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

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.

Study Overview

• Status: Recruiting
• Conditions: Melanoma; Breast Cancer; Gastrointestinal Neuroendocrine Tumor

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 Type: Observational
• Enrollment (Estimated): 500

Contacts and Locations

• Study Contact: Name: Celeste Yu, MSc; Phone Number: 5281 416-946-4501; Email: Celeste.Yu@uhn.ca
• Study Contact Backup: Name: Elizabeth Shah; Phone Number: 3833 416-946-4501; Email: elizabeth.shah@uhn.ca

Study Locations

• Canada
  • Ontario
    • Toronto, Ontario, Canada, M5G 2M9
      • Recruiting
      • Princess Margaret Cancer Centre
      • Contact: Celeste Yu; Phone Number: 5281 416-946-4501; Email: celeste.yu@uhn.ca
      • Principal Investigator: Lillian Siu, MD
      • Principal Investigator: Philippe Bedard, MD

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• 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.

Description

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

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort
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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Change from Baseline in ctDNA collected from biospecimens	Next-generation sequencing based ctDNA analysis	Through study completion, an average of 4 years

Secondary Outcome Measures

Outcome Measure	Time Frame
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

Collaborators and Investigators

• Sponsor: University Health Network, Toronto
• Collaborators: Princess Margaret Hospital, Canada
• Investigators: Principal Investigator: Lillian Siu, MD, Princess Margaret Hospital, Canada; Principal Investigator: Philippe Bedard, MD, Princess Margaret Hospital, Canada

Study record dates

Study Major Dates

• Study Start (Actual): July 20, 2022
• Primary Completion (Estimated): June 1, 2027
• Study Completion (Estimated): June 1, 2027

Study Registration Dates

• First Submitted: December 13, 2021
• First Submitted That Met QC Criteria: January 4, 2022
• First Posted (Actual): January 19, 2022

Study Record Updates

• Last Update Posted (Actual): July 2, 2025
• Last Update Submitted That Met QC Criteria: June 27, 2025
• Last Verified: June 1, 2025

More Information

Terms related to this study

• Keywords: Liquid Biopsy; Molecular Profiling; Circulating Tumor DNA; Minimal Residual Disease
• Additional Relevant MeSH Terms: Neoplasms; Neoplasms by Histologic Type; Neuroectodermal Tumors; Neoplasms, Germ Cell and Embryonal; Neoplasms, Nerve Tissue; Neuroendocrine Tumors
• Other Study ID Numbers: NIP IT!

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED
• IPD Plan Description: De-identified study data (including genetic data) may be potentially shared with approved research collaborators.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No