Identifying Novel Stool Biomarkers of Colorectal Cancer and Advanced Adenomas Using SomaScan Proteomics

 

Identifying Novel Stool Biomarkers of Colorectal Cancer and Advanced Adenomas Using SomaScan Proteomics

 

Lead: Vinaika Maruvada, Kamala Vanarsa

Team Members: Shriya Ramanan, Venkata Madhavi Latha, Yewei Ma

Collaborators:  Robert Bresalier

Project Summary:

Colorectal cancer (CRC) remains a leading cause of cancer-related mortality, largely due to limitations in early, noninvasive detection. While stool-based screening approaches are widely used, most current tests rely on occult blood detection or a limited set of molecular markers and may fail to capture the biological heterogeneity of colorectal neoplasia, particularly at precancerous stages. This project focuses on identifying novel stool protein biomarkers associated with colorectal cancer and advanced adenomas using high-plex aptamer-based proteomics.

Using SomaScan technology, we perform large-scale profiling of approximately 7,000 stool proteins across healthy controls, advanced adenoma patients, and colorectal cancer patients. By applying statistical analysis, clustering approaches, and machine learning-based classification, this work aims to identify disease-associated protein signatures that distinguish clinically relevant colorectal neoplasia from healthy states.

What is already known in the field?

  • Stool is a noninvasive and biologically informative source for detecting gastrointestinal disease.
  • Current stool-based CRC screening methods have limited sensitivity for early-stage disease and advanced adenomas.
  • Proteomic approaches offer an opportunity to uncover biologically meaningful biomarkers beyond targeted panels.

What is new?

  • This study represents one of the largest stool-based proteomic screens in colorectal cancer, leveraging a 7,000-plex SomaScan platform.
  • High-dimensional protein profiling is used to identify molecular patterns associated with advanced adenomas and colorectal cancer.
  • Data-driven approaches, including clustering analyses and machine learning classification, are applied to prioritize candidate biomarkers with high discriminatory potential.
  • Integration with public biological databases is used to contextualize findings and assess relevance to colorectal cancer biology.

Why is this important?

  • Identification of stool-based protein biomarkers supports development of noninvasive strategies for early detection of colorectal neoplasia.
  • High-plex proteomics enables discovery of biologically informed biomarker signatures, rather than reliance on single markers.
  • This approach may improve risk stratification and enhance the clinical utility of stool-based screening for colorectal cancer.

Ongoing/Future Steps

  • Complete additional data analyses to further refine and prioritize candidate stool protein biomarkers.
  • Explore biological pathways associated with top-ranked proteins to better understand disease mechanisms.
  • Assess robustness of selected biomarkers across relevant clinical subgroups.
  • Support future validation studies aimed at translating stool proteomic biomarkers into clinically actionable screening tools.