Multiplexed nanomaterial-assisted laser desorption/ionization for pan-cancer diagnosis and classification

Area of Science:

• Metabolomics
• Biomarker Discovery
• Mass Spectrometry

Background:

• Cancer is increasingly recognized as a metabolic disorder.
• Serum metabolite profiling offers a potential avenue for cancer detection.
• Non-invasive diagnostic methods are crucial for early cancer detection.

Purpose of the Study:

• To develop and validate a novel liquid biopsy assay for pan-cancer screening and classification.
• To assess the sensitivity and specificity of the assay in distinguishing cancer patients from healthy controls.
• To evaluate the accuracy of the assay in identifying the tissue of origin for various cancer types.

Main Methods:

• Development of Multiplexed Nanomaterial-Assisted LDI for Cancer Identification (MNALCI).
• Application of MNALCI to serum samples from 1,183 individuals (950 cancer patients, 233 healthy controls) across two cohorts.
• Utilizing machine learning for analysis of mass spectrometry fingerprints from nanostructured matrixes.

Main Results:

• MNALCI achieved 93% sensitivity and 91% specificity for cancer detection in internal validation.
• External validation showed 84% sensitivity and 84% specificity.
• Overall accuracy for identifying tumor origin was 92% (internal) and 85% (external), with up to eight metabolite biomarkers identified.

Conclusions:

• MNALCI is a promising high-throughput, non-invasive assay for pan-cancer screening and classification.
• The assay demonstrates excellent accuracy and requires minimal sample volume.
• Metabolite profiling via LDI mass spectrometry can serve as a viable approach for early cancer diagnosis.