Clinical trial NCT06440018: INSPIRE: a Multi-Cancer Early Detection Study
Official title: INSPIRE: Integrating Circulating DNA Methylation and Fragmentomics to Scan and Pinpoint Cancer Signals Effectively
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Lung Cancer
Gastric Cancer
Liver Cancer
Colorectal Cancer
Pancreatic Cancer
Esophageal Cancer
Breast Cancer
Cervical Cancer
Ovarian Cancer
Endometrial Cancer
Bladder Cancer
Prostate Cancer
Cholangiocarcinoma
Lead sponsor: Singlera Genomics Inc.
Sponsor class: INDUSTRY
Collaborators: name: Fudan University
name: Hubei Cancer Hospital, Huazhong University of Science and Technology
name: China-Japan Friendship Hospital
name: Shanxi Provincial Cancer Hospital
name: Xuhui Central Hospital, Fudan University
name: Shanghai Electric Power Hospital
name: East Hospital, Tongji University
name: The First Affiliated Hospital of Xi' an Jiaotong University
name: GaoZhou People's Hosipital
name: Anhui Provincial Hospital
name: Beijing Friendship Hospital
First posted: 2024-06-03
Enrollment: 5350
Sex: ALL
Minimum age (years): 40
Maximum age (years): 75
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: CASE_CONTROL
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: A consenting professional will invite participants, including cancer patients and healthy controls, from the main center and affiliated hospitals, to take part in a comprehensive case-control study.

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.

Eligibility criteria: Inclusion Criteria for Case Arm Participants:

* 40-75 years old
* Clinically and/or pathologically diagnosed cancer
* No prior or undergoing any systemic or local antitumor therapy, including but not limited to surgical resection, radiochemotherapy, endocrinotherapy, targeted therapy, immunotherapy, interventional therapy, etc.
* Able to provide a written informed consent and willing to comply with all part of the protocol procedures

Exclusion Criteria for Case Arm Participants:

* Pregnancy or lactating women
* Known prior or current diagnosis of other types of malignancies comorbidities
* Have participated in an "interventional" clinical trial within the past 30 days and have taken the experimental drug;
* Recipients of organ transplant or prior bone marrow transplant or stem cell transplant
* Recipients of blood transfusion within 30 days prior to screen
* Recipients of therapy in past 14 days prior to screen, including oral or IV antibiotics, glucocorticoid, azacitidine, decitabine, procainamide, hydrazine, arsenic trioxide
* Failure to collect blood on time according to plan
* The blood sample does not meet the requirements

Inclusion Criteria for Control Arm Participants:

* 40-75 years old
* Without confirmed cancer diagnosis
* Able to provide a written informed consent and willing to comply with all part of the protocol procedures

Exclusion Criteria for Control Arm Participants:

* Pregnancy or lactating women
* No previous history of malignancy in other sites
* Recipients of organ transplant or prior bone marrow transplant or stem cell transplant
* Recipients of blood transfusion within 30 days prior to screen
* Recipients of therapy in the past 14 days prior to screen, including oral or IV antibiotics, glucocorticoid, azacitidine, decitabine, procainamide, hydrazine, arsenic trioxide
* Unsuitable for this trial determined by the researchers

Primary outcomes:
measure: The AUC, sensitivity, specificity and tissue origin accuracy of the multi-cancer early detection model in detecting cancer or non-cancer
timeFrame: 12 months
Secondary outcomes:
measure: The performance of the multi-cancer early detection model in early stage cancer and precancerous lesion cases
timeFrame: 12 months
measure: The performance of the multi-cancer early detection model in different subgroups (such as age, gender, cancer pathological classification, and clinical stage)
timeFrame: 12 months
Other outcomes:
Locations:
city: Shanghai
country: China
facility: Fudan University
state: Shnaghai
Study officials:
References: