Proteomics, Genomics & Microarrays


Rethinking oncological drug discovery with advances in analytical proteomics technology


Aaron S. Gajadhar, PhD, Thermo Fisher Scientifi c


Despite advances in genomics, some aspects of cell behaviour can only be understood by exploring proteins. Cell signalling pathways, which commonly rely upon protein ‘receptors’ binding to signalling molecules to enable communication, allow a cell to interact with and respond to its environment appropriately, supporting healthy and normal growth, migration, division, and development and repair of tissue. These pathways can break down or become disrupted by various genetic or epigenetic alterations, leading to irregular cell function and the development of tumours and cancers.


To better understand the mechanisms behind cell health and disease, researchers aim to determine how cell signalling pathways are disrupted, and the points at which they are impeded along the sequential signalling pathway. Proteomics is a valuable tool here, as it can provide a level of information that other modalities cannot. By targeting specifi c proteins, proteomics directly measures the components of a cell rather than evaluating by inference based on RNA, DNA or other biomolecules. Quantitative proteomics advances these capabilities and identifi es not only what is in a cell, but how much of it is present and how different proteins are interacting, providing a ‘systems level’ understanding of what is going on in a given cell or disease state.


While it generates invaluable insight, proteomics remains challenging due to the sheer complexity of the proteome. While the genome is made up of approximately 20,000 genes, the proteome comprises up to one million protein forms, some of which are present at low (and, therefore, hard-to-detect) abundances. The proteome is also highly dynamic and variable in nature; it responds to environmental infl uences and changes with age and individual characteristics, making sampling and interpretation diffi cult. Additionally, many laboratories have not yet fully embraced proteomics, considering it to be a somewhat specialised approach.


However, the drug discovery landscape is changing, with innovative analytical technology playing an increasingly vital role at this stage of the pipeline and removing traditional barriers to entry. Advanced proteomics methodologies offer more accessible, usable ways to characterise the complex molecular mechanisms involved in disease pathogenesis – without compromising on performance.


The promise of protein quantitation


Quantitative proteomics aims to paint a reliable, accurate picture of the proteome - of its protein concentrations, post-translational modifi cations, protein-protein interactions, and more - in order to understand the molecular drivers of disease. As the proteome is the main functional entity within a cell, proteomic analysis of cells in various disease states can identify potential biomarkers for use in novel biotherapeutics, and enable researchers to better understand the mechanisms underpinning diseases, such as cancer (which, in turn, could unlock faster diagnosis and treatment for patients). While oncological research is a notable example of the value of proteomics, quantitative proteomics holds promise in many other areas of study, from the development of wider biotherapeutics and personalised medicines to metabolomics, anti-doping, food and beverage testing, and more.


Traditionally, clinical diagnosis relies upon a mix of pathology, microscopy and genomic stratifi cation – but this is insuffi cient for optimal understanding and diagnosis of health and disease given that many drugs act on proteins. With quantitative proteomics, diagnostic or prognostic protein biomarkers can be derived and used to monitor a specifi c set of targets, and map signalling networks and pathways. Such pathways are contextual in nature; measuring a specifi c node may return a specifi c set of conditions, but this gives just a snapshot of a particular pathway. For instance, a particularly active protein may appear to be in a certain state, but it may in fact be manipulated by a node further along the pathway. In this way, reaching a conclusion based on just one aspect of a sample’s total suite of proteins may return false conclusions and, in turn, prompt incorrect clinical research decisions – or in a diagnostic setting, incorrect decisions regarding patient treatment. Additionally, it may be easy to measure the most visible proteins in a cell, but some signalling pathway components exist only in very low abundances. As with an iceberg, the ‘tip’ of higher-abundance proteins may be visible - but what if the important information lies beneath?


Despite its promise, quantitative proteomics requires advanced technology. A key issue relates to the aforementioned dynamism and variability of the proteome. This is far more pronounced than for the genome, and can enhance sampling bias. Sampling different parts of the same tumour results in differences, and methods of collection and storage become important considerations since, for example, post-resection freezing delays can impact the activation of signalling networks. In such cases, researchers are not seeing the real physiology of the disease, but the artefacts of collection and biobanking. Issues of sampling bias, or of changes occurring during collection and storage, do not arise for genomics samples as the DNA is stable. The comparative instability of the proteome, and associated sample variability, highlights the challenges inherent in oncology and disease proteomics.


Current antibody- and mass spectrometry-


based approaches Currently, quantitative proteomics comprises a combination of discovery-based and targeted methods. Discovery methods aim to comprehensively survey a sample and identify all the components present, while targeted approaches instead seek to monitor and quantify selected targets of interest.


Many quantitative proteomics methodologies are of the former type – they look more broadly at the whole system – and are based on antibodies. Predominant antibody-based protein measurement methodologies include Luminex, tissue microarrays and western blotting. These techniques seek to determine which antigens within a sample are binding with specifi c and selective antibodies.


However, antibody-based methods are limited by the quality, availability and selectivity of the desired antibody, and can typically explore a maximum of several hundred or so targets. Mass spectrometry (MS), meanwhile, can explore thousands of targets without needing to verify the quality of an antibody, raising confi dence in results. More targeted methods of quantitative proteomics use MS – specifi cally, liquid chromatography MS with ‘selected reaction monitoring’ triple quadrupole workfl ows (LC-MS; SRM). An example of such a workfl ow is one in which peptides representing proteins of interest are used to generate an ‘assay’ for their detection and quantifi cation. For this, selected peptides are isolated and fragmented and characteristic fragment ions for that peptide are then sequentially isolated and detected. By tracing the properties of these precursor-product ion pairs, the target peptide – and, by extension, the target protein – can be quantifi ed.


Quick, straightforward and cost-effective SRM approaches are limited by mass resolution and selectivity, especially in samples with complex matrixes such a biological material. However, such limitations can be overcome using high-resolution, accurate-mass MS (HRAM MS) and parallel-reaction monitoring workfl ows (PRM), such as those implemented by Thermo Scientifi c Orbitrap-based mass spectrometers. PRM isolates a target precursor, fragments it and then detects all resulting product ions simultaneously, allowing quantifi cation of peptide abundance and comparison of results across multiple sample sets.


This brings greater selectivity and sensitivity alongside lower limits of detection, but remains limited by speed of acquisition, throughput and sometimes representation of the amount of target substance present (a property defi ned as peak area). Peak area is limited for PRM due to its slower acquisition, which decreases the chances of it achieving complete sampling of a peptide’s elution profi le.


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