Ignite Creation Date:
2025-12-25 @ 4:46 AM
Ignite Modification Date:
2026-03-02 @ 4:24 PM
Study NCT ID:
NCT06440018
Status:
RECRUITING
Last Update Posted:
2025-02-21
First Post:
2024-05-28
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
INSPIRE: a Multi-Cancer Early Detection Study
Sponsor:
Singlera Genomics Inc.
Organization:
Study Overview
Official Title:
INSPIRE: Integrating Circulating DNA Methylation and Fragmentomics to Scan and Pinpoint Cancer Signals Effectively
Status:
RECRUITING
Status Verified Date:
2024-05
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
INSPIRE
Brief Summary:
This research constitutes a multi-centric, case-control designed investigation aimed at developing and implementing a blinded validation of a machine learning-powered, multi-cancer early detection model. This is to be achieved through the prospective collection of blood specimens from newly diagnosed cancer patients and individuals devoid of a confirmed cancer diagnosis
Detailed Description:
Cancerous tissues, their adjacent non-cancerous tissues, along with white blood cells (WBCs) and normal tissue samples will be utilized to identify potential methylation candidate markers and investigate variations in methylation patterns among patients diagnosed with distinct cancer types. Building upon previous research and current study, a comprehensive methylation signature panel tailored specifically to cancer patients will be established.
We will prospectively collect blood samples from newly diagnosed cancer patients and non-cancer individuals to analyze and identify specific cancer signals via the detection of cfDNA methylation patterns. Following a rigorous and comprehensive research framework, a machine learning-driven model will be developed and validated through blinded testing in an independent cohort. The study aims to enroll approximately 2,650 cancer patients, with a focus on including early-stage cases to enhance the model's sensitivity in detecting cancers with favorable prognoses. Furthermore, around 2,400 control subjects, matched with cancer patients by age and gender and screened negative for cancer through routine tests, will participate as healthy or benign-condition volunteers in model development. Lastly, samples from an additional 300 patients with other tumors will be gathered to conduct interference testing, ensuring the robustness of the model's performance.
We will prospectively collect blood samples from newly diagnosed cancer patients and non-cancer individuals to analyze and identify specific cancer signals via the detection of cfDNA methylation patterns. Following a rigorous and comprehensive research framework, a machine learning-driven model will be developed and validated through blinded testing in an independent cohort. The study aims to enroll approximately 2,650 cancer patients, with a focus on including early-stage cases to enhance the model's sensitivity in detecting cancers with favorable prognoses. Furthermore, around 2,400 control subjects, matched with cancer patients by age and gender and screened negative for cancer through routine tests, will participate as healthy or benign-condition volunteers in model development. Lastly, samples from an additional 300 patients with other tumors will be gathered to conduct interference testing, ensuring the robustness of the model's performance.
Study Oversight
Has Oversight DMC:
False
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
False
Is an FDA AA801 Violation?: