Multidimensional proteomics and explainable AI feature selection identify cross-platform lung cancer molecular signature in blood plasma

Lung cancer is the leading cause of cancer mortality worldwide with 70% diagnosed late stage despite low-dose computed tomography (LDCT) screening availability. We combined data-independent acquisition mass-spectrometry (DIA-MS) and proximity extension assay (PEA) with explainable artificial intelligence (XAI)-led machine learning (ML) for plasma-based biomarker discovery. From a 490 lung cancer and 124 matched-control cohort, ML models were trained to predict lung cancer achieving an AUROC of 0.91 [95% CI: 0.88–0.93] and 0.97 [95% CI: 0.92–0.98] in DIA-MS and PEA, respectively. XAI characterised networks of model-consistent features primarily related to infection and inflammatory responses. We then introduced a DNA-aptamer proteomics method and identified a cross-platform concordance panel, with performances of 0.88 [95% CI: 0.80–0.90] and 0.88 [95% CI: 0.81–0.95] in DIA-MS and PEA, respectively. This study demonstrates that combining multi-dimensional proteomics with XAI-ML can characterise robust biomarker signatures.