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Record W4405012333 · doi:10.1136/jitc-2024-010094

Plasma arginine as a predictive biomarker for outcomes with immune checkpoint inhibition in metastatic colorectal cancer: a correlative analysis of the CCTG CO.26 trial

2024· article· en· W4405012333 on OpenAlexaff
X. Lucy, Emma Titmuss, Jonathan M. Loree, 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, Nathalie Aucoin, Sheryl Koski, Dongsheng Tu, Christopher O’Callaghan, Eric X. Chen

Bibliographic record

VenueJournal for ImmunoTherapy of Cancer · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Research and Treatments
Canadian institutionsQueen's UniversityAlberta Cancer FoundationCentre Hospitalier de l’Université de MontréalHôpital du Sacré-Cœur de MontréalMoncton HospitalTrillium Health CentreJuravinski Cancer CentreOttawa HospitalSaskatchewan Cancer AgencySt. John’s Health Sciences CentreHôpital Charles-Le MoyneHôtel-Dieu de QuébecQueen Elizabeth II Health Sciences CentrePrincess Margaret Cancer Centre
FundersEisaiTaiho PharmaceuticalNateraExelixisAstraZenecaBristol-Myers SquibbAmgen
KeywordsMedicineColorectal cancerInternal medicineOncologyImmune systemHazard ratioBiomarkerCancerGastroenterologyImmunologyBiologyConfidence interval

Abstract

fetched live from OpenAlex

BACKGROUND: Nutritional stress is a mechanism that allows tumor cells to evade the immune system. Arginine (ARG), an amino acid involved in immunomodulation, aids in regulating T-lymphocyte cell activity and the antitumor response. ARG deficiency in the tumor microenvironment can impair T-cell response while ARG supplementation may promote antitumor immune activity. In this exploratory post hoc analysis of the randomized phase II CO.26 trial, we investigated the role of plasma ARG in predicting response to immune checkpoint inhibitors (ICI) in patients with microsatellite stable refractory metastatic colorectal cancer (mCRC). METHODS: CO.26 randomized patients with refractory mCRC to durvalumab plus tremelimumab (D+T) versus best supportive care (BSC). Plasma ARG concentrations were determined from pretreatment blood samples using high-performance liquid chromatography-tandem mass spectrometry. The median plasma ARG value was used as a cut-off stratifying patients into ARG-high (≥10 700 ng/mL) versus ARG-low (<10 700 ng/mL) groups. Overall survival (OS) was estimated using the Kaplan-Meier method and compared using the log-rank test. Cox proportional hazard models were used to analyze the prognostic and predictive impacts of ARG on OS. RESULTS: Of 180 patients enrolled in CO.26, 161 (N=114 treated with D+T and 47 BSC) had pretreatment blood samples for ARG analysis. There were no significant differences in baseline characteristics between patients included in this analysis and the total study patients, or between ARG-high and ARG-low patients. In the BSC arm, the median OS was 3.09 months for ARG-high versus 4.27 months for ARG-low patients (univariable HR 0.89 (0.49-1.65), p=0.72). In the D+T arm, the median OS was 7.62 months for ARG-high versus 5.27 months for ARG-low patients (univariable HR 0.68, (0.48-1.0], p=0.048). In ARG-high patients, D+T significantly improved OS (median OS 7.62 months with D+T vs 3.09 months BSC; HR 0.61 (0.37-0.99), p=0.047; adjusted p=0.042 for interaction). In ARG-low patients there was no OS benefit with D+T (median OS 5.27 months D+T vs 4.27 months BSC; HR 0.87 (0.52-1.46), p=0.61). CONCLUSION: High baseline plasma ARG was predictive of improved OS in patients with mCRC treated with D+T. Further investigations are needed to validate ARG as a biomarker. Therapeutic approaches targeting the ARG pathway may augment ICI activity. TRIAL REGISTRATION NUMBER: NCT02870920.

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)0.000
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 score0.290
Threshold uncertainty score0.380

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.018
GPT teacher head0.370
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

Citations3
Published2024
Admission routes1
Has abstractyes

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