Topography of cancer-associated immune cells in human solid tumors

1Malignant tumors growing in an immunocompetent host elicit an immune response, evident by the presence of various inflammatory/immune cell in tumor tissue. In order to grow to a clinically relevant size, tumor cells develop specific escape mechanisms against the immune system by manipulating inflammatory cells for their benefit. One of the key strategies is that tumor cells interfere with immune signaling, hijacking immunosuppressive cells and thereby shaping the immune infiltrate, which allows for tumor cell proliferation.

2These mechanisms have been in the focus of oncology for several years. Currently a number of immunotherapeutic drugs are available which interfere with immune cells in the tumor microenvironment in order to facilitate tumor control. However, the complex nature of immune infiltrates impairs the development of more targeted approaches. Specifically, tailored combination treatments are widely proposed as a way to more effective cancer therapy. Systematically deciphering tumor-immune phenotypes is key to a better understanding and more effective tailoring of immunotherapies.

3Analysis of solid tumor tissue slides by immunohistochemistry (IHC) is the gold standard to assess tumor immune infiltrate because it allows for exact quantification of type, density and localization of immune cells. For more than a decade, digital pathology has been the method of choice to reliably and reproducibly analyze large cohorts of patient samples and can provide potential biomarkers for immunotherapy. Immune cell quantification in digitized tissue has been used to identify robust and clinically relevant biomarkers in numerous cancer entities, for example in colorectal cancer (CRC) primary tumors and liver metastases. Histological analysis of tumor-infiltrating lymphoid cells has been proven to be a reliable and prognostically relevant marker. Antitumor immunity arises in a complex ecosystem of various cell types that closely interact with one another, such as effector lymphocytes, macrophages, dendritic cells, granulocytes, innate lymphoid cells, regulatory T cells, natural killer cells, myeloid-derived suppressor cells and other cell types. In tumor tissue, T lymphocytes (T cells) and macrophages are among the most abundant immune cells and are closely related to clinical outcome.

4After years of detailed IHC analysis of solid tumor slides, a paradigm for the classification of tumor-immune phenotypes has emerged and three classes of tumors are generally assumed: ‘cold’ tumors (or ‘immune desert’, showing no immune cell infiltration), ‘immune-excluded’ tumors (with immune cells aggregating at the tumor boundaries) and ‘hot’ tumors (or ‘inflamed’ tumors, showing pronounced immune infiltrates in the tumor core. However, although this classification is generally accepted, it is backed by surprisingly little quantitative data. Fundamental biological questions regarding this concept are essentially unanswered, such as: Do these topographies exist in all tumor entities? Is there a difference between immunotherapy-sensitive and immunotherapy-insensitive tumor types? Does the concept of cold/excluded/hot apply to lymphoid and myeloid cells, or only to one of them? Do all tumors use the same strategies for immune escape?

5In the last years, several large studies have systematically investigated immune-tumor phenotypes in detail. However, most of these studies were not suitable to distinguish cold, excluded and hot tumors because they did not look at the tumor core and the invasive margin at the same time. Two recent studies of immunophenotypes in colorectal cancer (CRC) have shown that the average immune cell density is higher around the tumor than in the tumor core. Yet, the concept of cold/excluded/hot tumors was not investigated in these studies. Also, other comprehensive studies have not taken spatial patterns of immune cell phenotypes into account. Recent large-scale studies have looked at high-dimensional phenotypes of tumor-infiltrating immune cells, but have not specifically addressed different topographies. Another previous study investigated spatial patterns of lymphoid and myeloid cells in human solid tumors – however, only tissue microarrays (TMA) were included in that study, precluding any possible differentiation between invasive margin and tumor core. Lastly, the correlation of the tumor immunophenotype to its transcription profile was investigated previously, but was lacking a spatially resolved approach.

6In the present study, we have attempted to close this knowledge gap by a systematic analysis of a large cohort of various human tumors in a spatially resolved way. In particular, this included a systematic analysis of the following immune cells: CD3+ T-lymphocytes, CD8+ T-lymphocytes, PD1+ T lymphocytes, FOXP3+ T lymphocytes (which we assume to be largely ‘regulatory’, although the role of FOXP3 is more complex and CD68+ and CD163+ monocytes/macrophages.

7We measured immune cell densities (number of cells per mm², Figure 1A–B) in 965 immunostained histological tissue slides (listed in Supplementary file 3) in the pan-cancer cohort in three spatial compartments per slide (Figure 1C): outer invasive margin (0 – 500 µm outside the tumor invasion front), inner invasive margin (500 µm to the inside) and in the tumor core. In accordance with previous studies, colorectal adenocarcinoma primary tumors and liver metastases had a higher cell count in the outer invasive margin than in the tumor core (Figure 2—figure supplement 2 and Figure 2—figure supplement 3). Melanoma samples had higher cell counts in the inner tumor than outside of the tumor for all analyzed cell types. The other tumor types showed less clear-cut patterns, highlighting the need for a more detailed analysis.

8As previous studies have assumed the existence of ‘hot’, ‘cold’ and ‘immune excluded’ tumors with regard to lymphocyte infiltration, we assessed our data set for evidence of this clustering. We used cell densities for CD3+ and CD8+ cells in all three spatial compartments and used multiple methods to determine the optimal number of clusters. For CD3 and CD8 separately as well as for both together, the majority of the optimization runs converged on two clusters (Figure 2—figure supplement 4), mostly representing ‘hot’ and ‘cold’ tumors. There was no strong tendency to converge on three clusters, which means that there is no strong inherent grouping into ‘hot’, ‘cold’ and ‘immune excluded’ tumors.

9We asked whether there is a rationale to define ‘immune excluded tumors’ as a separate group although it does not naturally emerge in unsupervised clustering methods. With three spatial compartments, each of them having either high or low cell density, there are in theory eight possible topographies. We asked whether this number could be reduced and analyzed the statistical correlation between the spatial compartments for each type of staining in all tumor types. We found that in general, there was a high correlation between ‘tumor core’ and ‘inner invasive margin’, but not between either compartment and the ‘outer invasive margin’ (Figure 2—figure supplement 5). ‘Tumor core’ and ‘inner invasive margin’ can be collapsed into one compartment because cell counts in these compartments are highly correlated. This leaves only four possible topographies that are by equivalent to three previously postulated phenotypes: high density outside of the tumor with a low density inside the tumor can be described as ‘immune excluded’. Low density inside and outside is ‘cold’ and high density inside the tumor is ‘hot’ regardless of cell density outside of the tumor (Figure 2A–F).

10Having operationalized these definitions of immune topographies, we next asked how they are distributed in different tumor types in the pan-cancer cohort. As a cutoff value for high versus low cell density we used the median cell density for each cell type (median number of cells per mm² in any tumor type in any compartment, listed in Supplementary file 4). Based on these definitions, different tumor types showed very different distributions of immune topographies (Figure 3A–F). Most strikingly, tumor types that are to some degree sensitive to approved cancer immunotherapies (such as melanoma (MEL), lung adeno (LUAD), lung squamous (LUSC) and head and neck squamous (HNSC) had a large proportion (approximately half) of CD3-hot, CD8-hot and PD1-hot tumors. In accordance with previous studies, colorectal primary (COAD-PRI) and colorectal metastatic (COAD-MET) had a very high proportion of CD3-excluded tumors (Figure 3A). Differences between tumor types were most pronounced for regulatory T-cells (Foxp3+ cells, Figure 3D) with more than half of lung adeno (LUAD), head and neck squamous (HNSC), stomach adenocarcinoma (STAD) and esophageal (ESCA) cancer samples having Foxp3-hot topographies. Also, while close to half of all analyzed COAD-PRI samples were Foxp3-hot, the vast majority of all COAD-MET samples were Foxp3-cold (Figure 3A).

11Subsequently, we asked whether the topography for a given immune cell type co-occurs with the same topography for other immune cell types – or whether tumors can be ‘cold’ for one immune cell type and ‘hot’ or ‘excluded’ for another cell type at the same time. This question is important because in the clinic, immune topographies are often used as stratifying biomarkers for immunotherapy trials but it is unclear how many spatial compartments and which histological markers should be looked at.

12Indeed, we saw that tumors often had different topographies for a pair of immune cell markers (Figure 4). Especially, regarding lymphocytic and myeloid markers, there was a discordance in a substantial number of cases that were lymphoid-hot and myeloid-cold (Figure 4). We further stratified this by different tumor entities (Figure 5A–E) and found pronounced differences between the tumor types. CD3 and CD8 topographies were mostly concordant (Figure 5A) as can be expected because CD8+ lymphocytes are a subset of CD3+ lymphocytes. Regarding CD8 vs. Tregs (Figure 5B), there was a notable discordance. For example, most colorectal primary tumors (COAD-PRI) that were Foxp3-excluded were CD8-cold. Analyzing tumor-associated macrophages (TAMs) labeled with CD68 (Figure 5D), we found that several melanomas (MEL) that were CD68-hot were CD8-cold which was rarely observed in other tumor types. Colorectal cancer liver metastases (COAD-MET) were mostly CD8-excluded, corresponding to a CD68-excluded or CD68-hot phenotype. This was similarly observed for CD163+ macrophages (Figure 5E). These findings suggest that tumors of different immunophenotypes require different ways of immune escape and that these mechanisms can only be distinguished when considering a two-dimensional myeloid-lymphoid classification system.

13Using a full panel of immune cell markers (CD3, CD8, PD1, Foxp3, CD68 and CD163), we asked how similar different tumor types were in terms of immune cell spatial layout. For all 144 tumors that were stained for this full panel (Figure 6A–G, six immunostains, three compartments, 144 patients, after percentile normalization for each cell type), we used unsupervised hierarchical clustering to define similarity. We found that ovarian cancer (OV) was an outlier (Figure 6H) due to its low infiltration with almost all immune cell subsets (Figure 2—figure supplement 3). Highly immunogenic tumor types such as melanoma (MEL), stomach cancer (STAD) and non-small cell lung cancer (NSCLC: LUAD and LUSC) were close in hierarchical clustering.

14Having shown that hot, cold and excluded immune patterns exist, and that these patterns vary between tumor types and immune cell types, we investigated the clinical utility of this classification system in an independent patient cohort (DACHS cohort). We analyzed the topography of CD8+ lymphocytes and CD163+ macrophages in colorectal cancer (CRC) primary tumors because these cell types have previously been linked to prognosis and showed discordant topographies in the pan-cancer cohort (COAD_PRI in Figure 5E).

15We derived immune topographies for CD8 and CD163 from cell counts in the outer invasive margin and the tumor core for N = 287 patients using the median cell densities from the pan-cancer cohort as cutoff values (Figure 7A and B). As in the pan-cancer cohort, most patients were cold or immune excluded for these two cell types. The bivariate analysis showed that ‘CD8-cold, CD163-cold’ was the most prevalent category (Figure 7D).

16We then used multivariable Cox proportional hazard models (including tumor stage, age and sex as potential confounders) to analyze the association between cell counts and cell topographies with survival. Cell densities of CD8 and CD163 in the tumor core and the outer invasive margin were not significantly correlated to overall survival (hazard ratios [HR] for death from any cause were 1.00 for both cell types in both compartments, Supplementary file 5). However, bivariate immune topographies showed significant association to overall survival: With ‘CD8-cold, CD163-cold’ as a reference cohort, the HR was 1.75 for ‘CD8-excluded, CD163 excluded’ (p=0.041) and the HR was 2.71 for ‘CD8-excluded, CD163-hot’ (p=0.025, Supplementary file 6 and Figure 7D).

17The tumor microenvironment is a highly complex, heterocellular ecosystem. Multiple immune cell types are involved in this system and ultimately shift it towards a tumor-promoting or tumor-rejecting environment. Also, these immune cells determine whether a tumor will be responsive to immunotherapy. However, immunotherapy outcomes vary pronouncedly between different tumor entities and also between different patients within a given entity. The biological basis for these differences has been the subject of various studies but is still not entirely clear. Most importantly, we currently lack a comprehensive classification system for multiple effector parts of the immune system.

18In the present study, we have performed a large-scale systematic analysis of lymphoid and myeloid phenotypes of human solid tumors. We provide evidence for a classification of tumor-immune phenotypes into hot, cold and immune excluded spatial patterns. These patterns can be detected not only in lymphocyte infiltrates (lymphoid classification, as previously assumed by most studies) but also in macrophage infiltrates (myeloid classification). Interestingly, lymphoid and myeloid patterns are not always identical and a two-dimensional classification is needed to accommodate all possible lymphoid-myeloid phenotypes.

19Addressing the biological differences and the different responses to immunotherapy across tumor entities, we systematically compared different tumor types. We analyzed ten tumor entities in the framework of this classification and showed pronounced differences, but also unexpected cross-entity similarities: We found that lymphoid-hot tumors are more prevalent in immunotherapy-responsive tumor entities such as melanoma and lung adenocarcinoma than in other entities such as colorectal cancer. Immune-excluded tumors, such as lymphoid-excluded and myeloid-excluded tumors are common in head and neck tumors (HNSC). Lastly, we show that characteristic patterns of immune evasion (via T cell exhaustion or infiltration by Tregs) are not exclusive to specific tumor-immune phenotypes but can be detected across the defined categories.

20As part of this study, we present a clinical validation of the proposed classification system in the context of colorectal cancer (COAD). It has been previously shown that high density of CD8+ cells in COAD is associated with long survival while high density of CD163+ cells is associated with poor survival. However, some large studies have shown only a very modest impact of CD8+cell density on overall survival. Likewise, in the DACHS cohort which we analyzed as part of this study, CD8+ cell density alone and CD163+ cell density alone were not clearly associated with differences in overall survival. Also, for both cell types, the immune topographies (cold, excluded, hot) were not associated with such differences. However, classifying patients according to bivariate (CD8 combined with CD163) immune topographies identified statistically significant associations to survival in a multivariable statistical model (Figure 7D). This was particularly clear for CD8-excluded tumors: in this case, CD163-cold, excluded and hot tumors had a significantly (log rank p-value 0.019) different prognosis (Figure 7E). These findings suggest that a multivariate analysis of spatial distributions of multiple immune cell types may be superior to merely quantifying a particular type of immune cells in COAD and possibly other human solid tumors.

21Our study adds a systematic approach to a hitherto subjective classification of tumor-immune phenotypes. This new classification could constitute a novel framework to investigate immunotherapy responsiveness in clinical trials. Also, this classification sheds light on common immunological aspects by describing a shared immune topography across human solid tumors.

22All experiments were conducted in accordance with the Declaration of Helsinki, the International Ethical Guidelines for Biomedical Research Involving Human Subjects (CIOMS), the Belmont Report and the U.S. Common Rule. Anonymized archival tissue samples were retrieved from the tissue bank of the National Center for Tumor diseases (NCT, Heidelberg, Germany) in accordance with the regulations of the tissue bank and the approval of the ethics committee of Heidelberg University (tissue bank decision numbers 2152 and 2154, granted to NH and JNK, ovarian cancer tissues granted to SS; informed consent was obtained from all patients as part of the NCT tissue bank protocol, ethics board approval S-207/2005, renewed on 20 Dec 2017). Another set of tissue samples was provided by the pathology archive at UMM (University Medical Center Mannheim, Heidelberg University, Mannheim, Germany) after approval by the institutional ethics board (Ethics Board II at University Medical Center Mannheim, decision number 2017 – 806R-MA, granted to AM and waiving the need for informed consent for this retrospective and fully anonymized analysis of archival samples). Also, a set of melanoma samples was provided by the pathology archive at UMM after approval by the institutional ethics board (Ethics Board II at University Medical Center Mannheim, decision number 2014 – 835R-MA, granted to JU and waiving the need for informed consent for this retrospective and fully anonymized analysis of archival samples).

23We analyzed tissue samples of primary esophageal carcinoma (ESCA), primary gastric cancer (STAD), primary colorectal cancer (COAD-PRI), primary lung adenocarcinoma (LUAD), primary lung squamous cell carcinoma (LUSC), primary head and neck squamous cell carcinoma (HNSC), primary bladder cancer (BLAC), ovarian cancer primary tumors (OV) as well as melanoma primary tumors (MEL) and colorectal cancer liver metastases (COAD-MET).

24In addition to this pan-cancer cohort, we acquired a set of N = 287 primary surgical specimen of colorectal adenocarcinoma from the DACHS study which were provided by the NCT biobank under the same ethics board approval as stated above and including informed consent by all patients. Clinical data for this cohort are listed in Supplementary file 1.

25We performed histological staining for CD3 (dilution 1:100 with Leica antigen retrieval ER1 solution), CD8 (1:50 with ER1), Foxp3 (1:100 with Leica antigen retrieval ER2 solution), CD163 (1:500 with ER2), CD68 (1:2000 with Leica Fast Enzyme digestion) and PD1 (1:50 with ER1) on a Leica Bond automatic staining device using a hematoxylin-diaminobenzidine (DAB) staining protocol as described previously. For melanoma, FastRed (Leica #DS9390) was used as a chromogen. Stained whole slide tissue sections were digitized as described previously. Almost all samples were stained for these six immune cell markers. In cases of insufficient tissue availability, only CD3, CD8 and CD163 staining was performed.

26Our image analysis pipeline was composed of several steps: First, manual annotation of three regions of interest (ROI) in each histological whole slide image (Figure 1A). The ROIs were ‘tumor core’ (TU_CORE), ‘inner invasive margin’ (MARG_500_IN) and ‘outer invasive margin’ (MARG_500_OUT). Invasive margins were 500 µm wide. Second, we automatically counted all positively stained cells using the open source software QuPath (Figure 1B). Intensity thresholds and other parameters for cell detection and classification were set manually for each staining type and were identical for all samples in the pan-cancer cohort. All parameters are listed in Supplementary file 2. All cell detection scripts were manually checked for plausibility in all tumor entities. Examples for cell detection are shown in Figure 1—figure supplement 1. For all further analyses, cell density values were normalized by percentile within each staining type and were visualized as ‘target plots’ (Figure 1C). Staining intensity thresholds were slightly adapted for the DACHS cohort as listed in Supplementary file 2.

27To ensure reproducibility of our digital pathology pipeline, we randomly selected 60 tissue specimen and repeated all analysis steps. We used new tissue slides with a distance between 4 µm and 40 µm from the original slice. We stained 30 of these slides for a macrophage marker (CD163) and 30 slides for a lymphocyte marker (CD3) and a blinded observer delineated ROIs for cell quantification as before. There was a high correlation (Pearson’s correlation coefficient was >0.74, p-value<0.001) between these replicates (Figure 2—figure supplement 1).

28Furthermore, two slides that served as negative controls for CD3 and CD163 staining, 231/86516 (<<0.1%) and 1/38311 (<<0.1%) cells were false positive.

29Analysis of all six immunostains for 177 patients in our collective yielded 965 sets of cell density counts in three spatial compartments each (2895 data points in total, missing values due to tissue availability or quality). For visualization, cell densities were subjected to percentile normalization (quantile normalization with 100 quantiles) for each staining type, across all tumor entities. For clustering and all other analyses, we used absolute cell density (cell number per mm²).

30To determine the optimal number of clusters in this data set, we used three different methods for clustering: a gaussian mixture model, k-means and hierarchical clustering. For 1 up to 12 clusters, we computed three loss functions for each approach, using the Davies-Bouldin, the Calinski-Harabasz or the silhouette criteria for quality of clustering. For all optimization methods, we performed 10 technical replicates.

31All image analysis steps were implemented in QuPath (see key resources) and all downstream analyses were implemented in MATLAB (Mathworks, Natick, MA, USA) R2017a. All experiments were run on a standard workstation (Intel i7 Processor, 8 cores, 32 GB RAM, Microsoft Windows 10.1). We release all source codes under an open access license (; copy archived at https://github.com/elifesciences-publications/immuneTopography). Also, we release all raw data from our experiments (Supplementary file 3). All survival analyses were performed in R version 3.5.1 (R-project.org) using the packages survminer, survival, ggfortify and ggplot2.