Objective: This study aims to streamline the identification of immune biomarkers in plasma that predict tumour presence and burden, optimising a method that is both efficient and cost-effective for use in cancer research and clinical diagnostics.
Hypothesis: A two-step approach of combining broad initial screen with targeted validation will efficiently identify plasma biomarkers significantly altered by tumours, with high predictive accuracy for tumour detection and burden estimation.
Methods: We implemented a two-step strategy in murine model to identify potential plasma biomarkers. Subcutaneous models of colorectal (CT26) and triple-negative breast cancer (4T1) were established in adult Balb/C female mice. At the study endpoint (day-21 post-inoculation), blood samples were collected for plasma protein analysis, with tumour burden measured by solid tumour volume and mass.
Step-1: A shotgun approach was used for qualitative screening of proteins in pooled plasma from tumour-bearing mice versus no-tumour controls, employing a protein array. Proteins with a ≥1.5-fold change were selected for further analysis using a Luminex-custom panel.
Step-2: Protein levels in individual plasma samples were quantified via Luminex assay. Univariate analysis validated statistically significant alterations in protein levels due to tumours. Machine learning assessed biomarkers' predictive capacity.
To facilitate these analyses, a custom data analysis platform was developed in R, streamlining data processing, statistical analysis, and machine learning workflows, enhancing reproducibility and efficiency.
Results: The plasma levels of the majority of candidate biomarkers identified were validated as significantly altered by tumours. These biomarkers demonstrated excellent predictive capacity (classification accuracies >90%) for distinguishing tumour-bearing from non-tumour-bearing subjects, and with modest regression estimation of tumour burden.
Conclusion: This two-step approach significantly enhances the efficiency of biomarker identification, providing a powerful tool for cancer diagnosis and monitoring. It offers a cost-effective solutions, potentially accelerating both preclinical research and clinical application.