Simple blood test may allow for early cancer detection
[June 8, 2022: Kevin Pagel, Wiley]
The current gold standard in cancer diagnostics are biopsies from suspect tissue, which are examined using histology. (CREDIT: Creative Commons)
Cancer is the plague of our time. It is the leading cause of death worldwide, accounting for nearly one in six deaths. The mortality of cancer is reduced significantly when diagnosed and treated early enough, ideally before or immediately after the first symptoms occur.
The current gold standard in cancer diagnostics are biopsies from suspect tissue, which are examined using histology. However, the excision of specimen is often a very unpleasant and painful experience for the patient and potentially suffers from sampling inaccuracy due to tissue inhomogeneity.
An alternative strategy are so-called liquid biopsies: the sampling of circulating or excreted body fluids such as blood or saliva, which are subsequently screened for specific DNA or protein-based tumor markers using immune recognition or sequencing techniques.
A limiting factor of this approach is that each molecular probe is sensitive to only one particular feature, which may not be sufficient to come to a reliable conclusion. Multiplexing the detection of multiple DNA and protein-based features is generally possible, but causes further problems such as cross-reactivity and exploding costs.
Especially with the advent of easy-to-use chromatography and mass spectrometry instrumentation, another class of molecules got in focus for cancer detection: metabolites. As the name implies, metabolites are small molecular substrates or products of metabolism—a diverse potpourri ranging from amino acids, lipids, and sugars to hormones and reaction intermediates.
Each individual has a characteristic metabolic profile that can be traced back in body fluids. This metabolic fingerprint varies depending on the state and condition of the organism and therefore provides a snapshot of the underlying cellular processes and their activity.
Cancer cells generally grow much faster than normal cells and consume vast amounts of nutrients. Not surprisingly, this leads to drastic changes in their metabolic profile—an effect that is often referred to as “metabolic reprogramming.”
While the details of metabolic reprogramming are still not fully understood, it was shown for various examples that the resulting changes in the metabolic profile can be successfully used for the mechanistic elucidation5 and detection of cancer and other diseases.
Due to its exceptional sensitivity, speed, and the ability to obtain quantitative information, mass spectrometry is a perfectly suited tool to determine metabolic fingerprints containing hundreds of different components. However, the human metabolome database (HMDB) currently contains more than 200,000 entries. Reliably monitoring only a fraction of these to identify diagnostic cancer biomarkers represents a formidable challenge, not only for the MS-based measurement itself but also for the underlying data analysis.
In a research article recently published in Natural Sciences as well as a related study, Richard N. Zare and co-workers present an elegant way to drastically simplify the measurement as well as the analysis of metabolites for the detection of oral squamous cell carcinoma (OSCC).
The special beauty of the presented approach lies in the combination of multiple innovative techniques.
First, instead of using off-line capillaries or a chromatography-based infusion system, the authors use the much simpler conductive polymer spray ionization. This technique utilizes small triangles of a conductive polymeric substrate containing a solvent reservoir to which a few microliters of blood, serum, or saliva are applied. Using a nonporous polymer rather than the often-employed paper prevents unintended binding of apolar compounds such as lipids and significantly reduces the amount of background signal.
Application of a high voltage to the emitter generates molecular ions by a mechanism similar to that of electrospray ionization (ESI), which can subsequently be analyzed using the vast portfolio of available mass spectrometry and tandem mass spectrometry techniques. However, in contrast to ESI, virtually no sample work-up and no time-consuming cleaning steps are required as the sample is directly applied to the polymeric support. This prevents sample loss, makes the approach exceptionally fast, and, most importantly for clinical applications, cheap.
Zare and co-workers were able to measure an impressive 819 serum samples in 12 h, which highlights the potential of conductive polymer spray ionization for high-throughput clinical applications.
Second, the authors used a machine learning algorithm to identify diagnostic molecular markers that can be used to distinguish cancer form noncancer cases. Usually, mass spectrometry based metabolomics yields highly complex data, which are difficult to disentangle without applying a human bias. The presented machine learning approach circumvents this step and identifies markers purely based on a mathematical algorithm. This not only impedes human error but also helps to pinpoint markers and the correlations between markers that would otherwise remain hidden.
In total, 65 diagnostic metabolites, most of them lipids, were found to be significantly up- or downregulated in cancer cases. Monitoring these markers by mass spectrometry of serum can be used to quickly distinguish OSCC from healthy metabolites and even predict the stage of cancer, an aspect that is often crucial for the correct choice of treatment.
Finally, the authors elegantly validate their findings by cross-correlating their measurements with analyses on other body fluids such as saliva and molecular imaging of cancerous tissue using desorption ESI. The imaging data nicely illustrate the spatial distribution of the identified markers in excised tumor tissue.
The present study is a perfect showcase for how mass spectrometry-based metabolomics workflows can be simplified to make them usable in clinical applications. Using body fluids in conjunction with conductive polymer spray ionization drastically reduces the discomfort for patients and significantly lowers the cost and time expenditure of the analysis.
Recently, a comparable approach has been successfully employed for the metabolomics-based diagnosis of Parkinson's disease from sebum samples,15 which further highlights its universal utility. Likewise, artificial intelligence has demonstrated its exceptional potential in aiding the identification of diagnostic markers in various other applications.
The present paper therefore persuasively demonstrates the current transformation of mass spectrometry from an expensive research-only technique into a diagnostic tool that can be used in hospitals.
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Note: Materials provided above by Wiley. Content may be edited for style and length.
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