With a history that spans one hundred years, mass spectrometry (MS) is one of the most widely used analytical techniques in bioscience and medical research. Since the development of the first modern mass spectrometer in 19181, the technique has advanced steadily over time and found its’ way into a range of applications from forensic toxicology to cancer diagnostics. Bulky, expensive equipment that defined historical mass spectrometers are less common now, and have been replaced by faster, more sensitive, and cost-effective workflows. Newer spectrometers are also largely automated and user-friendly, making them more adaptable for clinical use.
Although histopathology is still the gold standard for diagnosing cancer, molecular analysis using MS has gained traction in the recent years for detecting tumors, monitoring progression, and even predicting treatment response.
MasSpec Pen Detects Cancer in Seconds and One-ups the iKnife
A hand-held device that connects to a mass spectrometer and identifies cancerous tissue during surgery hit the headlines last year, claiming to deliver results in ten seconds. Termed MasSpec Pen, the device contains a probe that can soak up molecules from human tissue and send to a mass spectrometer wheeled into the surgery room. Within a matter of seconds, molecular analysis by an ambient ionization MS technique indicates which areas of tissue are cancerous and should be removed by surgery. The device can also sample tissue non-destructively, displaying an advantage over a similar device called iKnife that causes tissue damage due to electrocauterization.
In a test of 253 patients with lung, breast, thyroid or ovarian cancer, the MasSpec Pen was able to provide a tissue diagnosis with over 96 percent accuracy. Even in marginal regions that contained mixed tissue and cellular compositions, the device was able to detect cancer.
During development, researchers used a benchtop liquid chromatography-tandem mass spectrometry (LC-MS/MS) system with quadruple precursor ion selection, and high-resolution, accurate-mass (HRAM) Orbitrap detection.2 Since this is a large instrument that takes up considerable space in the surgery room, developers are already looking into getting reliable data from a more compact spectrometer.
*Source: Youtube – The MasSpec Pen Can Detect Cancer By Touch
Using Quantitative MS to Help Guide Lung Cancer Therapy
A team of researchers at the Virginia Commonwealth University lead by Professors Adam Hawkridge (Department of Pharmaceutics, School of Pharmacy) and David Gewirtz (Department of Pharmacology and Toxicology, School of Medicine) are utilizing quantitative mass spectrometry to uncover clues related to treatment resistance and relapse of non-small cell lung cancer (NSCLC).
“Mass spectrometry can be used on a number of levels to better understand NSCLC signaling pathways for drug development, or to identify molecular signatures that can be exploited to overcome NSCLC treatment resistance,” says Hawkridge. “For example, in our most recent LC-MS/MS study of treatment resistance, we found that senescence – rather than autophagy – associated proteins were more prominently secreted in the treatment resistant-cells in response to ionizing radiation.”
In a previous study by Hawkridge’s team, LC-MS/MS analysis allowed the identification of 364 secreted proteins that could be further studied for their potential diagnostic value as a function of p53 expression, irradiation, and functional autophagy status in the context of NSCLC.3 The lead author of the study, Dr. Emmanuel Cudjoe, explained that cancer cells respond to stress by activating a process called autophagy in which organelles are recycled and energy production is maintained, leading to tumor growth, therapeutic resistance and relapse.
“Where autophagy contributes to treatment resistance, inhibition can restore tumor sensitivity to chemo/radiotherapy. Our long-term goal is to identify molecular signatures and pathways of autophagy that can be targeted to improve the sensitivity to radiotherapy.,” Cudjoe adds.
MS-based Test Helps to Select the Right Patients for the Right Type of Immunotherapy
For treating patients with metastatic melanoma, there are several different types of immunotherapeutic regimens available, including mono-therapies and combinations of immune-checkpoint inhibitors. However, selecting and optimizing the right treatment choice for each patient is challenging. To assist in treatment selection, a new MS-based blood test was developed by Biodesix, Inc. in collaboration with scientists from the NYU Langone Medical Center, New York, Yale University School of Medicine, New Haven, and the Massachusetts General Hospital, Boston.
“We developed an anti-programmed cell death protein-1 (PD-1) melanoma blood test using a machine learning platform with a combination of clinical outcome data and expression data of circulating proteins based on mass spectral analysis,” says Dr. Heinrich Röder, Chief Technology Officer at Biodesix. “We’ve shown this test to predict survival in patients receiving PD-1 inhibitors. It can also identify patients who are resistant to monotherapy, but may in fact benefit from combination immunotherapy instead.”
Scientists at Biodesix input data acquired from Matrix Assisted Laser Desorption/Ionization Time-of-Flight (MALDI-ToF) mass spectrometry into a machine learning platform for multivariate test design.4 “MALDI allows us to take a real-time snapshot of a patient’s circulating proteome, which is derived from the tumor, tumor microenvironment, and normal host tissue. These proteins have direct regulatory effects on the immune system, especially the interplay of the innate and adaptive system,” Röder explains. “We do not need to choose proteins based on pre-conceived notions of relevance, as one has to do in fixed panel approaches, and hence can observe effects that are often missed.”
Biodesix uses a special MALDI-ToF method for protein analysis, termed Deep MALDI™, that is aimed at reducing the noise in mass spectral data by increasing the number of laser shots. “With this method, we can measure more proteins in a robust and reproducible way with a high sample throughput and small sample volume,” says Röder. “We also experience a much wider coverage of reliably detectable proteins and peptides compared to traditional MALDI-ToF. “
Although Deep MALDI can function across an abundance range of 4-5 orders of magnitude, it struggles to pick up very low abundance proteins outside the range of the classical plasma proteome. Other limitations of the method include reduced sensitivity at masses higher than 30kDa, and low resolution of proteins that are very close in mass/charge ratio. Röder’s team is currently working on improving this technology and expanding it to investigate immunotherapy responses in other types of cancer such as NSCLC.
Future of MS-based Proteomics
With the advent of increasingly sophisticated technologies, MS instruments are becoming faster and more sensitive with respect to molecular characterization. Hawkridge believes that advances in analytical figures of merit will drive new molecular insight in areas such as protein post-translational modifications (e.g., glycosylation). He elaborates, “As genomic sequencing becomes more prevalent, these genetic perturbations are being incorporated into the proteomics databases for additional levels of data analysis. That is just a small sampling of the untapped potential of mass spectrometry-based cancer proteomics.”
1 Dempster, A. J. A New Method of Positive Ray Analysis. Phys. Rev. 1918, 11 (4): 316–325.
2 Zhang, J.; Rector, J.; Lin, J. Q.; Young, J. H. et al. Nondestructive tissue analysis for ex vivo and in vivo cancer diagnosis using a handheld mass spectrometry system. Sci. Transl. Med. 2017, 9 (406).
3 Cudjoe, E. K.; Saleh, T.; Gewirtz, D. A.; Hawkridge, A. M., Mass spectrometry-based proteomics analysis of the non-small cell lung cancer secretome. Cancer Res. 2017, 77 (13 Supplement), 227-227.
4 Weber, J. S.; Sznol, M.; Sullivan, R. J. et al. A Serum Protein Signature Associated with Outcome after Anti–PD-1 Therapy in Metastatic Melanoma. Cancer Immunol. Res. 2018, 6 (1), 79-86.