Characterizing the Tumor Immune Microenvironment with Tyramide-Based Multiplex Immunofluorescence.

Multiplex immunofluorescence (mIF) allows simultaneous antibody-based detection of multiple markers with a nuclear counterstain on a single tissue section. Recent studies have demonstrated that mIF is becoming an important tool for immune profiling the tumor microenvironment, further advancing our understanding of the interplay between cancer and the immune system, and identifying predictive biomarkers of response to immunotherapy. Expediting mIF discoveries is leading to improved diagnostic panels, whereas it is important that mIF protocols be standardized to facilitate their transition into clinical use. Manual processing of sections for mIF is time consuming and a potential source of variability across numerous samples. To increase reproducibility and throughput we demonstrate the use of an automated slide stainer for mIF incorporating tyramide signal amplification (TSA). We describe two panels aimed at characterizing the tumor immune microenvironment. Panel 1 included CD3, CD20, CD117, FOXP3, Ki67, pancytokeratins (CK), and DAPI, and Panel 2 included CD3, CD8, CD68, PD-1, PD-L1, CK, and DAPI. Primary antibodies were first tested by standard immunohistochemistry and single-plex IF, then multiplex panels were developed and images were obtained using a Vectra 3.0 multispectral imaging system. Various methods for image analysis (identifying cell types, determining cell densities, characterizing cell-cell associations) are outlined. These mIF protocols will be invaluable tools for immune profiling the tumor microenvironment.

Multiplexed immunohistochemistry, imaging, and quantitation: a review, with an assessment of Tyramide signal amplification, multispectral imaging and multiplex analysis.

Tissue sections offer the opportunity to understand a patient's condition, to make better prognostic evaluations and to select optimum treatments, as evidenced by the place pathology holds today in clinical practice. Yet, there is a wealth of information locked up in a tissue section that is only partially accessed, due mainly to the limitations of tools and methods. Often tissues are assessed primarily based on visual analysis of one or two proteins, or 2-3 DNA or RNA molecules. Even while analysis is still based on visual perception, image analysis is starting to address the variability of human perception. This is in contrast to measuring characteristics that are substantially out of reach of human perception, such as parameters revealed through co-expression, spatial relationships, heterogeneity, and low abundance molecules. What is not routinely accessed is the information revealed through simultaneous detection of multiple markers, the spatial relationships among cells and tissue in disease, and the heterogeneity now understood to be critical to developing effective therapeutic strategies. Our purpose here is to review and assess methods for multiplexed, quantitative, image analysis based approaches, using new multicolor immunohistochemistry methods, automated multispectral slide imaging, and advanced trainable pattern recognition software. A key aspect of our approach is presenting imagery in a workflow that engages the pathologist to utilize the strengths of human perception and judgment, while significantly expanding the range of metrics collectable from tissue sections and also provide a level of consistency and precision needed to support the complexities of personalized medicine.

Companion diagnostic requirements for spatial biology using multiplex immunofluorescence and multispectral imaging.

Immunohistochemistry has long been held as the gold standard for understanding the expression patterns of therapeutically relevant proteins to identify prognostic and predictive biomarkers. Patient selection for targeted therapy in oncology has successfully relied upon standard microscopy-based methodologies, such as single-marker brightfield chromogenic immunohistochemistry. As promising as these results are, the analysis of one protein, with few exceptions, no longer provides enough information to draw effective conclusions about the probability of treatment response. More multifaceted scientific queries have driven the development of high-throughput and high-order technologies to interrogate biomarker expression patterns and spatial interactions between cell phenotypes in the tumor microenvironment. Such multi-parameter data analysis has been historically reserved for technologies that lack the spatial context that is provided by immunohistochemistry. Over the past decade, technical developments in multiplex fluorescence immunohistochemistry and discoveries made with improving image data analysis platforms have highlighted the importance of spatial relationships between certain biomarkers in understanding a patient's likelihood to respond to, typically, immune checkpoint inhibitors. At the same time, personalized medicine has instigated changes in both clinical trial design and its conduct in a push to make drug development and cancer treatment more efficient, precise, and economical. Precision medicine in immuno-oncology is being steered by data-driven approaches to gain insight into the tumor and its dynamic interaction with the immune system. This is particularly necessary given the rapid growth in the number of trials involving more than one immune checkpoint drug, and/or using those in combination with conventional cancer treatments. As multiplex methods, like immunofluorescence, push the boundaries of immunohistochemistry, it becomes critical to understand the foundation of this technology and how it can be deployed for use as a regulated test to identify the prospect of response from mono- and combination therapies. To that end, this work will focus on: 1) the scientific, clinical, and economic requirements for developing clinical multiplex immunofluorescence assays; 2) the attributes of the Akoya Phenoptics workflow to support predictive tests, including design principles, verification, and validation needs; 3) regulatory, safety and quality considerations; 4) application of multiplex immunohistochemistry through lab-developed-tests and regulated in vitro diagnostic devices.

Multiplex Immunofluorescence and Multispectral Imaging: Forming the Basis of a Clinical Test Platform for Immuno-Oncology.

As immuno-oncology (I/O) emerges as an effective approach in the fight against cancer, multispectral imaging of multiplex immunofluorescence (mIF) is maturing as an analytical platform. The timing is fortuitous. Due to health economic considerations surrounding the use of I/O, there is an urgent need for tests that accurately predict response to the growing list of available therapies. Multispectral mIF provides several advantages over other biomarker modalities by enabling deeper interrogation of the intricate biology within the tumor microenvironment, including detection of cell-to-cell spatial interactions that correlate with clinical outcomes. It also provides a practical path for generating reliable and reproducible results in a clinically suitable, high-throughput workflow. In this article, we (1) describe the principles behind multispectral mIF; (2) provide advice and recommendations on assay development and optimization and highlight characteristics of a well-performing assay; and (3) discuss the requirements for translating this approach into clinical practice.

In vivo stable tumor-specific painting in various colors using dehalogenase-based protein-tag fluorescent ligands.

In vivo fluorescence cancer imaging is an important tool in understanding tumor growth and therapeutic monitoring and can be performed either with endogenously produced fluorescent proteins or with exogenously introduced fluorescent probes bound to targeting molecules. However, endogenous fluorescence proteins cannot be altered after transfection, thus requiring rederivation of cell lines for each desired color, while exogenously targeted fluorescence probes are limited by the heterogeneous expression of naturally occurring cellular targets. In this study, we adapted the dehalogenase-based protein-Tag (HaloTag) system to in vivo cancer imaging, by introducing highly expressed HaloTag receptors (HaloTagR) in cancer cells coupled with a range of externally injected fluorophore-conjugated dehalogenase-reactive reactive linkers. Tumor nodules arising from a single transfected cell line were stably labeled with fluorescence varying in emission spectra from green to near-infrared. After establishing and validating a SHIN3 cell line stably transfected with HaloTagR (HaloTagR-SHIN3), in vivo spectral fluorescence imaging studies were performed in live animals using a peritoneal dissemination model. The tumor nodules arising from HaloTagR-SHIN3 could be successfully labeled by four different fluorophore-conjugated HaloTag-ligands each emitting light at different wavelengths. These fluorophores could be alternated on serial imaging sessions permitting assessment of interval growth. Fluorescence was retained in histological specimens after fixation. Thus, this tagging system proves versatile both for in vivo and in vitro imaging without requiring modification of the underlying cell line. Thus, this strategy can overcome some of the limitations associated with the use of endogenous fluorescent proteins and exogenous targeted optical agents in current use.

Automated Multiplex Immunofluorescence Panel for Immuno-oncology Studies on Formalin-fixed Carcinoma Tissue Specimens.

Continued developments in immuno-oncology require an increased understanding of the mechanisms of cancer immunology. The immunoprofiling analysis of tissue samples from formalin-fixed, paraffin-embedded (FFPE) biopsies has become a key tool for understanding the complexity of tumor immunology and discovering novel predictive biomarkers for cancer immunotherapy. Immunoprofiling analysis of tissues requires the evaluation of combined markers, including inflammatory cell subpopulations and immune checkpoints, in the tumor microenvironment. The advent of novel multiplex immunohistochemical methods allows for a more efficient multiparametric analysis of single tissue sections than does standard monoplex immunohistochemistry (IHC). One commercially available multiplex immunofluorescence (IF) method is based on tyramide-signal amplification and, combined with multispectral microscopic analysis, allows for a better signal separation of diverse markers in tissue. This methodology is compatible with the use of unconjugated primary antibodies that have been optimized for standard IHC on FFPE tissue samples. Herein we describe in detail an automated protocol that allows multiplex IF labeling of carcinoma tissue samples with a six-marker multiplex antibody panel comprising PD-L1, PD-1, CD68, CD8, Ki-67, and AE1/AE3 cytokeratins with 4',6-diamidino-2-phenylindole as a nuclear cell counterstain. The multiplex panel protocol is optimized in an automated IHC stainer for a staining time that is shorter than that of the manual protocol and can be directly applied and adapted by any laboratory investigator for immuno-oncology studies on human FFPE tissue samples. Also described are several controls and tools, including a drop-control method for fine quality control of a new multiplex IF panel, that are useful for the optimization and validation of the technique.

Autofluorescence removal, multiplexing, and automated analysis methods for in-vivo fluorescence imaging.

The ability to image and quantitate fluorescently labeled markers in vivo has generally been limited by autofluorescence of the tissue. Skin, in particular, has a strong autofluorescence signal, particularly when excited in the blue or green wavelengths. Fluorescence labels with emission wavelengths in the near-infrared are more amenable to deep-tissue imaging, because both scattering and autofluorescence are reduced as wavelengths are increased, but even in these spectral regions, autofluorescence can still limit sensitivity. Multispectral imaging (MSI), however, can remove the signal degradation caused by autofluorescence while adding enhanced multiplexing capabilities. While the availability of spectral "libraries" makes multispectral analysis routine for well-characterized samples, new software tools have been developed that greatly simplify the application of MSI to novel specimens.

Multispectral imaging of formalin-fixed tissue predicts ability to generate tumor-infiltrating lymphocytes from melanoma.

BACKGROUND: Adoptive T cell therapy (ACT) has shown great promise in melanoma, with over 50 % response rate in patients where autologous tumor-reactive tumor-infiltrating lymphocytes (TIL) can be cultured and expanded. A major limitation of ACT is the inability to generate or expand autologous tumor-reactive TIL in 25-45 % of patients tested. Methods that successfully identify tumors that are not suitable for TIL generation by standard methods would eliminate the costs of fruitless expansion and enable these patients to receive alternate therapy immediately. METHODS: Multispectral fluorescent immunohistochemistry with a panel including CD3, CD8, FoxP3, CD163, PD-L1 was used to analyze the tumor microenvironment in 17 patients with melanoma among our 36-patient cohort to predict successful TIL generation. Additionally, we compared tumor fragments and enzymatic digestion of tumor samples for efficiency in generating tumor-reactive TIL. RESULTS: Tumor-reactive TIL were generated from 21/36 (58 %) of melanomas and for 12/13 (92 %) tumors where both enzymatic and fragment methods were compared. TIL generation was successful in 10/13 enzymatic preparations and in 10/13 fragment cultures; combination of both methods resulted in successful generation of autologous tumor-reactive TIL in 12/13 patients. In 17 patients for whom tissue blocks were available, IHC analysis identified that while the presence of CD8(+) T cells alone was insufficient to predict successful TIL generation, the CD8(+) to FoxP3(+) ratio was predictive with a positive-predictive value (PPV) of 91 % and negative-predictive value (NPV) of 86 %. Incorporation of CD163+ macrophage numbers and CD8:PD-L1 ratio did not improve the PPV. However, the NPV could be improved to 100 % by including the ratio of CD8(+):PD-L1(+) expressing cells. CONCLUSION: This is the first study to apply 7-color multispectral immunohistochemistry to analyze the immune environment of tumors from patients with melanoma. Assessment of the data using unsupervised hierarchical clustering identified tumors from which we were unable to generate TIL. If substantiated, this immune profile could be applied to select patients for TIL generation. Additionally, this biomarker profile may also indicate a pre-existing immune response, and serve as a predictive biomarker of patients who will respond to checkpoint blockade. We postulate that expanding the spectrum of inhibitory cells and molecules assessed using this technique could guide combination immunotherapy treatments and improve response rates.

Accumulation of intrahepatic islet amyloid in a nonhuman primate transplant model.

Islet amyloid is hypothesized to play a role in nonimmunologic transplanted islet graft loss. We performed a quantitative histologic analysis of liver biopsies from intrahepatic islet grafts transplanted in streptozotocin-induced diabetic cynomolgus macaques. Seven animals treated with antithymocyte globulin (ATG) and rapamycin or ATG and rituximab experienced islet graft rejection with lymphocytic infiltrates present on islet graft biopsies. Except for one case involving the oldest and largest donor where amyloid was present on initial biopsy 1 month after transplant, none of the six other cases with rejection contained amyloid, including one case biopsied serially to 25 months. In contrast, four out of six animals treated with ATG and rituximab and rapamycin had no evidence of rejection at the time of biopsy (two animals that discontinued rapamycin had mild periislet lymphocytes), and all four cases followed more than 4 months demonstrated amyloid deposition at subsequent time points. Amyloid severity increased with time after transplant (r = 0.68; P < 0.05) and with decreasing islet ß-cell area (r = -0.68; P < 0.05). In two islet recipients with no evidence of rejection and still normoglycemic and insulin independent at the first detection of amyloid, ß-cell secretory capacity declined over time coincident with increasing amyloid severity and decreasing ß-cell area, with both animals eventually becoming hyperglycemic and insulin dependent. Transplanted islet amyloid also developed in autologous islets placed sc. These results indicate that in cynomolgus macaques, transplanted islets may accumulate amyloid over time associated with subsequent decline in ß-cell mass and function and support the development of intrahepatic islet amyloid as a potential mechanism for nonimmunologic islet graft loss.

Distinguished photons: increased contrast with multispectral in vivo fluorescence imaging.

Noninvasive in vivo imaging is a rapidly growing field with applications in basic biology, drug discovery and clinical medicine. Because of the high cost of magnetic resonance (MR)- and computed tomography (CT)-based systems, a great deal of effort has gone into developing optical imaging methods, which offer, in some modalities, the promise of high spatial resolution and the ability to detect multiple markers simultaneously However, the ability to image and quantitate fluorescently labeled tumors and other fluorescently labeled markers in vivo has generally been limited by the autofluorescence of the tissue, which reduces the sensitivity of detection and accuracy of quantitation of the labeled target. Multispectral imaging methodology, which spectrally characterizes and computationally eliminates autofluorescence, enhances signal-to-background dramatically, revealing otherwise invisible labeled targets. Signal-to-noise considerations can guide the choice of appropriate sensors for fluorescence-based imaging, which generally does not benefit from the use of highly cooled (and expensive) cameras. Effective use of spectral tools to remove autofluorescence signal requires accurate spectra of the individual components. Using manual and automated algorithms to generate these spectra, it is possible to detect as many as three fluorescent protein-labeled tumors and two separate autofluorescent signals in a single subject.