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Record W4393076149 · doi:10.1158/1538-7445.am2024-586

Abstract 586: Tumor cell-intrinsic PD-1 activation drives therapeutic resistance in colorectal cancer cells

2024· article· en· W4393076149 on OpenAlexaff
Etienne Ho Kit Mok, Donald T. Yapp, Isabella T. Tai

Bibliographic record

VenueCancer Research · 2024
Typearticle
Languageen
FieldMedicine
TopicCancer, Stress, Anesthesia, and Immune Response
Canadian institutionsCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia
Fundersnot available
KeywordsColorectal cancerCancerMedicineCancer researchTumor cellsOncologyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background/Aim: Despite advancements in diagnosis and treatment, advanced CRC remains a formidable treatment challenge with limited therapeutic options because these tumors are resistant to chemo-, radio- and targeted-therapies. Emerging research shows that the presence of tumor cell-intrinsic PD-1 (ciPD-1) in various tumor types is involved in tumorigenicity and therapy-refractory disease. For example, ciPD-1 promotes tumor growth in melanoma and liver cancer, but in lung cancer, ciPD-1 exhibits tumor-suppressive behavior. These findings highlight the intriguing possibility that ciPD-1 may exert context specific functions within the tumor, beyond its well-known role in immune response suppression. Consequently, the present study addressed the pressing need to explore the effects of immune checkpoint inhibitors within broader tumor context and tumor microenvironment. We have established drug-resistant CRC cells and found that ciPD-1 was upregulated in these cells. This, together with an observation that ciPD-1-mediated survival signaling pathways was activated in CRC patient populations, prompted us to investigate the role of ciPD-1 in regulation of therapeutic resistance in CRC in the absence of immune cells. Methods: We evaluated the clinicopathological relevance of ciPD-1 and its correlation with disease progression in CRC samples from clinical cohorts. Monoclonal antibody inhibition and siRNA interference methods were used to characterize the functional roles of ciPD-1 in regulating therapeutic resistance in CRC. The gene expression profiles of PD-1-High and PD-1-Low patient groups retrieved from clinical cohorts were compared to identify ciPD-1-medatied survival pathways in CRC patients. Results: We found that ciPD-1 had a critical role in regulating CRC tumorigenicity and survival outcomes. Tumors with high expression levels of ciPD-1 also showed higher mutation rate and levels of microsatellite instability. Exposing CRC cells to chemotherapy, radiation, and nutrient deprivation increased ciPD-1 levels. In line with these observations, drug-resistant CRC cells also expressed higher levels of ciPD-1. The inhibition of ciPD-1 in CRC cells by monoclonal antibody or siRNA interference decreased cell proliferation and self-renewal rates, while enhancing treatment efficacy. Conclusion: Our studies reveal that CRC cells increase ciPD-1 expressions in response to drug treatment and targeting ciPD-1-mediated survival pathways can delay the onset of drug resistance and promote therapeutic sensitiveness in CRC cells. Citation Format: Ho Kit Mok, Donald Yapp, Isabella Tai. Tumor cell-intrinsic PD-1 activation drives therapeutic resistance in colorectal cancer cells [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 586.

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 score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0030.001

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 head0.042
GPT teacher head0.375
Teacher spread0.333 · 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
Published2024
Admission routes1
Has abstractyes

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