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Clinical Trials/NCT05196087
NCT05196087RecruitingNot Applicable

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

University Health Network, Toronto1 site in 1 country500 target enrollmentStarted: July 20, 2022Last updated:
Conditions

Trial Snapshot

Phase
Not Applicable
Status
Recruiting
Enrollment
500
Locations
1
Primary Endpoint
Change from Baseline in ctDNA collected from biospecimens

Study Overview

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 Design

Study Type
Observational
Observational Model
Cohort
Time Perspective
Prospective

Eligibility Criteria

Ages
18 Years to — (Adult, Older Adult)
Sex
All
Accepts Healthy Volunteers
No

Inclusion Criteria

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

Exclusion Criteria

  • Not provided

Outcomes

Primary Outcomes

Change from Baseline in ctDNA collected from biospecimens

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

Next-generation sequencing based ctDNA analysis

Secondary Outcomes

  • 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)

Investigators

Sponsor Class
Other
Responsible Party
Sponsor

Study Sites (1)

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