Reading time: 5 minutes
As technology has been advancing exponentially over the years, more efficient diagnostic tools have been developed for cancer detection. Recent studies1,2,3 have shown the need for this advancement, as current diagnostic tools are still not adequate enough to detect cancer. One of the new tools that have been advancing, especially over the past couple of years, is mass spectrometry. Mass spectrometry has not only advanced in methods but also in the way that samples are being prepped, thus creating new avenues in cancer diagnosis.
Current techniques that are used for cancer diagnosis, to name a few, are ELISA, PSA (prostate-specific antigen), PCa (suspected prostate cancer), FACS (Fluorescence-activated cell sorting), and IHC (immunohistochemistry). IHC is useful in discerning tissues associated with poor prognosis or response to treatment. Some cancers that are used to diagnose based on IHC include HIF1α and CD69+ activated lymphocytes. Each of these diagnostic tools has its pros and cons; however, the difficulty of adopting new techniques is the bias toward using the current ones. The main advantage of FACS is that it is detection-specific; however, factors like cost makes it difficult for providers to use it.
As cancer is a heavily cost-burdened disease state, researchers and doctors alike are looking for ways to decrease the cost and improve outcomes simultaneously. What if I say that we can take all these techniques away and just use mass spectrometry (MS)? It may seem unlikely that MS can be used for such a wide scope of human cancers; however, we are getting close to that with advancements in clinical proteomics in MS which allow clinicians to identify if a patient has cancer, based on certain proteins.
So what has been done in clinical proteomics for researchers and providers alike to be able to use it as a diagnostic tool? Well, various parts of the workflow in clinical proteomics have actually been improving so that individuals can use them. More patients’ tissues can be collected and analyzed, so ionization methods will have to be accommodated to investigate the need for the sample. Various sampling techniques that are being explored further are needle biopsies, fresh frozen tissues, formalin-fixed paraffin-embedded, and optimal cutting temperature embedded. Though it is not well understood how these techniques preserve the proteomics of the tissue, they seem to be more useful in comparison to just collecting samples when capable during surgery. Laser-capture microdissection is also useful when seeing the tissue in different spatial resolutions. Although it is useful to find biomarkers that have already been discovered, there is a high interest in investigating new biomarkers, especially in non-invasive/minimally invasive ways. Some samples that follow this criterion are using prostatic secretions, saliva, tears, urine, etc.
New development in ionization methods has unlocked new ways to process data without having the sample degraded too much during ionization. ESI (electron-spray ionization) and MALDI (matrix-assisted laser desorption/ionization) are recently advanced ionization methods that are considered “soft” ionization methods and have led to new ways of processing samples. Additionally, imaging MALDI has been able to detect masses on the sample that is on the matrix. Seeing this data in a spatial-based result can help researchers determine where the novel biomarkers are with respect to those already investigated. Some of the advances in MS scanning modes include DDA (data-dependent acquisition), BoxCar data acquisition method, and DIA (data-independent acquisition). Although it has been useful before, the downside of using these modes, from the previous sentence, is lower inter-sample reproducibility, which hampers the capability of finding novel biomarkers across various samples. BoxCar method works by filling smaller m/z windows which will increase ion injection time by more than ten-fold. This will increase the signal-to-noise ratio, leading to better resolution for both MS1 and MS2. Additionally, this method has been shown to have better reproducibility than DDA. DIA also fragments samples in a narrower m/z in comparison to DDA. DIA does have complex MS2 samples for complex samples and is also recommended to be used for less complex samples. Currently there is not much data in terms of detection, though this could possibly be a new route of optimizing.
Although there has been great advancement in using MS for cancer detection, there is still more progress that needs to be made to improve the diagnosis of cancer biomarkers. There are various aspects in an MS workflow that allows the opportunity to explore optimization and therefore have not only reproductive results but also more efficient ways to get results. There has been much work done on the ionization methods and scanning modes, but not much done in terms of MS detection in clinical proteomics. Thus, this will undoubtedly be an area of future exploration to understand how to use MS.
Edited by MaryAnn Bowyer
Header Image: NIH Image Gallery from Bethesda, Maryland, USA, CC BY 2.0 https://creativecommons.org/licenses/by/2.0, via Wikimedia Commons
- Macklin, A., Khan, S. & Kislinger, T. Recent advances in mass spectrometry-based clinical proteomics: applications to cancer research. Clin Proteom 17, 17 (2020). https://doi.org/10.1186/s12014-020-09283-w
- Vinaiphat, A., Low, J. K., Yeoh, K. W., Chng, W. J., & Sze, S. K. (2021). Application of Advanced Mass Spectrometry-Based Proteomics to Study Hypoxia Driven Cancer Progression. Frontiers in Oncology, 11. https://doi.org/10.3389/fonc.2021.559822
- Zhang, J., Sans, M., Garza, K. Y., Eberlin, L. S. Mass Spectrom. Rev. 2021, DOI: 10.1002/mas.21664