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370 Reilly Fankhauser et al.
dependent upon the expression levels of the target protein [62]. It is generally recommended to profile more than this lower limit. Due to limits of detection and spatial
resolution, the DSP, at its core, is not a single cell technology but can yield valuable information about tumor microenvironments or differences in larger biological compartments
(tumor vs stroma, for example).
2.6 Mass cytometry imaging
Thus far, we have discussed technologies that utilize antibodies coupled to HRP,
fluorophores, and oligonucleotides to interrogate proteins. The remaining modalities
that we will discuss in this chapter for analyzing protein expression that retain spatial
information utilize antibodies coupled to rare earth metals, read out by a mass cytometer.
These approaches are called mass cytometry imaging and include imaging mass cytometry
(IMC) and multiplexed ion beam imaging (MIBI, discussed below) [74].
2.6.1 Imaging mass cytometry
IMC is an approach to determine the concentration of a target protein or protein modification using metal isotope-conjugated antibodies. These metal isotopes are liberated
from the sample using a laser with resolution down to 1 μm, then identified and quantified using a mass cytometer to determine the target protein’s abundance [75]. In the
initial demonstration of IMC, Giesen et al. simultaneously detected 32 proteins and protein modifications with subcellular resolution [75]. Briefly, the steps for performing IMC
include tissue processing and slide preparation, marker staining with metal-labeled antibodies, laser ablation of metal labels, and analysis with a cytometry by time of flight
(CyTOF) mass cytometer. Then, signals are extracted for the measured markers, data
is processed and assembled into an image, single cells are segmented, and downstream
analysis is performed to make useful insights [75]. As additional isotopes become available, the authors anticipate this approach will be expanded to allow for the simultaneous
analysis of over 100 markers.
IMC overcomes the challenges of sample autofluorescence in fluorescence-based
imaging approaches and limitations in the number of fluorescent reporters that can be
detected in any given round of immunofluorescent imaging due to spectral overlap, thus
increasing the speed of this approach [75]. Staining of all markers can be performed
simultaneously, which increases speed; however, the detection rate is still quite slow.
At the time of publication, Giesen et al. suggested it takes 3.5 h to measure an area of
0.5 mm 0.5 mm at 1 μm resolution. It is anticipated that improvements in laser ablation chambers may shorten the acquisition time. Sensitivity may be increased by minimizing spray dispersion and increasing the number of metal atoms attached to each
antibody.
Limitations of IMC technology include the cost of acquiring nonstandard equipment
(Fluidigm CyTOF instrument), the costs to run and maintain the specialized equipment,
and while the resolution is quite high and enables single-cell analysis, the resolution is still
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less than that of IF approaches.
IMC technology has been demonstrated to analyze the tumor microenvironment and
predict outcomes to immunotherapy. Carvajal-Hausdorf et al. utilized IMC to investi-
gate CD8
+
T cell infiltration and other structural and signaling proteins in trastuzumab
(anti-HER2/neu) breast cancer patients [76]. They demonstrated that breast tumors that
do not express the extracellular domain (ECD) of HER2 are less likely to benefit from
trastuzumab. They also observed a spatial association of CD8
+
T cells and the extracellular domain of HER2, which supports the immune system’s role in the mechanism
of action of trastuzumab.
2.6.2 Multiplexed ion beam imaging (MIBI)
Multiplexed ion beam imaging (MIBI) is an alternative technology to IMC. Previous
reviews have performed extensive comparisons between the technologies, and a key difference is in the resolution [74, 77]. While IMC utilizes a laser with resolution down to
1 μm to liberate reporter metal isotopes, MIBI utilizes an oxygen duoplasmatron primary
ion beam to release reporter metal isotopes as ions resulting in a 260 nm resolution [78].
This approximates the resolution of standard microscopy at 200 nm, though the increased
resolution comes at the cost of lengthier acquisition times [79]. In both MIBI and IMC, the
liberated metal isotopes are identified and quantified using a mass cytometer.
Particularly pertinent to immunotherapies, Keren et al. used MIBI technology to ana-
lyze tissue from 41 triple-negative breast cancer patients [79]. Their panel of targets
included immune checkpoint molecules with antagonistic antibodies against PD-1,
anti-PD-1, Lag3, and IDO. They revealed highly variable tumor-immune compositions
between individuals. Analysis of the spatial distribution of these immune subsets led to the
categorization of cold, mixed, and compartmentalized tumors. Mixed and compartmentalized tumors coincided with the expression of PD-1, PD-L1, and IDO, dependent
upon cell type and location [79]. “Ordered immune structures” along the tumor immune
border were associated with survival [79]. All in all, this study promotes the usage of MIBI
technology to interpret the tumor-immune microenvironment, cellular composition,
spatial arrangement of cell subsets, and checkpoint-molecule expression, and to apply this
information to clinical decision making with immunotherapies. However, the clinical
utility of MIBI is limited by lengthy scanning times, nonstandard isotope-labeled probes,
poor detection of low-abundance antigens, and unconventional equipment and expertise
needed to run these experiments and analyze data [56].
It is important to note that MIBI and IMC (mass cytometry imaging approaches) are
different from mass spectrometry imaging (MSI) approaches, including Matrix-Assisted
Laser Desorption/Ionization (MALDI-MSI), which is discussed in the MS section of this
chapter. MALDI-MSI uses a laser to liberate molecules from within a sample without the
use of antibody labels [74]. These particles, ranging from proteins to metabolites, are
371Proteomic biomarker technology for cancer immunotherapy
372 Reilly Fankhauser et al.
identified by a mass spectrometer (MS) [74]. The range of potential analytes, in this case,
is much greater than that of MSI. However, there are other limitations to the technology,
including lower resolution, low sensitivity, and a lack of compatibility with FFPE tissues
[80–83], though some efforts have been made to analyze FFPE tissues using MALDI-
TOF as reviewed by Hermann et al. [84].
2.7 Multiplexed immunoimaging with nanostars
While mIHC and CyCIF approaches may overcome the problem of reliance on a single
biomarker to predict responses to ICB, capturing the dynamic changes in these biomarkers over time remains an issue. Current standards for predicting response require
invasive biopsies and rely on static measurements of PD-L1 expression. These static measurements lack critical information about temporal changes in protein expression, which
are increasingly important in monitoring response [85]. Recent advances in multimodal,
multiplexed imaging using gold nanostars may help overcome the challenges associated
with immunohistochemistry analysis of tumor sections [86]. In “Multimodal Multiplexed Immunoimaging with Nanostars to Detect Multiple Immunomarkers and
Monitor Response to Immunotherapies,” Ou and colleagues developed a set of immunoactive gold nanostars to simultaneously detect PD-L1 and CD8 + T cells in vivo using
positron emission tomography and surface-enhanced Raman spectroscopy (SERS) to
track both immunomarkers. Using these multimodal probes, they tracked response to
PD-L1 and CD137 agonists and segregated responders from nonresponders across multiple murine cancer cell lines. The authors acknowledge that the penetration depth of the
near infrared light that is used to facilitate SERS is 2–4 cm which could render this
approach difficult for deep tissues. However, the authors indicate that FDA-approved
optical fibers that can deliver light into deep tissues may help expand this approach’s
utility to include tumors deep within the body. This demonstration involved two biomarkers, PD-L1 and CD8, but the authors anticipate this approach can be upscaled to
include up to 10 biomarkers.
To this point, we have discussed technologies that retain spatial information, which is
crucial for investigating cell-cell interactions, ligand interactions, tumor architecture and
immune infiltration, and tissue heterogeneity. Next, we will discuss technologies that,
with some exceptions, do not retain spatial information, yet can still deliver critical
information about the tumor and immune cell phenotypes and peripheral changes in
biomarkers such as cytokines and cell populations.
2.8 Affinity technologies: Antibody and aptamer-based
2.8.1 Proximity extension assays: Olink
Proximity extension assay (PEA) technology, commercialized by the Swedish company
Olink, has enabled the simultaneous quantification of numerous proteins in low volumes
of biological samples, including sera, plasma, and cerebrospinal fluid. PEA technology
involves antibodies conjugated to an oligonucleotide tag that functions as a unique
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barcode for the protein type of interest [87]. When two of such oligonucleotide conjugated antibodies bind to a target epitope within close proximity to one another, the complementary oligonucleotides hybridize [87]. Successfully hybridized regions are
preamplified via PCR and digested with uracil DNA-glycosylase to remove all unbound
primers [87]. An expression can then be quantified through a targeted real-time qPCR
panel or an NGS-based exploratory assay [87]. The requirements for close proximity and
sequence-specific DNA hybridization render PEA technology a particularly sensitive
assay with low cross-reactivity [87] . PEA technology is similar to protein ligation assays
(PLA), except the ligation step in PLA is substituted for a DNA polymerization step in
PEA, which helps maximize recovery [88].
PEA technology has been successfully utilized to discover and validate biomarkers in
response to immunotherapy. Table 2 summarizes recent demonstrations of PEA technology in investigating responses to immunotherapy.
Table 2 Review of Olink PEA technology as demonstrated in investigating immunotherapies.
What they demonstrated using
Title Year Cancer type(s)
PEA technology
373Proteomic biomarker technology for cancer immunotherapy
In-depth plasma proteomics
reveals increase in circulating
PD-1 during anti-PD-1
immunotherapy in patients
with metastatic cutaneous
melanoma
[89]
2020 Cutaneous melanoma Used two complementary
techniques, high-resolution
isoelectric focusing liquid
chromatography–mass
spectrometry (HiRIEF
LC-MS/MS) and PEA
technology. Demonstrated
increased levels of circulating
PD-1 during anti-PD-1
therapy. Responders had
increased plasma levels of
proteins involved in various
immune processes including
T cell responses, neutrophil
degranulation, inflammation,
cell adhesion, and immune
suppression. Discovered
novel plasma proteins
associated with progression
free survival that may serve as
predictive biomarkers
including IL-6, IL-10,
PRAP1, desmocollin 3,
CCL-2, CCL-3, and CCL-4,
and VEGFA
Continued
374 Reilly Fankhauser et al.
Table 2 Review of Olink PEA technology as demonstrated in investigating immunotherapies—cont’d
What they demonstrated using
Title Year Cancer type(s)
PEA technology
A fine-needle aspiration-based
protein signature
discriminates benign from
malignant breast lesions
[90]
Age-associated changes in the
immune system may
influence the response to
anti-PD-1 therapy in
metastatic melanoma patients
[91]
Inflammatory cytokines and
ctDNA are biomarkers for
progression in advancedstage melanoma patients
receiving checkpoint
inhibitors [92]
2018 Breast cancer Used PEA technology and
nanostring DSP to compare
protein and RNA levels,
respectively, between breast
cancer biopsies and benign
lesions. Determined an
11-protein signature that can
discriminate cancerous tissue
from benign lesions.
Demonstrated results from
these technologies correlate
with current analysis tools
2020 Cutaneous melanoma Examined serum from patients
before and during anti-PD-1
treatment with the Olink
inflammation panel. Found
increased levels of CXCL9,
CXCL10, and CXCL11 after
1 month of anti-PD-1
therapy. These increases
were mainly found in
responders but not the
nonresponders. Also found
increased levels of NK-cell
stimulatory factor 2 (IL-12B)
and tumor necrosis factor
receptor superfamily member
9 (TNFRSF9) in responders
to therapy after 1 month of
treatment. IL-12B remained
higher in responders than
nonresponders after 3 months
of therapy
2020 Cutaneous melanoma Used the Olink
immunooncology panel to
determine that patien ts with
high pretreatment plasma
levels of monocyte
chemoattractant protein 1
(MCP1) and tumor necrosis
factor alpha (TNFa) were
associated with longer
Table 2 Review of Olink PEA technology as demonstrated in investigating immunotherapies—cont’d
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What they demonstrated using
Title Year Cancer type(s)
PEA technology
progression free survival than
those with low levels of these
cytokines. These results were
combined with an analysis of
circulating tumor DNA
(ctDNA) to predict responses
to anti-PD-1 therapy
Longitudinal immune
characterization of syngeneic
tumor models to enable
model selection for immune
oncology drug discovery [93]
2019 Murine colon
adenocarcinoma
(CT-26 and MC38
syngenic tumors)
Murine breast (4T1
syngenic tumors)
Applied flow cytometry, Olink
protein analysis, RT-PCR,
and RNAseq to characterize
immune populations in
mouse models of colon and
breast cancers over the course
of treatment with combined
anti-PD-L1 and anti-CTLA4
antibodies. Using the Olink
murine exploratory panel,
they demonstrated
differences in IL1B, IL-6,
CXCL1, CCL2, CCL3,
CCL5, and CSF2 expression
between CT-26 tumors
treated with combination
anti-PD-L1 and anti-CTLA4
compared to isotype control
A Phase I/IIa trial using CD19-
targeted third-generation
CAR T Cells for lymphoma
and leukemia
[94]
2018 B-Cell lymphoma or
leukemia
Utilized qPCR, flow
cytometry, and the Olink
immunooncology array to
investigate peripheral blood
changes over the course of
treatment for patients given
CAR-T cells targeting
CD19. They found that the
best predictor of response was
a good immune status prior
to treatment, including high
IL-12, DC-Lamp, Fas ligand,
and TRAIL. Responders had
low monocytic myeloid
derived suppressor cells and
low levels of IL-6, IL-8,
NAP3, sPDL1 and sPDL2
Continued
375Proteomic biomarker technology for cancer immunotherapy
376 Reilly Fankhauser et al.
Table 2 Review of Olink PEA technology as demonstrated in investigating immunotherapies—cont’d
What they demonstrated using
Title Year Cancer type(s)
PEA technology
Shaping the tumor stroma and
sparking immune activat ion
by CD40 and 4-1BB
signaling induced by an
armed oncolytic virus [95]
Adenovirus-mediated CD40L
gene transfer increases
teffector/tregulatory cell
ratio and upregulates death
receptors in metastatic
melanoma patients [96]
2017 Pancreatic cancer
(mouse models)
Used flow cytometry, and an
Olink 233-analyte ProSeek
array to interrogate
pancreatic cancer tumors in
mouse xenograft models
treated with an oncolytic
virus that drives expressio n of
CD40 and 4-1BBL. Infected
cells had reduced expression
of tumor-promoting factors
such as Spp-1, Gal-3, HGF,
TGF beta, and collagen type
1, while chemokines were
increased. Infected dendritic
cells upregulated
costimulatory molecules, and
infected endothelial cells
upregulated molecules
involved in migration. DC
activation led to an expansion
of antigen-specific T cells and
NK cells
2017 Cutaneous melanoma Investigated peripheral blood
changes in patients treated
with an adenovirus-based
CD40 ligand gene therapy.
Analyzed cells with a flowcytometry based panel and
plasma samples with an Olink
array. Patients had an
increased effector T cell/
Treg ratio due to therapy.
TNFR1 and TRAIL-R2
were upregulated. Stem cell
factor, E-selectin, and CD6
were associated with
improved OS while a high
level of granulocytic
myeloid-derived suppressor
cells, IL-8, IL-10, TGFb1,
CCL4, PIGF, and FI3t ligand
were associated with reduced
survival
Table 2 Review of Olink PEA technology as demonstrated in investigating immunotherapies—cont’d
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What they demonstrated using
Title Year Cancer type(s)
PEA technology
377Proteomic biomarker technology for cancer immunotherapy
Immunostimulatory AdCD40L
gene therapy combined with
low-dose cyclophosphamide
in metastatic melanoma
patients [97]
2016 Cutaneous melanoma Analyzed blood samples from
patients treated with an
adenoviral vector to express
the CD40 Ligand with flow
cytometry and the Olink
ProSeek Multiplex
Inflammation panel. The
authors found the patients
with the best survival had the
highest levels of activated
T cells and decreased
intratumoral IL-8
In conclusion, the Olink protein panels have shown a remarkable ability to discover
proteomic biomarkers that can predict immunotherapy response. Analyses are restricted
to a limited number of protein targets. However, rationally selecting the appropriate
panel for a given experiment has enabled the research community to identify numerous
statistically significant changes in cytokines and other protein targets in the tumor bed and
peripheral samples treated with various immunotherapies ranging from ICB to CAR-T
to oncolytic viruses.
2.8.2 Aptamer-based panels: Somalogic SomaScan
While PEA technology powered by oligonucleotide conjugated antibodies is typically
restricted to around 96 targets, recent developments with aptamer-based technologies
have enabled the interrogation of up to 5000 protein targets at a time across an 8-log
dynamic range from just 55 μL of blood [98]. The Somalogic SomaScan Platform uses
modified DNA aptamers: short oligonucleotides that bind to proteins with high affinity
and selectivity [98]. To run this assay, the analyte is incubated with aptamers containing a
photocleavable linker and biotin bound to a streptavidin bead. Unbound proteins are
washed away, and bound proteins are biotinylated [98]. UV light breaks a photocleavable
linker releasing the aptamer-protein complex [98]. Polyanionic competitors prevent
interaction with nonspecific complexes [98]. Biotinylated proteins and their bound
aptamers are then captured on streptavidin beads, and aptamers are isolated and quantified
using a fluorescence array [98].
The Somalogic SomaScan assay has been utilized in multiple studies investigating
lung cancer, which has resulted in protein panels that can be used to differentiate early
from late-stage disease [99–101]. The platform has also been used to discover biomarkers
expressed in prostate cancer exosomes [102]. Specific to immunotherapies, one study
378 Reilly Fankhauser et al.
raised the possibility of using SOMAmer (Slow Off-rate Modified Aptamer) reagents to
detect bound vs unbound EGFR in response to anti-EGFR therapies (Cetuximab and
Panitumumab) [103]. This approach could be used in the future to stratify patients with
high unbound EGFR who are less likely to respond to therapy and thus could benefit
from an alteration of the treatment strategy. Jin et al. used the SomaScan assay to determine biomarkers in the plasma of samples that could cause differences in expression of
HER2/neu on ex vivo cultured dendritic cells manufactured for use as immunotherapy
[104]. Lim et al. compared the SomaScan assay to the bead-based Eve Technologies Dis-
covery assay for their ability to identify circulatory biomarkers predictive of response to
immunotherapy [105]. Both panels detected circulating plasma proteins in
immunotherapy-treated patients, but the assays showed little correlation in protein quantification [105]. This study highlights the need for rigorous validation of assays that may
be used in clinical applications.
The field of immunotherapies could greatly benefit from using a panel that can detect
thousands of proteins to temporally identify dynamic biomarkers that change throughout
treatment to better predict responses. These aptamer-based panels may be more feasible for
clinical use than MS-based technologies (lower cost and complexity), and MS approaches
struggle to interrogate low abundance proteins without extensive preenrichment [106].
Some researchers suggest that biomarkers identified with these aptamer-based panels need
extensive validation before entering routine use, which could involve immobilizing individual aptamers on beads, incubating with sample analyte, then selectively eluting for analysis with MS [107]. This would validate the technology by determining the limit of
detection and analytic specificity of each aptamer in the panel [107].
2.9 Mass spectrometry
Historically, MS-based approaches to investigate highly heterogeneous biological samples have been limited by their ability to detect rare proteins [108]. Recent advances
in MS technologies and accompanying sample preparation strategies have helped overcome these limitations, enabling the investigation and quantification of proteins not limited to the targets in a panel, retaining high sensitivity and specificity, and enabling the
characterization of post-translational modifications [109, 110]. As discussed above, recent
technological developments combine mass spectrometry with spatial resolution information into a new powerful clinical tool called mass spectrometry imaging (MS-I) [80].In
addition, tissue preservation methods such as FFPE have previously been incompatible
with MS, but new sample preparation techniques have unlocked the proteomic potential
of the millions of banked FFPE samples in laboratories worldwide [111].
While there are a variety of mass spectrometers used in proteomic-based research,
each with individual strengths and weaknesses, the general concept is similar between
platforms. Proteins are ionized, separated using a mass-to-charge ratio (m/z), and then
quantified and identified. The instrument usually consists of three parts, including a
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source where the volatilization and ionization of the analyte occurs, an analyzer where
the ionized analyte is separated with electrical and/or magnetic fields and, the detector
where the mass-to-charge ratio (m/z) is measured [112]. The choice of ionization source,
analyzer, and detector depends on the scientific question of interest and depends on the
approach used for protein identification. Other important considerations when selecting
a mass spectrometer are mass resolution, sensitivity, and mass accuracy [113]. Modernized
techniques such as tandem mass spectrometry increase the specificity of the assay by coupling two mass spectrometers. A complete discussion of MS/MS is beyond the scope of
this chapter, but is reviewed in detail elsewhere [114–116]. Macklin et al. review additional pioneering MS scanning modes, detection, and quantitation techniques [117].
Two of the most common sample ionization methods are matrix-assisted laser
desorption ionization (MALDI) and electrospray ionization (ESI). MALDI-MS-I is performed on tissue sections and retains spatial information—an incredible advantage—
without destroying the tissue to enable subsequent histological staining [118]. The sample
is first crystallized into a matrix that is ionized into a gaseous phase with a laser, producing
protonated ions [119]. ESI is an alternative approach that utilizes a liquified sample for
ionization and thus does not retain spatial information. Historically, more aggressive ionization methods relied on gas chromatography as a separation technique, but recent
advances such as the aforementioned ESI and MALDI, utilize liquid chromatography
to separate ions, which offers several advantages [120].
After ionization, several analyzers are used in MS to separate particles based on their m/z
ratio,including ion trap, Fourier transformsion cyclotron resonance, quadrupole,Orbitrap,
and time-of-flight [121]. For a detailed review of the different analyzers, refer to Matthiesen
and Bunkenborg [122]. After separation by the analyzer, detectors identify and quantify
ionized particles. There are many detection options, and their description goes beyond
the scope of this chapter—for a more detailed description, refer to Medhe, 2018 [123].
With its ability to identify and quantify proteins derived from the entire human proteome, MS is uniquely poised to facilitate biomarker discovery. Recently, Berghmans
et al. investigated potential predictive biomarkers for anti-PD-1 therapy response in
NSCLC patients [124]. They utilized MALDI-MS-I and IHC to discover that the
expression of neutrophil defensin 1, 2, and 3 is predictive of response to anti-PD-1
and anti-PD-L1 treatment. Neutrophil defensin 1, 2, and 3 differ in only one amino acid,
a challenging distinction to identify, which required a combination of electron-transfer
dissociation and collision-induced dissociation in the MS/MS scan to resolve these
minute differences. Although more validation is necessary, this study is a good example
of using an approach that combines MS and IHC to determine putative predictive biomarkers for immunotherapy response.
In another example, Morales-Betanzos et al. developed an MS platform to quantify
several checkpoint molecules in a more robust method than the current IHC standard
379Proteomic biomarker technology for cancer immunotherapy
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