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\begin{document}

\title{Diagnosing Cancer from a Drop of Blood}



\author[1]{Kevin Pagel}%
\affil[1]{Freie Universitat Berlin}%


\vspace{-1em}



  
  \date{May 27, 2024}


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\begin{abstract}
This Research Highlight showcases the Research Paper entitled, Big
cohort metabolomic profiling of serum for oral squamous cell carcinoma
screening and diagnosis, https://doi.org/10.1002/ntls.20210071%
\end{abstract}%



\sloppy


RESEARCH HIGHLIGHTS

Diagnosing Cancer from a Drop of Blood

Kevin Pagel

Department of Biology, Chemistry, Pharmacy, Freie Universit\selectlanguage{ngerman}ät Berlin,
Berlin, Germany and Department of Molecular Physics, Fritz Haber
Institute of the Max Planck Society, Berlin, Germany

\textbf{Correspondence}

Prof. Dr. Kevin Pagel, Freie Universität Berlin, Institute of Chemistry
and Biochemistry, Altensteinstraße 23A, 14195 Berlin, Germany, Email:
kevin.pagel@fu-berlin.de

This Research Highlight showcases the Research Paper entitled, \emph{Big
cohort metabolomic profiling of serum for oral squamous cell carcinoma
screening and diagnosis} , https://doi.org/10.1002/ntls.20210071

Cancer is the plague of our time. It is the leading cause of death
worldwide, accounting for nearly one in six
deaths.11\textbf{References}Ferlay J, Ervik M, Lam F, Colombet M, Mery
L, Piñeros M, Znaor A, Soerjomataram I, Bray F (2020). Global Cancer
Observatory: Cancer Today. Lyon, France: International Agency for
Research on Cancer. Available from: https://gco.iarc.fr/today, accessed
{[}30 April 2022{]} 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
processes22Johnson C, Ivanisevic J, Siuzdak G. Metabolomics: beyond
biomarkers and towards mechanisms. \emph{Nat Rev Mol Cell Biol}2016;
\textbf{31:} 451--459. and their activity.33Rinschen MM, Ivanisevic J,
Giera M, Siuzdak G. Identification of bioactive metabolites using
activity metabolomics. \emph{Nat Rev Mol Cell Biol}2019; \textbf{20:}
353--367. 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''.44Hsu PP, Sabatini DM. Cancer
Cell Metabolism: Warburg and Beyond. \emph{Cell} 2008; \textbf{135:}
703--707. 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 elucidation55Jain M, Nilsson R, Sharma S, Madhusudhan N,
Kitami T, Souza AL, Kafri R, Kirschner MW, Clish CB, Mootha
VK.\emph{Science} 2012; \textbf{336:} 1040-1044. and detection of cancer
and other diseases.66Nagana Gowda GA, Zhang S, Gu H, Asiago V, Shanaiah
N, Raftery D. Metabolomics-based methods for early disease diagnostics,
\emph{Expert Rev Mol Diagn} 2008, \textbf{8:}
617-633.\textsuperscript{,}77Sinclair E, Trivedi DK, Sarkar D,
Walton-Doyle C, Milne J, Kunath T, Rijs AM, de Bie RMA, Goodacre R,
Silverdale M, Barran P. Metabolomics of sebum reveals lipid
dysregulation in Parkinson's disease. \emph{Nat Commun}2021;
\textbf{12:} 1592.

Due to its exceptional sensitivity, speed, and the ability to obtain
quantitative information, mass-spectrometry is a perfectly suited tool
to determine metabolic fingerprints comprising hundreds of different
components.88Alseekh S, Aharoni A, Brotman Y et al. Mass
spectrometry-based metabolomics: a guide for annotation, quantification
and best reporting practices. \emph{Nat Methods} 2021;\textbf{18:}
747--756. However, the human metabolome database (HMDB) currently
contains more than 200,000 entries.99Wishart DS, Guo AC, Oler E et al.
HMDB 5.0: the Human Metabolome Database for 2022.\emph{Nucleic Acids
Res} 2022, \textbf{50:} D622--D631. 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 Sciences1010Yang, X, Song, X, Yang, X, et al. Big
cohort metabolomic profiling of serum for oral squamous cell carcinoma
screening and diagnosis. \emph{Nat Sci} 2022; 2:e20210071. as well as a
related study1111Song X, Yang X, Narayanan R, Shankar V, Ethiraj S, Wang
X, Duan N, Ni Y-H, Hu Q, Zare RN Oral squamous cell carcinoma diagnosed
from saliva metabolic profiling. \emph{Proc Natl Acad Sci USA} 2020;
\textbf{117:}16167-16173., Richard N. Zare, Qingang Hu 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 substrate1212Song X,
Chen H, Zare RN. Conductive Polymer Spray Ionization Mass Spectrometry
for Biofluid Analysis.~\emph{Anal Chem} 2018;\textbf{90:} 12878--12885.
containing a solvent reservoir to which a few microliters of blood,
serum or saliva are applied. Using a non-porous polymer rather than the
often-employed paper1313Wang H, Liu J, Cooks R, Ouyang Z. Paper Spray
for Direct Analysis of Complex Mixtures Using Mass
Spectrometry.~\emph{Angew Chem Int Ed} 2010;\textbf{49:} 877-880.
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),1414Yamashita M, Fenn
JB. Electrospray ion source. Another variation on the free-jet
theme.~\emph{J Phys Chem.} 1984;\textbf{88:} 4451--4459. 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
purification 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, Hu and co-workers were able to measure an impressive 819
serum samples in 12 hours, 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 from
non-cancer 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 electrospray ionization. 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,1515Sarkar D, Sinclair E, Lim SH, et al. Paper Spray Ionisation
Ion Mobility Mass Spectrometry of Sebum Classifies Biomarker Classes for
the Diagnosis of Parkinson's Disease.\emph{ChemRxiv.} Cambridge:
Cambridge Open Engage; 2021; doi:10.26434/chemrxiv-2021-vsjwj-v2; This
content is a preprint and has not been peer-reviewed. 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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