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

Abstract

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 2513 case and control subjects. Multi-omics profiling detected 113,671 peptides corresponding to 8385 protein groups, 219,729 RNA transcripts, 71,756 RNA introns, and 1801 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.

timsTOF HT Improves Protein Identification and Quantitative Reproducibility for Deep Unbiased Plasma Protein Biomarker Discovery

“…We demonstrated that timsTOF HT identified up to 76% more plasma peptide precursors and resulted in a >2-fold increase in reproducible precursors (CV < 20%) compared to timsTOF Pro 2. In all experiments, the use of the Proteograph improved the performance of the mass spectrometry platform consistent with the significant improvement in depth of coverage described in prior publications that have ranged from 5 − 8X. − PG plasma analyzed at 600–1200 ng peptide loading mass on an EvosepOne-timsTOF HT enabled deep plasma proteome profiling with exceptional quantitative reproducibility and linearity. Qualitative and quantitative improvements of timsTOF HT translated into an approximate 50% increase in total and statistically significant ( q < 0.05) plasma precursors between lung cancer and control cohorts when plasma processed by the Proteograph were analyzed by timsTOF HT compared to Pro 2.…”

“…Qualitative and quantitative improvements of timsTOF HT translated into an approximate 50% increase in total and statistically significant ( q < 0.05) plasma precursors between lung cancer and control cohorts when plasma processed by the Proteograph were analyzed by timsTOF HT compared to Pro 2. In all experiments, the use of the Proteograph improved the performance of the mass spectrometry platform consistent with the significant improvement in depth of coverage described in prior publications tht have ranged from 5 – 8X. − Together, our results provide new insights into the advances in reliable plasma protein profiling at unprecedented depths, demonstrating the potential for improved plasma biomarker discovery studies and higher translational potential in the clinics.…”

“…Our data offer a comprehensive comparison of the timsTOF HT and Pro 2 systems for neat and Proteograph plasma proteomic profiling across a wide range of plasma peptide loading masses (100–1200 ng) and liquid chromatography (LC) gradients [100 samples per day (SPD), 60 SPD, 30 SPD]. The Proteograph workflow with the Proteograph Assay Kit (v1.2) leverages 5 distinct nanoparticles (NP1–5), in an automated workflow reproducibly compressing the dynamic range of the plasma proteome and thus enabling deeper proteome profiling. − In this paper, we propose an optimal range of liquid chromatography–mass spectrometry (LC–MS) conditions for deep and reproducible plasma proteomics analysis. Our data showcase the potential of timsTOF HT together with the Proteograph to identify putative cancer biomarkers in plasma using cohorts designed to exhibit distinct proteomic profiles [i.e., samples from 20 patients with stage IV nonsmall cell lung cancer (NSCLC) and 20 noncancer controls].…”

Deep Profiling of Plasma Proteoforms with Engineered Nanoparticles for Top-down Proteomics

“…Varying the physicochemical properties of the nanoparticles results in distinct protein enrichment behaviors, which can be analyzed by mass spectrometry using bottom-up proteomics (BUP) with enzyme digestion. This approach allowed the detection of over 8000 protein groups, 11,14 enabling increased depth for plasma proteome profiling and biomarker discovery. For example, it was used to identify distinct BMP1 isoforms in non-small cell lung cancer subjects.…”