Eight-Color Multiplex Immunohistochemistry for Simultaneous Detection of Multiple Immune Checkpoint Molecules within the Tumor Microenvironment

Mark A. J. Gorris, Altuna Halilovic, Katrin Rabold, Anne van Duffelen, Iresha N. Wickramasinghe, Dagmar Verweij, Inge M. N. Wortel, Johannes C. Textor, I. Jolanda M. de Vries and Carl G. Figdor
J Immunol January 1, 2018, 200 (1) 347-354; DOI: https://doi.org/10.4049/jimmunol.1701262

Abstract

Therapies targeting immune checkpoint molecules CTLA-4 and PD-1/PD-L1 have advanced the field of cancer immunotherapy. New mAbs targeting different immune checkpoint molecules, such as TIM3, CD27, and OX40, are being developed and tested in clinical trials. To make educated decisions and design new combination treatment strategies, it is vital to learn more about coexpression of both inhibitory and stimulatory immune checkpoints on individual cells within the tumor microenvironment. Recent advances in multiple immunolabeling and multispectral imaging have enabled simultaneous analysis of more than three markers within a single formalin-fixed paraffin-embedded tissue section, with accurate cell discrimination and spatial information. However, multiplex immunohistochemistry with a maximized number of markers presents multiple difficulties. These include the primary Ab concentrations and order within the multiplex panel, which are of major importance for the staining result. In this article, we report on the development, optimization, and application of an eight-color multiplex immunohistochemistry panel, consisting of PD-1, PD-L1, OX40, CD27, TIM3, CD3, a tumor marker, and DAPI. This multiplex panel allows for simultaneous quantification of five different immune checkpoint molecules on individual cells within different tumor types. This analysis revealed major differences in the immune checkpoint expression patterns across tumor types and individual tumor samples. This method could ultimately, by characterizing the tumor microenvironment of patients who have been treated with different immune checkpoint modulators, form the rationale for the design of immune checkpoint-based immunotherapy in the future.

This article is featured in In This Issue, p.1

Introduction

The success of mAb therapies directed toward molecules modulating antitumor responses has spurred many developments in the field of cancer immunotherapy. So far, mAbs against immune checkpoints CTLA-4, PD-1, and PD-L1 have been approved for the treatment of melanoma (1–3), lung (4, 5), head and neck (6), and bladder cancer (7, 8), and their use is rapidly expanding toward treatment of other cancer types (9–11). However, long-lasting (>2 y) responses are still limited to ∼20–40% of patients (12). This has led to the development of many new mAbs targeting other immune checkpoint molecules, which are currently being tested in clinical trials (13). These include targets such as TIM3 (14–16), CD27 (17–19), and OX40 (20–22), sometimes combined with already approved mAbs, such as ipilimumab, pembrolizumab, and nivolumab (23–26). To make educated decisions with the expanding choice of mAbs targeting different immune checkpoints, it is of great importance to investigate the expression profiles of these immune checkpoint molecules on both immune cells and tumor cells within the tumor microenvironment (TME).

The goal of this research is therefore to study the expression of multiple immune checkpoints simultaneously by means of immunohistochemistry (IHC). This technique offers detailed information regarding the distribution of different phenotypes within one sample, while also maintaining the morphological context of the tissue, which is lost with other techniques like gene expression profiling or flow cytometry. In clinical practice, tissue is often available only in formalin-fixed paraffin-embedded (FFPE) form, which hampers extensive expression analysis because of epitope destruction or limited applicability of Abs (27, 28). Also, the accuracy of the detection by Abs targeting novel immune checkpoint molecules can be questioned. Furthermore, performing IHC with more than one target requires the use of primary Abs raised in different species to prevent cross-reactivity of the secondary Abs that are used, which limits the choice of primary Abs. To circumvent all these limitations, we have made use of multiplex IHC with tyramide signal amplification (TSA), a powerful tool to study the expression of multiple markers at once in a single tissue section (29–33). This was carried out with Abs validated to target specific immune checkpoint molecules. Still, we observed that multiplex IHC using TSA with a maximized number of markers presents a high level of complexity and requires careful optimization to limit epitope damage and signal loss during the sequential staining protocol.

In this article, we report on the development, optimization, and application of an eight-color multiplex IHC panel, consisting of PD-1, PD-L1, OX40, CD27, TIM3, CD3, a tumor marker, and DAPI. This optimized panel allows for the simultaneous quantification of five different immune checkpoint molecules on individual cells within different tumor types.

Materials and Methods

IHC staining

Sections of 4-μm thickness were cut from FFPE tissue microarrays (TMAs) or control tonsil tissue. The slides were deparaffinized in xylene, rehydrated, and washed in tap water before boiling in Tris-EDTA buffer (pH 9; 643901; Klinipath) for epitope retrieval/microwave treatment (MWT). In chromogenic IHC experiments, endogenous peroxidase was blocked using 3% hydrogen peroxidase in methanol (76051800.1000 and 1.06009.1000, respectively; EMD Millipore). In TSA IHC experiments, protein blocking was performed using TBS-Tween 1% BSA (10735108001; Roche). Primary Abs CD3 (RM-9107, clone RM-9107; Thermo Fisher), PD-1 (315M-94, clone NAT105; Sanbio), PD-L1 (13684, clone E1L3N; Cell Signaling), OX40 (555863, clone ACT35; BD Biosciences), TIM3 (45208, clone D5D5R; Cell Signaling), CD27 (ab131254, clone EPR8569; Abcam), a melanoma mix consisting of HMB-45 (M063401, clone HMB45; Dako), Mart-1 (MS-799, clone A103; Thermo Immunologic), Tyrosinase (MONX10591, clone T311; Monosan), and SOX-10 (383R, clone EP268; Cell Marque), used for the tumor tissue within melanoma samples, and pan cytokeratin (ab86734, clone AE1/AE3 + 5D3; Abcam), to visualize tumor tissue within all other tumor samples, were incubated for 1 h at room temperature. Next, incubation with BrightVision poly-HRP-anti-Ms/Rb/Rt IgG (DPVO999HRP; ImmunoLogic) was performed at room temperature for 30 min.

Chromogenic visualization was performed with Bright-DAB (VWRKBS04-999; ImmunoLogic) for 7 min at room temperature. After dehydration, slides were counterstained with hematoxylin and were enclosed with Quick-D mounting medium (7281; Klinipath).

TSA visualization was performed with the Opal seven-color IHC Kit (NEL797B001KT; PerkinElmer) containing fluorophores DAPI, Opal 520, Opal 540, Opal 570, Opal 620, Opal 650, Opal 690, and TSA Coumarin system (NEL703001KT; PerkinElmer). MWT was performed to remove the Ab TSA complex with Tris-EDTA buffer (pH 9). TSA single stain slides were finished with MWT and counterstained with DAPI for 5 min and were enclosed in Fluoromount-G (0100-01; SouthernBiotech).

Multiplex TSA was optimized by performing a duplex (PD-L1, Opal 570 and PD-1, Opal 650), followed by a triplex (addition of OX40, Opal 690), 4plex (addition of CD27, Opal 540), 5plex (addition of TIM3, Opal 620), and 6plex (addition of CD3, Opal 520). All multiplex TSA experiments were performed by repeating staining cycles in series, with MWTs in between each cycle and at the end of the multiplex TSA. Finally, 7plex was performed with the addition of a “tumor marker” (pan cytokeratin or melanoma mix, both coumarin) and omission of the MWT at the end to prevent epitope damage or signal loss. All multiplex TSA stainings were finished with a DAPI counterstain and were enclosed in Fluoromount-G.

Tissue imaging and analysis

Slides were scanned using the PerkinElmer Vectra (Vectra 2.0.7 and 3.0.3; PerkinElmer). Multispectral images were unmixed using spectral libraries built from images of single stained tissues for each reagent using the inForm Advanced Image Analysis software (inForm 2.1.1 and 2.2.1; PerkinElmer). A selection of 15–25 representative original multispectral images was used to train the inForm software (tissue segmentation, cell segmentation, phenotyping tool, and positivity score). All the settings applied to the training images were saved within an algorithm to allow batch analysis of multiple original multispectral images of the same tumor.

Cell culture and transfection

Chinese hamster ovary (CHO) cells were cultured in Ham’s F-12 medium (21765-029; Invitrogen) supplemented with 10% FCS (758093; Greiner Bio-One) and antibiotic antimycotic (15240; Life Technologies). Cells were maintained at 37°C in a humidified atmosphere of 5% carbon dioxide. One day prior to transfection, CHO cells were seeded into T75 flasks at a confluency of ∼70%. Cells were transfected with the cDNA constructs cloned into the mammalian expression vector pcDNA3.1+/C-(K)-DYK encoding for PD-1, PD-L1, OX40, CD27, and TIM3 (OHu26320D, OHu22144D, OHu20164D, OHu19616D, and OHu20832D, respectively; all from Genscript) using Lipofectamine 2000 (11668-019; Invitrogen) according to manufacturers’ guidelines. Expression of the recombinant proteins was maintained for 48 h before measurement. Flow cytometry was performed on cells to check transfection efficiency. The remainder of cells was embedded in paraffin with the AgarCyto cell block preparation (34), and sections of 4-μm thickness were cut.

Flow cytometry

Flow cytometry was performed on (transfected) CHO cells with conjugated Abs suitable for flow cytometric assays. The following primary mAbs were used: anti–PD-1-PerCp-Cy5.5, anti–OX40-FITC, and anti–CD27-PE-Cy7 (561273, 555837, and 560609, respectively; all BD Biosciences), anti–PD-L1-allophycocyanin and anti-TIM3-PE (329708 and 345006, respectively; both from BioLegend). Flow cytometry was conducted with the FACS Verse (BD Biosciences). Flow cytometry data were analyzed using FlowJo software (v10; Tree Star).

Patient material

From the Radboud university medical center, a total of 33 tumor specimens was selected for this study, including melanoma, squamous cell carcinoma of the lung, adenocarcinoma of the lung, urothelial cell carcinoma of the bladder, colorectal adenocarcinoma, head and neck squamous cell carcinoma, diffuse large B cell lymphoma (DLBCL), ovarian serous adenocarcinoma, clear cell carcinoma of the kidney, invasive ductal breast carcinoma, and adenocarcinoma of the prostate. Three samples were included of each tumor type. Specimens were randomly included based on the availability of a resection specimen, with enough material to create a TMA of 2 mm. TMA cores were acquired at the tumor–stroma border, where immune infiltrates were detected. No ethical approval was required according to current Dutch legislation because we used leftover coded material (35) and patients were given the opportunity to object to their leftover material being used in (clinical) research.

Results

TSA IHC of immune checkpoint molecules is highly sensitive and requires extensive testing and titration of primary Abs for staining specificity

The knowledge on expression of immune checkpoint molecules within the TME remains incomplete. We set out to study immune checkpoint molecules for which mAb therapies have been approved or are currently being explored in clinical trials for the treatment of cancer. Of these, a wide variety of immune checkpoint Abs is commercially available claiming to work for IHC on FFPE tissue. However, most checkpoint targets are relatively new, and IHC staining Abs have not been validated for in vitro diagnostics. We screened Abs targeting a wide variety of immune checkpoint molecules for specific IHC staining signal. We continued with those Abs that showed specific IHC staining signals (Fig. 1A), after confirming the specificity of these Abs on CHO cells that expressed the targeted immune checkpoint molecule of interest (Fig. 1B, 1C). The most promising targets were scored for cell positivity from 3,3′-diaminobenzidine (DAB) chromogenic IHC on control tonsil tissue (Fig. 2A), which is considered to be the golden standard in pathology (36). To use these Abs within a multiplex panel, we applied a TSA staining technique, which is more sensitive than conventional fluorescence or DAB IHC (30). Equal concentrations of Ab yielded higher numbers of positive cells in the TSA application compared with DAB (Fig. 2B). It is therefore important to balance the signals through titration (Fig. 2C). Primary Ab dilutions were chosen for the TSA staining that correlated to the number of positive cells in the DAB IHC (Fig. 2D).

FIGURE 1.
FIGURE 1.

DAB single staining on tonsil tissue and CHO cells transfected with constructs cDNA encoding immune checkpoint molecules. DAB IHC screening was performed at dilutions recommended by the manufacturer on tonsil control tissue (A). CHO cells were transfected with constructs cDNA encoding immune checkpoint molecules. CHO AgarCyto sections were subjected to DAB IHC (B). Wild type cells do not show positive staining (B, top panel), whereas the transfected cells do show a positive signal (B, bottom panel). Original magnification ×20. Immune checkpoint molecules expression of the wild type and transfected CHO cells was confirmed by flow cytometry, stained with Ab clones suitable for this technique (C). Gray line represents CHO wild type, and black lines represents transfected CHO.

FIGURE 2.
FIGURE 2.

IHC staining of immune checkpoint molecules on tonsil control tissue. DAB IHC was performed at dilutions recommended by the manufacturer (A). Equal concentrations used in the TSA method resulted in a more intense staining and an increase in the number of positive cells (B). Primary Ab titrations were performed with the TSA method (C), and final dilutions (marked light gray) were chosen that yielded similar results to DAB IHC (D). Stainings were performed on sequential slides. Images are split up into composites (upper right halves) and results of the cell scoring (lower left halves). Cells were scored with inForm Cell Analysis by counting the number of positive cells of three ×20 fields (red = positive, blue = negative). Original magnification ×20.

The order of primary Abs within multiplex TSA affects signal intensity

A multiplex TSA can be established by performing sequential cycles of primary Ab, secondary Ab, and TSA application separated by MWT (used for epitope retrieval and removing the primary/secondary Ab complexes while leaving covalently bound TSA fluorophores in place) (30). However, caution should be taken in determining the order of Abs when optimizing a multiplex panel. Random integration of sequential Abs within a multiplex panel may lead to imbalanced signals, incomplete staining through interference with previously applied TSA, disruption of epitopes, and removal of TSA fluorophores because of repetitive MWTs (30, 33). Therefore, the sequence of Abs used in the panel was developed sequentially, starting with a duplex staining with two Abs and determining the optimal Ab order before continuing with the next step. Results of TSA single stains (Fig. 3A) were used as a reference to determine the optimal Ab order for the multiplex stains (Fig. 3B, 3C, Supplemental Fig. 1). This process was repeated until six different markers were combined in a 6plex. The duplex with PD-L1 first, followed by PD-1, most resembled the TSA single staining. Vice versa, starting with PD-1, followed by PD-L1, resulted in reduced PD-L1 staining and an overestimation of the PD-1 signal. Next, OX40 was implemented in the first, middle, or last position, within the predetermined order of PD-L1 and PD-1. OX40 at the second position yielded results most comparable with the single TSA staining. Other primary Ab orders resulted in an underestimation of OX40 or an overestimation of not only OX40 signal, but also PD-L1 and PD-1 signals (Supplemental Fig. 1). From this, we concluded that the optimal order for the triplex is PD-L1, OX40, and PD-1. We continued optimizing a 4plex, resulting in CD27 at the second position, followed by a 5plex, where TIM3 was optimal at the third position, and lastly a 6plex, where CD3 was best at the second position. We observed that the implementation of additional Abs within the panel affected not only the signal intensity of the new target, but also all other targets. Thus, reliable multiplex TSA staining requires a careful fine-tuning of the order in which primary Abs are used.

FIGURE 3.
FIGURE 3.

Primary Ab order optimization of multiplex IHC. Optimal multiplex IHC determined by comparing the TSA single staining signal (A) with the multiplex signal (B) on sequential slides of tonsil control tissue. Optimal combination of PD-1 and PD-L1 in duplex was determined first, followed by implementation of OX40 in triplex, CD27 in 4plex, TIM3 in 5plex, and finally CD3 in 6plex (B). Optimal Ab order was determined by visual inspection in combination with cell positivity score determined by inForm Software analysis of three ×20 fields. Mean positive cell score is shown per incorporated position (C). Only the cell positivity score of the single, first, last, and chosen position is shown to reduce complexity of the graphs. Final position in the multiplex panel is marked with light gray. Original magnification ×20.

Multiplex IHC reveals differential immune checkpoint molecule expression among tumor types and patients

As a proof of principle, the developed TSA multiplex panel was used to detect differences in the expression of immune checkpoint molecules on immune cells and tumor cells within different tumor tissues. TMAs consisting of invasive margin cores of different tumor types, which are known for their different levels of immunogenicity, were compared (37). Slides were stained with the developed immune checkpoint panel with the addition of a “tumor marker” (a mixture of melanoma-associated Abs for melanoma or pan cytokeratin for the other cancer types) at the last position (Fig. 4). Analysis was performed using inForm Cell Analysis, by segmenting the tumor from stroma (based on the tumor marker) and dividing the cells into immune cells (based on positivity of PD-1, PD-L1, OX40, CD27, TIM3, and/or CD3), tumor cells (tumor marker positive), and other cells (all markers negative) (Fig. 5A). Expression of immune checkpoint markers was determined on both tumor and immune cells, which were further separated into CD3+ and CD3− immune cells. As expected, DLBCL was tumor marker/cytokeratin negative, and therefore checkpoint expression could not be determined on tumor cells specifically. We observed that immune checkpoint molecule expression is heterogeneous on immune cells and tumor cells. Nonetheless, differential expression patterns were observed within and among tumor types (Fig. 5B). In melanoma and lung squamous cell carcinoma, for instance, a high percentage of CD3+ immune cells expressed CD27. OX40 was expressed by CD3+ immune cells in some cases in melanoma, lung, bladder, colorectal, head and neck, ovarian, and breast carcinoma. PD-1 expression was detected only on immune cells in melanoma and DLBCL. PD-L1 expression was found on CD3+ and CD3− immune cells in almost all cancers, whereas PD-L1 expression on tumor cells was visible in only a minority of cancers. PD-L1 on tumor cells was strongest in bladder cancer, followed by head and neck carcinoma, and partial on melanoma, lung, and breast carcinoma. TIM3 was mostly detected in head and neck, ovarian, kidney clear cell, and prostate carcinoma on both CD3+ and CD3− immune cells. These results demonstrate that the immune checkpoint landscape is highly diverse.

FIGURE 4.
FIGURE 4.

Eight-color multiplex IHC in different tumor types. Sections containing invasive margin tumor cores were stained with multiplex IHC with the optimal Ab order and tumor marker. Representative multicolor composite pictures of each tumor type are shown. Original magnification ×20. TM, tumor marker.

FIGURE 5.
FIGURE 5.

Assessment of immune checkpoint expression molecules in different tumor types. Representative overviews of each tumor type with stroma (gray), tumor (black), and immune marker–positive cell clusters (red circles) (A). DLBCL was tumor marker/cytokeratin negative. Expression of immune checkpoint molecules was determined on tumor cells and on immune marker–positive cells, subdivided into CD3+ and CD3− cells (B). Immune checkpoint molecule expression is shown as percentage of total tumor cells or total immune marker–positive cells. n = 3 per tumor type.

Discussion

The success or failure of immunotherapy might depend on the expression of immune-stimulating and -inhibiting checkpoint molecules. Understanding the balance between these molecules on (immune) cells and their location within the TME is therefore critical (38–40). Recent advances in multiple immunolabeling and multispectral imaging enable simultaneous analysis of more than three markers within a single FFPE tissue section, with accurate cell discrimination and spatial information (30–33). For this reason, multiplex IHC is one of the most sought-after techniques that might allow for the identification of new prognostic and predictive biomarkers in cancer immunotherapy. However, we and others have experienced that multiplex IHC using TSA is not straightforward, and caution should be taken in the choice of the primary Ab concentration and the order of Abs within a multiplex panel. TSA is a more sensitive technique compared with regular chromogenic IHC and can potentially detect lower levels of expressed proteins (30), but this can also lead to more unspecific background staining. In this study, a TSA IHC was developed that showed comparable staining results as obtained with chromogenic IHC. Furthermore, we discovered that the position of the Abs within the multiplex staining panel is an important factor that affects the signal levels of not only the newly added Ab, but of all Abs within a panel. A reason for this could be that a random order of Abs within a multiplex panel can lead to imbalanced signals, incomplete staining through interference of previous TSA applications, disruption of epitopes, and removal of TSA fluorophores because of repetitive MWTs (30, 33). Therefore, we compared our multiplex staining results throughout the whole optimization process with the TSA singles, starting from optimized singles to duplex staining and so on until eventually a 6plex panel was developed. Eight-color multiplex IHC was completed with the addition of a tumor marker for tumor cell/region discrimination and DAPI staining for cell nucleus identification.

Immune checkpoint molecules were studied for which mAb therapies have been approved or are being explored in clinical trials for the treatment of several types of cancers (12). Not all commercially available Abs that claim to work in FFPE tissue showed a strong and/or a specific staining in our hands (results not shown). A wide variety of primary Abs targeting immune checkpoint molecules were tested for FFPE tissue IHC, of which the ones targeting PD-1, PD-L1, OX40, CD27, and TIM3 worked well. Besides the Abs described in this manuscript, we also obtained specific immune checkpoint Abs for CTLA-4, LAG3, and ICOS, which worked well on FFPE tissue (M.A.J. Gorris, A. Halilovic, C. Wefers, M. Tazzari, I.M.N. Wortel, L.L. van der Woude, A. van Duffelen, D. Verweij, I.J.M. de Vries, C.G. Figdor, and J. Textor, manuscript in preparation). These targets can be implemented in additional multiplex panels because they are of major clinical interest and may result in novel (therapeutical) insights. Abs targeting PD-L2, 4-1BB, BTLA, and HVEM have thus far been tested by us without success.

Staining multiple TMA cores of various tumor types with this multiplex panel identified major differences in immune checkpoint expression, both within the same tumor type and among different tumor types. This was already apparent using the limited number of samples in this study. This demonstrates the necessity for analysis of immune checkpoint multiplex IHC in individual patients. For instance, PD-L1 expression was found on a fraction of both CD3+ and CD3− cells in almost all cancers. In contrast, PD-L1 expression on tumor cells was visible in only a minority of tumor types, with the strongest expression in carcinoma of the bladder, followed by head and neck carcinoma, and partly in melanoma and lung carcinoma. However, it is worth noting that because the TMA cores were selected at the invasive margin of the tumor where immune cell infiltrates were present, heterogeneity within a whole tumor section cannot be excluded (41–43). Therefore, these findings need to be further explored by performing analyses on whole tumor sections and larger cohorts.

To the best of our knowledge, this study is the first report on multiple immune checkpoint analyses simultaneously within FFPE sections of the TME. Importantly, this panel allows the simultaneous analysis of multiple immune checkpoint molecules on individual immune cells and tumor cells. This method could ultimately, by characterizing the TME of patients who have been treated with different immune checkpoint modulators, form the rationale for the design of individualized immune checkpoint-based immunotherapy for (treatment-naive) patients in the future.

Disclosures

The authors have no financial conflicts of interest.

Acknowledgments

We thank the technicians of the Immunodiagnostic Laboratory of the Department of Pathology at the Radboud university medical center, under the supervision of Monique Link. We thank Dr. Willeke Blokx for advice in selecting tumor tissue samples for this study. Irene Otte-Holler is acknowledged for efforts in the initial phase of this research.

Footnotes

  • This work was supported by a grant from the Netherlands Organisation for Scientific Research (NWO-Vici 916.14.655), a grant from the Dutch Cancer Society (KWF2009-4402), and a Radboud university medical center Ph.D. grant. C.G.F. received European Research Council Advanced Grant PATHFINDER (269019) and the Netherlands Organisation for Scientific Research Spinoza grant.

  • The online version of this article contains supplemental material.

  • Abbreviations used in this article:

    CHO
    Chinese hamster ovary
    DAB
    3,3′-diaminobenzidine
    DLBCL
    diffuse large B cell lymphoma
    FFPE
    formalin-fixed paraffin-embedded
    IHC
    immunohistochemistry
    MWT
    microwave treatment
    TMA
    tissue microarray
    TME
    tumor microenvironment
    TSA
    tyramide signal amplification.

  • Received September 1, 2017.
  • Accepted October 17, 2017.

References

  1. ↵
    1. Hodi, F. S.,
    2. S. J. O’Day,
    3. D. F. McDermott,
    4. R. W. Weber,
    5. J. A. Sosman,
    6. J. B. Haanen,
    7. R. Gonzalez,
    8. C. Robert,
    9. D. Schadendorf,
    10. J. C. Hassel, et al
    . 2010. Improved survival with ipilimumab in patients with metastatic melanoma. N. Engl. J. Med. 363: 711–723.
    OpenUrlCrossRefPubMed
    1. Robert, C.,
    2. J. Schachter,
    3. G. V. Long,
    4. A. Arance,
    5. J. J. Grob,
    6. L. Mortier,
    7. A. Daud,
    8. M. S. Carlino,
    9. C. McNeil,
    10. M. Lotem, et al, KEYNOTE-006 investigators
    . 2015. Pembrolizumab versus ipilimumab in advanced melanoma. N. Engl. J. Med. 372: 2521–2532.
    OpenUrlCrossRefPubMed
  3. ↵
    1. Ribas, A.,
    2. O. Hamid,
    3. A. Daud,
    4. F. S. Hodi,
    5. J. D. Wolchok,
    6. R. Kefford,
    7. A. M. Joshua,
    8. A. Patnaik,
    9. W. J. Hwu,
    10. J. S. Weber, et al
    . 2016. Association of pembrolizumab with tumor response and survival among patients with advanced melanoma. JAMA 315: 1600–1609.
    OpenUrlCrossRefPubMed
  4. ↵
    1. Brahmer, J.,
    2. K. L. Reckamp,
    3. P. Baas,
    4. L. Crinò,
    5. W. E. Eberhardt,
    6. E. Poddubskaya,
    7. S. Antonia,
    8. A. Pluzanski,
    9. E. E. Vokes,
    10. E. Holgado, et al
    . 2015. Nivolumab versus docetaxel in advanced squamous-cell non-small-cell lung cancer. N. Engl. J. Med. 373: 123–135.
    OpenUrlCrossRefPubMed
  5. ↵
    1. Reck, M.,
    2. D. Rodríguez-Abreu,
    3. A. G. Robinson,
    4. R. Hui,
    5. T. Csőszi,
    6. A. Fülöp,
    7. M. Gottfried,
    8. N. Peled,
    9. A. Tafreshi,
    10. S. Cuffe, et al, KEYNOTE-024 Investigators
    . 2016. Pembrolizumab versus chemotherapy for PD-L1-positive non-small-cell lung cancer. N. Engl. J. Med. 375: 1823–1833.
    OpenUrlCrossRef
  6. ↵
    1. Ferris, R. L.,
    2. G. Blumenschein Jr..,
    3. J. Fayette,
    4. J. Guigay,
    5. A. D. Colevas,
    6. L. Licitra,
    7. K. Harrington,
    8. S. Kasper,
    9. E. E. Vokes,
    10. C. Even, et al
    . 2016. Nivolumab for recurrent squamous-cell carcinoma of the head and neck. N. Engl. J. Med. 375: 1856–1867.
    OpenUrlCrossRef
  7. ↵
    1. Powles, T.,
    2. J. P. Eder,
    3. G. D. Fine,
    4. F. S. Braiteh,
    5. Y. Loriot,
    6. C. Cruz,
    7. J. Bellmunt,
    8. H. A. Burris,
    9. D. P. Petrylak,
    10. S. L. Teng, et al
    . 2014. MPDL3280A (anti-PD-L1) treatment leads to clinical activity in metastatic bladder cancer. Nature 515: 558–562.
    OpenUrlCrossRefPubMed
  8. ↵
    1. Massard, C.,
    2. M. S. Gordon,
    3. S. Sharma,
    4. S. Rafii,
    5. Z. A. Wainberg,
    6. J. Luke,
    7. T. J. Curiel,
    8. G. Colon-Otero,
    9. O. Hamid,
    10. R. E. Sanborn, et al
    . 2016. Safety and efficacy of durvalumab (MEDI4736), an anti-programmed cell death ligand-1 immune checkpoint inhibitor, in patients with advanced urothelial bladder cancer. J. Clin. Oncol. 34: 3119–3125.
    OpenUrlAbstract/FREE Full Text
  9. ↵
    1. Motzer, R. J.,
    2. B. Escudier,
    3. D. F. McDermott,
    4. S. George,
    5. H. J. Hammers,
    6. S. Srinivas,
    7. S. S. Tykodi,
    8. J. A. Sosman,
    9. G. Procopio,
    10. E. R. Plimack, et al, CheckMate 025 Investigators
    . 2015. Nivolumab versus everolimus in advanced renal-cell carcinoma. N. Engl. J. Med. 373: 1803–1813.
    OpenUrlCrossRefPubMed
    1. Nghiem, P. T.,
    2. S. Bhatia,
    3. E. J. Lipson,
    4. R. R. Kudchadkar,
    5. N. J. Miller,
    6. L. Annamalai,
    7. S. Berry,
    8. E. K. Chartash,
    9. A. Daud,
    10. S. P. Fling, et al
    . 2016. PD-1 blockade with pembrolizumab in advanced merkel-cell carcinoma. N. Engl. J. Med. 374: 2542–2552.
    OpenUrlCrossRefPubMed
  11. ↵
    1. Seiwert, T. Y.,
    2. B. Burtness,
    3. R. Mehra,
    4. J. Weiss,
    5. R. Berger,
    6. J. P. Eder,
    7. K. Heath,
    8. T. McClanahan,
    9. J. Lunceford,
    10. C. Gause, et al
    . 2016. Safety and clinical activity of pembrolizumab for treatment of recurrent or metastatic squamous cell carcinoma of the head and neck (KEYNOTE-012): an open-label, multicentre, phase 1b trial. Lancet Oncol. 17: 956–965.
    OpenUrlCrossRefPubMed
  12. ↵
    1. Sharma, P.,
    2. J. P. Allison
    . 2015. The future of immune checkpoint therapy. Science 348: 56–61.
    OpenUrlAbstract/FREE Full Text
  13. ↵
    1. Pardoll, D. M.
    2012. The blockade of immune checkpoints in cancer immunotherapy. Nat. Rev. Cancer 12: 252–264.
    OpenUrlCrossRefPubMed
  14. ↵
    1. Sakuishi, K.,
    2. L. Apetoh,
    3. J. M. Sullivan,
    4. B. R. Blazar,
    5. V. K. Kuchroo,
    6. A. C. Anderson
    . 2010. Targeting Tim-3 and PD-1 pathways to reverse T cell exhaustion and restore anti-tumor immunity. [Published erratum appears in 2011 J. Exp. Med. 208: 1331.] J. Exp. Med. 207: 2187–2194.
    OpenUrlAbstract/FREE Full Text
    1. Guo, Z.,
    2. D. Cheng,
    3. Z. Xia,
    4. M. Luan,
    5. L. Wu,
    6. G. Wang,
    7. S. Zhang
    . 2013. Combined TIM-3 blockade and CD137 activation affords the long-term protection in a murine model of ovarian cancer. J. Transl. Med. 11: 215.
    OpenUrlCrossRefPubMed
  16. ↵
    1. Kim, J. E.,
    2. M. A. Patel,
    3. A. Mangraviti,
    4. E. S. Kim,
    5. D. Theodros,
    6. E. Velarde,
    7. A. Liu,
    8. E. W. Sankey,
    9. A. Tam,
    10. H. Xu, et al
    . 2017. Combination therapy with Anti-PD-1, Anti-TIM-3, and focal radiation results in regression of murine gliomas. Clin. Cancer Res. 23: 124–136.
    OpenUrlAbstract/FREE Full Text
  17. ↵
    1. He, L. Z.,
    2. L. Thomas,
    3. J. Weidlick,
    4. L. Vitale,
    5. T. O’Neill,
    6. N. Prostak,
    7. K. Sundarapandiyan,
    8. H. Marsh,
    9. M. Yellin,
    10. T. A. Davis,
    11. T. Keler
    . 2011. Development of a human Anti-CD27 antibody with efficacy in lymphoma and leukemia models by two distinct mechanisms. Blood 118: 2861.
    OpenUrl
    1. Ramakrishna, V.,
    2. K. Sundarapandiyan,
    3. B. Zhao,
    4. M. Bylesjo,
    5. H. C. Marsh,
    6. T. Keler
    . 2015. Characterization of the human T cell response to in vitro CD27 costimulation with varlilumab. J. Immunother. Cancer 3: 37.
    OpenUrlCrossRefPubMed
  19. ↵
    1. Ahrends, T.,
    2. N. Bąbała,
    3. Y. Xiao,
    4. H. Yagita,
    5. H. van Eenennaam,
    6. J. Borst
    . 2016. CD27 agonism plus PD-1 blockade recapitulates CD4+ T-cell help in therapeutic anticancer vaccination. Cancer Res. 76: 2921–2931.
    OpenUrlAbstract/FREE Full Text
  20. ↵
    1. Weinberg, A. D.,
    2. M. M. Rivera,
    3. R. Prell,
    4. A. Morris,
    5. T. Ramstad,
    6. J. T. Vetto,
    7. W. J. Urba,
    8. G. Alvord,
    9. C. Bunce,
    10. J. Shields
    . 2000. Engagement of the OX-40 receptor in vivo enhances antitumor immunity. J. Immunol. 164: 2160–2169.
    OpenUrlAbstract/FREE Full Text
    1. Gough, M. J.,
    2. C. E. Ruby,
    3. W. L. Redmond,
    4. B. Dhungel,
    5. A. Brown,
    6. A. D. Weinberg
    . 2008. OX40 agonist therapy enhances CD8 infiltration and decreases immune suppression in the tumor. Cancer Res. 68: 5206–5215.
    OpenUrlAbstract/FREE Full Text
  22. ↵
    1. Piconese, S.,
    2. B. Valzasina,
    3. M. P. Colombo
    . 2008. OX40 triggering blocks suppression by regulatory T cells and facilitates tumor rejection. [Published erratum appears in 2008 J. Exp. Med. 205: 1505.] J. Exp. Med. 205: 825–839.
    OpenUrlAbstract/FREE Full Text
  23. ↵
    1. Aspeslagh, S.,
    2. S. Postel-Vinay,
    3. S. Rusakiewicz,
    4. J. C. Soria,
    5. L. Zitvogel,
    6. A. Marabelle
    . 2016. Rationale for anti-OX40 cancer immunotherapy. Eur. J. Cancer 52: 50–66.
    OpenUrlCrossRefPubMed
    1. Murtaza, A.,
    2. H. Laken,
    3. J. D. S. Correia,
    4. P. McNeeley,
    5. L. Altobell,
    6. J. Zhang,
    7. P. Vancutsem,
    8. K. Wilcoxen,
    9. D. Jenkins
    . 2016. Discovery of TSR-022, a novel, potent anti-human TIM-3 therapeutic antibody. Eur. J. Cancer 69: S102.
    OpenUrl
    1. Sanborn, R. E.,
    2. M. J. Pishvain,
    3. M. K. Callahan,
    4. N. Rizvi,
    5. H. Kluger,
    6. M. Yellin,
    7. T. Rawls,
    8. L. Vitale,
    9. A. Halim,
    10. T. Davis,
    11. T. Keler
    . 2016. Phase I results from the combination of an immune-activating anti-CD27 antibody (varlilumab) in combination with PD-1 blockade (nivolumab): activation across multiple immune pathways without untoward immune-related adverse events. Cancer Res. 76: CT023.
    OpenUrl
  26. ↵
    1. Swart, M.,
    2. I. Verbrugge,
    3. J. B. Beltman
    . 2016. Combination approaches with immune-checkpoint blockade in cancer therapy. Front. Oncol. 6: 233.
    OpenUrl
  27. ↵
    1. van der Loos, C. M.
    2008. Multiple immunoenzyme staining: methods and visualizations for the observation with spectral imaging. J. Histochem. Cytochem. 56: 313–328.
    OpenUrlCrossRefPubMed
  28. ↵
    1. Ramos-Vara, J. A.,
    2. M. A. Miller
    . 2014. When tissue antigens and antibodies get along: revisiting the technical aspects of immunohistochemistry—the red, brown, and blue technique. Vet. Pathol. 51: 42–87.
    OpenUrlCrossRefPubMed
  29. ↵
    1. Huang, W.,
    2. K. Hennrick,
    3. S. Drew
    . 2013. A colorful future of quantitative pathology: validation of Vectra technology using chromogenic multiplexed immunohistochemistry and prostate tissue microarrays. Hum. Pathol. 44: 29–38.
    OpenUrlCrossRefPubMed
  30. ↵
    1. Stack, E. C.,
    2. C. Wang,
    3. K. A. Roman,
    4. C. C. Hoyt
    . 2014. Multiplexed immunohistochemistry, imaging, and quantitation: a review, with an assessment of Tyramide signal amplification, multispectral imaging and multiplex analysis. Methods 70: 46–58.
    OpenUrlCrossRefPubMed
    1. Feng, Z.,
    2. S. Puri,
    3. T. Moudgil,
    4. W. Wood,
    5. C. C. Hoyt,
    6. C. Wang,
    7. W. J. Urba,
    8. B. D. Curti,
    9. C. B. Bifulco,
    10. B. A. Fox
    . 2015. Multispectral imaging of formalin-fixed tissue predicts ability to generate tumor-infiltrating lymphocytes from melanoma. J. Immunother. Cancer 3: 47.
    OpenUrlCrossRefPubMed
    1. Feng, Z.,
    2. S. M. Jensen,
    3. D. J. Messenheimer,
    4. M. Farhad,
    5. M. Neuberger,
    6. C. B. Bifulco,
    7. B. A. Fox
    . 2016. Multispectral imaging of T and B cells in murine spleen and tumor. J. Immunol. 196: 3943–3950.
    OpenUrlAbstract/FREE Full Text
  33. ↵
    1. Stack, E. C.,
    2. P. G. Foukas,
    3. P. P. Lee
    . 2016. Multiplexed tissue biomarker imaging. J. Immunother. Cancer 4: 9.
    OpenUrl
  34. ↵
    1. Kerstens, H. M. J.,
    2. J. C. M. Robben,
    3. P. J. Poddighe,
    4. W. J. G. Melchers,
    5. H. Boonstra,
    6. P. C. M. de Wilde,
    7. M. V. E. Macville,
    8. A. G. J. M. Hanselaar
    . 2000. AgarCyto: a novel cell-processing method for multiple molecular diagnostic analyses of the uterine cervix. J. Histochem. Cytochem. 48: 709–718.
    OpenUrlCrossRefPubMed
  35. ↵
    The Medical Research Involving Human Subjects Act (WMO, Wet medisch-wetenschappelijk onderzoek met mensen). Available at: http://www.ccmo.nl/en/non-wmo-research. Accessed: September 15, 2016.
  36. ↵
    1. Shi, S. R.,
    2. C. Liu,
    3. C. R. Taylor
    . 2007. Standardization of immunohistochemistry for formalin-fixed, paraffin-embedded tissue sections based on the antigen-retrieval technique: from experiments to hypothesis. J. Histochem. Cytochem. 55: 105–109.
    OpenUrlCrossRefPubMed
  37. ↵
    1. Lawrence, M. S.,
    2. P. Stojanov,
    3. P. Polak,
    4. G. V. Kryukov,
    5. K. Cibulskis,
    6. A. Sivachenko,
    7. S. L. Carter,
    8. C. Stewart,
    9. C. H. Mermel,
    10. S. A. Roberts, et al
    . 2013. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499: 214–218.
    OpenUrlCrossRefPubMed
  38. ↵
    1. Tumeh, P. C.,
    2. C. L. Harview,
    3. J. H. Yearley,
    4. I. P. Shintaku,
    5. E. J. M. Taylor,
    6. L. Robert,
    7. B. Chmielowski,
    8. M. Spasic,
    9. G. Henry,
    10. V. Ciobanu, et al
    . 2014. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 515: 568–571.
    OpenUrlCrossRefPubMed
    1. Blank, C. U.,
    2. J. B. Haanen,
    3. A. Ribas,
    4. T. N. Schumacher
    . 2016. CANCER IMMUNOLOGY. The “cancer immunogram”. Science 352: 658–660.
    OpenUrlAbstract/FREE Full Text
  40. ↵
    1. Gnjatic, S.,
    2. V. Bronte,
    3. L. R. Brunet,
    4. M. O. Butler,
    5. M. L. Disis,
    6. J. Galon,
    7. L. G. Hakansson,
    8. B. A. Hanks,
    9. V. Karanikas,
    10. S. N. Khleif, et al
    . 2017. Identifying baseline immune-related biomarkers to predict clinical outcome of immunotherapy. J. Immunother. Cancer 5: 44.
    OpenUrl
  41. ↵
    1. Madore, J.,
    2. R. E. Vilain,
    3. A. M. Menzies,
    4. H. Kakavand,
    5. J. S. Wilmott,
    6. J. Hyman,
    7. J. H. Yearley,
    8. R. F. Kefford,
    9. J. F. Thompson,
    10. G. V. Long, et al
    . 2015. PD-L1 expression in melanoma shows marked heterogeneity within and between patients: implications for anti-PD-1/PD-L1 clinical trials. Pigment Cell Melanoma Res. 28: 245–253.
    OpenUrlCrossRefPubMed
    1. Boddupalli, C. S.,
    2. N. Bar,
    3. K. Kadaveru,
    4. M. Krauthammer,
    5. N. Pornputtapong,
    6. Z. Mai,
    7. S. Ariyan,
    8. D. Narayan,
    9. H. Kluger,
    10. Y. Deng, et al
    . 2016. Interlesional diversity of T cell receptors in melanoma with immune checkpoints enriched in tissue-resident memory T cells. JCI Insight 1: e88955.
    OpenUrlCrossRef
  43. ↵
    1. Mansfield, A. S.,
    2. S. J. Murphy,
    3. T. Peikert,
    4. E. S. Yi,
    5. G. Vasmatzis,
    6. D. A. Wigle,
    7. M. C. Aubry
    . 2016. Heterogeneity of programmed cell death ligand 1 expression in multifocal lung cancer. Clin. Cancer Res. 22: 2177–2182.
    OpenUrlAbstract/FREE Full Text
View Abstract
Back to top

In this issue

Print
Download PDF
Article Alerts
Email Article
Citation Tools

Jump to section