci were detected in clinical samples; on average, only 25% of tumor nuclei exhibited one or more gammaH2AX foci before treatment and 74% after the start of treatment. CONCLUSION: gammaH2AX can provide useful information on the response of human tumors to the combination of cisplatin and radiation, but prediction becomes less accurate as more time elapses between treatment and tumor biopsy. Use of residual gammaH2AX as a biomarker for response is feasible when cell survival exceeds approximately 20%, but heterogeneity in endogenous and treatment-induced gammaH2AX must be considered.
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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 | 0.000 | 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) | 0.001 | 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".