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Record W4407285891 · doi:10.1093/jcag/gwae059.121

A121 DEVELOPMENT OF COLON CANCER MODELS TO STUDY THE INTERACTION BETWEEN CANCER STEM CELLS AND THE MICROENVIRONMENT

2025· article· en· W4407285891 on OpenAlexaffabout
M G Sedeuil, Éric Grenier, Véronique Giroux

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2025
Typearticle
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsCancerCancer stem cellColorectal cancerCancer researchTumor microenvironmentBiologyOncologyMedicineInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background Colon cancer is the 2nd deadliest cancer worldwide. The main treatment combines surgery with 5-fluorouracil (5-FU) chemotherapy. However, the number of relapses remains frequent due to a loss of sensitivity to treatment, known as chemoresistance. Over the last decades, studies have highlighted the importance of two tumoral components: cancer stem cells (CSC) and the microenvironment. CSC are tumor cells characterized by their enhanced capacity for self-renewal, potency and resistance to anti-cancer treatments. The microenvironment, for its part, comprises all components surrounding the tumor cells including cancer-associated fibroblasts (CAF). While their respective importance in resistance has been studied, it is important to better understand their interactions during resistance and how this could affect their respective functions. Aims Identify the specific changes occurring in CSC and the impact of their interaction with CAF during the induction of 5-FU resistance in colon cancer. Methods To achieve this goal, we established a model of colon cancer organoids resistant to 5-FU. Results First, we validated that the parental organoids maintained their expected cellular heterogeneity. Indeed, the presence of proliferative cells (Ki67+) and CSC (ALDH1+) was confirmed by immunofluorescence. We also confirmed that they are sensitive to 5-FU by assessing cell survival using a WST1 assay. With a baseline IC50 of 7µM, we concluded that they can be used to generate a 5-FU resistant line. To induce 5-FU resistance, we performed cyclic treatments to mimic clinical reality using a 5-FU dose 4-5 times greater than the baseline IC50. Resistance is currently being validated by WST1 assay to compare IC50. Second, our initial findings indicate that the conditioned media of CAF cultures reduce the sensitivity of parental organoids to 5-FU, highlighting the necessity for further exploring the interaction between these two tumor components. Conclusions In summary, our preliminary results suggest that the interaction between tumor cells and CAF influences the sensitivity to chemotherapy. We are currently investigating the interaction between CAF and CSC with secretome and surfaceome analysis. Additionally, we are investigating the phenotypic and transcriptomic changes occurring specifically in CSC isolated from resistant organoids. Ultimately, the aim of this project is to enhance our understanding of the changes required for resistance, paving the way for new targeted therapies. Funding Agencies Canada foundation for innovation, Canada Research Chairs

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)0.000
metaresearch head score (Gemma)0.000
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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.022
GPT teacher head0.271
Teacher spread0.250 · 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 routes2
Has abstractyes

Explore more

Same venueJournal of the Canadian Association of Gastroenterology→Same topicCancer Cells and Metastasis→French-language works237,207→