Transcriptomic analysis of the tumor microenvironment to guide prognosis and immunotherapies

Abstract

Tumors are highly heterogeneous tissues where malignant cells are surrounded by and interact with a complex tumor microenvironment (TME), notably composed of a wide variety of immune cells, as well as vessels and fibroblasts. As the dialectical influence between tumor cells and their TME is known to be clinically crucial, we need tools that allow us to study the cellular composition of the microenvironment. In this focused research review, we report MCP-counter, a methodology based on transcriptomic markers that assesses the proportion of several immune and stromal cell populations in the TME from transcriptomic data, and we highlight how it can provide a way to decipher the complex mechanisms at play in tumors. In several malignancies, MCP-counter scores have been used to show various prognostic impacts of the TME, which we also show to be linked with the mutational burden of tumors. We also compared established molecular classifications of colorectal cancer and clear-cell renal cell carcinoma with the output of MCP-counter, and show that molecular subgroups have different TME profiles, and that these profiles are consistent within a given subgroup. Finally, we provide insights as to how knowing the TME composition may shape patient care in the near future.

Prognostic value of the expression of chemokines and their receptors in regional lymph nodes of melanoma patients

“…This approach is not affected by the expression of the same surface marker by different cell types. Moreover, samples can be easily processed and stored in a standardized manner, alleviating problems that negatively affect the quality of data collected at different times and locations . The results obtained using CIBERSORT to calculate lymphocytic infiltration are consistent with the data generated by flow cytometry, and this methodology has been applied to the study of multiple diseases .…”

Quantifying tumor-infiltrating immune cells from transcriptomics data

“…EPIC showed superior performance to ISOpure in the prediction of cancer cell fractions from RNA-seq data [21], whereas quanTIseq demonstrated high accuracy in the quantification of the unknown tumor content in 1700 simulated data sets from bulk tumor RNA-seq [22]. Besides being robust to the unknown tumor content, these approaches quantify the immune cell fractions referred to the total bulk tissue, allowing both intra- and inter-sample comparison; the latter is not guaranteed, instead, when cell proportions are referred only to the screened immune cell types [56]. …”

Impact of chemotactic factors and receptors on the cancer immune infiltrate: a bioinformatics study revealing homogeneity and heterogeneity among patient cohorts

“…The total number of tumors analyzed in this study amounts to 5953 (Table 1). We first examined the expression variability of each of the metagenes relevant to tumor-infiltrating immune cell types (determined by using the MCP counter method) 27,30 and mRNAs encoding annexins (ANXA), chemokines (CCL, CXCL, XCL), chemokine receptors (CCR, CXCR, XCR, CCRL), formyl peptide receptors (FPR), purinergic receptors (ADOR, P2RX, P2RY), compared to housekeeping genes (ACTB, GADPH, TUB), as this is typically done when protein expression is measured by immunoblot analysis. However the variability in the expression of housekeeping genes was not expected to be smaller than that of the genes of interest (including immune metagenes) due to the normalization methods of microarrays.…”

“…26 Methods for estimating the abundance of leukocyte subsets in tumor, based on gene expression data (microarray or RNAseq), have been developed. 27–30 …”