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Artemin and immune checkpoint inhibitor (ICI) efficacy in metastatic colorectal cancer (mCRC): A correlative analysis of the Canadian Cancer Trials Group (CCTG) CO.26 trial.

2024· article· en· W4399879795 on OpenAlexaffabout
Lucy Xiaolu, Emma Titmuss, Derek J. Jonker, Hagen F. Kennecke, Scott Berry, Félix Couture, Chaudhary E. Ahmad, John R. Goffin, Petr Kavan, Mohammed Harb, Bruce Colwell, Setareh Samimi, Benoit Samson, Tahir Abbas, Sheryl Koski, Dongsheng Tu, Christopher J. O’Callaghan, Jonathan M. Loree, Eric Xueyu Chen

Bibliographic record

VenueJournal of Clinical Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsCentre Hospitalier de l’Université de MontréalHôpital du Sacré-Cœur de MontréalJuravinski Cancer CentreCancer Care Nova ScotiaJewish General HospitalHôpital Charles-Le MoyneSt. John’s Health Sciences CentreHôtel-Dieu de QuébecGenome British ColumbiaSaskatchewan Cancer AgencyUniversity Health NetworkOttawa HospitalPrincess Margaret Cancer CentreQueen's UniversityMoncton Hospital
Fundersnot available
KeywordsMedicineColorectal cancerOncologyInternal medicineCancerImmune checkpointCancer researchImmunotherapy

Abstract

fetched live from OpenAlex

3587 Background: ICIs have limited efficacy in microsatellite stable (MSS) mCRC. The mechanisms of resistance to ICI remain incompletely understood. Recent findings suggest that erythyroid progenitor cells (EPCs) in the tumour microenvironment can exert immunosuppressive properties and promote tumour progression through the secretion of artemin, a neurotrophic factor. We conducted this post-hoc analysis of the Phase II CCTG CO.26 trial (NCT02870920) to investigate the relationship between artemin and ICI treatment outcomes in MSS mCRC. Methods: The CO.26 trial randomized patients (pts) with refractory mCRC to durvalumab plus tremelimumab (D+T) and best supportive care (BSC) compared to BSC alone in a 2:1 fashion. Serum artemin concentrations were determined from pre-treatment and serial on-treatment blood samples. The median artemin value (1.051ng/ml) was used to stratify pts into high versus low artemin groups. Progression-free (PFS) and overall survival (OS) were estimated using the Kaplan-Meier method and compared between groups using the log-rank test. Cox proportional hazard models were used to analyze prognostic and predictive impacts of artemin on PFS and OS. Results: Of 180 pts enrolled in CO.26, blood samples were available for 161 pts (N= 114 D+T, 47 BSC) at baseline, 94 at week 8 and 69 at progression. In the BSC arm, OS was lower for pts with high artemin at baseline compared to low pts (mOS 3.17 vs. 5.65 months, HR 1.74 [0.94-3.25], p=0.080). In artemin high pts, D+T improved OS compared to BSC (mOS 6.44 vs. 3.17 months, HR 0.54 [0.33-0.91] p=0.020). There was no difference in OS between D+T and BSC arms in artemin low pts (mOS 6.64 vs. 5.65 months, HR 0.87 [0.53-1.43], p=0.58, multivariable p-interaction=0.061). In the D+T arm, pts with progressive disease had significant increases in artemin levels at week 8 compared to baseline (p=0.026), while there was no significant change in week 8 artemin levels in pts with stable disease (p=0.14). Conclusions: High baseline artemin may be a poor prognostic marker in MSS mCRC and increase in artemin level during treatment can be indicative of progressive disease. High baseline artemin may predict benefit from ICI treatment. Studies are warranted to better understand and explore targeting the EPC-artemin axis to enhance efficacy of ICI in MSS mCRC.

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)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.161
GPT teacher head0.496
Teacher spread0.335 · 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 designObservational
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 routes2
Has abstractyes

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