HiTIMED: hierarchical tumor immune microenvironment epigenetic deconvolution for accurate cell type resolution in the tumor microenvironment using tumor-type-specific DNA methylation data

Beyond clonally-derived tumor cells, abundant and heterogenous cells that harbor these tumor cells constitute the tumor microenvironment (TME) [1]. The TME plays an essential role in tumor differentiation, growth, and invasion [2]. The TME comprises a spectrum of cell types responsible for immune and angiogenic responses [2]. When antitumor immune responses are triggered, inflammatory cells populate the TME, including natural killer (NK) cells, active cytotoxic CD8 T cells, memory CD4 T cells, pro-inflammatory macrophages, and dendritic cells (DC). In contrast, a TME that contributes to functional evasion of tumor immune response includes Foxp3 + regulatory T cells (Tregs), exhausted CD8 T cells, inactive macrophages, and myeloid-derived suppressor cells (MDSCs) [1]. Non-tumor stromal cells and endothelial cells remodel the angiogenic microenvironment to support tumor growth and invasion [3]. Also, the plasticity of epithelial cells plays a critical role in tumor progression [4]. The dynamic interactions between tumor cells and other cells in their microenvironment can promote tumor progression [3].

Tumor immune subtypes can be identified based on immunological gene expression profiling [5]. Tumors that are highly characterized by pro-inflammatory cytokines and T cell infiltration, i.e., immunologically hot tumors, have a better response rate to immune checkpoint inhibitors compared to immunologically cold tumors, which have a relatively low level of immune cell infiltration [6]. However, the binary classification of hot and cold tumors oversimplifies the broader underlying immune landscape in TME. In the angiogenic microenvironment, tumors that are inclined to promote endothelial cell proliferation by producing vascular endothelial growth factor (VEGF) to develop new blood vessels can be targeted by angiogenesis inhibitors [7], e.g., cancers of the lung, kidney, breast, colon, and rectum [8]. Thus, understanding the heterogeneity of TME can guide therapy response and prognosis [1].

Gene expression and DNA methylation have been used to estimate cell composition in complex mixtures and include both reference-based and reference-free methods. CIBERSORT is a prominent reference-based method developed for deconvolving immune cell types using mRNA expression data [9]. The accuracy of cell composition estimates using gene expression approaches is limited by variability in cell-specific gene expression across cells and the feature-space of gene expression data. DNA methylation is an epigenetic modification associated with gene regulation and is essential to lineage specification in development to establish and preserve cellular identity [10]. There are three notable advantages to reference-based DNA methylation methods compared with RNA-based approaches in estimating cell composition. First, DNA is more stable than RNA. Second, the covalent addition of a methyl group to a cytosine is binary, tracking with cell count. Third, using standard measurement approaches, the feature space to define reference profiles of cell-specific DNA methylation is at least 40-fold that of the typical gene expression feature space and can be up to 2000-fold higher [11]. We have established and created extended libraries for reference-based DNA methylation deconvolution that result in improved accuracy and performance for peripheral blood immune cell deconvolution [12, 13]. Tissue-specific reference-based libraries have also been developed to infer cell-type composition in the brain, breast, and skin [14, 15].

Initial approaches to deconvolve the TME using DNA methylation have been described. MethylCIBERSORT and MethylResolver have succeeded in resolving 10 and 12 cell types, respectively [16, 17]. However, due to the complexity and heterogeneity of the cell types in the TME, existing methods lack accuracy, specificity, and detailed cell types. Both the MethylCIBERSORT and MethylResolver methods used data from cancer cell lines rather than data from primary cancer cells. This is potentially problematic for deconvolution as cancer cell lines harbor additional epigenetic alterations as compared to primary tumors. [18]. Also, instead of using organ-specific epithelial cell type DNA methylation signatures, MethylResolver used a universal standard reference for tumor purity estimation in all tumor types.

To address the limitations of existing methods and to enhance the accuracy and utility of TME deconvolution, we developed a novel DNA methylation-based algorithm that employs a tumor-type-specific hierarchical model and broadens the number of immune cell types that are deconvolved. Our method, called Hierarchical Tumor Immune Microenvironment Deconvolution (HiTIMED), uses deconvolution libraries specific to tumor type, identifying the most cell-discriminatory CpG sites for each cell type in each tumor type context, resulting in 12 libraries per tumor type. Our method also organizes deconvolution into the three major tumor microenvironment components (tumor, angiogenic, immune), resulting in the ability to resolve a total of 17 cell types in the TME: tumor, epithelial, endothelial, stromal, basophil, eosinophil, neutrophil, monocyte, dendritic cell (DC), B naïve (Bnv), B memory (Bmem), CD4T naïve (CD4nv), CD4T memory (CD4mem), CD8T naïve (CD8nv), CD8T memory (CD8mem), T regulatory (Treg), and natural killer (NK) cells, in 20 carcinoma types. HiTIMED's ability to resolve tumor cellular composition with high resolution promises a better understanding of cell heterogeneity in the TME and offers new opportunities to study more complex relationships of the TME with etiologic exposures, patient outcomes, and response to treatment.

Previous gene expression and DNA methylation-based deconvolution approaches for TME cell composition have had some success for major cell types [16, 17, 35]. However, due to the across-tumor-type diversity and within-tumor-type heterogeneity of the TME, substantial gaps still exist in tumor type specificity, cell projection accuracy, and cell-type resolution for TME deconvolution. Here, we present HiTIMED, optimized to more accurately, specifically, and exhaustively deconvolve the TME. HiTIMED has three major advantages compared to the existing algorithms: high cell-type resolution, tumor-specific libraries, and cell-projection accuracy optimization. Firstly, HiTIMED provides high-resolution profiling of the cell types in TMEs. Seventeen cell types in total among 3 TME components (tumor, immune, angiogenic) are projected by HiTIMED. In the immune microenvironment, closely related lymphocyte subtypes, including subtypes of CD4T and CD8T cells, and granulocyte subtypes are captured by HiTIMED. In the angiogenic/non-immune microenvironment, epithelial, endothelial, and stromal cells are profiled by HiTIMED separately as their roles in TME could be functionally very different. Furthermore, numerous variables from HiTIMED predicted cell types offer more opportunities to study the associations between TMEs and clinically relevant outcomes. For instance, studies have demonstrated CD8mem to Treg ratio as an indicator of the immune balance between cytotoxic and regulatory immunity, corresponding to the immunotherapy response [36‐38]. Also, DC to NK ratio was studied in a mouse colon cancer model to enhance the antitumor effect as DC plays a crucial role in NK cell activation [39]. The high resolution of HiTIMED projection provides novel opportunities to exploit the cellular composition of the TME to discern patient prognosis and response to therapy. Although it can be argued that single-cell RNA sequencing technologies can offer a similar resolution of cell profiling in TME, DNA methylation-based deconvolution is immensely more cost-effective, less laborious, and is amenable to archival biospecimens where cells are no longer intact. Secondly, HiTIMED uses DNA methylation signatures that are specific to tumor type. Most of the existing methods developed a universal reference library for all types of tumors [16, 40]. Although, it is possible to estimate tumor purity with a signature that captures generalizable DNA methylation changes across all tumor types. The use of tumor-specific DNA methylation signatures maximizes the power of detecting most differentially methylated CpGs as tumors are genetically and epigenetically very different by tumor type. Although one algorithm has developed multiple libraries based on tumor type, cell lines were used rather than primary tumors [17]. Studies have shown consistently differential DNA methylation profiles between cancer cell lines and primary tumor samples [18, 41]. Finally, HiTIMED optimizes cell projection accuracy by employing a novel hierarchical model for deconvolution. With the high resolution of cell mixture deconvolution, bias can be generated with inevitable noise for cells under similar or the same lineage. The hierarchical model enhances the projection of the primary cell types in the specific lineage niche in a stepwise manner. For example, Library L3A in HiTIMED was designed to target angiogenic microenvironment deconvolution. As a result, the library collapsed all immune cells into one group but separated epithelial, endothelial, and stromal cells for optimal discernment. Although tumor purity and major immune cells were validated for accuracy in the previously existing methods, unlike HiTIMED, extensive deconvolution of immune cell types has not been validated in other methods [16, 17].

Understanding the TME with a standardized and cost-effective approach enables precision medicine. Studies have demonstrated TME's association with chemo- and immunotherapy responses and prognosis [1, 42, 43]. The balance between cytotoxic and regulatory immunity dictates tumor behavior in the immune microenvironment [36]. When the balance favors cytotoxic immunity, tumor elimination is promoted. On the contrary, tumor escape is facilitated when the balance tips toward regulatory immunity. CD8T cells are one of the cytotoxic representatives, whereas Tregs are a proxy for regulatory immunity [36]. Studies have shown the CD8T to Treg ratio as a significant biomarker for chemo- and immunotherapy responses [36, 38]. Our analyses with HiTIMED on TCGA showed better 5-year survival rates with higher CD8T memory cell levels in lung adenocarcinoma and better long-term survival in liver hepatocellular carcinoma, head and neck squamous cell carcinoma, and endocervical adenocarcinoma, which are consistent with its cytotoxic role in anti-tumoral activities. In kidney clear cell renal cell carcinoma, a higher level of Treg is associated with a worse survival outcome, indicating its role in immunosuppression [36]. Interestingly, in endometrial carcinoma, we observed significantly better survival with a higher level of Treg. This finding is consistent with a previous report on Treg being beneficial for survival in endometrial carcinoma [44]. The impact of Treg in cancer survival varies greatly by tumor site, suggesting differential physiological functions and roles of Tregs in different tumor types [45]. Based on TME composition, immune hot tumors are defined as tumors with a high level of immune cell infiltration and, thus, more likely to respond to immunotherapy [6, 42]. In our analysis, the unsupervised dichotomous classification of TCGA tumors by HiTIMED immune projection demonstrated the potential identification of immune hot and cold tumors. Future supervised training on paired data on immunotherapy response with HiTIMED immune projection promises a potential on systematically rating a tumor for immunotherapy response rate.

The angiogenic microenvironment supports tumor proliferation and metastasis [46]. The formation of new blood vessels relies heavily on endothelial and stromal cell proliferation [7]. In our study, a higher level of endothelial and stromal cells identified by HiTIMED was associated with worse survival rates in multiple cancers. Interestingly, in kidney clear cell renal cell carcinoma, a higher level of endothelial cells is beneficial for survival. This result is consistent with a single-cell analysis on kidney clear cell carcinoma, showing a better survival outcome in tumors with more endothelium [47]. A unique role of endothelial cells in prognostication of survival and immunotherapy response in kidney clear cell renal cell carcinoma patients has been hypothesized [47]. Worse 5-year survival outcomes were observed in multiple cancers for angiogenic hot tumors compared to angiogenic cold tumors in our analyses. Interestingly, immune hot and cold tumors were not significantly associated with 5-year survival after adjusting for age, gender, and tumor stage. Taken together, these data lead us to hypothesize that there is a closer relationship between the angiogenic microenvironment in TME with prognosis.

The cell type heterogeneity in TME complicates epidemiological analyses of TME and clinical outcomes. The association between cell type prevalence in TME and patient survival has previously been studied primarily by counting certain cells in TME using immunohistochemical quantification [29]. However, the cells in TME are dynamically interactive, making such analysis susceptible to other cell type confounders. The high resolution of HiTIMED makes it possible to adjust for such cell type confounders. Further, traditional EWAS analyses are susceptible to the cell type heterogeneity confounding. For instance, EWAS can identify valuable epigenetic biomarkers for early cancer detection and prognosis [52]. However, the sensitivity and precision of identifying such biomarkers are compromised when the tissue cell heterogeneity is ignored [53]. HiTIMED-projected cell composition in TME provides new opportunities for EWAS studies to unveil cell-type independent epigenetic biomarkers in cancer. Our results clearly show that much of the vast DNA methylation dysregulation previously observed in tumors is attributable to cell heterogeneity. Further application of HiTIMED cell estimates to models that identify tumor-specific DNA methylation is poised to enable a clearer understanding of early DNA methylation drivers alterations in carcinogenesis and disease progression.

While HiTIMED points to a valid method for estimating cell proportions in TME and the potential application to cancer research, we recognize some limitations. First, HiTIMED shows modest over-prediction for CD8T cells and under-prediction for CD4T cells, especially Tregs, in artificial mixtures. The HiTIMED libraries were developed to optimize the deconvolution in specific tumor microenvironments. We posited that the bias we observed in artificial mixtures would be minimized in specifically targeted tumor types. However, the hypothesis is hard to examine without known cell composition in TME. When collapsing the subtypes of T cells, HiTIMED is highly accurate, even in artificial mixtures. Also, immune cells are possibly reprogrammed by interaction with the TME. Thus using normal cells as a reference may generate noise. Second, the stomach adenocarcinoma showed least methylation distinction between tumor versus normal tissues compared to other carcinomas. This may attribute to the heterogeneity of stomach tumor cell subtypes. Future work on stomach cancer subtype specific libraries may be necessary for stomach TME deconvolution. Third, macrophages were not included in HiTIMED. Macrophage is a highly heterogeneous cell type in TME [48, 49]. Our initial effort on including macrophage generates substantial noise that confounds other mononuclear cells. Future effort on epigenetically defining tumor specific macrophages may help to address the issue. Fourth, only carcinomas are currently included in HiTIMED, and future work is needed to add other complex tumor types. Fifth, tumor subclones were not captured in all existing deconvolution methods. Future epigenetic data on tumor subclones guarantee a more extensive deconvolution of TME. Finally, the angiogenic/non-immune microenvironment profiled by HiTIMED cannot distinguish normal and tumor-impacted epithelial, endothelial, and stromal cells. However, our HiTIMED-profiled angiogenic microenvironment reflects angiogenesis globally, providing relevant information. Differentiating TME affected cells and normal cells may provide additional research avenues beyond the scope of this method.

In summary, we developed HiTIMED, a DNA-methylation-based method to deconvolve the TME. This approach employs a novel tumor-type-specific hierarchical model with optimized libraries for each layer of deconvolution in each tumor type. HiTIMED provides higher cell type resolution compared to other methods, providing new opportunities to study the relation of the TME with etiologic factors, disease progression, and response to therapy.