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Record W4393096869 · doi:10.1158/1538-7445.am2024-2881

Abstract 2881: Comparing the effects of proton and photon therapy on promoting cancer aggressiveness in ovarian cancer and glioblastoma

2024· article· en· W4393096869 on OpenAlexaff
Yeonkyu Jung, Ann Morcos, Aaron Keniston, Ashley Antonissen, Sharon Asariah, Antonella Bertucci, Marcelo E. Vazquez, Juli Unternaehrer

Bibliographic record

VenueCancer Research · 2024
Typearticle
Languageen
FieldMedicine
TopicHydrogen's biological and therapeutic effects
Canadian institutionsCanadian Nuclear Laboratories
Fundersnot available
KeywordsCancerGlioblastomaMedicineProton therapyOvarian cancerOncologyInternal medicineCancer researchRadiation therapy

Abstract

fetched live from OpenAlex

Abstract Ovarian cancer is the 12th most common cancer among women in the United States and the 5th leading cause of cancer-related death for women. About 80% of the patients are diagnosed at stages III or IV, classified as High-Grade Serous Ovarian Cancer (HGSOC). HGSOC is highly aggressive, exhibiting an 80% recurrence rate within 24 months after cancer treatment. Brain and nervous system cancer ranks as the 10th leading cause of cancer-related death in the U.S., with Glioblastoma Multiforme (GBM) accounting for 47.7% of all brain cancer cases. GBM is the most aggressive primary brain cancer, with a 90% recurrence rate and a 4% 2-year survival rate. Patients with these cancers encounter limited treatment options, and the recurrence of cancer exacerbates these challenges. Therefore, a deeper understanding of the factors contributing to cancer treatment-induced aggressiveness is greatly needed. In our study, we first examined the treatment efficacy of proton and photon radiation by conducting apoptosis assays. Our findings demonstrate that proton radiation is significantly more effective at eliminating both HGSOC and GBM cells compared to photon radiation. Subsequently, we assessed the level of cancer aggressiveness in the surviving cells after radiation exposure by measuring stemness and epithelial-mesenchymal transition (EMT) levels. While proton radiation proved to be more effective in eliminating a greater number of cancer cells, we hypothesize that both proton and photon irradiation promote cancer aggressiveness in the surviving cells of HGSOC and GBM. To assess the stemness and EMT level of cancer cells following radiation, we integrated a SORE6-GFP reporter to identify the cancer stem cell population expressing SOX2/OCT4 and a 3’ UTR-ZEB1-GFP reporter to detect the cell population with mesenchymal traits. Cells transduced with the reporters were treated with 0, 1, 2, 4, and 8 Gy of 250 MeV proton and 6 MeV photon beams, and at 72 hours post-radiation, GFP levels were measured via flow cytometry. Our data demonstrated a dose-dependent increase in stemness and EMT levels in live cells after both types of radiation. Furthermore, cancer cells exposed to radiation were harvested at 72 hours and 240 hours post-radiation for RT-qPCR analysis. Our preliminary data indicate an upregulation of stemness genes including POU5F1 and SOX2, as well as EMT transcription factor genes ZEB1, SNAI1, and TWIST1 in response to both types of radiation in most cell lines. These findings indicate that both proton and photon radiation therapies promote cancer aggressiveness in the surviving cancer cells. Citation Format: Yeonkyu Jung, Ann Morcos, Aaron Keniston, Ashley Antonissen, Sharon Asariah, Antonella Bertucci, Marcelo Vazquez, Juli Unternaehrer. Comparing the effects of proton and photon therapy on promoting cancer aggressiveness in ovarian cancer and glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2881.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score0.426

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.068
GPT teacher head0.421
Teacher spread0.352 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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