A workflow for visualizing human cancer biopsies using large-format electron microscopy

Recent developments in large format electron microscopy have enabled generation of images that provide detailed ultrastructural information on normal and diseased cells and tissues. Analyses of these images increase our understanding of cellular organization and interactions and disease-related changes therein. In this manuscript, we describe a workflow for two-dimensional (2D) and three-dimensional (3D) imaging, including both optical and scanning electron microscopy (SEM) methods, that allow pathologists and cancer biology researchers to identify areas of interest from human cancer biopsies. The protocols and mounting strategies described in this workflow are compatible with 2D large format EM mapping, 3D focused ion beam-SEM and serial block face-SEM. The flexibility to use diverse imaging technologies available at most academic institutions makes this workflow useful and applicable for most life science samples. Volumetric analysis of the biopsies studied here revealed morphological, organizational and ultrastructural aspects of the tumor cells and surrounding environment that cannot be revealed by conventional 2D EM imaging. Our results indicate that although 2D EM is still an important tool in many areas of diagnostic pathology, 3D images of ultrastructural relationships between both normal and cancerous cells, in combination with their extracellular matrix, enables cancer researchers and pathologists to better understand the progression of the disease and identify potential therapeutic targets.

Quantification of fluorophore distribution and therapeutic response in matched in vivo and ex vivo pancreatic cancer model systems

Therapeutic resistance plagues cancer outcomes, challenging treatment particularly in aggressive disease. A unique method to decipher drug interactions with their targets and inform therapy is to employ fluorescence-based screening tools; however, to implement productive screening assays, adequate model systems must be developed. Patient-derived pancreatic cancer models (e.g., cell culture, patient-derived xenograft mouse models, and organoids) have been traditionally utilized to predict personalized therapeutic response. However, cost, long read out times and the inability to fully recapitulate the tumor microenvironment have rendered most models incompatible with clinical decision making for pancreatic ductal adenocarcinoma (PDAC) patients. Tumor explant cultures, where patient tissue can be kept viable for up to weeks, have garnered interest as a platform for delivering personalized therapeutic prediction on a clinically relevant timeline. To fully explore this ex vivo platform, a series of studies were completed to quantitatively compare in vivo models with tumor explants, examining gemcitabine therapeutic efficacy, small molecule uptake and drug-target engagement using a novel fluorescently-labeled gemcitabine conjugate. This initial work shows promise for patient-specific therapeutic selection, where tumor explant drug distribution and response recapitulated the in vivo behavior and could provide a valuable platform for understanding mechanisms of therapeutic response and resistance.

Early Prediction of Breast Cancer Therapy Response using Multiresolution Fractal Analysis of DCE-MRI Parametric Maps

We aimed to determine whether multiresolution fractal analysis of voxel-based dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parametric maps can provide early prediction of breast cancer response to neoadjuvant chemotherapy (NACT). In total, 55 patients underwent 4 DCE-MRI examinations before, during, and after NACT. The shutter-speed model was used to analyze the DCE-MRI data and generate parametric maps within the tumor region of interest. The proposed multiresolution fractal method and the more conventional methods of single-resolution fractal, gray-level co-occurrence matrix, and run-length matrix were used to extract features from the parametric maps. Only the data obtained before and after the first NACT cycle were used to evaluate early prediction of response. With a training (N = 40) and testing (N = 15) data set, support vector machine was used to assess the predictive abilities of the features in classification of pathologic complete response versus non-pathologic complete response. Generally the multiresolution fractal features from individual maps and the concatenated features from all parametric maps showed better predictive performances than conventional features, with receiver operating curve area under the curve (AUC) values of 0.91 (all parameters) and 0.80 (K^trans), in the training and testing sets, respectively. The differences in AUC were statistically significant (P < .05) for several parametric maps. Thus, multiresolution analysis that decomposes the texture at various spatial-frequency scales may more accurately capture changes in tumor vascular heterogeneity as measured by DCE-MRI, and therefore provide better early prediction of NACT response.

Predicting primary site of secondary liver cancer with a neural estimator of metastatic origin

Purpose: Pathologists rely on relevant clinical information, visual inspection of stained tissue slide morphology, and sophisticated molecular diagnostics to accurately infer the biological origin of secondary metastatic cancer. While highly effective, this process is expensive in terms of time and clinical resources. We seek to develop and evaluate a computer vision system designed to reasonably infer metastatic origin of secondary liver cancer directly from digitized histopathological whole slide images of liver biopsy. Approach: We illustrate a two-stage deep learning approach to accomplish this task. We first train a model to identify spatially localized regions of cancerous tumor within digitized hematoxylin and eosin (H&E)-stained tissue sections of secondary liver cancer based on a pathologist's annotation of several whole slide images. Then, a second model is trained to generate predictions of the cancers' metastatic origin belonging to one of three distinct clinically relevant classes as confirmed by immunohistochemistry. Results: Our approach achieves a classification accuracy of 90.2% in determining metastatic origin of whole slide images from a held-out test set, which compares favorably to an established clinical benchmark by three board-certified pathologists whose accuracies ranged from 90.2% to 94.1% on the same prediction task. Conclusions: We illustrate the potential impact of deep learning systems to leverage morphological and structural features of H&E-stained tissue sections to guide pathological and clinical determination of the metastatic origin of secondary liver cancers.

Moving beyond the current limits of data analysis in longevity and healthy lifespan studies

Living longer with sustainable quality of life is becoming increasingly important in aging populations. Understanding associative biological mechanisms have proven daunting, because of multigenicity and population heterogeneity. Although Big Data and Artificial Intelligence (AI) could help, naïve adoption is ill advised. We hold the view that model organisms are better suited for big-data analytics but might lack relevance because they do not immediately reflect the human condition. Resolving this hurdle and bridging the human-model organism gap will require some finesse. This includes improving signal:noise ratios by appropriate contextualization of high-throughput data, establishing consistency across multiple high-throughput platforms, and adopting supporting technologies that provide useful in silico and in vivo validation strategies.

Variational Autoencoding Tissue Response to Microenvironment Perturbation

This work applies deep variational autoencoder learning architecture to study multi-cellular growth characteristics of human mammary epithelial cells in response to diverse microenvironment perturbations. Our approach introduces a novel method of visualizing learned feature spaces of trained variational autoencoding models that enables visualization of principal features in two dimensions. We find that unsupervised learned features more closely associate with expert annotation of cell colony organization than biologically-inspired hand-crafted features, demonstrating the utility of deep learning systems to meaningfully characterize features of multi-cellular growth characteristics in a fully unsupervised and data-driven manner.

Deuterated Polyunsaturated Fatty Acids Reduce Oxidative Stress and Extend the Lifespan of C. elegans

Chemically reinforced essential fatty acids (FAs) promise to fight numerous age-related diseases including Alzheimer’s, Friedreich’s ataxia and other neurological conditions. The reinforcement is achieved by substituting the atoms of hydrogen at the bis-allylic methylene of these essential FAs with the isotope deuterium. This substitution leads to a significantly slower oxidation due to the kinetic isotope effect, inhibiting membrane damage. The approach has the advantage of preventing the harmful accumulation of reactive oxygen species (ROS) by inhibiting the propagation of lipid peroxidation while antioxidants potentially neutralize beneficial oxidative species. Here, we developed a model system to mimic the human dietary requirement of omega-3 in Caenorhabditis elegans to study the role of deuterated polyunsaturated fatty acids (D-PUFAs). Deuterated trilinolenin [D-TG(54:9)] was sufficient to prevent the accumulation of lipid peroxides and to reduce the accumulation or ROS. Moreover, D-TG(54:9) significantly extended the lifespan of worms under normal and oxidative stress conditions. These findings demonstrate that D-PUFAs can be used as a food supplement to decelerate the aging process, resulting in extended lifespan.

Lipid bilayer stress and proteotoxic stress-induced unfolded protein response deploy divergent transcriptional and non-transcriptional programmes

The unfolded protein response (UPR) is activated by endoplasmic reticulum (ER) stress and is designed to restore cellular homeostasis through multiple intracellular signalling pathways. In mammals, the UPR programme regulates the expression of hundreds of genes in response to signalling from ATF6, IRE1, and PERK. These three highly conserved stress sensors are activated by the accumulation of unfolded proteins within the ER. Alternatively, IRE1 and PERK sense generalised lipid bilayer stress (LBS) at the ER while ATF6 is activated by an increase of specific sphingolipids. As a result, the UPR supports cellular robustness as a broad-spectrum compensatory pathway that is achieved by deploying a tailored transcriptional programme adapted to the source of ER stress. This review summarises the current understanding of the three ER stress transducers in sensing proteotoxic stress and LBS. The plasticity of the UPR programme in the context of different sources of ER stress will also be discussed.

Robust Cell Detection and Segmentation for Image Cytometry Reveal Th17 Cell Heterogeneity

Image cytometry enables quantitative cell characterization with preserved tissue architecture; thus, it has been highlighted in the advancement of multiplex immunohistochemistry (IHC) and digital image analysis in the context of immune-based biomarker monitoring associated with cancer immunotherapy. However, one of the challenges in the current image cytometry methodology is a technical limitation in the segmentation of nuclei and cellular components particularly in heterogeneously stained cancer tissue images. To improve the detection and specificity of single-cell segmentation in hematoxylin-stained images (which can be utilized for recently reported 12-biomarker chromogenic sequential multiplex IHC), we adapted a segmentation algorithm previously developed for hematoxlin and eosin-stained images, where morphological features are extracted based on Gabor-filtering, followed by stacking of image pixels into n-dimensional feature space and unsupervised clustering of individual pixels. Our proposed method showed improved sensitivity and specificity in comparison with standard segmentation methods. Replacing previously proposed methods with our method in multiplex IHC/image cytometry analysis, we observed higher detection of cell lineages including relatively rare T_H 17 cells, further enabling sub-population analysis into T_H 1-like and T_H 2-like phenotypes based on T-bet and GATA3 expression. Interestingly, predominance of T_H 2-like T_H 17 cells was associated with human papilloma virus (HPV)-negative status of oropharyngeal squamous cell carcinoma of head and neck, known as a poor-prognostic subtype in comparison with HPV-positive status. Furthermore, T_H 2-like T_H 17 cells in HPV-negative head and neck cancer tissues were spatiotemporally correlated with CD66b⁺ granulocytes, presumably associated with an immunosuppressive microenvironment. Our cell segmentation method for multiplex IHC/image cytometry potentially contributes to in-depth immune profiling and spatial association, leading to further tissue-based biomarker exploration. © 2019 International Society for Advancement of Cytometry.

Modeling Tumor Phenotypes In Vitro with Three-Dimensional Bioprinting

The tumor microenvironment plays a critical role in tumor growth, progression, and therapeutic resistance, but interrogating the role of specific tumor-stromal interactions on tumorigenic phenotypes is challenging within in vivo tissues. Here, we tested whether three-dimensional (3D) bioprinting could improve in vitro models by incorporating multiple cell types into scaffold-free tumor tissues with defined architecture. We generated tumor tissues from distinct subtypes of breast or pancreatic cancer in relevant microenvironments and demonstrate that this technique can model patient-specific tumors by using primary patient tissue. We assess intrinsic, extrinsic, and spatial tumorigenic phenotypes in bioprinted tissues and find that cellular proliferation, extracellular matrix deposition, and cellular migration are altered in response to extrinsic signals or therapies. Together, this work demonstrates that multi-cell-type bioprinted tissues can recapitulate aspects of in vivo neoplastic tissues and provide a manipulable system for the interrogation of multiple tumorigenic endpoints in the context of distinct tumor microenvironments.

IFNγ-activated dermal lymphatic vessels inhibit cytotoxic T cells in melanoma and inflamed skin

Mechanisms of immune suppression in peripheral tissues counteract protective immunity to prevent immunopathology and are coopted by tumors for immune evasion. While lymphatic vessels facilitate T cell priming, they also exert immune suppressive effects in lymph nodes at steady-state. Therefore, we hypothesized that peripheral lymphatic vessels acquire suppressive mechanisms to limit local effector CD8+ T cell accumulation in murine skin. We demonstrate that nonhematopoietic PD-L1 is largely expressed by lymphatic and blood endothelial cells and limits CD8+ T cell accumulation in tumor microenvironments. IFNγ produced by tissue-infiltrating, antigen-specific CD8+ T cells, which are in close proximity to tumor-associated lymphatic vessels, is sufficient to induce lymphatic vessel PD-L1 expression. Disruption of IFNγ-dependent crosstalk through lymphatic-specific loss of IFNγR boosts T cell accumulation in infected and malignant skin leading to increased viral pathology and tumor control, respectively. Consequently, we identify IFNγR as an immunological switch in lymphatic vessels that balances protective immunity and immunopathology leading to adaptive immune resistance in melanoma.

Lipid bilayer stress-activated IRE-1 modulates autophagy during endoplasmic reticulum stress

Metabolic disorders, such as non-alcoholic fatty liver disease (NAFLD), are emerging as epidemics that affect the global population. One facet of these disorders is attributed to the disturbance of membrane lipid composition. Perturbation of endoplasmic reticulum (ER) homeostasis through alteration in membrane phospholipids activates the unfolded protein response (UPR) and causes dramatic transcriptional and translational changes in the cell. To restore cellular homeostasis, the three highly conserved UPR transducers ATF6, IRE1 (also known as ERN1 in mammals) and PERK (also known as EIF2AK3 in mammals) mediate adaptive responses upon ER stress. The homeostatic UPR cascade is well characterised under conditions of proteotoxic stress, but much less so under lipid bilayer stress-induced UPR. Here, we show that disrupted phosphatidylcholine (PC) synthesis in Caenorhabditis elegans causes lipid bilayer stress, lipid droplet accumulation and ER stress induction. Transcriptional profiling of PC-deficient worms revealed a unique subset of genes regulated in a UPR-dependent manner that is independent from proteotoxic stress. Among these, we show that autophagy is modulated through the conserved IRE-1-XBP-1 axis, strongly suggesting of the importance of autophagy in maintaining cellular homeostasis during the lipid bilayer stress-induced UPR.

Tissue Factor Activity of Syncytiotrophoblast Plasma Membranes and Tumoral Trophoblast Cells in Culture

During pregnancy, important modifications of hemostasis occur resulting in mothers in hypercoagulability and the role of placental cells such as trophoblast cells has been hypothesized. In this study, we first showed that syncytiotrophoblast plasma membranes, isolated from normal human placenta, expressed a strong tissue factor (TF) activity. We then studied TF activity of two continuous trophoblast cell lines (JEG-3 and BeWo) in comparison to human umbilical vein endothelial cells (HUVEC) and transformed human endothelial cells (ECV-304). TF assays were performed on intact detached confluent cells. Unstimulated JEG-3 and BeWo cells exhibited a very high TF activity which slightly increased after 2 to 4 h TNF-α stimulation. In contrast, HUVEC and ECV-304 had a lower basal TF activity which was mainly inducible by TNF-a, with a maximum effect after 4 to 6 h stimulation. For both cell types, TF activity was decreased to basal value after 16-hour TNF-α stimulation. These results support that trophoblast cells are able to express TF but the involvement of this property in the hemostatic physiological changes observed during pregnancy, remains to be demonstrated.

Abstract 220: Intra-tumor immune heterogeneity is associated with prognosis of oropharyngeal head and neck squamous cell carcinoma

As cell-cell interactions among tumor and immune cells are known to contribute to tumor progression, in depth evaluation of tumor-immune heterogeneity will enable biomarker-guided patient stratification and improvement of treatment response. Here we examined head and neck squamous cell carcinomas (HNSCC) to reveal the prognostic significance of leukocytes in tumors with regards to their complexity, effector status and spatial characteristics via multiplex immunohistochemistry and image cytometry. To accomplish this, we examined oropharyngeal HNSCC (N = 38), where lymphoid, myeloid, and hypo-inflamed leukocyte complexity correlated with HPV-status (Tsujikawa et al. Cell Reports, 2017). In the present study, quantitative analysis of cell density and distribution of 14-distinct immune cell lineages (e.g., CD8+ T cells, regulatory T cells, Th1, Th2, Th17, Th0 lymphocytes, B cells, NK cells, CD163+ and CD163- macrophages, mature and immature dendritic cells, mast cells, granulocytes) was analyzed via immune cell density mapping and tissue segmentation algorithms. We revealed tropism of polarized Th1-type cells within tumor nests versus stroma in HPV-associated HNSCC. In addition, Cox regression analysis of cell density and distribution of the 14 immune populations revealed that CD66b+ granulocyte infiltration within tumor nests reflected a negative prognostic indicator for HNSCC outcome. Furthermore, cell-cell proximity analysis in HPV-associated HNSCC further revealed a correlation between PD-L1 positive immune cells, and micro-regionally polarized immune characteristics biased towards Th1, coincident with high density of CD8+ T cells. These results reveal intra-tumor immune heterogeneity is associated with micro-regional immune complexity profiles, and provide insight into in situ immune characteristics that may aid patient stratification for immune therapy going forward. Acknowledgement: This project was supported by the Japan Society for the Promotion of Science Grant-in-Aid for Young Scientists (Start-up, 17H07016), Oregon Clinical and Translational Research Institute (OCTRI), grant number (UL1TR000128) from the National Center for Advancing Translational Sciences (NCATS) at the National Institutes of Health (NIH), and P30 CA069533-17 OHSU Knight Cancer Institute. LMC acknowledges support from the NIH/NCI, DOD BCRP Era of Hope Scholar Expansion Award, Susan G. Komen Foundation, Stand Up To Cancer - Lustgarten Foundation Pancreatic Cancer Convergence Dream Team Translational Research Grant, Breast Cancer Research Foundation, and the Brenden-Colson Center for Pancreatic Health.

Citation Format: Takahiro Tsujikawa, Guillaume Thibault, Young Hwan Chang, Edward El Rassi, Daniel R. Clayburgh, David Sauer, Akihito Arai, Molly F. Kulesz-Martin, Motomi Mori, Shigeru Hirano, Paul W. Flint, Lisa M. Coussens. Intra-tumor immune heterogeneity is associated with prognosis of oropharyngeal head and neck squamous cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 220.

Analysis of DCE-MRI for Early Prediction of Breast Cancer Therapy Response

Positive response to neoadjuvant chemotherapy (NACT) has been correlated to better long-term outcomes in breast cancer treatment. Early prediction of response to NACT can help modify the regimen for non-responding patients, sparing them of potential toxicities of ineffective therapies. It has been observed that tumor functions such as vascularization and vascular permeability change even before noticeable changes occur in the tumor size in response to the treatment. Therefore, it is essential to have reliable imaging based features to measure these changes. Texture analysis on parametric maps from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has shown to be a good predictor of breast cancer response to NACT at an early stage. But hand crafted texture features might not be able to capture the rich spatio-temporal information in the parametric maps. In this work, we studied the ability of convolutional neural networks in predicting the response to NACT at an early stage.

Deep learning based Nucleus Classification in pancreas histological images

Tumor specimens contain a variety of healthy cells as well as cancerous cells, and this heterogeneity underlies resistance to various cancer therapies. But this problem has not been thoroughly investigated until recently. Meanwhile, technological breakthroughs in imaging have led to an explosion of molecular and cellular profiling data from large numbers of samples, and modern machine learning approaches including deep learning have been shown to produce encouraging results by finding hidden structures and make accurate predictions. In this paper, we propose a Deep learning based Nucleus Classification (DeepNC) approach using paired histopathology and immunofluorescence images (for label), and demonstrate its classification prediction power. This method can solve current issue on discrepancy between genomic- or transcriptomic-based and pathology-based tumor purity estimates by improving histological evaluation. We also explain challenges in training a deep learning model for huge dataset.

From the unfolded protein response to metabolic diseases - lipids under the spotlight

The unfolded protein response (UPR) is classically viewed as a stress response pathway to maintain protein homeostasis at the endoplasmic reticulum (ER). However, it has recently emerged that the UPR can be directly activated by lipid perturbation, independently of misfolded proteins. Comprising primarily phospholipids, sphingolipids and sterols, individual membranes can contain hundreds of distinct lipids. Even with such complexity, lipid distribution in a cell is tightly regulated by mechanisms that remain incompletely understood. It is therefore unsurprising that lipid dysregulation can be a key factor in disease development. Recent advances in analysis of lipids and their regulators have revealed remarkable mechanisms and connections to other cellular pathways including the UPR. In this Review, we summarize the current understanding in UPR transducers functioning as lipid sensors and the interplay between lipid metabolism and ER homeostasis in the context of metabolic diseases. We attempt to provide a framework consisting of a few key principles to integrate the different lines of evidence and explain this rather complicated mechanism.

Quantitative Multiplex Immunohistochemistry Reveals Myeloid-Inflamed Tumor-Immune Complexity Associated with Poor Prognosis

Here, we describe a multiplexed immunohistochemical platform with computational image processing workflows, including image cytometry, enabling simultaneous evaluation of 12 biomarkers in one formalin-fixed paraffin-embedded tissue section. To validate this platform, we used tissue microarrays containing 38 archival head and neck squamous cell carcinomas and revealed differential immune profiles based on lymphoid and myeloid cell densities, correlating with human papilloma virus status and prognosis. Based on these results, we investigated 24 pancreatic ductal adenocarcinomas from patients who received neoadjuvant GVAX vaccination and revealed that response to therapy correlated with degree of mono-myelocytic cell density and percentages of CD8⁺ T cells expressing T cell exhaustion markers. These data highlight the utility of in situ immune monitoring for patient stratification and provide digital image processing pipelines to the community for examining immune complexity in precious tissue sections, where phenotype and tissue architecture are preserved to improve biomarker discovery and assessment.

Quantitative analysis of histological tissue image based on cytological profiles and spatial statistics

The cellular heterogeneity and complex tissue architecture of most tumor samples is a major obstacle in image analysis on standard hematoxylin and eosin-stained (H&E) tissue sections. A mixture of cancer and normal cells complicates the interpretation of their cytological profiles. Furthermore, spatial arrangement and architectural organization of cells are generally not reflected in cellular characteristics analysis. To address these challenges, first we describe an automatic nuclei segmentation of H&E tissue sections. In the task of deconvoluting cellular heterogeneity, we adopt Landmark based Spectral Clustering (LSC) to group individual nuclei in such a way that nuclei in the same group are more similar. We next devise spatial statistics for analyzing spatial arrangement and organization, which are not detectable by individual cellular characteristics. Our quantitative, spatial statistics analysis could benefit H&E section analysis by refining and complementing cellular characteristics analysis.

DCE-MRI Texture Features for Early Prediction of Breast Cancer Therapy Response

This study investigates the effectiveness of hundreds of texture features extracted from voxel-based dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parametric maps for early prediction of breast cancer response to neoadjuvant chemotherapy (NAC). In total, 38 patients with breast cancer underwent DCE-MRI before (baseline) and after the first of the 6-8 NAC cycles. Quantitative pharmacokinetic (PK) parameters and semiquantitative metrics were estimated from DCE-MRI time-course data. The residual cancer burden (RCB) index value was computed based on pathological analysis of surgical specimens after NAC completion. In total, 1043 texture features were extracted from each of the 13 parametric maps of quantitative PK or semiquantitative metric, and their capabilities for early prediction of RCB were examined by correlating feature changes between the 2 MRI studies with RCB. There were 1069 pairs of feature-map combinations that showed effectiveness for response prediction with 4 correlation coefficients >0.7. The 3-dimensional gray-level cooccurrence matrix was the most effective feature extraction method for therapy response prediction, and, in general, the statistical features describing texture heterogeneity were the most effective features. Quantitative PK parameters, particularly those estimated with the shutter-speed model, were more likely to generate effective features for prediction response compared with the semiquantitative metrics. The best feature-map pair could predict pathologic complete response with 100% sensitivity and 100% specificity using our cohort. In conclusion, breast tumor heterogeneity in microvasculature as measured by texture features of voxel-based DCE-MRI parametric maps could be a useful biomarker for early prediction of NAC response.

Abstract NG02: Lymphatic vessels: Balancing immune priming and immune evasion in melanoma

Lymphatic vessel remodeling and lymphangiogenesis is correlated with melanoma progression and lymph node metastasis. While lymphatic vessels provide an important route for disseminating tumor cells, they are also a crucial interface between a developing malignancy and the host immune response. Rather than acting as passive conduits, lymphatic vessels directly regulate their transport function and facilitate leukocyte trafficking for efficient induction of adaptive immunity in downstream draining lymph nodes. We recently published that in the absence of dermal lymphatic vessels, the tumor microenvironment of murine melanoma remains completely uninflamed and fails to induce a robust T-cell response (1). Consistently, TCGA analysis of human cutaneous metastatic melanoma identified positive correlations between LEC gene expression and immune genes, including a T cell-inflamed signature, indicating a relationship between lymphatic vessel remodeling and local immunity. Furthermore, others have recently demonstrated that lymphatic vessel density (LVD) in combination with intratumoral T cell function stratified best overall survival in nonmetastatic and metastatic colorectal cancer (2). In contrast to this, however, many reports independently correlate peritumoral LVD with lymph node metastasis and some poor prognosis (3). Furthermore, our previous work demonstrated that vascular endothelial growth factor C (VEGFC)-driven lymphangiogenesis in the context of murine melanoma drove increased leukocyte infiltration but associated with poor CD8+ T cell priming in draining lymph nodes (4). We therefore hypothesize that lymphatic vessels are (A) required for induction of adaptive immunity but (B) acquire immunosuppressive activity as a function of the accumulation of local cytotoxic immunity. Furthermore, we predict that LVD may be a relevant biomarker of in situ immune responsiveness and response to therapy (5).

To test the first part of this hypothesis (A), we have continued our published work and used a cutaneous model of viral infection to demonstrate the requirement for lymphatic vessel drainage in cutaneous immunity. Following cutaneous vaccinia infection we demonstrate that in the absence of lymphatic vessel transport, both cellular and humoral adaptive immune responses fail to initiate, leading to enhanced cutaneous immunopathology and persistent viral load. The complete absence of primed CD8+ and CD4+ T cells in cutaneous tissue following challenge mirrors our observations in melanoma and is consistent with correlations of intratumoral lymphocytes and LVD both by our group in cutaneous metastatic melanoma as well as by others. This unequivocal requirement for a functional lymphatic vasculature in the priming of cutaneous immunity further supports the prediction that LVD may be a novel biomarker of immune reactivity within tumor parenchyma. To test this, we simultaneously evaluated immune and vascular components in human primary melanoma samples using a multiplex-immunohistochemistry-based approach. Tissue regions that include tumor/stroma borders and show high CD8+ T-cell infiltrates are selected for analysis, followed by tissue segmentation, and automated detection of cell populations within intratumoral regions and bordering stroma. Interestingly, those tumors with enhanced hematopoietic infiltrate (CD68 and CD8) also appear to demonstrate increased vasculature, both blood (CD31 and CD34) and lymphatic (D2-40 and Prox1). Preliminary data demonstrate that lymphatic vessels, blood vessels, and CD8+ T cells are significantly enriched at the tumor-stroma border in primary melanoma and positively correlate with one another, indicating that lymphatic vessels may be a dynamic component of the “T cell-inflamed” microenvironment.

While we demonstrate that lymphatic vessels are necessary for immune induction, we further hypothesized (B) that in the context of an ongoing immune response lymphatic vessels adapt their function to promote immune resolution. The adaptive resistance hypothesis proposes that upon accumulation of local cytotoxic immunity, both tumors as well as stromal components adapt and acquire therapeutically relevant immunosuppressive function. We demonstrate that peripheral, tumor-associated lymphatic endothelial cells (LEC; CD45-CD31+gp38+) acquire expression of immunoregulatory proteins, most notably programmed death receptor ligand-1 (PD-L1) and major histocompatibility complex II (MHCII), coincident with CD8+ T-cell infiltration in an interferon gamma (IFNg)-dependent manner. Adoptive transfer of activated CD8+ T cells induced higher expression of PD-L1 by LECs in B16 F10 tumors, while neutralization of IFNg reduced levels to that of naïve skin. Furthermore, conditional knockout of the IFNgR (Lyve1-Cre) prevented upregulation of PD-L1 on tumor-associated LECs. Notably, lymph node LECs constitutively express PD-L1 and this expression contributes to the attenuation of self-reactive CD8+ T-cell responses (6). Importantly, in the absence of IFNgR on peripheral LECs we observed significantly enhanced accumulation of antigen-specific CD8+ T cells in cutaneous tissue. Thus, cutaneous lymphatic vessels, while necessary for immune induction, acquire immunodulatory properties in a context-dependent manner and may participate in immune evasion within tumor microenvironments.

In conclusion, our work across multiple model systems provides strong experimental evidence to indicate that the lymphatic vasculature is an important, active component of the antitumor immune response and may represent a biomarker to stratify patient response and survival for effective clinical immunotherapy. These data indicate a need to revisit the passive paradigm of lymphatic vessel involvement in tumor progression and metastasis to a more active model whereby lymphatic vessels are both required for antitumor immunity but functionally evolve in response to accumulating cytotoxicity to drive immune evasion.

References:

1. Lund AW et al. Lymphatic vessels regulate immune microenvironments in human and murine melanoma. J Clin Invest 2016;126:3389-402. doi: 10.1172/JCI79434.

2. Mlecnik B et al. The tumor microenvironment and Immunoscore are critical determinants of dissemination to distant metastasis. Sci Transl Med 2016;8:327ra26.

3. Pasquali S et al. Lymphatic biomarkers in primary melanomas as predictors of regional lymph node metastasis and patient outcomes. Pigment Cell Melanoma Res 2013;26:326-37.

4. Lund AW et al. VEGF-C promotes immune tolerance in B16 melanomas and cross-presentation of tumor antigen by lymph node lymphatics. Cell Rep 2012;1:191-9.

5. Lund AW. Rethinking lymphatic vessels and antitumor immunity. Trends Cancer 2016;2:548-51. doi: 10.1016/j.trecan.2016.09.0056. Tewalt EF et al. Lymphatic endothelial cells induce tolerance via PD-L1 and lack of costimulation leading to high-level PD-1 expression on CD8 T cells. Blood 2012;120:4772-82.

Citation Format: Ryan S. Lane, Julia Femel, Jamie Booth, Christopher Loo, Nicholas Nelson, Takahiro Tsujikawa, Guillaume Thibault, Amanda W. Lund. Lymphatic vessels: Balancing immune priming and immune evasion in melanoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr NG02. doi:10.1158/1538-7445.AM2017-NG02

A fully integrated, three-dimensional fluorescence to electron microscopy correlative workflow

While fluorescence microscopy provides tools for highly specific labeling and sensitive detection, its resolution limit and lack of general contrast has hindered studies of cellular structure and protein localization. Recent advances in correlative light and electron microscopy (CLEM), including the fully integrated CLEM workflow instrument, the FEI CorrSight with MAPS, have allowed for a more reliable, reproducible, and quicker approach to correlate three-dimensional time-lapse confocal fluorescence data, with three-dimensional focused ion beam-scanning electron microscopy data. Here we demonstrate the entire integrated CLEM workflow using fluorescently tagged MCF7 breast cancer cells.

BREAST CANCER HISTOPATHOLOGY IMAGE ANALYSIS PIPELINE FOR TUMOR PURITY ESTIMATION

The translation of genomic sequencing technology to the clinic has greatly advanced personalized medicine. However, the presence of normal cells in tumors is a confounding factor in genome sequence analysis. Tumor purity, or the percentage of cancerous cells in whole tissue section, is a correction factor that can be used to improve the clinical utility of genomic sequencing. Currently, tumor purity is estimated visually by expert pathologists; however, it has been shown that there exist vast inter-observer discrepancies in tumor purity scoring. In this paper, we propose a quantitative image analysis pipeline for tumor purity estimation and provide a systematic comparison between pathologists' scores and our image-based tumor purity estimation.