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RecruitingNCT05196087NIP IT!Updated Sep 28, 2026

Non-Invasive Artificial Intelligence-Based Platform MonIToring Program (NIP IT!)

An observational study in Breast Cancer, Melanoma and Gastrointestinal Neuroendocrine Tumor, sponsored by University Health Network, Toronto. Recruiting at 1 site in Canada. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2026-09-28.

Sponsored by University Health Network, Toronto · Observational

Study type
Observational
Model
Cohort
Time perspective
Prospective
Enrollment
800
Ages
18 Years and older
Sex
All
01

Study summary

Patients who have undergone curative treatment may be at risk of relapse. This study will collect, annotate, and sequence biospecimens (blood, stool, and tissue) from patients across different tumor types to detect molecular residual disease (MRD) before metastases become radiographically or clinically detectable. This will allow for early cancer interception, and hopefully prolong relapse-free survival across tumor types.

Read the detailed description

The development of anticancer drugs typically starts with patients with advanced cancers who have exhausted standard treatments. Yet even the most active new drugs produce only modest benefits in patients with advanced cancers because of the emergence of resistance, similar to the resistance that bacteria develop when they are repeatedly exposed to antibiotics. In order to achieve larger magnitude gains in survival and make greater impact in the field of cancer, promising drugs must be tested in patients with curable malignancies who have undergone definitive treatment but are at high risk of relapse. Interception is the active intervention of cancers at an early stage, offering an opportunity to eliminate molecular residual disease (MRD) before clinical relapse. MRD describes the situation in which cancer-derived biomarkers are detectable, typically using highly sensitive and specific molecular assays in blood or other body substances that are below the threshold of detection by conventional tests such as CT scans or radiological imaging. Using innovative technologies to monitor patients at high risk of relapse, and applying them to serial samples of their circulating tumor DNA, other body fluids, stool and radiological images, the goal is to develop AI-based models to identify those who are at the highest risk of relapse. This will allow interception studies to be conducted to target microscopic tumor cells in these patients to increase cancer cure rates.

02

Conditions studied

  • Breast Cancer
  • Melanoma
  • Gastrointestinal Neuroendocrine Tumor

Keywords

  • Molecular Profiling
  • Minimal Residual Disease
  • Liquid Biopsy
  • Circulating Tumor DNA
03

Who can participate

Ages eligible
18 Years and older
Sexes eligible
All
Accepts healthy volunteers
No
Sampling method
Non-probability sample

Study population

Early stage or locally advanced disease that is planned for or have undergone curative treatment.

Inclusion criteria

  1. Patients with histological confirmation of a solid tumor.
  2. Patients must have early stage or locally advanced disease that is planned for or have undergone curative treatment.
  3. Patient must be ≥ 18 years old.
  4. All patients must have signed and dated an informed consent form.

Exclusion criteria

Exclusion Criteria:

None

04

Study design

Observational model
Cohort
Time perspective
Prospective
Enrollment
800 participants (estimated)
Patient registry
No
Biospecimen retention
Samples with dna

Groups and cohorts

  • NIP IT!

    Patients with early stage or locally advanced disease that is planned for or have undergone curative treatment will have next-generation sequencing (NGS)-based ctDNA analysis performed on blood samples to determine minimal residual disease (MRD). Blood samples, stool samples, and additional archival/fresh tumor specimens will be collected for banking and future research purposes.

05

What researchers measure

Primary outcomes

  1. Change from Baseline in ctDNA collected from biospecimens

    Next-generation sequencing based ctDNA analysis

    Time frame: Through study completion, an average of 4 years

Secondary outcomes

  1. Number of participants that are identified as high risk of clinical relapse with artificial intelligence (AI) and machine learning algorithms

    Time frame: Through study completion, an average of 4 years

06

Study locations

1 of 1 sites recruiting
  • Princess Margaret Cancer Centre
    Toronto, Ontario M5G 2M9, Canada
    • Celeste Yu · Contact · celeste.yu@uhn.ca · 416-946-4501
    • Lillian Siu, MD · Principal investigator
    • Philippe Bedard, MD · Principal investigator
    Recruiting
07

References and documents

Individual participant data

Plan to share: Undecided — De-identified study data (including genetic data) may be potentially shared with approved research collaborators.

No publications or documents are linked to this record.

08

Registry details

Key details

Study ID
NCT05196087
Lead sponsor
University Health Network, Toronto
Collaborators
Princess Margaret Hospital, Canada
Responsible party
Sponsor
First posted
Jan 19, 2022
Start date
Jul 20, 2022
Primary completion
Jun 2028 (estimated)
Completion
Jun 2028 (estimated)
Last update
Sep 28, 2026

Study contacts

Celeste Yu, MSc
Contact
celeste.yu@uhn.ca
416-946-4501 ext. 5281
Elizabeth Shah
Contact
elizabeth.shah@uhn.ca
416-946-4501 ext. 3833
Lillian Siu, MD
principal investigator · Princess Margaret Hospital, Canada
Philippe Bedard, MD
principal investigator · Princess Margaret Hospital, Canada

Oversight

Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

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