Quality of radiation shapes survival, invasiveness, and migration in ovarian cancer cell lines with different molecular profiles and varying alpha/beta ratios: an in vitro study on behalf of the Multicenter Italian Trials in Ovarian Cancer (MITO) group
Bibliographic record
Abstract
Objective The results of radiotherapy (RT) in oligometastatic ovarian cancers (OCs) lead to the query whether it is possible to stratify patients based on tumor hallmarks to ensure the best-personalized RT treatment. To address this question, we designed a preclinical study to evaluate the effects of high and low linear energy transfer (LET) radiation while considering molecular features and alpha/beta ratios of different OC cell lines.Methods Exponentially growing human OVSAHO, OVCAR8, COV362, and OVCAR3 cells cultured in T-25 and T-75 flasks were exposed to different single physical doses of photons, protons, and carbon ion (CIRT) irradiation. We assessed ovarian cells’ in vitro response using clonogenic survival (fitted using LQ model), migration by Boyden chamber assay, and invasion through BioCoat Matrigel invasion assay.Results Following photon irradiation, OVCAR3 was the most radioresistant and OVCAR8 the most radiosensitive cell line. OC cell migration decreased in a dose-dependent manner after irradiation, with CIRT showing the strongest effect, evident by the α/β ratio. The number of invading cells decreased following irradiation with all types. However, the greatest reduction was seen in CIRT, particularly at higher α/β ratios. Proton irradiation demonstrated similar potential to photons but did not match the effects of carbon ions in terms of survival, migration, and invasion. Conclusion: CIRT markedly reduced survival, migration, and invasion of OC cells, particularly in BRCA wild-type OVCAR3 emphasizing its potential to improve local control and lower metastasis risk. This preliminary study serves as a foundation for developing personalized clinical radiation strategies to treat oligometastatic OCs.
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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".