Proteomics Is Ready for Primetime


Over the past two decades, medicine has been moving towards data-driven diagnoses and personalized therapies based on molecular information.1 Oncology is a prime example, with an ever-growing range of precision drugs designed to target specific driver mutations in cancer cells. However, while these drugs have provided some benefits, they have failed to transform survival in most tumor types.

Frustratingly, identification of a particular genetic alteration is not enough to guarantee that a treatment will work. For example, drugs targeting the overactive V600E form of the signaling protein BRAF are effective in melanoma but have no activity in colorectal tumors with exactly the same mutation.

Attempting to treat disease based on a catalogue of genetic mutations is therefore likely to be an overly simplistic approach. 

Genes don’t tell the whole story – proteomics can fill in the gaps 

DNA sequencing is now rapid, reliable, low cost, and widely available, enabling rapid acquisition of large amounts of genetic and genomic information. Next-generation sequencing has been used in a range of clinical trials and has recently been approved for routine use by the US Food and Drug Administration (FDA).2

However, in practice, genes don’t always tell the whole story of what goes on in a cell or tissue at a molecular level (phenotype). Epigenetic regulation of gene activity and expression may alter the molecular pathways within a cell, resulting in dysfunction and disease even in the absence of underlying genetic alterations. As a result, genetic analysis is at best a proxy for the underlying molecular workings of a cell and may be misleading when it comes to selecting the best therapy.

Transcriptomics – sequencing of the RNA produced within a tissue or cell – is one way to estimate how genes are expressed, providing a more detailed insight into which cellular processes are dysfunctional in disease and aiding therapy selection.

Although transcriptomics provides a more realistic description of phenotype than genomic or genetic analysis, particularly while in a steady state. However, it is much less accurate during times of transition and turbulence – the correlation between transcription and translation is much weaker and RNA content alone is insufficient to predict protein abundance in many situations. For example, RNA may not be translated into a protein, and proteins can be modified after transcription in ways that can be unpredictable.3

As a result, there is often a mismatch between the predicted molecular profile of a cell based on RNA sequencing and its actual phenotype. Analyzing the protein content of cells (proteomics) would be more relevant for patient phenotyping and precision medicine. Yet protein analysis has historically been perceived as more technically challenging than DNA or RNA sequencing and requiring a large amount of starting material.

Proteomics reveals what’s going on inside

Faulty or dysregulated proteins lie at the heart of most disease pathways and are the usual target of precision therapies. Furthermore, there is a growing realization that the molecular chaos within a disease like cancer emerges as a result of complex dysregulated pathways and cannot be pinned on a handful of specific gene faults. Studying the entire set of proteins produced by a cell can therefore give a broad overview of the cell function and disease phenotype in a way that genomic analysis cannot.

Proteomics presents a significant challenge for analytical scientists: proteomes are highly complex, containing thousands of proteins, and it is easy to miss molecules that are present in a cell at low levels. However, researchers predict that decoding the proteome will impact the life sciences and clinical practice even more significantly than the genome revolution, so the challenge is worthwhile.

In recent years, mass spectrometry has emerged as the analytical method of choice for proteomics, due to its high sensitivity and specificity, providing reliable, high-throughput identification and quantification of proteins in biological samples.

Ultimately, proteomics could be used routinely to complement genomics and transcriptomics data in research and development, diagnosis, and treatment selection. But there are significant challenges that are only now being overcome.

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