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Identifying the optimal post-surgical timing of molecular residual disease (MRD) detection in colorectal cancer (CRC) using an ultra-sensitive assay: Interim results from the VICTORI study.

2025· article· en· W4406869526 on OpenAlexaff
João Paulo Solar Vasconcelos, Emma Titmuss, Fábio C. P. Navarro, Charles W. Abbott, Brendan J. Chia, James T. Topham, Gale Ladua, Tharani Krishnan, Daniela Hegebarth, Howard J. Lim, Karamjit S. Gill, Sharlene Gill, Carl J. Brown, Amandeep Ghuman, Adam Meneghetti, Daniel J. Renouf, David F. Schaeffer, Richard Chen, Sean Michael Boyle, Jonathan M. Loree

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsVancouver General HospitalSt. Paul's HospitalProvidence Health CareBC Cancer AgencyGenome British ColumbiaPancreas Centre (Canada)University of British Columbia
Fundersnot available
KeywordsMedicineColorectal cancerInterimCancerOncologyInternal medicineDiseaseInterim analysisMinimal residual diseaseClinical trial

Abstract

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275 Background: While detection of MRD using ctDNA is prognostic for recurrence in CRC, some patients still recur prior to MRD detection. VICTORI is prospectively investigating NeXT Personal, an ultra-sensitive NGS-based MRD assay, to profile patients with resected CRC. Methods: Patients with CRC treated with curative intent (all stages) are tested for MRD using NeXT Personal, a bespoke assay with up to ~1,800 tumor-informed single nucleotide variants (SNVs) identified from whole-genome sequencing. Plasma is collected prior to surgery, every 2 weeks post-surgery up to week 8 (MRD landmark window), and every 3 months for up to 3 years (surveillance). We present preliminary results on 397 samples from the first 62 patients. Results: A total of 62 patients (N=36 rectal [58%], N=26 colon [42%]; N=46 stage I-III [74%], N=16 stage IV [26%]) were included in our analysis. Baseline pre-surgical sensitivity (treatment naive) was 93.5% [N=29/31]. Pre-surgical positivity rate in patients who had received neoadjuvant therapy and had residual cancer at the time of surgery was 67% [N=16/24]. 60 patients were evaluable for clinical outcomes. At a median follow-up of 355 days, 15 patients (25%) had a recurrence. Of these, all patients were ctDNA-positive prior to recurrence (100%, 14/14; 1 pt excluded due to lack of samples prior to recurrence). ctDNA detection preceded clinical relapse by a median of 194 days [range: 5-397]); ctDNA for 78.6% (N=11/14) were first detected in the MRD landmark window. All landmark-positive recurrences occurred within one year of surgery. The 14 recurrent cancers with samples were first detected at a median ctDNA concentration of 28.7 parts per million (PPM) (range 2.4-111,120), with 64.3% (9/14) of those detections in the ultra-low range of <100 PPM. MRD detection at week 4 and week 8 had the greatest reduction in RFS (HR 12.86 [2.74-60.28], p=0.0012 week 4; HR 16.14 [3.52-74.09], p=0.0004 week 8), with weeks 4, 6 and 8 having similar higher prevalence of ctDNA detection (36.0% [18/50], 35.3% [18/51], 38.5% [20/52] respectively). Week 2 detection rate was 17.0% (8/46). cfDNA concentration was highest at week 2 (4.63ng/ml vs. 2.22 at baseline, p=0.00028) and 4 (3.68 vs. 2.22, p=0.0081), returning to baseline levels at week 6 (2.28 vs. 2.22, p=0.67) and 8 (2.48 vs. 2.22, p=0.64). Conclusions: In this interim report after a median ~1 year follow-up, NeXT Personal detected MRD for all patients prior to disease recurrence. Most initial MRD detection was in the ultra-sensitive range <100ppm and detection at 4-8 weeks after surgery was highly prognostic for recurrence.

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)0.003
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.152
GPT teacher head0.501
Teacher spread0.349 · 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".

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Citations2
Published2025
Admission routes1
Has abstractyes

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