Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5588_Библиотеки_им_академика_М_И_Перельмана
.pdf
360 Reilly Fankhauser et al.
demonstrated that corticosteroid therapy severely reduced peripheral CD4+and CD8+,
altered peripheral CD8
+
/Treg ratios, and diminished the efficacy of anti-PD-1 therapy in
peripheral tumors, but not in intracranial tumors [17].
Two critical and unmet needs must be addressed to realize the full potential of immunotherapies: (1) how do we increase the response rate to immunotherapies? and (2) how
do we predict and control off-target toxicities due to immunotherapies? One promising
approach to answering these questions comes from studying proteomic biomarkers for
cancer immunotherapy. Proteomic analyses of tumor tissues, blood biomarkers, or even
the microbiome can help pair a patient with the treatment option that they are most likely
to respond to, or a personalized therapy (Fig. 1). After initial treatment, longitudinal
monitoring of the patient through proteomic analyses can enable treatment alteration
if biomarkers demonstrate early signs of a lack of response or can elucidate targetable
pathways to treat immune-related adverse events (Fig. 1). This approach to treatment,
driven by proteomic analyses, will ultimately help improve patient outcomes. Here,
we will describe advances in proteomic technologies and their applications in investigating and improving cancer immunotherapies.
Fig. 1 Proteomic analyses enable precision medicine. Created with BioRender.com.
Before discussing proteomic biomarker technologies for immunotherapy, it is impor-
Данная книга находится в списке для перевода на русский язык сайта https://meduniver.com/
tant to define the different types of biomarkers and understand what makes a good biomarker. Biomarkers can be predictive, diagnostic, or prognostic. Predictive biomarkers
can be used to “predict” the effect of a therapeutic intervention on a patient and indicate
how likely a patient is to benefit from a given treatment [18]. Predictive biomarkers can
be used to determine specific, actionable characteristics in cancers, such as the presence of
a BRAF V600E mutation in melanoma [19]. Using a therapeutic specifically in patients
harboring this driver mutation may help reduce the toxicity associated with traditional
cytotoxic chemotherapies, improve treatment responses, and spare the patient from
undergoing treatments that they are unlikely to benefit from. Diagnostic biomarkers
are used to narrow down a diagnosis for a patient. A classic example of a diagnostic biomarker in medicine is prostate-specific antigen (PSA), used to diagnose prostate cancer
[20]. A prognostic biomarker gives information about a patient’s overall outcome regard-
less of therapy [18]. Some biomarkers can be both predictive and prognostic [21]. HER2
amplification in breast cancer, for example, is prognostic for a worse overall response but
predictive of a positive response to treatment with anti-HER2 mAbs or tyrosine kinase
inhibitors [22].
With the rise of precision medicine and the increased role of companion diagnostic
tests in shaping clinical care, it is increasingly important that biomarkers used to direct
care are analytically and clinically validated and demonstrate clinical utility [23]. Analytical validity is determined by how reliably and accurately an assay measures the analyte of
interest [24]. Clinical validity is determined by how reliably an assay divides a population
into two or more groups based on expected treatment outcome or biological characteristics [24]. An assay holds clinical utility if it significantly improves clinical outcomes or
aids in patient management and clinical decision making [24]. Ideally, assays to test for
these biomarkers should be inexpensive both financially and in terms of time and expertise required to interpret the data to be widely integrated into clinical practice (i.e., the
technology should be accessible to community hospitals and major academic health centers alike) [23]. As we discuss various biomarkers and the analytic techniques used to
interrogate them, we should evaluate them through the lens of analytic validity, clinical
validity, clinical utility, cost, and complexity.
Proteomic analyses are by far the most popular tool for clinical tests and assays. Studying the proteome has a distinct advantage over transcriptomic analyses in that the correlation between mRNA abundance and protein expression is only about 40% [25, 26].
Additionally, many biomarkers relevant to immunotherapy are cytokines and other
protein-based signaling molecules; thus, studying their expression relies upon proteomic
analysis techniques. The preferred targets for many drugs, including checkpoint inhibitors, are proteins; investigating these proteins and their interactions with drugs is paramount. The proteome also provides more cellular information than the genome; while
the human genome has approximately 20,000 genes, researchers propose that the human
361Proteomic biomarker technology for cancer immunotherapy
362 Reilly Fankhauser et al.
proteome consists of several million protein variants, a staggering number that emphasizes
the need for precise and accurate protein detection methods [27, 28]. Due to SNPs, splice
variants, and other posttranslational modifications, one gene does not necessarily equal
one protein [29]. Therefore, one can acquire significantly more biologically relevant
information from a comprehensive analysis of the proteome than the genome.
It is also important to highlight the limitations of protein analysis. Unlike genomics,
which benefits from nucleotide amplification to allow for signal amplification, protein
identification methods cannot currently amplify peptide sequences, relying on initial
sample input quantity. This can limit the detection of rare proteins and proteins that
degrade quickly. Another challenge in proteomics is the complexity of the sequence
space; the genome consists of four complementary base pairs, yet the proteome consists
of 20 standard amino acids arranged to form chains of peptides leading to increased
complexity and requiring additional computational power to analyze [30].
2. Proteomic technologies
2.1 Immunohistochemical and immunofluorescence approaches
The FDA-approved biomarkers for predicting response to pembrolizumab (anti-PD-1)
are PD-L1 expression and mismatch repair (MMR) deficiency or high microsatellite
instability. Tumor PD-L1 expression, as determined by standard immunohistochemistry
(IHC) analysis of FFPE tumor tissue, was one of the earliest predictive biomarkers investigated. In a landmark study by Topalian et al., patients with >5% PD-L1 expression had a
36% overall response rate while patients with <5% PD-L1 expression had no response
[31]. Due to the inextricable role of PD-L1 in cancer development and the mechanism of
anti-PD-1/PD-L1 therapeutics, PD-L1 expression has been extensively studied as a predictive biomarker. As a result, five different testing platforms utilizing different antibody
clones and staining platforms have been developed. These platforms use different scoring
systems and cutoffs to determine PD-L1 positivity [32]. Concordantly, data relying solely
on PD-L1 expression status in the tumor to predict response to anti-PD-1 inhibition has
been mixed [32, 33]. These mixed outcomes are likely due to differences in antibody
clones, slide preparation and staining strategies, reliability of reader/pathologist interpretation, scoring mechanisms, and tumor heterogeneity. Thus, relying solely on PD-L1
expression status as a predictive biomarker is controversial.
Beyond PD-L1 expression, microsatellite instability and MMR deficiency have been
FDA-approved as predictive biomarkers that invoke treatment with pembrolizumab.
Microsatellite instability is determined through PCR by measuring the length of repetitive areas of DNA known as microsatellites and comparing these lengths in tumor and
normal tissue. Five microsatellite regions have been validated as targets for testing microsatellite instability—BAT25, BAT26, NR-21, NR-24, and NR-27 [34]. If two or more
out of the five markers show instability (different size repeats in tumor relative to normal
Данная книга находится в списке для перевода на русский язык сайта https://meduniver.com/
tissue), it is classified as microsatellite instability-high [34]. The tumor tissue is classified as
microsatellite instability-low if only one of the five markers shows instability [35]. The
distinction between microsatellite instability-low and microsatellite instability-stable
requires testing additional markers [35, 36].
More pertinent to proteomic technologies, MMR deficiency is determined through
IHC as the loss of expression of one of the four MMR proteins; MLH1, MSH2, MSH6,
or PMS2 [35]. MMR deficiency is a tissue-agnostic test. It was demonstrated to be predictive of response to pembrolizumab in a variety of cancers, including colorectal cancer
(CRC), ampullary cancer, cholangiocarcinoma, endometrial cancer, gastroesophageal
cancer, neuroendocrine tumors, osteosarcomas, pancreatic cancer, prostate cancer, cancer of the small intestine, thyroid cancer, and carcinomas of unknown primary [37,38].
Since MMR deficiency was demonstrated to be predictive of response to anti-PD-1 therapy in numerous cancers, anti-PD-1 therapy was granted FDA approval for patients with
MMR-deficient or microsatellite instability-high, unresectable or metastatic, solid
tumors that have progressed on prior therapies [39].
ICB therapies rely on a complex interplay between tumor cells, immune cells, the
tumor microenvironment, and other environmental factors including the microbiome
[40]. Therefore it makes sense that a single biomarker is likely insufficient to stratify
patient outcomes completely, and approaches that analyze numerous parameters are most
likely to be clinically valid. For example, tumors that express PD-L1 and are infiltrated
with CD4
+
and CD8+T cells are most likely to benefit from PD-L1 inhibitors [41].
Approaches that utilize both PD-L1 expressions (as determined through immunohistochemistry) and lymphocytic infiltration in the tumor (as determined through analysis of
standard H&E stained tumor sections) may be the most parsimonious, clinically useful
tool for predicting response to ICB [32].
Quantitative immunofluorescence using multiple markers that do not spectrally overlap
to stain the same tissue section simultaneously may help overcome the challenges posed by
reliance on a single marker. In a recent example, Johnson et al. utilized quantitative spatial
profiling to determine that a close interaction between PD-L1 and PD-1 and/or expression
of IDO1 and HLA-DR were predictive of response to anti-PD-1 therapy [42].Individual
biomarkers or other combinations of markers were not able to differentiate responders
from nonresponders. Translating this approach into clinical practice is highly feasible—it
requires only a few tumor tissue sections, has high predictive capacity, and scoring is performed objectively through automated scoring systems and visual verification.
363Proteomic biomarker technology for cancer immunotherapy
2.2 Multiplexed immunohistochemistry (mIHC)
Multiplexed immunohistochemistry (mIHC) allows the evaluation of numerous biomarkers within the same tissue sample, which may better capture the complex nature
364 Reilly Fankhauser et al.
of cancer tissues and tumor-infiltrating lymphocytes (TILs). These approaches allow for
the quantitative measurement of numerous proteins’ expression levels while retaining
spatial information [43]. This can ultimately be used to determine the activation states
and spatial distribution of immune cells within a tissue specimen and their interaction
with tumor cells [43]. Such information is critical for classifying tumors and predicting
clinical outcomes [44–48].
Several variations of immunofluorescence (IF) and mIHC have been developed in
recent years, as reviewed by Tan et al. [43]. The major differences between technologies
are in the detection chemistry and the methods used to quench the signal and/or strip off
previous rounds of antibodies. The predominant detection methods include HRPconjugated antibodies developed with a chromogenic die, metal isotope tagged antibodies, fluorophore-conjugated tyramide molecules (which react with HRP to generate
an antigen-associated fluorescence signal), and oligonucleotide-conjugates that can be
cleaved and counted to derive expression data [43].
mIHC approaches have been used to analyze immune contexture in immunotherapy-
treated tumors. The resulting analysis has informed prognostic and predictive parameters
for various cancer types. In one example, Tsujikawa et al. developed two mIHC panels
with 12 markers each to analyze the immune microenvironment of head and neck squamous cell carcinoma (HNSCC) and pancreatic ductal adenocarcinoma (PDAC) [49]. The
third panel of 11 markers was used to determine the functional/activation states of immune
cells within tumors [49]. The authors were able to identify three classes of HNSCC tissue,
including lymphoid-inflamed, myeloid-inflamed, and hypo-inflamed. The myeloidinflamed subgroup was associated with the shortest overall survival (OS), regardless of
HPV status. The authors also demonstrated different immune signatures based on HPV
status. HPV-positive tissues had high CD8
ciated with high natural killer cells, DC-SIGN
+
T cells, and HPV-negative tissues were asso-
+
dendritic cells, and CD66b+granulocytes.
The authors also investigated PDAC tissue responses to GVAX, a tumor vaccine
containing GM-CSF-secreting cancer cells. They described two subcategories of PDAC
tissue with differing levels of myeloid cell densities, but not lymphoid cell densities.
Immunosuppressive profiles dominated the high-myeloid group and were associated
with a shorter OS. The authors also determined that the high-myeloid PDAC classes
were associated with a PD-1
+
, EOMES+, CD8+T cell compartment with low Ki67
expression, indicating exhausted T cell function. Lastly, the authors demonstrated that
PD-L1 expression on myeloid cells was associated with CD8
+
cytotoxic
T lymphocyte activation and a favorable prognosis following GVAX therapy. In contrast,
the low proliferation status of CD8
+
T cells and low activation status of granzyme B were
associated with shorter OS in response.
In summary, this study highlights the immense clinical potential for such technologies. The tissue requirement is minimized by performing iterative rounds of staining on
the same section, which is critical in the clinical setting where tumor specimens are scarce
[50, 51]. While this approach utilizes standard wet-lab equipment and conventionally
Данная книга находится в списке для перевода на русский язык сайта https://meduniver.com/
processed FFPE tissues, current limitations include lengthy processing times during
the iterative staining, signal development, image acquisition, signal removal, and heat
stripping stages, as well as complex computational analysis of slide images. Advances
in the automation of the staining and imaging processes and automated analysis of slide
scans will help realize the clinical potential of this technology. Recently, the approach
described in Tsujikawa et al. was expanded upon to enable the quantitative assessment
of 29 biomarkers in a single FFPE tissue section [52].
2.3 Quantitative multiplexed fluorescence immunohistochemistry
Quantitative multiplexed fluorescence immunohistochemistry, combined with RNA
analysis, has been successfully utilized to investigate the immune contexture of testicular
germ cell tumors (TGCTs) [53]. TGCT responds quite well to platinum-based chemotherapy, yet a subset of patients are refractory to treatment or relapse and succumb to
the disease [54]. Therefore, there is a strong motivation to pursue developing additional
lines of therapy to treat TGCT. Siska et al. assessed PD-1, PD-L1, CD3, CD4, CD8,
CD25, and FOXP3 expression in 35 TGCT samples (11 seminomas and 24 nonseminomas) using quantitative multiplexed fluorescence immunohistochemistry [53].
In this approach, secondary antibodies are conjugated to HRP, which reacts with
fluorophore-conjugated tyramine molecules. This reaction amplifies the fluorescence signal, enabling the detection of low-abundance targets. Slides were imaged, then analyzed
using AQUAnalysis software. Seminomas displayed increased CD3
decreased Tregs, increased PD-L1 expression, and increased spatial interaction of
PD-1/PD-L1 compared to nonseminomas. Transcriptional profiling demonstrated
increased expression of T cell markers, as well as cancer-testis antigens (CTAs) in
seminomas, and high neutrophil and macrophage signatures in nonseminomas. T cell
and NK signatures decreased with the disease stage, while Treg, neutrophil, mast cell,
and macrophage signatures increased with the disease stage in both seminomas and
nonseminomas. CTA expression, neutrophil, and CD8
+
/Treg signatures correlated with
recurrence-free survival. Given the high expression of CTAs, PD-1/PD-L1 interaction,
and immune infiltration in both seminoma tumors and treatment-refractory nonseminoma tumors, the authors suggest these patients have a strong potential to respond
to anti-PD-1/anti-PD-L1 therapy, particularly in earlier stages [53]. Subsequent data from
clinical trials investigating the use of ICB agents in cisplatin-refractory TGCTs has been
mixed, as reviewed in Kalavska et al. [55]. Therefore, additional studies must be carried
out to understand the predictive markers for response to immunotherapy in treatmentrefractory TGCTs. Combining immunotherapy with cisplatin-based chemotherapy
beginning immunotherapy in earlier stages of treatment may be warranted.
+
T cell infiltration,
365Proteomic biomarker technology for cancer immunotherapy
366 Reilly Fankhauser et al.
2.4 Cyclic immunofluorescence
Cyclic immunofluorescence (CyCIF) is an alternative to mIHC that utilizes repetitive
rounds of staining with a fluorophore-conjugated primary antibody, imaging, and fluorophore quenching reactions. This approach addresses some concerns about colorimetric
IHC readouts, including harsh stripping methods and the fact that developing a signal
using enzymatic amplification to produce diamino-benzidine (DAB) (or similar) precipitates is inherently less quantitative than fluorescence signals and may poorly correlate
with target antigen concentration [56, 57].
The initial demonstration of tissue-cyclic immunofluorescence (t-CyCIF) included an
analysis of 61 different targets in single FFPE tissue sections and was compatible with DNA
fluorescence in situ hybridization (FISH) and standard H&E stains [58]. The approach has
subsequently been demonstrated with various tissue types and antibodies [56]. Recent
advances in CyCIF technologies have reduced slide processing time burdens. McMahon
et al. utilized oligonucleotide-conjugated primary antibodies to bind to protein epitopes
[59]. Subsequently, a complementary single-stranded oligonucleotide conjugated to a
conventional fluorophore is introduced that binds specifically to its target antibody. After
imaging on a conventional fluorescence microscope, the fluorescence signal is removed by
applying UV light that breaks a photocleavable linker between the fluorophore and oligo
sequence. In this approach, all oligo-conjugated antibodies are incubated simultaneously,
preventing steric hindrance and increasing speed. Signal removal by UV treatment
preserves tissue integrity and antigenicity over multiple staining cycles. This concept
was demonstrated with 14 color imaging of FFPE breast cancer sections [59].
CODEX (codetection by indexing) is a similar technology that utilizes DNA-
barcoded antibodies, except the reporters (fluorescent dNTP analogs) are specifically
bound to DNA targets using an in situ polymerization-based indexing technique [60].
This approach has been utilized to compare the immune architecture of normal and lupus
mouse spleens using a 30-antibody panel [60]. In future studies, CODEX may be used to
interrogate tissue responses to immunotherapy, though antibody-oligo technology may
have a more parsimonious and less laborious workflow, making it more translatable to
clinical applications.
Advances in CyCIF technology make the clinical utilization of these approaches to
stratify patient responses to immunotherapies far more realistic. CyCIF approaches are
well poised to address clinical necessities in cancer therapies, including differentiating
TIL subsets, exploring tumor heterogeneity, and understanding the tumor microenvironment’s role in predicting response to immunotherapy [61].
2.5 Nanostring digital spatial profiler (DSP)
Nanostring’s digital spatial profiler (DSP) allows for the digital readout of up to 96 protein
or RNA targets with spatial resolution in FFPE tissues [62]. To perform the analysis, the
user begins by staining slides with three fluorescently labeled antibodies and a nuclear
Данная книга находится в списке для перевода на русский язык сайта https://meduniver.com/
marker to define morphological regions of interest [62]. Simultaneously, antibody or
RNA probes coupled to UV photocleavable oligonucleotide tags are incubated. After
selecting the desired region of interest, the oligonucleotide tags are liberated using
UV light, collected with a microcapillary tube, and counted using the nCounter platform. Oligo counts act as a proxy for expression levels and are mapped back to their origin
to derive a spatially resolved profile of analyte abundance [62]. The region of interest is
scalable and can range from 650 to 10 μm in diameter (roughly a single cell) [63].
The instrument is gaining popularity in the research community due to the tissuesparing nature of the technique, which enables downstream analysis, a high degree of
automation, flexibility in deciding regions of interest, and the ability to profile both
RNA and protein. Table 1 summarizes recent demonstrations of nanostring DSP technology that contribute to the investigation of responses to immunotherapy.
However, there are some limitations to DSP technology, predominantly on resolution and the limits of detection. Even with the DSP’s smallest region of interest (10 μm
diameter), mapping expression data back to this region renders significantly less spatial
resolution than microscopy-based (IF, IHC) techniques. Only the three fluorescent morphology markers used to define the regions of interest have a truly single-cell resolution.
The remaining targets in the panel are mapped back to a much broader region without
any visualization. This leads to a loss of spatial information about the distribution of cells
within the region of interest and their respective expression levels.
As for limits of detection, to generate a sufficient signal for successful analysis, one
must often profile the area to include at least 20 rare cells of interest, though the lower
limit for the number of cells profiled is different for RNA and protein analysis, and is
367Proteomic biomarker technology for cancer immunotherapy
Table 1 Recent publications utilizing the Nanostring DSP to investigate responses to immunotherapy.
What they demonstrated using DSP
Title Year Cancer type(s)
LAG3: a novel immune checkpoint
expressed by multiple
lymphocyte subsets in diffuse
large B-cell lymphoma [64]
2020 Large diffuse
B-cell
lymphoma
(LDBCL)
technology
Utilized the DSP, standard
immunohistochemistry, flow
cytometry, and the nCounter
platform for analysis of RNA
extracted from FFPE tissues to
demonstrate LAG3 expression on
malignant B-cells and near
universal coexpression with
PD-1 and TIM3 on CTLs and
+
Tregs, indicating potential
CD4
clinical efficacy from a
combination blockade of PD-1
and LAG3
Continued
368 Reilly Fankhauser et al.
Table 1 Recent publications utilizing the Nanostring DSP to investigate responses to
immunotherapy—cont’d
What they demonstrated using DSP
Title Year Cancer type(s)
technology
Biomarkers associated with
beneficial PD-1 checkpoint
blockade in nonsmall cell lung
cancer (NSCLC) identified using
high-plex digital spatial profiling
[65]
Immune landscapes predict
chemotherapy resistance and
immunotherapy response in
acute myeloid leukemia
[66]
Linking transcriptomic and imaging
data defines features of a
favorable tumor immune
microenvironment and identifies
a combination biomarker for
primary melanoma [67]
High-plex predictive marker
discovery for melanoma
immunotherapy-treated patients
using digital spatial profiling
([68])
Neoadjuvant vs adjuvant
ipilimumab plus nivolumab in
macroscopic stage III melanoma
[69]
2020 NSCLC Analyzed specimens from
67 patients treated with anti-PD1 and demonstrated that high
CD56 and CD4 expression on
CD45 cells was associated with
increased overall and progression
free survival. High expression of
VISTA and CD127 in the tumor
compartment was associated with
resistance to immunot herapy
2020 AML Utilized immune gene profiling and
the DSP to demonstrate a high
IFN-gamma signature was
predictive of patients likely to
respond to flotetuzum ab, a
bispecific antibody that targets
CD3 and CD123
2020 Cutaneous
melanoma
Utilized DSP and gene expression
profiling using the nCounter
platform to demonstrate that
combining the CD8 to
macrophage ratio with an IFNbased gene signature stratifies
patient prognosis. Notably, the
high risk group may benefit from
adjuvant combination
immunotherapies
2019 NSCLC,
cutaneous
melanoma
Demonstrated that DSP highly
agreed with the AQUA method
of quantitative
immunofluorescence. They also
identified 26 biomarkers
associated with response and
survival including PD-L1
expression on macrophages, but
not melanocytes. This
demonstrates the discover y
potential of the DSP
2018 Cutaneous
melanoma
Demonstrated that patients with
relapsed melanoma have lower
pretreatment levels of B2M,
PD-L1, and CD3 in tumorenriched regions of interest
Table 1 Recent publications utilizing the Nanostring DSP to investigate responses to
Данная книга находится в списке для перевода на русский язык сайта https://meduniver.com/
immunotherapy—cont’d
What they demonstrated using DSP
Title Year Cancer type(s)
technology
369Proteomic biomarker technology for cancer immunotherapy
Neoadjuvant immune checkpoint
blockade in high-risk resectable
melanoma [70]
Phase Ib/II trial testing combined
radiofrequency ablation and
ipilimumab in uveal melanoma
(SECIRA-UM) [71]
B cells and tertiary lymphoid
structures promote
immunotherapy response [72]
Tertiary lymphoid structures
improve immunotherapy and
survival in melanoma [73]
2018 Cutaneous
melanoma
2020 Uveal
melanoma
2020 Cutaneous
melanoma
2020 Cutaneous
melanoma
Identified immune correlates of
response to ICB including
including:
(A) Higher lymphoid infiltrates
in responders to both adjuvant nivolumab and
ipilimumab + nivolumab
(B) More clonal and diverse
T cell infiltrates in
responders to nivolumab
monotherapy
Explained the lack of response to
ipilimumab plus radiofrequency
ablation by a weak induction of
inflammation, counteracted by
induction of IDO, FOXP3
(Tregs), CD68, and CD163
(TAMS). The patients with a
longer OS and PFS had higher
expression of markers of immune
infiltration including CD45,
CD4, and CD8
Combined data from the DSP,
RNA-sequencing, and ma ss
cytometry to characterize B and
T cells in melanoma tumors with
and without tertiary lymphoid
structures (TLS). Discovered that
tumors from patients responding
to immune checkpoint blockade
had clonally expanded and
memory-switched B cells
Investigated melanoma tumors with
and without tertiary lymphoid
structures and determined that
infiltrating T cells in tumors
without TLS were dysfunctional,
therefore, adaptive immune
responses may be partly driven by
B cells in response to
immunotherapy
Соседние файлы в папке Библиотека им академика М.И. Перельмана