Multi-omics profiling with untargeted proteomics for blood-based early detection of lung cancer

Blood-based approaches to detect early-stage cancer provide an opportunity to improve survival rates for lung cancer, the most lethal cancer world-wide. Multiple approaches for blood-based cancer detection using molecular analytes derived from individual 'omics (cell-free DNA, RNA transcripts, proteins, metabolites) have been developed and tested, generally showing significantly lower sensitivity for early-stage versus late-stage cancer. We hypothesized that an approach using multiple types of molecular analytes, including broad and untargeted coverage of proteins, could identify biomarkers that more directly reveal changes in gene expression and molecular phenotype in response to carcinogenesis to potentially improve detection of early-stage lung cancer. To that end, we designed and conducted one of the largest multi-omics, observational studies to date, enrolling 2,513 case and control subjects. Multi-omics profiling detected 113,671 peptides corresponding to 8,385 protein groups, 219,729 RNA transcripts, 71,756 RNA introns, and 1,801 metabolites across all subject samples. We then developed a machine learning-based classifier for lung cancer detection comprising 682 of these multi-omics analytes. This multi-omics classifier demonstrated 89%, 80%, and 98-100% sensitivity for all-stage, stage I, and stage III-IV lung cancer, respectively, at 89% specificity in a validation set. The application of a multi-omics platform for discovery of blood-based disease biomarkers, including proteins and complementary molecular analytes, enables the noninvasive detection of early-stage lung cancer with the potential for downstaging at initial diagnosis and the improvement of clinical outcomes. ### Competing Interest Statement All authors are current or former employees and shareholders of PrognomiQ and have declared no other conflicts of interest. ### Funding Statement This work was supported by PrognomiQ. PrognomiQ was involved in the study design, study execution, analysis and interpretation (no grant number) ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Western Institutional Review Board (WIRB) of WCG gave ethical approval for this work I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The data referenced in this study will be made available upon reasonable written request and following submission to all study institutional review boards and approval at all participating sites