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A user’s perspective on GeoMxTM Digital Spatial Profiling (DSP)


Abstract and Figures

Characterization of spatial protein expression for multiple targets from a single tissue is difficult to perform, especially due to the limitations of multiplex immunohistochemistry and tissue heterogeneity. Therefore, a new technology is required that permits detailed and simultaneous expression profiling of proteins within a defined region of interest (ROI). To address this unmet need, NanoString Technologies developed a new technology, GeoMxTM digital spatial profiling (DSP), which currently enables simultaneous and guided detection of up to 40 antibodies (probes) from a single formalin-fixed paraffin-embedded (FFPE) tissue. DSP probes are tagged with unique photocleavable DNA oligos that are released after guided ultraviolet exposure in specific ROIs. Digital quantification of the released oligos by NanoString's nCounter® system provides a detailed expression profile of proteins within these discrete ROIs. In this article, we will describe our experience with the GeoMx DSP platform using cancer FFPE tissues. These expression profiles will provide better characterization and understanding of tumor heterogeneity and the tumor micro-environment, enabling the improvement of patient therapy and the identification of potential biomarker signatures. The purpose of this article is to offer potential future users an independent insight into the DSP platform and a comprehensive idea of usability, including advantages and current limitations of the technology based on our current experience with the beta version of NanoString's DSP platform as part of the DSP beta-testing program. The GeoMxTM Digital Spatial Profiling (DSP) platform is a non-destructive technique for regional in-depth protein expression profiling. Using oligonucleotide detection technologies, the GeoMxTM DSP enables simultaneous high-level multiplexing on a single FFPE tissue. Here, we focus on our current experience derived from our biomarker research using the beta version of the DSP instrument.
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Technology Explained
A user's perspective on GeoMx
digital spatial proling
Trieu My Van
, Christian U. Blank
Netherlands Cancer Institute, Amsterdam, The Netherlands
Region of interest
Translational research
Characterization of spatial protein expression for multiple targets from a single tissue is difcult to perform,
especially due to the limitations of multiplex immunohistochemistry and tissue heterogeneity. Therefore, a new
technology is required that permits detailed and simultaneous expression proling of proteins within a dened
region of interest (ROI). To address this unmet need, NanoString Technologies developed a new technology,
digital spatial proling (DSP), which currently enables simultaneous and guided detection of up to 40
antibodies (probes) from a single formalin-xed parafn-embedded (FFPE) tissue. DSP probes are tagged with
unique photocleavable DNA oligos that are released after guided ultraviolet exposure in specic ROIs. Digital
quantication of the released oligos by NanoString's nCounter®system provides a detailed expression prole of
proteins within these discrete ROIs. In this article, we will describe our experience with the GeoMx DSP platform
using cancer FFPE tissues. These expression proles will provide better characterization and understanding of
tumor heterogeneity and the tumor micro-environment, enabling the improvement of patient therapy and the
identication of potential biomarker signatures. The purpose of this article is to offer potential future users an
independent insight into the DSP platform and a comprehensive idea of usability, including advantages and
current limitations of the technology based on our current experience with the beta version of NanoString's DSP
platform as part of the DSP beta-testing program. The GeoMx
Digital Spatial Proling (DSP) platform is a non-
destructive technique for regional in-depth protein expression proling. Using oligonucleotide detection tech-
nologies, the GeoMx
DSP enables simultaneous high-level multiplexing on a single FFPE tissue. Here, we focus
on our current experience derived from our biomarker research using the beta version of the DSP instrument.
Regional and quantitative protein signature analysis of clinical
formalin-xed parafn-embedded (FFPE) tissues has proved to be dif-
cult to perform in many research areas. In particular, in the eld of
immuno-oncology (IO), detailed characterization of immune cell subsets
within a tumor area would improve our understanding of antitumor
immunity and resistance to immune checkpoint inhibition. Tumors are
highly heterogeneous in their architecture, (immune) cell composition,
abundance and distribution. Understanding tumor and immune cell co-
localization may also be important for biomarker identication and
precision immunotherapy [15]. At present, the CD8þT cell:regulatory T
cell ratio is an improved biomarker compared with CD8 expression alone
[611]. Furthermore, characterizing co-expression of co-stimulatory and
inhibitory receptors would improve therapeutic strategies. For example,
characterization of inhibitory markers expressed on T cells (e.g. LAG-3
and TIM-3) in the tumor micro-environment may impact the decision
for new (combination) therapies [12,13]. Detailed tissue analysis is
highly necessary but involves a laborious staining procedure and is
hampered by limited patient tissue samples. Therefore, multiplex
immunohistochemistry (IHC) will provide a more comprehensive insight
into the interaction and crosstalk between tumor and immune cells
within the tumor micro-environment.
Multiplexing more than ve antibodies has largely been restricted by
the spectral overlap of available uorophores or chromogens. Moreover,
current multiplex IHC tools provide objective information about the
presence and histological location of immune cells, but neglect detailed
phenotypical description. Due to this limitation, additional ow cytom-
etry analysis is required. To advance standard IHC-based multiplex tissue
analyses, NanoString Technologies (Seattle, WA) developed a novel im-
aging and tissue-sampling platform: GeoMx
digital spatial proling
(DSP). This is a high-level multiplexing technique that provides infor-
mation about the presence and histological location of immune cell
subsets, and detailed characterization of their activation, differentiation
* Corresponding author. Trieu M. Van, Netherlands Cancer Institute, Amsterdam, The Netherlands. Tel.: þ31 20 512 2066.
E-mail address: (T.M. Van).
Postal Adress: Plesmanlaan 121, 1066 CX Amsterdam
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Immuno-Oncology Technology
journal homepage:
Available online 30 May 2019
2590-0188/©2019 Published by Elsevier Ltd on behalf of European Society for Medical Oncology. This is an open access article under the CC BY-NC-ND license
Immuno-Oncology Technology 1 (2019) 1118
and immune checkpoint expression [14].
The capability of DSP technology in the eld of IO holds promise in
advancing the current standard analysis of tumor biopsies. Thus, this
article aims to give an insight into the DSP technology, the current rec-
ommended experimental set-up and an independent view on the ad-
vantages and limitations of the system based on our own experience and
research. It should be noted that all observations are derived from our
experience with the beta version of the DSP instrument, which we are
currently testing, and may not apply fully to the commercially available
The working procedure of protein DSP
DSP technology simultaneously characterizes regional and quantita-
tive protein expression of up to 40 markers related to immune cell acti-
vation and tumor cell classication on a single FFPE tissue section [14].
The DSP procedure implements ve non-destructive steps: a standard
FFPE tissue preparation step, a tissue incubation step with a mixture of
visualization markers (VMs) and DSP probes, an imaging and region of
interest (ROI) selection step, an ultraviolet (UV) exposure and oligo
collection step, and a quantication step on NanoString's nCounter®
system (Figure 1)[14]. In total, this procedure takes 3 days from tissue
slide preparation until data analysis, with a throughput of four slides per
day. Below, we will provide a more detailed review of our experiences
with the DSP platform and its advantages and limitations.
Tissue preparation
Tissue preparation for DSP analysis is comparable to standard IHC,
and is therefore an established procedure that does not involve additional
steps or reagents. A major advantage of DSP is the non-destructive
staining procedure. Tissue sections can be preserved after staining/
acquisition for further haematoxylin and eosin or chromogenic IHC
staining. Moreover, processed slides can be stored long term for addi-
tional DSP analysis. Further analysis will require additional antigen
retrieval and re-incubation with a cocktail of VMs and DSP probes.
Tissue incubation with VMs and DSP probes
For DSP analysis, the tissue sample requires an incubation step with a
master mix containing the VMs and DSP probes (Table 1). The VMs are
tissue-compartment-specicuorescent-labeled antibodies and a DNA
marker used to visualize the tissue morphology. ROI selection from VMs
has the advantage of allowing researchers to select areas based on the
immune cell type of interest and the surrounding environment. The DSP
platform enables multispectral imaging but also minimizes emission-
spectral overlap by including four distinct light-emitting diodes. This
limits the morphological characterization of the tissue to three different
markers. Currently, there are no options within the DSP platform to
extend the number of VMs, and thus thorough selection of markers or the
use of serial tissue sections is required. Based on our experience,
combining more than four VMs by applying an intermediate bleaching
step is not recommended due to remaining tissue stain and the conse-
quent possibility of false-positive signals.
The tissue is also incubated with the DSP probe panel, which consists
of a xed core panel that can be extended with two additional modules
(Table 1B). These DSP probes are not multiplex-limited by spectral res-
olution, as they are not uorescently labelled but are labelled with bar-
code indexing oligos. These oligos are linked to each antibody via a UV-
cleavable linker modied from a previous concept [15]. During the beta
test phase, the DSP probe panel consists of 40 antibodies. Theoretically,
the DSP probe panel could be extended to a maximum of 96 antibodies
Figure 1. Protein digital spatial proling (DSP) working procedure. Formalin-xed parafn-embedded tissue slide preparation involves incubation with an antibody
mixture which contains up to four visualization markers and 40 DSP probes. Following imaging, regions of interest (ROIs) are selected based on visualization of the
tissue. Sequential ultraviolet (UV) light exposure of each ROI results in the release of indexing oligos from the DSP probes, allowing their quantication on Nano-
String's nCounter®system.
Table 1
Overview of (A) visualization markers used currently to distinguish between the
tumor and immune cell compartment, and (B) digital spatial proling protein
probes included in the core panel and the additional modules
Visualization markers Compartment
Syto13 DNA
S100B/Pmel17 or PanCK Tumor
CD45 Immune cell
CD3 Immune cell
Core panel Module 1 Module 2
Beta-2-microglobulin CD56 CD137 CD127
CD3 Fibronectin TIM-3 CD86
CD68 GZMB B7-H3 CD40
CD8 Ki-67 IDO1 CD40L
T.M. Van, C.U. Blank Immuno-Oncology Technology 1 (2019) 1118
due to the technical capability of the quantication process in the
nCounter system. For our projects, we extended the standard DSP probe
panel with 11 additional antibodies which were conjugated to unique
oligos by NanoString's barcoding service [16]. Using standard IHC, we
conrmed the efcient staining performance of the antibodies after oligo
conjugation (Figure 2A) and sufcient DSP counts above the background
control (Figure 2B). In total, antibody selection, conjugation and vali-
dation took 4 months; hence, thorough advance planning of projects is
ROI selection procedure, UV exposure and oligo collection
The DSP platform provides additional benet to standard IHC or full-
section multiplex platforms by enabling regional/spatial analysis. Based
on the tissue morphology, ROIs can be selected which vary in size
m in diameter) and form (Figure 3). The shape of the ROI can
vary from geometric to a rare cell population level. Additional segmen-
tation within a geometric ROI allows distinction to be made between the
tumor and the tumor micro-environment, or between multiple immune
cell types (Figure 3). However, the selection of single cells is not rec-
ommended for the beta instrument at present due to the low signal:noise
ratio of the DSP probes, requiring at least 10 cells/ROI for sufcient
counts. Selection of ROIs results in guided UV light exposure using two
digital micromirror devices (DMDs) in the instrument. These DMDs are
small mechanical systems that contain an array of steerable reective
micromirrors (Texas Instruments, Dallas, TX). DMD-directed UV light
illuminates all selected ROIs sequentially, resulting in the release of
indexing oligos solely within the boundaries of the set ROI. To conrm
precise guidance of UV light by the DMDs, we used CAGE-532, a dye that
is initially colorless and non-uorescent but which releases a highly
uorescent signal when illuminated with UV light (Figure 4A). Further
validation revealed low counts in a control glassROI compared with a
Figure 2. Comparison of staining efciency before and after oligo conjugation of antibodies. (A) Standard immunohistochemical staining for CD16 and CD39 before
and after oligo conjugation of the antibodies. Rabbit immunoglogulin G (IgG) was used as the background control. Scale bar 100
m. (B) Housekeeping (HK)-
normalized digital spatial proling counts after oligo conjugation of antibodies from three discrete regions of interest (ROI13). Violet bars, rabbit IgG; pink bars
(upper gure), CD16; pink bars (lower gure), CD39.
T.M. Van, C.U. Blank Immuno-Oncology Technology 1 (2019) 1118
neighboring tissue ROI (Figure 4B), and precise protein expression ac-
cording to the segment type (Figure 4C).
In our experience, dening representative ROIs requires strong
guidance from an experienced pathologist to avoid biased analysis,
especially with heterogeneous tissues. A detailed description of the
manner of ROI selection will also be a prerequisite in DSP publi-
cations. Our experiments show high reproducibility between com-
parable sections (Figure 5), but strong variations occur in
comparisons of tumor sections with different immune cell abun-
dance (hotvs coldtumors) (data not shown). Ideally, spatially
structurally delimited but impure tumor regions should be selected.
We therefore prefer the selection of ROIs according to the tumor
structure (e.g. intratumor, peritumor and pure stromal ROIs). To
focus on immune cell populations, segmentation within these
impure tumor ROIs can be performed. In the current state, the DSP
analysis tools could be improved to incorporate the needs of users
analysing complex tissue samples, especially in high-density regions
(Figure 4D). By selecting distinct ROIs, this platform enables users
to obtain spatial data for protein targets relative to each other, as
well as in relation to tissue architecture and immune inltration.
However, spatial information between certain ROIs can only be
assumed and cannot be measured, and a tool which incorporates a
distance of spatial information would be valuable.
In addition to area-specic characterization of the tissue, full
tissue section analysis can also be performed on this platform by
selecting adjacent square ROIs throughout the tissue. However, in
our opinion, it is not recommended or feasible to carry out this
time-consuming and costly procedure. Therefore, the DSP platform
is not the preferred tool and we recommend the use of specialized
sequential staining platforms, such as CODEX (Akoya Biosciences,
West Coast, 1505 O'Brien Drive, Suite A-1, Menlo Park, CA, USA) or
InsituPlex (Ultivue Inc., Cambridge, MA) [17,18].
Quantication using NanoString's nCounter instrument and data analysis
For digital quantication, the photocleaved oligos are hybridized to
NanoString barcodes and processed on the nCounter instrument. This
results in digital counts corresponding to the abundance of each targeted
protein within each ROI. These digital counts can be analysed in the
provided web-based software, in which data are associated with the
tissue scan and ROIs. Several implemented functions allow comparison of
the acquired DSP probe counts from different ROIs in (clustered) heat-
maps, boxplots and bar graphs, and perform statistical analysis. Our
experience to date with the software is that comparison of data from two
to three slides is simple. However, experimental groups involving more
than three tissues and three points of comparison (e.g. patients' response,
treatment and dose) require careful annotation to enable clustering of the
data, or bioinformatics support.
Due to the high-dimensional analysis of the DSP platform, technical
and biological variations need to be controlled for during data compar-
ison and interpretation. To address various sources of technical variation,
NanoString implemented several internal control mechanisms to
normalize for variables, including External RNA Control Consortium
(ERCC) controls, housekeeping (HK) proteins and immunoglobulin G
(IgG) controls (Table 2). ERCCs are included as positive and negative
controls for technical variation during hybridization, while HK proteins,
number of nuclei or area size are included to normalize the variation of
cellularity within the ROIs (Table 2). To date, there is no established
guideline for normalization; IO pathologists, researchers and bio-
informaticians need to develop consensus guidelines. We found the best
method for analysis was to normalize to ERCC, scale counts to nuclei
counts, and then normalize to either HK proteins or IgG controls. How-
ever, we have also identied some exceptions that require careful
normalization in order to avoid articial elevation of counts. For
example, when using a geometric ROI on glassas a background control
Figure 3. Selection of type of region of interest (ROI). Three types of regions can be selected within the digital spatial proling platform. Tissue biopsy was stained
with S100B/PMEL17 (green), LDH (red) and CD45 (yellow) visualization markers. (AC) Geometric ROIs can be selected, ranging from circles (A) to rectangles (B) to
user-dened polygons (C). (D,E) Segmentation within a geometric ROI is generated based on visualization markers, and can currently distinguish between tumor and
stroma (D) or specic cell type populations (E). (F) Single-cell ROIs are generated based on visualization markers which allow the analysis of either one or multiple
single cells within a eld of view. Scale bar 100
T.M. Van, C.U. Blank Immuno-Oncology Technology 1 (2019) 1118
Figure 4. Precise ultraviolet (UV) guidance by digital mirror devices. (A) CAGE-dye-stained tonsil tissue with geometric region of interest (ROI) before and after UV
exposure. Scale bar 100
m. (B) Circular ROIs (200-
m diameter) on cell pellet array and glassand respective protein expression levels after digital spatial proling
(DSP) analysis. DSP counts are normalized to immunoglobulin G (IgG) controls to correct for noise. Scale bar 100
m. (C) Geometric ROI on colorectal cancer tissue
with internal segmentation for tumor(red) and stroma(green). Scale bar 100
m. Heatmap of region-specic nCounter counts normalized to nuclei. (D) Circular
ROI (200-
m diameter) with segmentation on PanCKþCD45- CD3-(blue), PanCK- CD45þCD3-(yellow) and PanCK- CD45þCD3þ(red) cells and respective DSP
counts after normalization to IgG controls. Scale bar 100
T.M. Van, C.U. Blank Immuno-Oncology Technology 1 (2019) 1118
or when comparing tumor and rare cell segmentation with a signicant
difference in cell number, data cannot be scaled to nuclei or normalized
to HK proteins. This would result in false-positive counts; instead,
normalization to IgG controls is recommended.
Of note, analysis and interpretation of counted indexing oligos have
to be carried out carefully as a single count does not necessarily reect
protein expression on a single cell. This is due to potential variation in
antigen density between cells and the fact that cells may bind simulta-
neously to multiple oligo-conjugated antibodies. Heterogeneous oligo-
antibody conjugation is regulated and controlled by NanoString to a
certain extent by analysis of the degree of labeling. Using the nCounter
system, they estimate the oligo numbers for each clone, and remaining
unconjugated antibodies are puried via high-performance liquid chro-
matography [16]. Normalization of the counts to cell nuclei will give an
indication of the density of positive cells or protein expression. However,
these variabilities hinder estimation of the percentage of cells
co-expressing certain markers, unlike other tools such as ow cytometry.
Figure 5. Reproducibility of digital spatial proling analysis. Independent expression analyses of depicted proteins were performed on two serial sections of (A) cell
pellet array and (B) colorectal cancer tissue (B). Expression levels are shown for one 200-
m-diameter circular region of interest (ROI)/section and counts were
normalized to nuclei and housekeeping proteins. ROIs of serial sections were chosen in the same tissue area to allow close comparison. r
value indicates correlation
between expression proles from sections 1 and 2. Section 1, dark grey bars; section 2, light grey bars.
Table 2
Data analysis and normalization: (A) Technical and biological sources of varia-
tion during the digital spatial proling (DSP) working process. To subtract the
variation, NanoString Technologies offers different typesof normalization for
protein DSP; (B) Isotype controls and housekeeping proteins included in the DSP
probe panel
Source of variation Type of normalization
ERCC correction
ROI size Area normalization
ROI cellularity Reference protein/gene
Nuclei count normalization
ROI background Three isotype controls
Isotype controls Mouse IgG1, IgG2a and rabbit IgG
Housekeeping proteins Histone H3, GAPDH and ribosomal protein S6
ROI, region of interest; ERCC, External RNA Control Consortium.
T.M. Van, C.U. Blank Immuno-Oncology Technology 1 (2019) 1118
At present, co-expression of proteins can be identied only for a cell
population within an ROI, but not at single cell level due to the limitation
of single cell analysis. Taking these variabilities into account, we
currently validate our top protein candidates derived from DSP analyses
by standard IHC methods. Our comparisons of DSP counts with staining
obtained using VMs (Figure 6A) or standard IHC reveal good correlation,
enhancing the reliability of the DSP platform (Figure 6B and C).
Conclusion and future perspectives
NanoString's DSP platform is an innovative technology that combines
imaging and tissue sampling to advance the standard IHC procedures
currently used by pathologists. In our view, the major advantages of the
platform are its high multiplexing ability on FFPE samples, requiring low
hands-on time, and the non-destructive straightforward procedure that
will contribute to dene biomarker signatures in discrete ROIs. More-
over, the segmentation tool enables users to focus the analysis on, for
example, distinct immune cell inltrates within a tumor area. As a beta
test site, we have encountered a few correctable instabilities in the
software but not in the hardware of the DSP instrument. Nonetheless,
during the testing phase, we have encountered restrictions in the
simultaneous combination of more than four VMs, which requires the use
of serial tumor tissue sections for different research questions. During this
testing period, single cell analyses were not feasible, which limited our
analytical approaches.
The DSP platform is a rapidly evolving platform that, in the future,
will likely include more DSP probes to cover other aspects of tissue
proling. In addition to the protein DSP, in the near future, NanoString
will also provide the possibility to characterize RNA expression on FFPE
tissues [14]. However, to implement the DSP platform for translational
research, established guidelines are needed for data analysis, including
normalization strategies, and a more comprehensive understanding of
the effects of staining intensity and oligo labelling on DSP counts. This
will likely be addressed in the near future with the upcoming possibility
Figure 6. Correlation of digital spatial proling (DSP) counts with immunouorescent or standard immunohistochemical staining. (A) Tonsil tissue stained with
PanCK (green), CD45 (red) and CD3 (yellow) visualization markers. Scale bar 100
m. Graphs depict nuclei and housekeeping (HK)-normalized DSP counts of
indicated proteins in tumoror immune-enriched regions of interest (ROIs). (B) Nuclei and HK-normalized counts for CD163 and PD-L1 obtained from melanoma
patient groups 1 (n¼22) and 2 (n¼32). Each dot represents one ROI/patient selected by similar tissue morphology. *P<0.005, unpaired t-test. ns, non-signicant. (C)
Representative images for CD163 and PD-L1 were obtained from melanoma tissue samples from groups 1 and 2 using standard immunohistochemistry. Scale bar
T.M. Van, C.U. Blank Immuno-Oncology Technology 1 (2019) 1118
to characterize a single cell. Similar to the recent publication by Decalf et
al. [19], methodological cross-laboratory comparisons between the DSP
platform and other multiplex staining methods, such as sequential
chromogenic IHC multiplexing, Akoya's Codex and Vectra systems, are
essential to optimize the use of each technology based on the research
question and/or clinical need.
In conclusion, in our view, the DSP platform will be an important
addition to current single staining IHC methods in clinical diagnostics.
Recent published data, and our unpublished data, reveal sufcient
characterization of the melanoma protein prole in patient cohorts that
received immune checkpoint blockade therapy using protein DSP [20,
21]. We envisage that with further standardization and optimization of
the working process, data analysis and extension of the visualization and
DSP probe panels, DSP-based multiplex analyses can become a helpful
tool towards personalized immunotherapies.
CB and TMV received a research grant from NanoString for testing the
beta version of the DSP machine.
CB and TMV received a research grant from NanoString for testing the
beta version of the DSP machine.
TMV thanks NanoString Technologies for the opportunity to join the
DSP development team for a 6-month training rotation. We thank Dr AM
Terry for proofreading the manuscript.
[1] Chen DS, Mellman I. Oncology meets immunology: the cancer-immunity cycle.
Immunity 2013;39:110.
[2] Morisaki T, Katano M, Ikubo A, Anan K, Nakamura M, Nakamura K, et al.
Immunosuppressive cytokines (IL-10, TGF-beta) genes expression in human gastric
carcinoma tissues. J Surg Oncol 1996;63:2349.
[3] Fleming V, Hu X, Weber R, Nagibin V, Groth C, Altevogt P, et al. Targeting myeloid-
derived suppressor cells to bypass tumor-induced immunosuppression. Front
Immunol 2018;9:398.
[4] Blank CU, Haanen JB, Ribas A, Schumacher TN. Cancer immunology. The "cancer
immunogram". Science 2016;352:65860.
[5] Chen DS, Mellman I. Elements of cancer immunity and the cancer-immune set
point. Nature 2017;541:32130.
[6] Baras AS, Drake C, Liu JJ, Gandhi N, Kates M, Hoque MO, et al. The ratio of CD8 to
Treg tumor-inltrating lymphocytes is associated with response to cisplatin-based
neoadjuvant chemotherapy in patients with muscle invasive urothelial carcinoma of
the bladder. Oncoimmunology 2016;5:e1134412.
[7] Preston CC, Maurer MJ, Oberg AL, Visscher DW, Kalli KR, Hartmann LC, et al. The
ratios of CD8þT cells to CD4þCD25þFOXP3þand FOXP3- T cells correlate with
poor clinical outcome in human serous ovarian cancer. PLoS One 2013;8:e80063.
[8] Shang B, Liu Y, Jiang SJ, Liu Y. Prognostic value of tumor-inltrating FoxP3þ
regulatory T cells in cancers: a systematic review and meta-analysis. Sci Rep 2015;5:
[9] Gajewski TF. The expanding universe of regulatory T cell subsets in cancer.
Immunity 2007;27:1857.
[10] Togashi Y, Shitara K, Nishikawa H. Regulatory T cells in cancer immunosuppression
implications for anticancer therapy. Nat Rev Clin Oncol 2019;16:35671.
[11] Blackburn SD, Shin H, Haining WN, Zou T, Workman CJ, Polley A, et al.
Coregulation of CD8þT cell exhaustion by multiple inhibitory receptors during
chronic viral infection. Nat Immunol 2009;10:2937.
[12] Das M, Zhu C, Kuchroo VK. Tim-3 and its role in regulating anti-tumor immunity.
Immunol Rev 2017;276:97111.
[13] McGranahan N, Furness AJ, Rosenthal R, Ramskov S, Lyngaa R, Saini SK, et al.
Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune
checkpoint blockade. Science 2016;351:14639.
[14] Merrit CR, Ong GT, Church S, Barker K, Geiss G, Hoang M, et al. High multiplex,
digital spatial proling of proteins and RNA in xed tissue using genomic detection
methods. bioRxiv 2019. [epub ahead of print].
[15] Agasti SS, Liong M, Peterson VM, Lee H, Weissleder R. Photocleavable DNA
barcode-antibody conjugates allow sensitive and multiplexed protein analysis in
single cells. J Am Chem Soc 2012;134:18499502.
[16] Lee J, Geiss GK, Demirkan G, Vellano CP, Filanoski B, Lu Y, et al. Implementation of
a multiplex and quantitative proteomics platform for assessing protein lysates using
DNA-barcoded antibodies. Mol Cell Proteomics 2018;17:124558.
[17] Goltsev Y, Samusik N, Kennedy-Darling J, Bhate S, Hale M, Vazquez G, et al. Deep
proling of mouse splenic architecture with CODEX multiplexed imaging. Cell
[18] Jungmann R, Avenda~
no MS, Woehrstein JB, Dai M, Shih WM, Yin P. Multiplexed
3D cellular super-resolution imaging with DNA-PAINT and Exchange-PAINT. Nat
Methods 2014;11:3138.
[19] Decalf J, Albert ML, Ziai J. New tools for pathology: a user's review of a highly
multiplexed method for in situ analysis of protein and RNA expression in tis sue.
J Pathol 2019;247:65061.
[20] Amaria RN, Reddy SM, Tawbi HA, Davies MA, Ross MI, Glitza IC, et al. Neoadjuvant
immune checkpoint blockade in high-risk resectable melanoma. Nat Med 2018;24:
[21] Blank CU, Rozeman EA, Fanchi LF, Sikorska K, van de Wiel B, Kvistborg P, et al.
Neoadjuvant versus adjuvant ipilimumab plus nivolumab in macroscopic stage III
melanoma. Nat Med 2018;24:165561.
T.M. Van, C.U. Blank Immuno-Oncology Technology 1 (2019) 1118
... Then, a single step of reagents is applied to the tissue, which consists of a cocktail of immunofluorescence biomarkers and probes or antibodies linked to photo-cleavable DNA tags. The immunofluorescence biomarkers will be used as visualization markers (VMs), and they include a DNA marker (SYTO13) and up to three specific antibodies or RNA probes conjugated to fluorophores (13,15). ...
... Markers with low counts should be looked at with caution (71) (e.g., PD-L1), especially since counts below 1 (found, for example, in immunologically cold areas) are equalized to 1 in the initial dataset, which can alter the final data when normalized by SNR (86). It has been previously recommended to normalize to ERCC, scale counts to nuclei counts, and then normalize to HK proteins or IgG controls (15). The methodology to select the normalization strategy is still not standardized. ...
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Characterization of the tumor microenvironment through immunoprofiling has become an essential resource for the understanding of the complex immune cell interactions and the assessment of biomarkers for prognosis and prediction of immunotherapy response; however, these studies are often limited by tissue heterogeneity and sample size. The nanoString GeoMx® Digital Spatial Profiler (DSP) is a platform that allows high-plex profiling at the protein and RNA level, providing spatial and temporal assessment of tumors in frozen or formalin-fixed paraffin-embedded limited tissue sample. Recently, high-impact studies have shown the feasibility of using this technology to identify biomarkers in different settings, including predictive biomarkers for immunotherapy in different tumor types. These studies showed that compared to other multiplex and high-plex platforms, the DSP can interrogate a higher number of biomarkers with higher throughput; however, it does not provide single-cell resolution, including co-expression of biomarker or spatial information at the single-cell level. In this review, we will describe the technical overview of the platform, present current evidence of the advantages and limitations of the applications of this technology, and provide important considerations for the experimental design for translational immune-oncology research using this tissue-based high-plex profiling approach.
... The GeoMx ® DSP platform uses a cocktail of antibodies conjugated to photocleavable DNA-barcoded oligos to provide high multiplex capacity, and the use of guided ultraviolet light exposure (by way of adjustable 1 million micromirrors) provides a high degree of flexibility in the selection of regions of interest for study. DSP technology and the GeoMx ® platform have been extensively reviewed [14][15][16][17]. DSP of pre-and post-treatment FFPE tumor tissue was performed to measure the relative levels of 28 protein markers associated with immuno-oncology. ...
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Background This window of opportunity trial evaluated the safety of intratumoral Copaxone® and profiled immune markers in biopsies before and after treatment. Methods Patients with percutaneously accessible malignancies scheduled for surgical resection with curative intent were eligible to participate. Adverse events from one, two, or three injections of Copaxone® were monitored leading up to surgical resection. Using RNA sequencing and spatial protein profiling of immune-related targets, changes in mRNA and protein expression patterns, respectively were assessed in tumor biopsy samples pre- and post-treatment. Results Adverse events at the injection site were mild and consistent with historic subcutaneous administration of Copaxone®. Increased intratumoral immune activity was evident in most patients, including the upregulation of genes associated with immune stimulation and targets of checkpoint inhibitor therapy. Conclusions Intratumoral injection of Copaxone® was well tolerated, and immune profile changes in the tumor microenvironment warrant its further evaluation as human intratumoral immunotherapy. Trial registration, NCT03982212 First posted June 11th, 2019
... In this technique it is needed to manually select different regions of interest (ROI) that will further be subjected UV light in order to release the target probes associated with the barcoded tags, for both RNA and protein experiments. GeoMx has limitations such as low multiplexing capacity, regional analysis of the tissue in an unbiased way and for small ROI (≈ 10µm) it has a low efficiency in protein detection (Van and Blank, 2019;Asp et al., 2020). With the same goal, but using a different approach, microfluidic deterministic barcoding in tissue (DBiT-seq) was developed (Liu et al., 2020). ...
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Genome sequencing projects of humans and other organisms reinforced that the complexity of biological systems is largely attributed to the tight regulation of gene expression at the epigenome and RNA levels. As a consequence, plenty of technological developments arose to increase the sequencing resolution to the cell dimension creating the single-cell genomics research field. Single-cell RNA sequencing (scRNA-seq) is leading the advances in this topic and comprises a vast array of different methodologies. scRNA-seq and its variants are more and more used in life science and biomedical research since they provide unbiased transcriptomic sequencing of large populations of individual cells. These methods go beyond the previous “bulk” methodologies and sculpt the biological understanding of cellular heterogeneity and dynamic transcriptomic states of cellular populations in immunology, oncology, and developmental biology fields. Despite the large burden caused by mycobacterial infections, advances in this field obtained via single-cell genomics had been comparatively modest. Nonetheless, seminal research publications using single-cell transcriptomics to study host cells infected by mycobacteria have become recently available. Here, we review these works summarizing the most impactful findings and emphasizing the different and recent single-cell methodologies used, potential issues, and problems. In addition, we aim at providing insights into current research gaps and potential future developments related to the use of single-cell genomics to study mycobacterial infection.
... Currently, over 300 antibodies have been validated for use with the DSP platform in addition to pre-designed reagent panels and custom-designed antibody or RNA probes [34]. Finally, the DSP platform is simple to use, automated, and does not require additional instrumentation to image, quantify and undertake spatial profiling [33,36]. However, DSP is restricted to analyzing information within each region of interest and cannot profile the complete slide, unlike CODEX and sequential immunofluorescence imaging [33]. ...
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Over the past decade, our understanding of human diseases has rapidly grown from the rise of single-cell spatial biology. While conventional tissue imaging has focused on visualizing morphological features, the development of multiplex tissue imaging from fluorescence-based methods to DNA- and mass cytometry-based methods has allowed visualization of over 60 markers on a single tissue section. The advancement of spatial biology with a single-cell resolution has enabled the visualization of cell-cell interactions and the tissue microenvironment, a crucial part to understanding the mechanisms underlying pathogenesis. Alongside the development of extensive marker panels which can distinguish distinct cell phenotypes, multiplex tissue imaging has facilitated the analysis of high dimensional data to identify novel biomarkers and therapeutic targets, while considering the spatial context of the cellular environment. This mini-review provides an overview of the recent advancements in multiplex imaging technologies and examines how these methods have been used in exploring pathogenesis and biomarker discovery in cancer, autoimmune and infectious diseases.
... The DSP allows selection of a region of interest, which is then exposed to UV light to cause delinking of the oligonucleotide tags. Selection of the region of interest is a critical step and can be facilitated with guidance from expert pathologists [53]. These oligonucleotide tags can then be quantified using a standard nCounter assay to assess the amount of protein or mRNA present in different morphological structures. ...
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In recent years there has been increased interest in using the immune contexture of the primary tumors to predict the patient’s prognosis. The tumor microenvironment of patients with cancers consists of different types of lymphocytes, tumor-infiltrating leukocytes, dendritic cells, and others. Different technologies can be used for the evaluation of the tumor microenvironment, all of which require a tissue or cell sample. Image-guided tissue sampling is a cornerstone in the diagnosis, stratification, and longitudinal evaluation of therapeutic efficacy for cancer patients receiving immunotherapies. Therefore, interventional radiologists (IRs) play an essential role in the evaluation of patients treated with systemically administered immunotherapies. This review provides a detailed description of different technologies used for immune assessment and analysis of the data collected from the use of these technologies. The detailed approach provided herein is intended to provide the reader with the knowledge necessary to not only interpret studies containing such data but also design and apply these tools for clinical practice and future research studies.
... As such, direct interrogation tools are required to enable characterization of localized transcriptomic changes in discrete tissue while preserving additional tissue for testing. The GeoMxTM Digital Spatial Profiling (DSP) platform robustly quantifies high-plex RNA expression data from single, 5 μm sections, capturing genome-wide expression patterns in spatially resolved locations throughout the tissue, and it has already been used to study many types of cancers (Van and Blank 2019;Chandramohan et al. 2021;Kalita-de Croft et al. 2021). In this article, we present the DSP method to study PC. ...
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Background Pancreatic cancer (PC) is a malignancy with a poor prognosis and high mortality. Surgical resection is the only “curative” treatment. However, only a minority of patients with PC can obtain surgery. Improving the overall survival (OS) rate of patients with PC is still a major challenge. Molecular biomarkers are a significant approach for diagnostic and predictive use in PCs. Several prediction models have been developed for patients newly diagnosed with PC that is operable or patients with advanced and metastatic PC; however, these models require further validation. Therefore, precise biomarkers are urgently required to increase the efficiency of predicting a disease-free survival (DFS), OS, and sensitivity to immunotherapy in PC patients and to improve the prognosis of PC. Methods In the present study, we first evaluated the highly and selectively expressed targets in PC, using the GeoMxTM Digital Spatial Profiler (DSP) and then, we analyzed the roles of these targets in PCs using TCGA database. Results LAMB3, FN1, KRT17, KRT19, and ANXA1 were defined as the top five upregulated targets in PC compared with paracancer. The TCGA database results confirmed the expression pattern of LAMB3, FN1, KRT17, KRT19, and ANXA1 in PCs. Significantly, LAMB3, FN1, KRT19, and ANXA1 but not KRT17 can be considered as biomarkers for survival analysis, univariate and multivariate Cox proportional hazards model, and risk model analysis. Furthermore, in combination, LAMB3, FN1, KRT19, and ANXA1 predict the DFS and, in combination, LAMB3, KRT19, and ANXA1 predict the OS. Immunotherapy is significant for PCs that are inoperable. The immune checkpoint blockade (ICB) analysis indicated that higher expressions of FN1 or ANXA1 are correlated with lower ICB response. In contrast, there are no significant differences in the ICB response between high and low expression of LAMB3 and KRT19. Conclusions In conclusion, LAMB3, FN1, KRT19, and ANXA1 are good predictors of PC prognosis. Furthermore, FN1 and ANXA1 can be predictors of immunotherapy in PCs.
... The main advantages of DSP was the high reuse capability for FFPE samples, the low operating time, and the non-destructive direct process to de ne biomarkers in a discrete ROI. 20,21,52 In the eld of immunotherapy companion diagnostics, Gupta S. et al. and Zugazagoitia J. et al. have con rmed that DSP seems to have quantitative potential compared to IHC, and the technology has the capability to do concomitant diagnostic tests for immunotherapy. 53,54 DSP was also used in nding biomarkers. ...
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Background Immunotherapy, targeting programmed death ligand-1 (PD-L1) and cytotoxic T lymphocyte-associated antigen-4, has shown significant antitumor activity and favourable safety profile in patients with advanced non-small cell lung cancer (NSCLC), while predictive biomarkers remain largely unknown. Here, we investigated the predictive effect of spatial protein expression signature of KN046, a bispecific antibody (bsAb), as a treatment in advanced NSCLC patients. Methods Digital spatial profiling was used to investigate protein expression in both tumor and stroma areas of formalin-fixed paraffin-embedded sections at baseline. Regions of interests (ROIs) were recorded after tricolor fluorescence labeling. The geometric means of different protein expression of different response groups were combined to construct the tumor signature, the stroma signature and the tumor + stroma signature. The accurate score was determined by receiver operating characteristic curve and the area under the curve (AUC). Results Eight patients with partial response (PR) and nine with progressive disease (PD) were enrolled in the study. Among 133 ROIs, 14 proteins expressed differently between PR and PD groups (both P < 0.05). Among tumor areas, tumor necrosis factor superfamily member 4, CD276, B-cell lymphoma-2 and CD45RO were highly expressed in PR group, while transmembrane protein 173 was highly expressed in PD group (both P < 0.05). Among stroma areas, PR group exhibited a greater expression of T cell immunoglobulin and mucin domain 3 (Tim-3), PD-L1, complement component 3 receptor 4 subunit (CD11c) and Beta-2-microglobulin (B2M) than those in PD group (P < 0.05). The stroma signature showed a superior predictive performance (AUC = 0.840, P < 0.05) to the tumor + stroma signature (AUC = 0.732, P < 0.05) and the tumor signature (AUC = 0.670, P < 0.05). The predictive effect of the stroma signature was much better than that of PD-L1 expression or tumor mutation burden. Conclusions In NSCLC, we firstly found that there are differences in the expression of immune-related proteins in different spatial regions. And we successfully developed a stroma signature with Tim-3/PD-L1/CD11c/B2M which could better predict the response to KN046. This signature might potentially complement the limitations of PD-L1 and TMB measurements and be useful in clinical practice about bsAbs.
... DSP is highly sensitive and can generate usable data with 60-100 cells, 55 and can also be used for quantitative protein characterization. 56 APEX-Seq defines the association of specific subcellular locations of RNA in living cells with corresponding function. 41,42 DBiT-seq contains three resolutions of 10, 25, and 50 μm, of which each pixel at 10 μm resolution can capture about 2000 genes. ...
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The combination of spatial transcriptomics (ST) and single cell RNA sequencing (scRNA-seq) acts as a pivotal component to bridge the pathological phenomes of human tissues with molecular alterations, defining in situ intercellular molecular communications and knowledge on spatiotemporal molecular medicine. The present article overviews the development of ST and aims to evaluate clinical and translational values for understanding molecular pathogenesis and uncovering disease-specific biomarkers. We compare the advantages and disadvantages of sequencing- and imaging-based technologies and highlight opportunities and challenges of ST. We also describe the bioinformatics tools necessary on dissecting spatial patterns of gene expression and cellular interactions and the potential applications of ST in human diseases for clinical practice as one of important issues in clinical and translational medicine, including neurology, embryo development, oncology, and inflammation. Thus, clear clinical objectives, designs, optimizations of sampling procedure and protocol, repeatability of ST, as well as simplifications of analysis and interpretation are the key to translate ST from bench to clinic.
The tumor microenvironment (TME) is a heterogeneous milieu of cellular and molecular factors that play a crucial role in tumor evolution and disease progression. These factors are important in all aspects of tumorigenesis as they reveal how cell types within the TME interact with one another. Characterizing the TME therefore paves the way for deeper insights into the tumor biology and addresses several unanswered questions in tumor progression and drug resistance. The emerging cellular and molecular profiling technologies with spatial phenotyping capabilities are rapidly changing our understanding of the TME architecture. These approaches allow for high-plex transcriptomic and proteomic phenotyping while also providing valuable spatial information on cell types within the TME. Here, we discuss tissue biomarkers associated with therapy response and describe cutting-edge technologies giving us new insights into cancer biology.
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Digital Spatial Profiling (DSP) is a method for highly multiplex spatial profiling of proteins or RNAs suitable for use on formalin-fixed, paraffin-embedded (FFPE) samples. The approach relies on (1) multiplexed readout of proteins or RNAs using oligonucleotide tags; (2) oligonucleotide tags attached to affinity reagents (antibodies or RNA probes) through a photocleavable (PC) linker; and (3) photocleaving light projected onto the tissue sample to release PC oligonucleotides in any spatial pattern across a region of interest (ROI) covering 1 to ~5,000 cells. DSP is capable of single-cell sensitivity within an ROI using the antibody readout, with RNA detection feasible down to ~600 individual mRNA transcripts. We show spatial profiling of up to 44 proteins and 96 genes (928 RNA probes) in lymphoid, colorectal tumor and autoimmune tissues by using the nCounter system and 1,412 genes (4,998 RNA probes) by using next-generation sequencing (NGS). DSP may be used to profile not only proteins and RNAs in biobanked samples but also immune markers in patient samples, with potential prognostic and predictive potential for clinical decision-making. A turnkey system allows for spatial profiling of proteins and RNA in fixed tissues, providing a window on cellular heterogeneity.
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We have developed Digital Spatial Profiling (DSP), a non-destructive method for high-plex spatial profiling of proteins and RNA, using oligonucleotide detection technologies with unlimited multiplexing capability. The key breakthroughs underlying DSP are threefold: (1) multiplexed readout of proteins/RNA using oligo-tags; (2) oligo-tags attached to affinity reagents (antibodies/RNA probes) through a photocleavable (PC) linker; (3) photocleaving light projected onto the tissue sample to release PC-oligos in any spatial pattern. Here we show precise analyte reproducibility, validation, and cellular resolution using DSP. We also demonstrate biological proof-of-concept using lymphoid, colorectal tumor, and autoimmune tissue as models to profile immune cell populations, stroma, and cancer cells to identify factors specific for the diseased microenvironment. DSP utilizes the unlimited multiplexing capability of modern genomic approaches, while simultaneously providing spatial context of protein and RNA to examine biological questions based on analyte location and distribution.
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Adjuvant ipilimumab (anti-CTLA-4) and nivolumab (anti-PD-1) both improve relapse-free survival of stage III melanoma patients1,2. In stage IV disease, the combination of ipilimumab + nivolumab is superior to ipilimumab alone and also appears to be more effective than nivolumab monotherapy³. Preclinical work suggests that neoadjuvant application of checkpoint inhibitors may be superior to adjuvant therapy⁴. To address this question and to test feasibility, 20 patients with palpable stage III melanoma were 1:1 randomized to receive ipilimumab 3 mg kg⁻¹ and nivolumab 1 mg kg⁻¹, as either four courses after surgery (adjuvant arm) or two courses before surgery and two courses postsurgery (neoadjuvant arm). Neoadjuvant therapy was feasible, with all patients undergoing surgery at the preplanned time point. However in both arms, 9/10 patients experienced one or more grade 3/4 adverse events. Pathological responses were achieved in 7/9 (78%) patients treated in the neoadjuvant arm. None of these patients have relapsed so far (median follow-up, 25.6 months). We found that neoadjuvant ipilimumab + nivolumab expand more tumor-resident T cell clones than adjuvant application. While neoadjuvant therapy appears promising, with the current regimen it induced high toxicity rates; therefore, it needs further investigation to preserve efficacy but reduce toxicity. © 2018, The Author(s), under exclusive licence to Springer Nature America, Inc.
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Preclinical studies suggest that treatment with neoadjuvant immune checkpoint blockade is associated with enhanced survival and antigen-specific T cell responses compared with adjuvant treatment¹; however, optimal regimens have not been defined. Here we report results from a randomized phase 2 study of neoadjuvant nivolumab versus combined ipilimumab with nivolumab in 23 patients with high-risk resectable melanoma (NCT02519322). RECIST overall response rates (ORR), pathologic complete response rates (pCR), treatment-related adverse events (trAEs) and immune correlates of response were assessed. Treatment with combined ipilimumab and nivolumab yielded high response rates (RECIST ORR 73%, pCR 45%) but substantial toxicity (73% grade 3 trAEs), whereas treatment with nivolumab monotherapy yielded modest responses (ORR 25%, pCR 25%) and low toxicity (8% grade 3 trAEs). Immune correlates of response were identified, demonstrating higher lymphoid infiltrates in responders to both therapies and a more clonal and diverse T cell infiltrate in responders to nivolumab monotherapy. These results describe the feasibility of neoadjuvant immune checkpoint blockade in melanoma and emphasize the need for additional studies to optimize treatment regimens and to validate putative biomarkers. © 2018, The Author(s), under exclusive licence to Springer Nature America, Inc.
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A highly multiplexed cytometric imaging approach, termed co-detection by indexing (CODEX), is used here to create multiplexed datasets of normal and lupus (MRL/lpr) murine spleens. CODEX iteratively visualizes antibody binding events using DNA barcodes, fluorescent dNTP analogs, and an in situ polymerization-based indexing procedure. An algorithmic pipeline for single-cell antigen quantification in tightly packed tissues was developed and used to overlay well-known morphological features with de novo characterization of lymphoid tissue architecture at a single-cell and cellular neighborhood levels. We observed an unexpected, profound impact of the cellular neighborhood on the expression of protein receptors on immune cells. By comparing normal murine spleen to spleens from animals with systemic autoimmune disease (MRL/lpr), extensive and previously uncharacterized splenic cell-interaction dynamics in the healthy versus diseased state was observed. The fidelity of multiplexed spatial cytometry demonstrated here allows for quantitative systemic characterization of tissue architecture in normal and clinically aberrant samples.
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Molecular analysis of tumors forms the basis for personalized cancer medicine and increasingly guides patient selection for targeted therapy. Future opportunities for personalized medicine are highlighted by the measurement of protein expression levels via immunohistochemistry, protein arrays, and other approaches; however, sample type, sample quantity, batch effects, and "time to result" are limiting factors for clinical application. Here, we present a development pipeline for a novel multiplexed DNA-labeled antibody platform which digitally quantifies protein expression from lysate samples. We implemented a rigorous validation process for each antibody, and show that the platform is amenable to multiple protocols covering nitrocellulose and plate-based methods. Results are highly reproducible across technical and biological replicates, and there are no observed "batch effects" which are common for most multiplex molecular assays. Tests from basal and perturbed cancer cell lines indicate that this platform is comparable to orthogonal proteomic assays such as Reverse-Phase Protein Array, and applicable to measuring the pharmacodynamic effects of clinically-relevant cancer therapeutics. Furthermore, we demonstrate the potential clinical utility of the platform with protein profiling from breast cancer patient samples to identify molecular subtypes. Together, these findings highlight the potential of this platform for enhancing our understanding of cancer biology in a clinical translation setting.
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The immune system has many sophisticated mechanisms to balance an extensive immune response. Distinct immunosuppressive cells could protect from excessive tissue damage and autoimmune disorders. Tumor cells take an advantage of those immunosuppressive mechanisms and establish a strongly immunosuppressive tumor microenvironment (TME), which inhibits antitumor immune responses, supporting the disease progression. Myeloid-derived suppressor cells (MDSC) play a crucial role in this immunosuppressive TME. Those cells represent a heterogeneous population of immature myeloid cells with a strong immunosuppressive potential. They inhibit an antitumor reactivity of T cells and NK cells. Furthermore, they promote angiogenesis, establish pre-metastatic niches, and recruit other immunosuppressive cells such as regulatory T cells. Accumulating evidences demonstrated that the enrichment and activation of MDSC correlated with tumor progression, recurrence, and negative clinical outcome. In the last few years, various preclinical studies and clinical trials targeting MDSC showed promising results. In this review, we discuss different therapeutic approaches on MDSC targeting to overcome immunosuppressive TME and enhance the efficiency of current tumor immunotherapies.
Regulatory T (Treg) cells, an immunosuppressive subset of CD4⁺ T cells characterized by the expression of the master transcription factor forkhead box protein P3 (FOXP3), are a component of the immune system with essential roles in maintaining self-tolerance. In addition, Treg cells can suppress anticancer immunity, thereby hindering protective immunosurveillance of neoplasia and hampering effective antitumour immune responses in tumour-bearing hosts, thus promoting tumour development and progression. Identification of the factors that are specifically expressed in Treg cells and/or that influence Treg cell homeostasis and function is important to understanding cancer pathogenesis and to identifying therapeutic targets. Immune-checkpoint inhibitors (ICIs) have provided a paradigm shift in the treatment of cancer. Most immune-checkpoint molecules are expressed in Treg cells, but the effects of ICIs on Treg cells, and thus the contributions of these cells to treatment responses, remain unclear. Notably, evidence indicates that ICIs targeting programmed cell death 1 (PD-1) might enhance the immunosuppressive function of Treg cells, whereas cytotoxic T lymphocyte antigen 4 (CTLA-4) inhibitors might deplete these cells. Thus, although manipulation of Treg cells is a promising anticancer therapeutic strategy, approaches to controlling these cells require further research. Herein, we discuss novel insights into the roles of Treg cells in cancer, which can hopefully be used to develop Treg cell-targeted therapies and facilitate immune precision medicine.
Tumor cell heterogeneity and tumor cell ‐ stromal interactions are being explored as determinants of disease progression and treatment resistance in solid tumor and hematologic malignancies. As such, tools simultaneously capable of highly‐multiplexed profiling of tissues’ protein and RNA content, as well as interrogation of rare or single cells, are required to precisely characterize constituent tumor cell populations, infiltrating lymphocytes and stromal elements. Access to spatial relationships will enable more precise characterization of tumors, support patient stratification and may help to identify novel drug targets. Multiple platforms are being developed to address these critical unmet needs. The NanoString Digital Spatial Profiling (DSP) platform enables highly multiplexed, spatial assessment of protein and/or RNA targets in tissues by detecting oligonucleotide barcodes conjugated via a photocleavable linker to primary antibodies or nucleic acid probes. While this platform enables high‐dimensional spatial interrogation of tissue protein and RNA expression, a detailed understanding of its composition, function and chemistry is advisable to guide experimental design and data interpretation. The purpose of this review is to provide an independent, comprehensive description of the DSP technology including an overview of NanoString's capture and antibody barcode conjugation chemistries, experimental workflow, data output and analysis methods. The DSP technology will be discussed in the context of other highly multiplexed immunohistochemistry methods, including imaging mass cytometry (IMC) and multiplexed ion beam imaging (MIBI), to inform potential users of the advantages and limitations of each. Additional issues such as pre‐analytical variability, sampling and specimen adequacy will be considered with respect to the platforms to inform potential experimental design. This article is protected by copyright. All rights reserved.
Immunotherapy is being increasingly recognized as a key therapeutic modality to treat cancer and represents one of the most exciting treatments for the disease. Fighting cancer with immunotherapy has revolutionized treatment for some patients and therapies targeting the immune checkpoint molecules such as CTLA-4 and PD-1 have achieved durable responses in melanoma, renal cancer, Hodgkin's diseases and lung cancer. However, the success rate of these treatments has been low and a large number of cancers, including colorectal cancer remain largely refractory to CTLA-4 and PD-1 blockade. This has provided impetus to identify other co-inhibitory receptors that could be exploited to enhance response rates of current immunotherapeutic agents and achieve responses to the cancers that are refectory to immunotherapy. Tim-3 is a co-inhibitory receptor that is expressed on IFN-g-producing T cells, FoxP3+ Treg cells and innate immune cells (macrophages and dendritic cells) where it has been shown to suppress their responses upon interaction with their ligand(s). Tim-3 has gained prominence as a potential candidate for cancer immunotherapy, where it has been shown that in vivo blockade of Tim-3 with other check-point inhibitors enhances anti-tumor immunity and suppresses tumor growth in several preclinical tumor models. This review discusses the recent findings on Tim-3, the role it plays in regulating immune responses in different cell types and the rationale for targeting Tim-3 for effective cancer immunotherapy.