# A state-of-the-art database enables a macro perspective on tumor microenvironment

Krisha Mehta, Talha Ahmad Khan, Marouen Ben Guebila

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## To cite this version:

Krisha Mehta, Talha Ahmad Khan, Marouen Ben Guebila. A state-of-the-art database enables a macro perspective on tumor microenvironment. 2023. hal-04089593

HAL Id: hal-04089593

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Preprint submitted on 4 May 2023

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Distributed under a Creative Commons Attribution 4.0 International License
A state-of-the-art database enables a macro perspective on tumor microenvironment

Krisha Mehta¹,*, Talha Ahmad Khan¹,*, Marouen Ben Guebila¹

¹Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA

*Equal contribution

Abstract

The dramatic growth in the use of single-cell assays in the study of various cancers has produced a wealth of new data, but methods for integrating and interpreting the resulting complex data have limited the use of these data and interpretation of their potential clinical significance. IMMUcan scDB, developed by Camps, Noël, and colleagues in "*Meta-Analysis of Human Cancer Single-Cell RNA-Seq Datasets Using the IMMUcan Database*" (PMID: 36459564), is a database that includes 144 single-cell datasets covering 56 cancers in a single online resource. IMMUcan scDB aims to enable biological discovery by allowing combined analyses of these heterogeneous datasets. The authors illustrate the value of IMMUcan scDB for meta-analyses of single-cell data and their associations with cancer phenotypes in two examples by identifying markers of response to immunotherapy and unique cell compositions in the microenvironment of specific cancer types.

There is a growing body of evidence that the interaction between tumor cells and the local tumor microenvironment (TME) plays a significant role in cancer progression and response to therapy. The TME is an ecosystem consisting of diverse cell types whose interactions drive chemical changes within both tumor and host cells that include modulating immune and stromal cells, blood vessels, and extracellular matrices in ways that to enhance tumor growth.¹ The heterogeneity of the TME has been found to hinder the efficacy of immunotherapy treatment for many patients, possibly by confounding the recognition of tumor cells.² These and other lines of evidence suggest that investigating TME cell type composition and characterizing gene expression dynamics could both improve our understanding of how tumors develop and suggest ways to improve immunotherapy response.
<figure><img src="image_3.png" /><figcaption>**Figure 1** – IMMUcan scDB enables large-scale analyses of therapeutic response phenotypes and single-cell dissection of the tumor microenvironment based on its cell composition, marker genes, and gene coexpression.</figcaption></figure>

The advent of single-cell RNA sequencing³ (scRNA-Seq) has given us unprecedented information about the composition of tumors and their microenvironments and the way in which those cells contribute tumor progression and metastasis⁴ and how cell-cell interactions alter tumor properties,⁵ and allowed detection of rare cell types. More than 1300 studies have released scRNA-Seq data with samples from a variety of species, tissues, cell counts, and data formats. There are about 190 cancer-related scRNA-Seq data sets available, but analyzing these together presents a number of challenges including an absence of standardization for data processing⁶ and the difficulty of clinical interpretation given the sparsity of annotation, not to mention the inherent challenges of single-cell data analysis.

Databases such as Recount3,⁷ ReMap2022,⁸ and ARCHS4⁹ (Table1) have demonstrated the value of genomic data standardization, providing researchers with uniformly processed and curated data with efficient search and user-friendly interfaces that facilitate multi-study analyses. Similar standardized databases for scRNA-Seq data have begun to appear. CancerSEA¹⁰ was one of the first databases with tumor sample datasets consisting of 41,900 cells from 25 cancer types focused on identifying functional states (stemness, invasion, metastasis, proliferation, EMT, angiogenesis, apoptosis, cell cycle, differentiation, DNA damage, DNA repair, hypoxia, inflammation and quiescence) of the collected cells. CancerSCEM¹¹ provides data on 638,341 cells across 20 types of cancer and provides tools for analyzing cell types within samples and the expression of genes within and between samples. TISCH¹² has assembled data from both human and mouse studies representing over two million cells with an emphasis on exploring the tumor microenvironment. But all of these databases primarily enable analyses that are somewhat limited in scope.
IMMUscan scDB<sup>13</sup> is the newest entry in the world of scRNA-Seq databases and represents a significant step forward. Camps, Noël, and colleagues have collected and standardized scRNA-Seq data from 144 single-cell studies representing 56 types of cancer. The IMMUscan initiative, of which IMMUscan scDB is a part, is a public-private partnership to profile cancer and its microenvironment in more than 3000 patients. As such, it has been engineered to allow complex, multi-study analyses and to be able to scale to store thousands of single-cell profiling datasets, in a large-scale, annotated, and searchable database that can be accessed by non-specialists.

IMMUscan scDB builds on its parent project's well-designed database that includes manually derived annotation that has been extended using accurate automatic data processing algorithms. IMMUscan scDB is populated using a four-step pipeline that collects, processes, clusters, and annotates scRNA-Seq data. First, data are collected from peer-reviewed and non-peer-reviewed datasets in other public databases using keywords as filters. Next, to ensure that the database consists of uniform, high-quality metadata that supports effective search, the authors captured metadata and organized it into four broad categories: study-wise information, sample-specific attributes, information about the applied single-cell technology workflow, and annotated, standardized information about the single-cell data (when available). Datasets were downloaded in raw read counts format to better facilitate downstream analyses and analyzed using scProcessoR, a bespoke R pipeline that performs normalization, variable gene selection, PCA transformations and scaling, clustering, and dimensionality reduction. Finally, cell types were annotated using the CHETAH<sup>14</sup> algorithm to annotate cells automatically and further analyzed using CopyKAT<sup>15</sup> to identify malignant cells.

IMMUscan scDB is unique in providing information about patient treatment (available for 56% of the datasets) and co can be used to study therapeutic response (Figure 1). In their paper, they show the utility of their system by conducting a cell-centric analysis of response to anti PD-1 treatment in which they found an enrichment for B cells (and their CD20marker gene). They also reported gene-centric analysis of CXCL9 and found it to be expressed primarily in melanoma and basal cell carcinoma, an interesting result in that it had previously been reported as expressed in T follicular helper cells and exhausted CD8<sup>+</sup> T cells and implicated in response to immunotherapy. They also found a correlation across datasets between expression of CXCL13—another immunotherapy marker gene that has been associated with the levels of PD1 expression, particularly in exhausted CD8<sup>+</sup> T cells. Finally, they assessed cell type frequency in 25 datasets and found regulatory T cells in-the tissue microenvironment but not in normal tissue; further analysis identified a tumor-specific pattern for Treg activation and that might be linked to cells responding to PD-1 blockade therapy. These exemplar analyses clearly demonstrate the value of the resource as it allows complex analyses through an intuitive web portal that also offers visualizations such as scatterplots and Venn diagrams.

IMMUscan scDB is valuable addition to the set of annotated scRNA-Seq databases and its transparent analytical pipeline and intuitive a query an analysis tools that support cross-experiment. The extensive collection of cellular data and phenotypic information allows complex analyses exploring the interface between gene expression, tumor composition, and response to
therapy. These assets put it ahead of its competitors and its robust and extensible software framework position it to continue to be a leading resource in the field for a long time to come.

<table><caption>**Table 1** – Transcriptomics databases and resources</caption><thead><tr><th>Database</th><th>Summary</th><th>URL</th></tr></thead><tbody><tr><td colspan="3">Tumor microenvironment single-cell databases</td></tr><tr><td>IMMUCan scDB</td><td>Tumor microenvironment single-cell data</td><td>https://immucanscdb.vital-it.ch/</td></tr><tr><td>TISCH</td><td>Tumor microenvironment single-cell data</td><td>http://tisch.comp-genomics.org/</td></tr><tr><td>CancerSCEM</td><td>Tumor microenvironment single-cell data</td><td>https://ngdc.cncb.ac.cn/cancerscem/</td></tr><tr><td>CancerSEA</td><td>Cancer single-cell data</td><td>http://biocc.hrbmu.edu.cn/CancerSEA/</td></tr><tr><td colspan="3">Uniformly processed genomic databases</td></tr><tr><td>Recount 3</td><td>RNA-Seq data</td><td></td></tr><tr><td>ARCHS4</td><td>RNA-Seq and ChIP-Seq data</td><td>https://maayanlab.cloud/archs4/</td></tr><tr><td>REMAP2022</td><td>ChIP-Seq data</td><td>https://remap2022.univ-amu.fr/</td></tr></tbody></table>

### Competing interests

The authors declare that they have no competing interests.

### Data availability

Database resources were summarized in Table 1.

## References

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