Introduction

The burgeoning notion of cellular heterogeneity within a tumor has attracted much attention in cancer research1, 2. In addition to genetic heterogeneity embodied by the diversity of genomes in each cancer cell due to genome instability3,4,5, it is increasingly recognized that the nongenetic variability of cell phenotypes within an isogenic (clonal) cell population contributes to functional heterogeneity of cancer cells6, 7. Even not considering the variety from stromal cells (endothelium, stromal fibroblasts and infiltrating immune cells) but just within the tumor parenchyma, the neoplastic cells raise the possibility of functional specialization and cooperation among the cancer cells themselves8,9,10,11. Slow proliferating cancer stem cells vs. fast proliferating vs. migratory cells are examples of (overlapping and dynamic) subsets of cells among the neoplastic cells12, 13. Cooperation would require cell-cell communication which is mainly mediated by physical cell-cell contact, shared extracellular-matrix and secreted diffusible signaling molecules14,15,16. While interaction between distinct genetic clones of cancer cells in a tumor, has received attention in past years17, 18 the diversification of cell phenotypes within the same clone (non-genetic heterogeneity7, 19, 20) must also be considered as a source of interaction between distinct types of cells.

Here we study crosstalk between cells via secreted (paracrine) soluble or matrix proteins as well as cell surface signaling (juxtacrine) proteins that bind specifically to their cognate receptors. Herein the principle of autocrine stimulation in which a cell secretes a growth factor and also expresses its cognate receptor has long been used to explain the observed co-expression of both ligand and receptor in the tumor cell population which was originally considered to be homogeneous and consisting of only one cell type21. But the notion of the diversity of cell subtypes among the tumor parenchyma cells which can differ in their capacity to secrete different sets of mediators and to express distinct receptors for these signaling molecules by necessity entails that networks of heterotypic paracrine interactions arise in addition to homotypic interactions between the cells capable of autocrine stimulation.

Studies of heterotypic cell-cell communication mediated by secreted factors and of their role in tumor progression have largely focused on the tumor-stroma cooperation22,23,24. For instance, IL8 secreted by many carcinoma attracts tumor promoting macrophages25, and secretion of VEGF stimulates endothelial migration and proliferation, fostering tumor angiogenesis26. But with the growing awareness of heterogeneity of the tumor-parenchyma proper, attention has also been given to cooperation amongst distinct cancer cell subpopulations9, 27, 28. For instance, in a study of small cell lung cancer in animal models, the grafting of tumor cell clones genetically engineered to produce different cytokines revealed the effect of cooperative interactions between distinct (engineered) clones of cancer cells on disease progression and metastatic processes. Importantly, in such work, implantation of individual clones separately could not exert any detectable effect29.

Similar findings were obtained with a WNT1-signaling-dependent breast cancer model where cooperation between two different differentiation lineages (luminal and basal) were required for tumor growth30. In a p53-deficient mouse breast cancer model, a crosstalk between tumor initiating cells and a more differentiated mesenchymal cell population was observed9. In this study, interaction between different cell populations (defined by different levels of CD29 and CD24 markers, respectively) was documented by the detection of secreted ligands, such as WNT9A, WNT2, IL-6 and CXCL12 from one cell population which induced a response in the other population through the respective cognate receptors. Furthermore, cytokines produced by the more differentiated mesenchymal cell population were able to stimulate the self-renewal and tumor initiating capacity of the tumor initiating cells. In another case based on the prostate cancer PC3 cell line, a non-cancer stem cell subpopulation rendered a cancer cell subpopulation metastasis-prone31. Here, a paracrine interaction between two clonally distinct subpopulations mediated by several diffusible factors, among which protein SPARC had a prominent role, resulted in enhanced invasiveness and metastatic dissemination of the cancer stem cell-rich subpopulation of the PC-3 cells.

As described in the paper of Marusyk et al.11 and more generally in review papers of cytokines and chemokines32,33,34,35,36, a large set of growth factors and cytokines/chemokines along with their cognate receptors mediate the cell-cell interactions between different cells in the cancer tissue or even between sub-clones among the cancer cells. Thus, in addition to cross-talk with the non-neoplastic stroma, a large array of secreted proteins and their receptors are produced by the cancer cells themselves as signal for other cancer cells within a tumor. From such a viewpoint, Wang et al. systematically reviewed the influence on cancer behaviors and drug resistance of genetic subclones of cancer cells, subclone interaction network, clone-clone-stromal cell interactions and their dynamics17, 18. Given the wide functional variety of cells among tumor cells of the same clone (intraclonal or non-genetic heterogeneity), we should consider that a substantial portion of such intra-tumor cell-cell signaling consists of heterotypic interactions between cells of distinct phenotypes (independent of their genetic identity) whereby some cells send signals designated only for another functional subset of cells, perhaps only minimally distinct, that expresses the respective cognate receptors.

Since communication is mediated by specific ligand-receptor interactions information about the set of specific “sender-receiver cell pairs” will allow one to define an entire cell-cell communication network in the cancerous tissue in which both neoplastic and stromal cells participate. This network is “hardwired” by the distribution of the cell identities that also dictate the expression of ligand and receptors. Mapping out this communication network would require single-cell resolution gene expression analysis to determine which cell type expresses which ligand or receptor. Such data are, despite the arrival of single-cell transcriptome analysis37 still scant.

Currently, the databases of gene expression of ligands and receptors are derived from bulk tumor (whole tissue) analysis of gene expression (transcript) profiles. To evaluate if such aggregate measurements, which lack single-cell level information but are readily available for a large number of tumors, provides information on cell-cell communication, we searched for a relationship in the expression of cyto/chemokines and their specific cognate receptors in tumor tissues at the transcription level in the transcriptome data of The Cancer Genome Atlas (TCGA) project4. The rationale was that a difference in the expression levels and/or in the correlation of expression of receptors and their ligands between the tumor and the corresponding normal tissues may indicate a role of specific cell-cell communication that is augmented/altered in neoplastic compared to non-neoplastic tissue. The analysis of a total of 5,362 transcriptomes from ten different solid tumors and comparison with corresponding normal tissues revealed a significant alteration of the expression levels and of the correlation of cyto/chemokine transcript abundances with that of their cognate receptors in the tumor tissues, suggesting possible shifts in the communication network associated with malignancy. This finding suggests a role for the non-cell autonomous processes in tumor growth. Constructing cell-cell communication network from whole-tumor tissue profiles may also help in designing new therapeutic strategies that target not intracellular networks - which cannot longer be justified in view of cellular heterogeneity - but inter-cellular molecular networks that govern tumor behavior a whole. The latter are operated by the signaling between the cytokines/chemokines and their receptors which are readily accessible for pharmacological interventions.

Results

Ligand-receptor genes differentially expressed between normal and cancer tissues in TCGA transcriptomes

To test the hypothesis that cancer cells are embedded in a network of “cross-talking” activity among themselves as well as with cells from the tissue microenvironment, we first curated a list of 709 ligands and 693 cognate receptors (totally 2,558 pairs of chemokine/cytokine) from various known public protein interaction databases (see details in Material and methods). We reasoned that since co-expression of a ligand and its cognate receptor is necessary (albeit not sufficient) for cell-cell communication via secreted signals, the simultaneous up- or down-regulation of expression of ligands and their cognate receptors can be the first proxy to detect gain or loss of cell-cell communication when comparing gene expression profiles from cancerous to normal tissue (Fig. 1a).

Figure 1
figure 1

Schema & examples for the change of intercellular signaling. (a) Schema to identify the changes of intercellular signaling between normal and cancer tissues from human ligand-receptor database and TCGA transcriptomes. (b,c) Scatter plots of top 10 ligand-receptor pairs up-regulated/down-regulated in cancer tissues compared with normal tissues. Each dot or triangle represents a population average of a ligand (y axis) and a receptor (x axis). (dot –normal tissue; triangle – cancer tissue).

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We first searched for ligand-receptor pairs that were differentially expressed between the normal tissue and the cancer tissues using the TCGA database4, which contains a large number of patient samples. We focused on the most frequent cancer types which was necessary to achieve statistical significance for analyzing the expression changes of ligand/receptor genes in the same tumor type across the patient population. From the 30 cancer types available, we selected 10 cancers for which TCGA had at least ≥30 normal samples and ≥60 samples from distinct cancer patients. We analyzed the ligand or receptor genes that were significantly differentially expressed between the normal tissues and the cancer samples using the linear regression model (up-/down 2 folds with P-value = 0.05, more details can be found in Methods Section). The top 10 up-regulated ligand-receptor pairs across 10 cancer types are shown in Fig. 1b while the top 10 down-regulated ligand-receptor pairs are shown in Fig. 1c. To demonstrate the distribution of these most changed genes in different cancer types, boxplot of most down-regulated genes (either ligands or receptors) are shown in Fig. 2a while the most up-regulated ones are shown in Fig. 2b. For a more detailed analysis of these genes, we listed these up-/down-regulated ligand-receptor pairs which were most commonly shared across 10 cancer types for the four possible scenarios:

  1. i.

    Expression of both receptor and ligand increased in cancer (Table S1), such as COL11A1-ITGA2, COL7A1-ITGA2, MDK-GPC2 and MMP1-ITGA2. Excess expression of both Collagen (COL11A1, COL7A1) and Integrin (ITGA2) is known to be associated with cell invasiveness and tumor formation38. MMP1 is an enzyme which degrades the extracellular matrix (ECM) – hence promoting cell invasiveness39. MDK is a growth factor and GPC2 involves with Wnt Signaling pathway – both promote cell growth, migration and angiogenesis during tumorigenesis40.

  2. ii.

    Expression of either receptor or ligand is increased in cancer but not both (Table S2), such as CALM1-HMMR, SPP1-ITGB1, TNFSF18-TFNRSF18, COL11A1-ITGB1 etc. Not surprisingly, collagen and integrin were found also here. CALM1-HMMR is involved in Ca2+ controlled cell motility and invasiveness. SPP1-ITGB1 is related to cell-matrix interactions and up-regulate stimulates the immune response. TNFSF18-TFNRSF18 is involved with T-cell activation and lymphocytes and endothelial cell interactions.

  3. iii.

    Expression of both receptor and ligand genes is decreased in cancer (Table S3), such as ANGPT1-TEK, CCL21-CCBP2, CCL14-CCBP2, L1CAM-CNTN1 and NCAM1-GFRA1. In quiescent tissues, ANGPT1 form complexes with TEK molecules to maintain cell-cell contacts, inhibiting angiogenesis and promotes the vascular quiescence. Chemokine (C-C motif) ligands (CCL21, CCL14) and Chemokine-binding protein (CCBP2) control chemokine levels and localization. L1CAM-CNTN1 and NCAM1-GFRA1 are both involved in cell adhesion and NOTCH signaling activation.

  4. iv.

    Expression of either ligand or receptor is decreased in cancer but not both (Table S4), such as CNTFR, LIFR, SSTR1, CXCL12, NPY1R, RELN, RSPO3, TGFBR3 etc. CNTFR involves with neuronal cell survival and differentiation; LIFR involves cellular differentiation and proliferation. SSTR1 regulates various cellular functions such as cell proliferation and endocrine signaling – inhibiting the release of many hormones. CXCL12 together with CXCR4 regulates intracellular calcium ions and chemotaxis. NPY1R mediates diverse of biological functions, such as food intake and circadian rhythm. RELN is involved with extracellular matrix and regulates microtubule functions. RSPO3 has been implicated in angiogenesis, Wnt/beta-catenin pathways and TGF-beta pathways. Decreasing expression of TGFBR3 receptors is commonly observed in cancers.

Figure 2
figure 2

Boxplots of the most differentially expressed ligand or receptor genes across 10 cancer-normal tissue comparisons. (a) The boxplots of top 10 down-regulated ligand/receptor genes in cancer tissues compared with normal tissues. Each small panel is the gene expression boxplot comparison between normal (left) and cancer (right) tissues in one cancer type (brca – breast cancer etc.). Different color represents different genes. (b) The boxplots of top 6 up-regulated ligand/receptor genes in cancer tissues compared with normal tissues.

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Altered correlation of ligand-receptor gene expression in cancer tissues in the TCGA transcriptomes

Besides considering the up-/down-regulation of ligands and their cognate receptors as proxy for cell-cell communication, correlation in the abundance of transcripts of these communication molecules across tumors would also be an indicator of cell-cell communication – irrespective of whether of the autocrine or the paracrine type. In other words, if co-expression has a biological role, one can postulate that, as a more stringent criterion for functionality, the expression change of ligands and receptors across tumors of multiple patients would be commensurate: Tumors with higher numbers of “sender” cells may also contain a larger number of “receiver” cells, which would be manifest in an increased correlation of the abundance of transcripts of ligands and receptors. Moreover, if individual cells are both sender and receiver (autocrine scenario), the varying content of such autocrine-capable cells in tumors, postulated to be a hallmark of malignancy21, would further increase the correlation between ligand and receptor expression across tumors.

We systematically calculated the Spearman correlation coefficients for the transcript abundances of the curated ligand-receptor pairs (i.e., the correlation between the ligand expression vector and the receptor expression vector across the samples for each pair) for the normal and the corresponding primary tumor tissues of all 10 cancer types. In cases where the ligands or receptors have non-bijective relationships, e.g. one ligand binds to multiple receptors, the correlation is calculated between the ligand and the sum of all receptors. The overall patterns of correlation of many ligand-receptor pairs changed significantly (Kolmogorov test for change of distributions of all the values ligand-receptor correlation coefficients): examples of ligand-receptor pairs are shown in Fig. 3. For some ligand-receptor pairs the correlation between ligands and receptors in the cancer tissue was higher, as was the case for CCL2-CCR5, CCL3-CCR5 and PLAU-ITGA5. By contrast, some cancers lost the positive correlation seen in the healthy tissue, such as LIPH-LPAR2 and SEMA4G-PLXNB2, and others lost the negative correlations, such as CGN-TGFBR2 and SEMABD-TYROBP etc.

Figure 3
figure 3

The scatter plots of seven typical ligand-receptor pairs demonstrated the altered correlations between five cancer (orange dots) and their corresponding normal tissues (blue dots). We showed the Spearman correlations of the gene expressions of ligand-receptor pairs in both normal and cancer sample for five cancer types. The ligand-receptor pairs were grouped into 3 categories: 1) gaining the correlation in cancer tissues (first 3 rows); 2) losing the positive correlation in cancer tissues (4th ad 5th row); 3) lose the negative correlations in cancer tissues (6th and 7th row). Each dot represent one ligand-receptor gene expression in one sample. The line is a regression line to visualize the trend of the data.

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To obtain a comprehensive view of ligand-receptor correlation of all curated pairs, we plotted the histograms of the values of the ligand-receptor Spearman correlation coefficients for all ligand-receptor pairs in normal and cancers tissues for the 10 cancer types (as shown in Fig. 4a). This aggregate analysis revealed that the mean of the distribution of ligand-receptor Spearman correlation coefficients consistently decrease: tumors of most cancer types generally had reduced correlation compared with the normal tissues. To rule out the confounding factor, we compute the correlations of random pairs in both tumor and normal tissues. It is shown that random pairs have no correlations compared with the specific pairs in the normal tissues (Suppl. Fig. S1). This change of the distribution of correlation coefficients between cancer and normal tissues was significant (Kolmogorov-Smirnov test, P-value < 0.01, see Table S5 and details in Materials and methods). Figure 4b shows the ligand/receptor pairs with the most altered correlation detected by Spearman correlation.

Figure 4
figure 4

The overall Spearman correlation distribution between ten normal and cancer tissues. (a) The histograms of Spearman correlation coefficients of 2,558 ligand-receptor pairs between 10 cancer samples (in red) and their corresponding normal tissue samples (in blue). The ligand-receptor pairs generally dexreease the correlation in most cancer tissues. (b) The ligand-receptor pairs shared in most cancers with altered correlations between 10 cancer and normal tissues.

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As negative control for the significance of the correlation in the expression of ligands and their cognate receptors, we computed the correlation of random pairs of receptors and ligands (which do not interact specifically). The ligand-receptor correlation for all possible random pairs (2,558 × (2,558 − 1)/2 = 3,270,403 pairs) of ligands and receptors, irrespective of their binding specificity, was computed to serve as background signal. As shown in Fig. 5a, the distribution of correlation coefficients of ligand-receptor pairs of cancer tissues extracted from the TCGA gene expression data have a much higher peak around zero than the one derived from the random pairs, indicating that overall, specific ligand-receptor pairs exhibit lower variability of correlation than one would expect for random pairs, warranting the use of ligand-receptor correlation as an indicator of a biological change in the tumor tissue. For most cancer types (breast, head and neck, liver, lung (LUSC) and thyroid), the distribution had a fat long tail of higher correlation values for specific ligand/receptor pairs compared to random pairs – in line with the small subset of ligand-receptor pairs that displayed a significantly higher correlation in cancer tissues. Note that the differences of the fat tails do not exist in the comparison between observed ligand-receptor pairs and random pairs in the normal tissues (Fig. 5b). We also calculated the coefficients of variation (CV) and found that indeed the CVs for the specific pairs are much smaller than those of the random pairs – which confirms that specific pairs have lower variability (Table 1).

Figure 5
figure 5

Spearman correlations in cancer and normal tissues are significantly different from randomly chosen pairs. (a) The Spearman correlation coefficients distribution (random (in red color) vs. defined (in blue) ligand-receptor gene pair) in cancer tissues. (b) The Spearman correlation coefficients distribution (random vs. defined ligand-receptor gene pair) in normal tissues.

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Table 1 The coefficients of variation of the specific ligand-receptor pairs and the random pairs of ten cancer types from TCGA.
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Next, to display the nature of the changes, we plotted the values of Spearman correlation coefficients of the 2,558 ligand/receptor pairs as scatter plots for a cancer (Y-axis) against the corresponding normal tissue (X axis) (Fig. 6). The ligand-receptor pairs whose relationship of expression in cancer differed most from that in normal tissue could be divided in three categories, indicated by the three rectangle boxes in the scatter plots away from the diagonal: (i) uncorrelated in normal (Spearman correlation coefficients between −0.25 and 0.25) but correlated in cancer (higher than 0.5), located in rectangle area I in the scatter plot of Fig. 6c; (ii) negatively correlated in normal (lower than −0.5) but uncorrelated (between −0.25 and 0.25), in rectangle area II of Fig. 6c and (iii) positively correlated in normal (higher than 0.5) but uncorrelated (between −0.25 and 0.25) in cancer, located in rectangle area III (Fig. 6c). In order to estimate the background noise, we had randomized two groups of 65 samples from the 112 samples of normal breast tissue and compared their fluctuations in the scatter plots (Fig. 6A and B). This indicates that variations of ±0.25 from the value of correlation coefficients were within the range of measurement noise; hence we defined as ‘uncorrelated’ Spearman correlation coefficients between −0.25 and 0.25, and as ‘positively’ or ‘negatively correlated’ values higher than 0.5 or lower than −0.5, respectively. With these criteria we systematically compared ligand-receptor correlation differences through all scatter plots for all ten cancer tissues (Fig. 6c–o).

Figure 6
figure 6

The scatter plots show the altered ligand-receptor correlations between normal and cancer tissues of 10 cancer types. (a,b). The background noise of correlations in the normal breast tissue. The scatter plots of 2,558 ligand-receptor pairs from 65 samples which are randomly chosen 1,000 times from breast tissue (performed twice); (c–o). The scatter plots of ligand-receptor correlations (normal vs. cancer) of ten cancer types. Three types of altered correlations: i) area I: changing from uncorrelated (Spearman correlation coefficients between −0.25 and 0.25) to correlated (higher than 0.5); ii) area II: changing from positively correlated (lower than −0.5) to uncorrelated (between −0.25 and 0.25); iii) area III: changing from negatively correlated (higher than 0.5) to uncorrelated (between −0.25 and 0.25).

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Among the several pairs identified in this survey, some have been implicated in cancer initiation and progression, often impacting angiogenetic processes, immune responses facilitating interactions between cancer cells and carcinoma associated fibroblasts. This was, for instance, the case with CCR5 which, with the two ligands i.e. CCL2 and CCL3, formed the two ligand-receptors pairs that exhibited the largest increase in correlation when comparing tumor to normal tissues. CCR5 is expressed on several tumor-associated immune cells, including T cells, dendritic cells, and NK cells as well as endothelial and cancer cells. CCR5 and its ligands CCL2, CCL3, CCL4 and CCL8 play crucial role in migration, activation and proliferation of T-cells and polarization of macrophages. It is also involved in metastasis and mediates the recruitment of endothelial cells, thus contributing to angiogenesis41, 42. Furthermore, the CCR5-CCL4 axis can contribute to breast cancer metastasis to bone by mediating the interaction between cancer cells and fibroblasts in bone cavity43. Another well-known pair is the CCL2–CCR2 axis which mediates macrophage recruitment, promotes tumor growth, progression and metastases in breast and prostate cancers44, 45. Furthermore, evidence suggests that carcinoma associated fibroblasts (CAFs) recruit monocytes mostly through the CCL2-CCR2 axis in breast and melanoma cancers46, 47. In a mouse model of pancreatic cancer, CCR2 inhibitors depleted inflammatory monocytes and macrophages, which resulted in decreased tumor growth and reduced metastasis48. It is well known that CCL2 promotes lymphocyte, keratinocyte, and endothelial cell activation; furthermore, CCL2 can indirectly affect angiogenesis. The dysregulation of chemokines that activate neutrophils or monocytes/macrophages can negatively impact angiogenesis by limiting the secretion of proangiogenic factors49. The top 15 ligand-receptor pairs for which the ten cancer types showed most significant alteration in ligand-receptor correlation in three categories are listed in Tables S6 to S8 separately.

Role of immune cell infiltration in modulating the ligand-receptor correlations

The altered expression and correlation of ligand-receptor pairs in tumors most likely originates from the altered interaction patterns of tumor cells with the infiltrating immune cells. Inflammatory cells in the tumor stroma interact with tumor cells in a bi-directional way via cytokines, such as IL6, IL8 and many others, which can promote tumor progression or mediate an immune response to the neoplastic cells25. Analysis of gene expression profiles in cancer cell lines50, 51 as well as measurements in cancer cell culture conditioned media52, 53 suggest that these cytokines, originally associated with immune cells, can also be produced by carcinoma cells.

Thus, we next sought to assess the contribution of immune cell infiltrates to the altered correlation of ligand-receptors expression in tumors. We took advantage of the information in TCGA that offers an estimate of the immune and other stromal cell contents in each tumor sample. This had previously been calculated by Carter et al. from somatic DNA alternations in exome sequencing data54. We divided the primary tumor samples into two groups: tumors in which the proportion of infiltrating non-neoplastic cells was (i) high 25% quantile or (ii) low 25% quantile. This distinction was done with respect to four types of non-neoplastic admixture cells: lymphocyte, monocytes, neutrophil and stromal cells. If these micro-environmental cells contribute substantially to cell-cell communication, that is, to the change of the correlation of expression between ligand and receptor in tumors, the correlation coefficient should be significantly different in between tumors of these two groups that have very different non-neoplastic cell content. Figure 7 described the results of two analysis: (a) we divided the breast cancer samples into two subpopulations: top 25% high inflammation cell infiltration group and bottom 25% low inflammation cell infiltration group. We plot the Q-Q plot of ligand-receptor correlation distributions of two groups, which shows that the two distributions are very similar. (b) We plot the histograms of ligand-receptor correlation distributions of two groups of tumor samples. They show that the ligand-receptor correlation coefficients was not different between tumor samples with high or low content of stromal infiltrates. This result indirectly suggests that the co-scaling of transcript expression of ligands and their cognate receptors in tumors, which indicates cell-cell communication, is not simply a manifestation of increased interaction between cells of the immune infiltrate and the tumor parenchyma.

Figure 7
figure 7

Spearman correlation coefficient distributions of ligand-receptor pairs of breast cancer tissues with different percentage of immune cells. The Q-Q plots and the histograms of ligand-receptor correlation distributions of two groups of tumor samples: top 25% high inflammation cell infiltration group (in green) vs. bottom 25% low inflammation cell infiltration group (in blue). (a) Lymphocyte (b) monocyte (c) Neutrophil (d) Stromal cells.

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Ligand-receptor co-expression analysis in single-cell gene expression data

The arrival of single-cell transcriptome data now offers the opportunity for mapping a fine-grained cell-cell communication scheme for how the receiving (receptor-expressing) and signaling (ligand expressing) cells are distributed among the various cell subpopulations of cells in the tumor. Single cell transcriptomes of the mixed population of dissociated cells from a tumor readily identifies the cell types in addition to providing information on expression of ligands and receptors by individual cells, and thus allows us to determine “who talks to whom” in the cell-cell communication network. We demonstrate this using one of the first data sets available as an example for future analyses as such single-cell resolution data becomes available for many tumor types. Garraway et al. published a set of single cell RNA-Seq data that revealed intratumoral heterogeneity of gene expression in primary and metastatic melanoma55. We analyzed the same 2,558 ligand-receptor pairs in the transcriptomes of ~4,000 individual cells isolated from melanoma which include both malignant tumor cells and non-tumor stroma and immune cells. We systematically compared the ligand-receptor expression patterns among seven cell types present in the melanoma-derived single-cell suspension (Melanoma cells, T cells, B cells, macrophage, NK cells, CAF and endothelial cells)55. We then built the interaction network of indicating which cell types “talks” to which other cell type, based on the specific ligand-receptor link. The gene expression distribution in single-cell transcriptomes obtained from RNA-Seq is usually modeled as a negative binomial distribution56. If the gene expression values x i of both the ligand and the receptor were above a certain threshold for two individual cells, this would indicate a possible intercellular signaling between these two cells. We used the following criteria to call a cell-cell dialogue between two different cells as existent:

We calculated the mean \(\overline{{x}_{i}}\) and standard deviation σ i of the expression level of a gene i (either ligand or receptor) across all seven cell subpopulations representing the seven cell types. If a subpopulation’s average gene expression level was larger than \(\overline{{x}_{i}}+3{\sigma }_{i}\), it was considered to be highly expressed in that subpopulation. Where both members of a ligand-receptor pair were highly expressed in two cell types out of the seven cell subpopulations, we interpreted this as a potential cell-cell communication line between these two cell types. The above threshold is tunable and we used here the above value for demonstration purposes. A hence identified ligand-receptor pair also indicates the direction of a communication line (sender→receiver), allowing us to draw a communication network as a directed graph, as show in Fig. 8a.

Figure 8
figure 8

The proposed cell-cell interaction network between cancer and the normal cells in the microenvironment derived from melanoma single-cell data. To highlight the crosstalk between cancer cells and the microenvironment, we filtered out all the interactions not involved in cancer cells. (a) The network shows the number of all the proposed ligand-receptor interactions involving melanoma cancer cells and the normal cells in the micro-environment. The majority of the interactions seem to involve the endothelial cells, CAFs and cancer cells themselves. (b) The top 2% of the ligand-receptor pairs cross-talking between cancer cells and normal cells. The ranking is based on the exclusiveness score of the interaction (exclusiveness score: gene expression level of one cell type divided by the mean across all cell types).

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Using this analysis framework we found that by far most communication lines occurred between cancer and endothelial cells, and between cancer cells and cancer associated fibroblasts (CAF), as well as among the cancer cells themselves. Using this analysis, we could also label the links in the cell-cell interaction network map in the melanoma tissues with the specific by ligand-receptor pairs (Fig. 8b): melanoma tumor cells talked to CAF cells mainly through the three ligand-receptor pairs CTGF-LRP1, C1QB-LRP1 and COL1A2-ITGA11; conversely, CAF cells “talked back” through CYR61-CAV1, CTGF-LRP1 and COL3A1-DDR2. Tumor cells talked to macrophages through the ligand receptor pairs C1QB-LRP1, COL1A2-LRP1 and SERPINE2-LRP1, and macrophages talked back through SERPINA-LRP1, C1QB-LRP1 and C1QA-CSPG4; tumor cells talked to T-cells through COL1A2-ITGA1 and T cells talked back through GZMB-PGRMC1. Tumor cells talked to endothelial through the pairs COL1A2-FLT4, COL1A2-CD93 and COL4A2-CD93; and endothelial cells talked back through THBS1-LRP1, CYR61-CAV1 and TGPI-LRP1. Finally, tumor cells talked to NK cells through CYR61-ITGAM and CYR61-ITGA5 and NK cells talked back through GZMB-PGRMC1. As can be seen from this list, many interactions where mediated by collagen deposition, acting as a ligand to a variety of receptors.

Discussion

In recent years many reports, such as the discovery of cancer without driver mutations57 and collective metastasis58 etc., have challenged the traditional paradigm of tumorigenesis in which cancer cells accumulate mutations in genes that control growth, which lead to clonal expansion and metastasis at late stage59 while emphasizing the importance of non-cell-autonomous forces. Among the recent explanations proposed to address inconsistencies within the oncogene paradigm, a more comprehensive and dynamic view describes tumors as complex tissues consisting of multiple different cell types that communicate with each other9, 11. Thus, cancer is an emergent system: tumor growth and progression are promoted by the intricate network of cooperation among cancer cells themselves and between cancer cells and (non-neoplastic) cells and matrix of the tumor micro-environment.

The emphasis on cellular interactions among cancer and normal cell populations is based on several findings and one of the most convincing is the presence of a large array of growth factors, chemokine/cytokines and matrix proteins detected in tumor tissues. Furthermore, many of these secreted molecules are produced not only by the non-neoplastic stromal tissues but also by the cancer cells themselves and directed at other cancer cells. Since it is expensive and time-consuming to directly measure the ligand-receptor interactions in hundreds of tumor samples, the up/down-regulation and the Spearman correlation of ligand-receptor pair was used as the surrogate of the existence of the ligand receptor interaction. If a ligand interacts with a receptor, depending on their molecular structure and on whether one receptor could bind to one, two or even more ligands, certain quantitative relationship can exist between the ligand and the receptor. Our analysis revealed the pronounced changes in gene expression or correlation of several cyto-chemokines/receptors pairs in most cancer tissues. Among the most frequent pairs of chemokines/receptors that whose transcript expression was upregulated or the correlations increased in tumors were: COL11A1-ITGA2, COL7A1-ITGA2 and MMP1-ITGA2, likewise CCR5 and the ligands CCL2, CCL3 etc. All these receptors/ligands molecules are endowed with several tumorigenic properties, such as collagen-integrin interaction, cell-ECM(extracellular matrix) interactions and Ca2+ controlled cell-ECM interactions. The most frequently down-regulated pair of chemokines/receptors were ANGPT1-TEK, CCL21-CCBP2, CCL14-CCBP2, L1CAM-CNTN1 and NCAM1-GFRA1 etc. They inhibit angiogenesis, maintain vascular quiescence and cell adhesion, modulate immune-regulatory and inflammatory processes etc.

We calculated the Spearman correlation of mRNA expression to detect such quantitative relationship – the higher the correlation, the more a quantitative relationship may exist between the ligand-receptor pair. However, these issues may complicate the analysis and interpretation: (1) Most ligand receptor gene expression data is quite noisy (Spearman correlation coefficients mostly vary from −0.3 to 1 in cancer tissues), thus the correlative measurements presented here represents only a first, broadly applied step towards identifying receptor ligand pairs involved in tumorigenesis. We will need more accurate measure to represent the ligand receptor interaction; (2) although we have analyzed the influence of immune cells and found no obvious effect, this negative result could be due to lack of sensitivity and explicit experiments will be needed to distinguish whether the quantitative changes in the ligand-receptor relationship are caused either by cell interactions among the cancer cells or between cancer cells and the infiltration by immune cells; (3) all our analyses, including the single-cell data, are based on transcript expression which may not always correlate with protein expression – but the use of CyTOF60 may in the foreseeable future provide the protein level complement of our analysis and at single cell resolution. (4) Finally, one should be reminded that correlation of ligand and receptor expression is only a suggestion for possible interaction. Whether the suggested communication is critical for the tumor will require functional experiments (such ligand depletion or receptor blockade studies). However our type of analysis may generate the hypothesis to motivate such experiments.

The cell-cell communication network that we calculated from the single-cell expression data of melanoma relies on the specificity of the interaction between a given ligand produced by a given cell and the its cognate receptor expressed by a different cell, on the large variety of cancer and stromal cells, and on the expression profiles which define which ligand and which receptor they express. Since cancer heterogeneity can arise from both genetic subclones and non-genetic diversity of states, their interactions through ligand-receptor can both influence the cancer’s behaviors. Both underscore the importance of cancer heterogeneity and may explain why a single target-selective drug which may targets a single subpopulation may have limited effect. Drug resistance can arise from the evolutionary selection given the presence of variation of phenotypes. In our study we do not consider genetic diversity of cells and thus cannot distinguish between interactions between clones17, 18 – which may affect the standard somatic evolution model of cancer, and intra-clonal interactions. The availability of single-cell RNA-Seq, combined with future accurate whole-genome single-cell sequencing technology61 will permit the combination of ligand-receptor expression analysis with clonal analysis to clarify the extent of intra and inter-clonal heterogeneity in forming heterotypic cell-cell interaction networks. But the structure of the cell-cell communication network – independent of the clonal structure may itself contain untapped relevant information for comparison, classification and even prognosis and prediction of tumor behaviors.

Methods

Curated ligand-receptor Database

We used the database provided by Ramilowski et al.62 which includes 2,558 unique human ligand-receptor interactions. They firstly merged the lists of ligand-receptor pairs from Database of Ligand-Receptor Partners (DLRP)21, IUPHAR63 and Human Plasma Membrane Receptome (HPMR)64, then further extended by inferring experimentally supported interactions in the HPRD and STRING database.

Ten solid tumor transcriptomes from TCGA

Gene expression data were retrieved from The Caner Genome Atlas (TCGA) project data portal (https://tcga-data.nci.nih.gov/). We focused our analysis on ten tissues: breast invasive carcinoma (BRCA), Uterine Corpus Endometrial Carcinoma (UCEC), Colon adenocarcinoma (COAD), Head and Neck squamous cell carcinoma (HNSC), Kidney renal clear cell carcinoma (KIRC), Kidney renal papillary cell carcinoma (KIRP), Liver hepatocellular carcinoma (LIHC), Lung adenocarcinoma (LUAD), Lung squamous cell carcinoma (LUSC), Thyroid carcinoma (THCA). We considered the samples annotated as 01 to be “cancer” (primary solid tumor) and the ones annotated as 11 to be “normal” (solid tissue normal).

Linear regression model to identify the differentially expressed genes between normal and cancer samples using LIMMA

We assume a linear regression model E[y j ]X α j where y j is a vector of the expression data of gene j across n samples, X is a design matrix with n row (n - sample number) and m columns (m - group number) – here m = 2 (ex., x ij  = 0 for normal tissue and x ij  = 1 for cancer tissue), α j is a vector of coefficients which can be fitted using linear regression model according to the above equation. We can obtain the differentially expressed gene list by computing moderated t-statistics on the contrasts between the estimated coefficients α j of normal and cancer tissue as well as setting folds changes, relevant P-value and false discovery rate.

Calculate correlation of ligand-Receptor pairs

For each pair of ligand and receptor we calculated the Spearman correlation coefficient. We used the Spearman correlation function from the base package of R version 3.3.1. Three different groups of interactions were defined based on the value of their correlation. The group that gain positive correlation in cancer: we focused on the pairs that have a coefficient >0.5 in cancer and a coefficient >−0.25 & <0.25 in healthy tissues; the group that lose positive correlation in cancer: we focused on the pairs that have a coefficient >0.5 in the healthy tissues and a coefficient >−0.25 & <0.25 in cancer. The group that lose negative correlation in cancer: we focused on the pairs that have a coefficient <−0.5 in healthy tissues and a coefficient >−0.25 & <0.25 in cancer. Once the pairs in each group defined, we focused our attention on the most recurrent ligand-receptor pairs across the ten cancer types analyzed.

Kolmogorov-Smirnov test of the significance of correlation coefficients distribution differences between cancer and normal tissue

We use the Kolmogrov-Sminov test to calculate the distances and P-values of Spearman correlation coefficients distribution between 10 normal and cancer tissues from TCGA. The distribution is estimated from the numeric sets of correlation coefficients. The null hypothesis to calculate P-values is that we draw two datasets from the same continuous distribution.

Data availability statement

The transcriptome data of 10 cancer types from TCGA and single-cell RNASeq data of melanoma are publicly available.