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

Official Title

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

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 tumour 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 tumour types.

Trial Description

Primary Outcome:

  • Change from Baseline in ctDNA collected from biospecimens
Secondary Outcome:
  • Number of participants that are identified as high risk of clinical relapse with artificial intelligence (AI) and machine learning algorithms
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 tumour 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 tumour cells in these patients to increase cancer cure rates.

View this trial on ClinicalTrials.gov

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Resources

Canadian Cancer Society

These resources are provided in partnership with the Canadian Cancer Society