Skip to content

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

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

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT05196087
Acronym
NIP IT!
Enrollment
500
Registered
2022-01-19
Start date
2022-07-20
Completion date
2027-06-30
Last updated
2025-07-02

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Breast Cancer, Gastrointestinal Neuroendocrine Tumor, Melanoma

Keywords

Molecular Profiling, Minimal Residual Disease, Liquid Biopsy, Circulating Tumor DNA

Brief 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.

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.

Interventions

None listed

Sponsors

Princess Margaret Hospital, Canada
CollaboratorOTHER
University Health Network, Toronto
Lead SponsorOTHER

Study design

Observational model
COHORT
Time perspective
PROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No

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

None

Design outcomes

Primary

MeasureTime frameDescription
Change from Baseline in ctDNA collected from biospecimensThrough study completion, an average of 4 yearsNext-generation sequencing based ctDNA analysis

Secondary

MeasureTime frame
Number of participants that are identified as high risk of clinical relapse with artificial intelligence (AI) and machine learning algorithmsThrough study completion, an average of 4 years

Countries

Canada

Contacts

Primary ContactCeleste Yu, MSc
celeste.yu@uhn.ca416-946-4501
Backup ContactElizabeth Shah
elizabeth.shah@uhn.ca416-946-4501

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 19, 2026