Prospective Study of oncRNA Stratification of Cancer by Size and Stage
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Cancer
- Sponsor
- Exai Bio Inc.
- Enrollment
- 2400
- Locations
- 2
- Primary Endpoint
- Optimize non-invasive cancer biomarker tests based on its small noncoding (snc)RNA technology
- Status
- Recruiting
- Last Updated
- last year
Overview
Brief Summary
Cancer strikes about one in three women and one in two men in the U.S. and more than 600,000 die from it each year. The best chance to reduce these numbers and save lives is through early detection and intervention.
The investigators are developing a blood test to detect cancer from a simple blood draw also referred to as a liquid biopsy. This test is based on orphan non-coding RNAs (oncRNAs) that are abundant in the blood of patients with cancer and largely absent in people without cancer. Using artificial intelligence (AI) and machine learning (ML) investigators are able to interpret the thousands of oncRNAs found in the blood of patients with cancer by identifying unique, cancer-specific patterns. oncRNA patterns can be used to detect several types of cancer and detect cancer at the earliest stages.
This is a prospective, observational study to collect blood samples and medical information from participants with and without cancer to represent the population in the USA. The investigators have designed the study to include participants without cancer, participants with conditions that are a predisposition for cancer, participants with pre-malignant lesions, and participants with cancer. Patients with a wide variety of cancers are going to be included i.e. bladder, breast, colorectal, esophageal, gastric, kidney, liver, lung, ovarian, pancreatic, prostate, and uterine cancer.
Each participant will be asked to donate a small blood sample and to share their medical information. The participant's medical information will be updated during the course of the study. The blood will be tested for oncRNA. The objective is to create a blood repository and associated medical database to develop a blood test for cancer, for different cancer types. The study is designed to be inclusive and represent the population in America.
If this study is successful, the results will enable a world where cancer can be detected early with a simple blood test and diagnosed accurately, with better chances of cure. The investigators believe this study has the potential to transform cancer detection in America.
Investigators
Eligibility Criteria
Inclusion Criteria
- •Age ≥18 years
- •Able and willing to provide informed consent
- •Able and willing to have 35-40 mL of whole blood drawn
Exclusion Criteria
- •Age \<18 years
- •Prior history of cancer, except for non-melanoma skin cancer
- •Prior history of receipt of any blood products within 30 days of enrollment
- •Receipt of any cancer therapy, e.g., surgical, radiation, or medical including neoadjuvant treatment, prior to study enrollment
- •Prior history of receipt of any non-cancer system immune modulation therapy within the last 60 days (ex: monoclonal antibodies)
- •Prior history of organ transplantation
- •Current or prior pregnancy within the last 12 months
- •Unable or unwilling to provide informed consent
Outcomes
Primary Outcomes
Optimize non-invasive cancer biomarker tests based on its small noncoding (snc)RNA technology
Time Frame: Seven years
Analyze small noncoding (snc)RNA profiles differentially expressed in specimens obtained from case and control subjects to identify candidate sncRNA biomarkers for use in deriving and optimizing oncRNA diagnostic models for multiple cancer applications, including screening, early diagnosis, minimal residual disease and monitoring for tumor recurrence
Optimize oncRNA test clinical performance
Time Frame: Seven years
Assess small noncoding (snc)RNA profiles potentially associated with demographic parameters, medication use, comorbid conditions, and/or cancer-predisposing conditions which might overlap with candidate cancer biomarkers to optimize oncRNA model clinical specificity performance, i.e., minimize false positives.