Machine perception liquid biopsy identifies brain tumors via systemic immune and tumor microenvironment signature

The detection and identification of intracranial tumors is limited by the lack of accurate biomarkers and requires invasive biopsy procedures. We investigated a machine perception liquid biopsy approach to detect and identify intracranial tumors from peripheral blood and to discover biomarkers responsible for the predictions. Quantum well defect-modified single-walled carbon nanotubes stabilized with single standed DNA, interrogating 739 plasma samples from brain tumor patients, were used to train and validate machine learning models to detect intracranial tumors with 98% accuracy and identify tumor type. The protein corona of the top model-contributing nanosensor was interrogated using quantitative proteomics, resulting in the identification of tumor ecosystem-secreted factors, both previously-reported and newly-discovered, originating from intracranial tumor cells, the tumor microenvironment, and the innate immune system of patients with glioblastoma and meningioma. Newly-discovered factors elicited linear nanosensor responses and were elevated in one or both tumor types, matching the original protein corona enrichment. This investigation reveals that a perception-based detection of disease in blood can identify biomarkers responsible for the signal and also amplify cancer detection signals by detecting factors beyond tumour cells, thereby recruiting the entire tumour ecosystem for cancer diagnosis.