Global population growth and increasing life expectancy mean that cancer now affects more people than ever before. Cancer is the second most common cause of death worldwide, after cardiovascular disease, and responsible for around one in every six lives lost.1 Against these stark statistics, international research efforts continue to seek out strategies for earlier cancer detection and more effective treatment. Translational proteomics has come to play an increasingly important role in many avenues of pursuing these goals, including precision oncology research, supporting the discovery, and development of promising protein biomarkers for improved cancer diagnosis and monitoring, and even personalized treatment.
A number of exciting cancer biomarker discoveries over the last decade have resulted from the considerable improvements in the analytical power of liquid chromatography-mass spectrometry (LC-MS) technologies, in particular high-resolution MS systems. The enhanced mass resolution, accuracy, and sensitivity of modern MS systems have been fundamental in delivering confident identification and quantification of proteins and peptides in proteomics studies. However, while LC-MS-based proteomics techniques are revealing new protein biomarkers through discovery approaches every year, translating these into clinically informative diagnostic assays has been more challenging.2,3
Biomarker verification and validation workflows bridge this translational gap between the lab bench and the patient bedside. These large-scale studies are designed to build upon the positive findings of early-stage experiments and reflect the heterogeneity of the target population by involving a greater number and diversity of patients than those participating at the discovery stage. Often, these proteomics studies are performed across multiple locations, so the MS workflows used must not only provide high-throughput, deep coverage of the proteome but also deliver reproducible results that are consistent between instruments and laboratories. Here, we look at some of the latest MS techniques that are enabling exceptional reproducibility, multiplexing capacity, and quantitative accuracy in translational proteomics workflows for cancer biomarker development.
Advanced label-free data dependent acquisition workflows
Given the increased demands on throughput, proteomics studies involving large patient cohorts require more scalable MS workflows than discovery stage efforts. MS methods based on label-free data dependent acquisition (DDA) meet this challenge and have therefore become well-established for proteomics studies involving large sample numbers.
The improved scalability of label-free DDA workflows stems from the fact that tandem MS analysis is performed on the most abundant precursor ions individually, rather than collectively, to extend proteome coverage and minimize redundant peptide precursor selection. As a result, label-free DDA methods allow multiple samples to be compared with much greater throughput. However, despite their widespread use, inconsistent sample preparation or instrument use have led to reduced measurement precision.3 Consequently, experiments employing DDA methods have typically required more repeat measurements to boost statistical confidence in findings.
Advances in LC-MS technologies are helping to circumvent this issue by enabling more robust method standardization and reproducibility. Improvements in the analytical sensitivity of modern capillary flow high-performance LC systems, for example, are delivering more stable retention times and peak areas, ultimately resulting in more consistent data. Furthermore, the enhanced accuracy, precision, and sensitivity of Orbitrap mass analyzers are leading to more comprehensive peptide coverage and greater inter-run measurement consistency, helping researchers more easily apply standardized methods across different laboratories. These combined improvements in LC-MS technologies are furthering the development of so-called “DDA+” workflows, which are more transferable, reproducible, and scalable than conventional DDA methods.
High-resolution MS1 data-independent acquisition workflow
Although DDA workflows have proven to be highly effective for large-scale biomarker verification and validation studies, obtaining the necessary levels of analytical sensitivity remains challenging for certain experiments and biological samples. MS methods based on data-independent acquisition (DIA) offer an alternative route forward, and the proteomics community has seen these approaches gain traction for translational biomarker workflows in recent years. Using data acquisition windows of 10–100 Daltons to fragment peptides over a broad mass-to-charge (m/z) range, DIA workflows routinely provide excellent multiplexing capacity for proteome-wide quantitation.
Recent improvements in the resolution of MS technologies have enhanced the power of DIA workflows even further. By harnessing the exceptional mass resolution of hybrid quadrupole-Orbitrap mass analyzers, the latest high-resolution MS1 DIA (HRMS1-DIA) workflows can use much narrower m/z acquisition windows to collect data, yielding more useful MS information. This ability to deconvolute feature-rich spectra is particularly valuable for the analysis of complex clinical, samples such as blood plasma and other biofluids, which typically include a diverse range of peptides with a broad dynamic range. By pushing the limits of precursor selectivity and improving proteome coverage and data fidelity, HRMS1-DIA workflows are resulting in more reproducible protein profiling—a factor of key importance in the development of standardized methods for verification studies.
Next-generation proteomics workflows: Accelerating success in biomarker development
The well-recognized reproducibility, transferability, and scalability challenges associated with incumbent MS proteomics methods are major bottlenecks restricting the progression of biomarkers through the translational pipeline. In response, large-scale collaborative programs, led by the Cancer Moonshot initiative, are turning to the most advanced MS methods to accelerate protein biomarker development.
The next-generation MS proteomics methods highlighted here were recently used in a multisite biomarker study, with participation from several Cancer Moonshot labs, which sought to evaluate the effectiveness of these workflows for biomarker development. An international network of 11 laboratories, located across nine countries/regions, applied online capillary LC coupled with an HRMS1-DIA method using a hybrid quadrupole-Orbitrap MS system for the verification and validation of proteins identified from the large-scale analysis of human, yeast, and E. coli proteomes, and compared the interlaboratory and day-to-day reproducibility of the results.
The workflows delivered consistent results across all sites over seven consecutive days in 24/7 operation mode, with more than 7000 proteins identified with 1-hour capillary LC-HRMS1-DIA workflow and only a 1% false-discovery rate. The study found that over 80% of the protein groups were identified and quantified in common across different days at the same site (Figure 1), while similar proportions were identified and quantified in common between labs (Figure 2), demonstrating the scalability and measurement robustness of the proteomics workflows used. When used more broadly across other translational proteomics workflows, it is hoped that these advanced MS methods will accelerate the verification and validation of protein biomarkers and hasten their clinical application.
To bridge the gap between biomarker discovery and translational research, it is essential that the issues around method reproducibility, transferability, and scalability are addressed. These recent findings from this interlaboratory ring trial initiative highlight how HRMS1-DIA workflows can deliver reliable, consistent, and high-throughput data in large-scale proteomics studies. By improving method standardization and reproducibility within verification and validation workflows, these advanced techniques could provide the catalyst that is needed to accelerate progress toward earlier diagnosis, improved monitoring, and more targeted treatment of cancer.
1. World Health Organization, Cancer Key Facts, www.who.int/news-room/fact-sheets/detail/cancer
2. Parker CE, Borchers CH. Advances in mass spectrometry-based clinical biomarker discovery. Mol. Oncol. 2014; 8: 840–858.
3. Anderson L. Six decades searching for meaning in the proteome. J. Proteomics 2014; 107: 24–30.
4. Britton D et al. Quantification of Pancreatic Cancer Proteome and Phosphorylome: Indicates Molecular Events Likely Contributing to Cancer and Activity of Drug Targets. PLoS One 2014; 9: e107077.
Yue Xuan, PhD, is a senior product marketing manager, precision medicine, at Thermo Fisher Scientific. She specializes in multiomics research, including proteome profiling workflow development. She is the project lead for this international Cancer Moonshot multi-site study and has an MS. in chemistry from Free University Berlin and a PhD in chemistry from Technical University of Dortmund.