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Record W4409629099 · doi:10.1158/1538-7445.am2025-5542

Abstract 5542: A novel approach for establishing clinically relevant models of chemoresistance

2025· article· en· W4409629099 on OpenAlexaff
Jared R. Fischbach, Geoffrey A. Wood, Courtney R. Schott

Bibliographic record

VenueCancer Research · 2025
Typearticle
Languageen
FieldMedicine
TopicCancer Research and Treatment
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMedicineCancerComputational biologyIntensive care medicineBiologyInternal medicine

Abstract

fetched live from OpenAlex

Abstract This study provides a novel methodology for generating in vitro chemoresistance models that maximize clinical relevance, aimed at improving the translatability of downstream experimental insights to clinical contexts. Our approach prioritizes an accurate translation of clinical chemotherapy treatment regimens to in vitro applications. We present our methodology in this study using canine osteosarcoma (OS), but our approach is designed to broadly apply to other cancer types. Chemoresistance poses a significant challenge to improving cancer treatment outcomes, as the underlying mechanisms driving it are not well understood. One approach to studying these mechanisms is generating in vitro chemoresistance models. However, there has yet to be a consensus on the optimal method for developing clinically relevant chemoresistance models. Many existing guidelines either produce levels of chemoresistance that are not clinically achievable or offer incomplete strategies for producing clinically relevant models. To establish a clinically relevant model of chemoresistance, we treated canine OS cell lines with carboplatin under conditions designed to precisely mimic the standard-of-care (SOC) treatment regimen used in clinical practice. To translate this clinical regimen to our in vitro application, we utilized numerous pharmacokinetic parameters derived from serum concentration curves for carboplatin in dogs treated in accordance with the SOC regimen, including area under the curve (AUC), peak serum concentration (Cmax), time to peak serum concentration (Tmax), and time from treatment initiation to when the serum concentration falls below detectable levels. Utilizing these pharmacokinetic parameters, a biphasic treatment protocol was developed to provide a SOC-equivalent treatment regimen for in vitro application. Before commencing model generation, the baseline chemosensitivity of each parental canine OS cell line was determined in triplicate via its respective half-maximal inhibitory concentration (IC50) of carboplatin. These parental cell lines were then split into two identical plates each, with one plate from each cell line treated periodically with our SOC-equivalent regimen, administered every twenty-one days for six cycles, in accordance with the SOC regimen. Untreated cell lines were passaged alongside their treated counterparts as model controls. Twenty-one days after the final treatment, the IC50s of all treated cell lines and their respective controls will be re-assessed in triplicate to quantify the fold change in chemoresistance induced by our SOC-equivalent regimen. We anticipate that treated cell lines will exhibit appreciable increases in chemoresistance, highlighting the potential of our methodology to establish clinically relevant in vitro models of chemoresistance that support future investigations into the underlying mechanisms of chemoresistance. Citation Format: Jared R. Fischbach, Geoffrey A. Wood, Courtney R. Schott. A novel approach for establishing clinically relevant models of chemoresistance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5542.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.208
GPT teacher head0.491
Teacher spread0.283 · 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 source (direct Gemma or distilled Codex), 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
Published2025
Admission routes1
Has abstractyes

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