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The addition of monoclonal antibodies to chemotherapy in metastatic early-onset colorectal cancer: A comparative effectiveness study using real-world data.

2025· article· en· W4410821434 on OpenAlexaffabout
Robert B. Basmadjian, Dylan E. O’Sullivan, Tamer N. Jarada, Winson Y. Cheung, Patricia A. Tang, Sharlene Gill, Safiya Karim, Robert J. Hilsden, Colleen Cuthbert, Khara M. Sauro, Joon Lee, Christie Farrer, Barry Stein, Darren R. Brenner

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Treatments and Studies
Canadian institutionsAlberta Health ServicesUniversity of Calgary
Fundersnot available
KeywordsMedicineColorectal cancerMonoclonal antibodyOncologyChemotherapyInternal medicineCancerAntibodyImmunology

Abstract

fetched live from OpenAlex

e15580 Background: The incidence of early-onset colorectal cancer (eoCRC) in adults under the age of 50 years is rising in Canada and the United States. Despite this trend, eoCRCs account for only ~10% of colorectal cancer diagnoses and are underrepresented in trials of novel chemotherapies and targeted agents. The benefit of adding monoclonal antibodies to chemotherapy for stage IV disease remains uncertain, with limited knowledge on factors beyond RAS genes that influence treatment response. Given the distinct molecular features of eoCRC tumours compared to later-onset tumours, identifying optimal treatment regimens for metastatic eoCRC population is crucial. Objective: This project leveraged existing real-world data to investigate the effectiveness of initiating first line combination chemotherapy vs. combination chemotherapy plus monoclonal antibodies in stage IV eoCRC in Alberta. Methods: We conducted a population-based, retrospective cohort study including all patients aged 18to 49 years in Alberta diagnosed with metastatic CRC from 2004 to 2020. Data from electronic medical records, administrative data claims, and vital statistics were merged. Observational data were used to emulate a target trial comparing the initiation of any combination chemotherapy (fluorouracil/capecitabine plus oxaliplatin regimens) vs. combination chemotherapy plus monoclonal antibodies (bevacizumab/cetuximab/panitumumab) within 16 weeks of diagnosis. The primary outcome was overall survival. Follow-up began at time of diagnosis and patients were followed until death, last known date of contact with the healthcare system, or administrative end of follow-up (April 2022), whichever occurred first. To address time-varying selection bias and confounding, marginal Cox models with inverse-probability censoring weights and artificial cloning were employed to estimate hazard ratios (HR) and 95% confidence intervals (95% CI). Results: A total of 674 patients were included, where 313 (46%) initiated chemotherapy and 146 (22%) initiated chemotherapy plus monoclonal antibodies. Patients initiating monoclonal antibodies were more likely to have proximal tumours (34.2% vs. 21.1%) and less likely to receive surgery (41.1% vs. 52.1%) and radiation (4.8% vs 24%) than patients initiating chemotherapy alone. The risk of death from any cause death was 5% higher (HR:1.05; 95%CI: 0.88-1.26) among those who initiated monoclonal antibodies relative to those who did not, but statistical significance was not achieved. Conclusions: Our study did not demonstrate survival benefits of initiating first line monoclonal antibodies in stage IV eoCRC, which is likely explained by the fact that survival depends on compliance and subsequent lines of therapy. We did not have access to RAS gene status, an important predictor of treatment response.

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)0.016
metaresearch head score (Gemma)0.023
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 score0.031
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.023
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.258
GPT teacher head0.573
Teacher spread0.315 · 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
Published2025
Admission routes2
Has abstractyes

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