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

Recruiting

• Conditions: Gastrointestinal Neuroendocrine Tumor; Breast Cancer; Melanoma

• Registration Number: NCT05196087
• Lead Sponsor: University Health Network, Toronto

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.

Study Timeline

• Study First Submitted: 2021-12-13
• Study First Submitted QC: 2022-01-04
• Study First Posted: 2022-01-19
• Study Start: 2022-07-20
• Status Verified: 2024-06
• Last Update Submitted: 2024-06-14
• Last Update Posted: 2024-06-18
• Primary Completion: 2026-06
• Study Completion: 2026-06

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 500

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.

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Exclusion Criteria

None

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Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Change from Baseline in ctDNA collected from biospecimens	Through study completion, an average of 4 years	Next-generation sequencing based ctDNA analysis

Secondary Outcome Measures

Name	Time	Method
Number of participants that are identified as high risk of clinical relapse with artificial intelligence (AI) and machine learning algorithms	Through study completion, an average of 4 years

Trial Locations

Locations (1)

Princess Margaret Cancer Centre; 🇨🇦 Toronto, Ontario, Canada