Program Official
Principal Investigator
Robert E.
Schoen
Awardee Organization
University Of Pittsburgh At Pittsburgh
United States
Fiscal Year
2024
Activity Code
U01
Early Stage Investigator Grants (ESI)
Not Applicable
Project End Date
NIH RePORTER
For more information, see NIH RePORTER Project 5U01CA271884-03
Blood-Based Testing for Advanced Adenoma
The goal of our Clinical Validation Center (CVC) is to advance and validate blood-based detection of colorectal advanced adenoma. Whereas blood-based testing for invasive colorectal cancer is progressing, with multiple companies pursuing products, the ability of blood-based testing to detect advanced adenomas, the premalignant lesions closest to invasive cancer, is uncertain. To optimally impact colorectal cancer incidence, blood-based biomarkers cannot just detect cancer, they should also have sufficient sensitivity to identify subjects with advanced adenomas. For our CVC, we organized and assembled a network of highly skilled clinical centers including a focus on minority populations, to prospectively collect well-characterized, highquality blood specimens from a large number of subjects with advanced adenoma prior to undergoing polypectomy and serially post-polypectomy. From the same clinical centers, we will collect blood specimens from control subjects. Specimens will be collected and processed using standard protocols, and relevant demographic and clinical variables will be captured to facilitate biomarker validation studies. Specifically, we will use these well-characterized specimens to validate our data on the utility of original, innovative techniques for molecular detection of advanced adenomas including RealSeqS in combination with custom machine learning algorithms such as SignaL. We propose two Specific Aims. In Specific Aim 1, we will conduct a phase 2 case-control study to validate our novel methods for advanced adenoma detection. We will prospectively recruit patients with advanced adenoma (N=400) and site-specific control subjects (N=400) for comparison to the case subjects. In Specific Aim 2, we will utilize serial blood specimens systematically collected postpolypectomy from our case subjects to determine whether our novel molecular detection techniques can be used to predict likelihood of recurrence and potentially guide surveillance colonoscopy exams. Advanced adenomas are the important, next frontier in non-invasive colorectal cancer screening, and our CVC is equipped to profoundly advance blood-based detection of advanced adenoma.
Publications
- Douville C, Lahouel K, Kuo A, Grant H, Avigdor BE, Curtis SD, Summers M, Cohen JD, Wang Y, Mattox A, Dudley J, Dobbyn L, Popoli M, Ptak J, Nehme N, Silliman N, Blair C, Romans K, Thoburn C, Gizzi J, Schoen RE, Tie J, Gibbs P, Ho-Pham LT, Tran BNH, Tran TS, Nguyen TV, Goggins M, Wolfgang CL, Wang TL, Shih IM, Lennon AM, Hruban RH, Bettegowda C, Kinzler KW, Papadopoulos N, Vogelstein B, Tomasetti C. Machine learning to detect the SINEs of cancer. Science translational medicine. 2024 Jan 24;16(731):eadi3883. Epub 2024 Jan 24. PMID: 38266106