Molecular Taxonomy of Complex Diseases Using High-Plex Liquid Biopsy Proteomics

 

Molecular Taxonomy of Complex Diseases Using High-Plex Liquid Biopsy Proteomics

 

Lead: Vinaika Maruvada

Team Members: Yewei Ma, Kamala Vanarsa, Shri Chada

Collaborators: Ramesh Saxena

Project Summary:

Autoimmune, inflammatory, and malignant diseases often share overlapping molecular pathways. Conventional disease classification systems, which rely on clinical features or histopathology, frequently fail to capture this biological heterogeneity. This project applies high-plex body-fluid proteomics to define a molecular taxonomy of disease, enabling data-driven patient stratification that transcends traditional diagnostic boundaries.

Using large-scale proteomic profiling of urine and stool samples (“liquid biopsy”), we investigate lupus nephritis (LN), diabetic kidney disease (DKD), bladder cancer (BC), colorectal cancer (CRC), inflammatory bowel disease (IBD), and healthy controls. By integrating unsupervised clustering and Bayesian network analysis, this work aims to identify shared molecular endotypes and pathway-level signatures that underlie diverse disease states.

What is already known in the field?

  • Urine and stool are rich, noninvasive sources of biomarkers reflecting systemic and organ-specific pathology.
  • High-plex proteomic technologies enable simultaneous measurement of thousands of proteins, but their use for cross-disease comparisons and classification remains limited.
  • Renal and gastrointestinal diseases exhibit substantial molecular heterogeneity that is poorly captured by current clinical or histologic classifications.

What is new?

  • Application of 7,000–11,000-plex proteomic assays to urine and stool samples across multiple autoimmune, inflammatory, and cancer populations.
  • Use of unsupervised molecular taxonomy to classify patients based on shared protein expression patterns and up/down-regulated molecular pathways, rather than clinical diagnosis.
  • Bayesian network analysis to uncover relationships between protein modules, biological pathways, clinical features, and demographic variables.

Why is this important?

  • Molecular taxonomy enables biologically driven patient classification, revealing shared mechanisms across seemingly unrelated diseases.
  • Identification of pathway-level signatures supports noninvasive biomarker discovery and improves disease subtyping and risk stratification.
  • Redefining each patient’s disease as a network of up/down-regulated molecular pathways may facilitate pathway-targeted therapies.

Ongoing/Future Steps

  • Expand molecular taxonomy analyses to larger autoimmune, inflammatory, and cancer cohorts, where comprehensive urine or stool proteomic data is available
  • Identify shared and disease-specific molecular pathways that define patient endotypes.
  • Correlate proteomic signatures with clinical outcomes to enable prognostic and predictive biomarker development.
  • Devise pathway-targeted therapies, as guided by the OMICs data.