Gene Expression In Tumor Microenvironment Research Articles

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

BackgroundCellular compositions of solid tumor microenvironments are heterogeneous, varying across patients and tumor types. High-resolution profiling of the tumor microenvironment cell composition is crucial to understanding its biological and clinical implications. Previously, tumor microenvironment gene expression and DNA methylation-based deconvolution approaches have been shown to deconvolve major cell types. However, existing methods lack accuracy and specificity to tumor type and include limited identification of individual cell types.ResultsWe employed a novel tumor-type-specific hierarchical model using DNA methylation data to deconvolve the tumor microenvironment with high resolution, accuracy, and specificity. The deconvolution algorithm is named HiTIMED. Seventeen cell types from three major tumor microenvironment components can be profiled (tumor, immune, angiogenic) by HiTIMED, and it provides tumor-type-specific models for twenty carcinoma types. We demonstrate the prognostic significance of cell types that other tumor microenvironment deconvolution methods do not capture.ConclusionWe developed HiTIMED, a DNA methylation-based algorithm, to estimate cell proportions in the tumor microenvironment with high resolution and accuracy. HiTIMED deconvolution is amenable to archival biospecimens providing high-resolution profiles enabling to study of clinical and biological implications of variation and composition of the tumor microenvironment.

Tumor-Derived Lactate Creates a Favorable Niche for Tumor via Supplying Energy Source for Tumor and Modulating the Tumor Microenvironment.

Tumor evolution is influenced by events involving tumor cells and the environment in which they live, known as the tumor microenvironment (TME). TME is a functional and structural niche composed of tumor cells, endothelial cells (ECs), cancer-associated fibroblasts (CAFs), mesenchymal stromal cells (MSCs), and a subset of immune cells (macrophages, dendritic cells, natural killer cells, T cells, B cells). Otto Warburg revealed the Warburg effect in 1923, a characteristic metabolic mechanism of tumor cells that performs high glucose uptake and excessive lactate formation even in abundant oxygen. Tumor tissues excrete a large amount of lactate into the extracellular microenvironment in response to TME’s hypoxic or semi-hypoxic state. High lactate concentrations in tumor biopsies have been linked to metastasis and poor clinical outcome. This indicates that the metabolite may play a role in carcinogenesis and lead to immune escape in TME. Lactate is now recognized as an essential carbon source for cellular metabolism and as a signaling molecule in TME, forming an active niche that influences tumor progression. This review summarized the advanced literature demonstrating the functional role of lactate in TME remodeling, elucidating how lactate shapes the behavior and the phenotype of both tumor cells and tumor-associated cells. We also concluded the intriguing interactions of multiple immune cells in TME. Additionally, we demonstrated how lactate functioned as a novel function factor by being used in a new histone modification, histone lysine lactylation, and to regulate gene expression in TME. Ultimately, because lactate created a favorable niche for tumor progression, we summarized potential anti-tumor strategies targeting lactate metabolism and signaling to investigate better cancer treatment.

The development of a computational machine learning tool to decipher malignant cell gene expression from complex tumor tissue.

e15050 Background: Complex tumor tissue is composed of malignant cells and diverse tumor microenvironment (TME) cellular populations. Depending on the cancer type, the percentage of malignant cells present in tumor tissue varies, with percentages sometimes below 10%. TME cellular transcripts may comprise the majority of the total transcripts in a tumor, potentially resulting in biases during biomarker development and for clinical decision-making. However, TME gene expression can be subtracted from total tumor gene expression, resulting in only malignant cell expression. Methods: A computational tool was developed for the “subtraction” of TME-specific gene expression from total gene expression in an array of solid tumors, producing in silico “purified'' malignant cell gene expression. To extract the malignant cell RNA expression values, a machine learning model was trained on an artificial transcriptome dataset created from different solid tumor cancer cell lines with the addition of various TME cellular proportions. The artificial transcriptomes, both training and test, were composed of 7,114 samples of purified TME cell types and 3,143 cancer cell lines. The final model relied on the following three major parameters: 1) RNA percentages of deconvolved TME cell types; 2) the weighted sum of the average expression of a malignant cell target gene produced by different TME cell populations where the RNA percentages (weights in the weighted sum) of the TME cell populations were predicted by deconvolution; and 3) a set of genes expressed predominantly in the TME. To experimentally validate the computational model, different proportions of COLO829 (cutaneous melanoma), MCF-7 (invasive ductal carcinoma), and K562 (chronic myeloid leukemia) cell lines were mixed in vitro with PBMCs, creating controlled representations of tumor tissue, and RNA was extracted and sequenced. Gene expression was quantified and analyzed with the computational model, with comparisons to pure cancer cell line expression. Results: The expression of at least 120 clinically relevant biomarkers was reconstructed by applying the model to artificial transcriptomes in which the percentage of malignant cells varied from 10% to 90%. The concordance correlation coefficient between pure cancer cell lines and the extracted malignant cell expression increased on average from 0.75 to 0.9 compared to unprocessed data (e.g., PTEN from 0.2 to 0.88, RB1 from 0.57 to 0.89, ERBB2 from 0.83 to 0.99). In vitro validation showed that the tool improved the concordance correlation coefficient and mean absolute error (MAE) for many tumor biomarkers. For example, the PTEN correlation coefficient increased by 0.33 and its MAE was reduced 3-fold. Conclusions: This novel computational tool can aid in treatment decision-making based on malignant cell expression, promoting the use of gene expression for personalized therapeutics.

Abstract B18: YAP1 and TAZ mediate BET inhibitor-dependent immune regulation in NSCLC

Abstract Bromodomain and extra-terminal domain proteins inhibitors (BETi) are promising immunomodulatory agents, able to regulate gene expression in tumor microenvironment (TME), influencing immune cell differentiation and activation. However, immune-related molecular mechanisms underlying BETi activity need to be clarified. We demonstrated that BET protein BRD4 is an important regulator of YAP1/TAZ oncogene expression in several cancer cell lines, including non-small cell lung cancer (NSCLC) cells. YAP1 and TAZ are paralogue genes involved in tumor cell proliferation, migration, and survival. BETi affect YAP1/TAZ expression and impair their transcriptional program. As previously reported, YAP1 and TAZ oncogenes are also involved in tumor immune escape, through the impairment of lymphocyte activation and enhanced PD-L1 expression. We investigated whether BETi-dependent immune regulation depends on YAP1/TAZ activity in NSCLC patients. To this end, we isolated NSCLC patient-derived tissue-originated spheroids (CTOS) and tumor-infiltrating lymphocytes (TILs) and treated both populations with BETi (JQ1 or OTX-015). BETi treatment significantly increased the activation of TILs, which was also associated with decreased expression of the immune checkpoint receptors PD-1, LAG-3, TIM-3, BTLA, and CD160. Notably, BETi treatment also downregulated the expression of corresponding immune checkpoint ligands (PD-L1, LGALS9, and TNFRSF14). Intriguingly, patients displaying higher expression of immune checkpoint molecules exhibited also higher expression of YAP1 and TAZ, suggesting that these transcription factors may be involved in this regulatory network. Accordingly, YAP1 and TAZ were significantly downregulated after BETi treatment in both CTOS and TILs. To confirm specific YAP/TAZ activity in immune regulation, the knockout (KO) of YAP1 or TAZ was achieved through CRISPR/Cas9 approach in NSCLC cell lines. Of interest, YAP1 and TAZ display distinct regulatory effects, suggesting a not overlapping function. The KO of YAP1, but not of TAZ, was sufficient to downregulate PD-L1 expression. Conversely, KO of TAZ alone efficiently downregulated LGALS9, which was not affected by YAP1 KO. TNFRSF14 was significantly reduced in both TAZ and YAP1 KO. These preliminary data suggest that BETi exert a YAP/TAZ-dependent immunomodulatory activity, acting on both tumor cells and TME immune landscape. In this context, BETi may reactivate immune response against tumor cells, acting on both immune checkpoint inhibitor expression and TIL-specific activation. These findings further support the use of BETi as immunomodulatory agents in NSCLC patients. Citation Format: Francesca Reggiani, Giulia Gobbi, Filippo Lococo, Massimiliano Paci, Eleonora Zznetti, Simonetta Piana, Alessia Ciarrocchi, Valentina Sancisi. YAP1 and TAZ mediate BET inhibitor-dependent immune regulation in NSCLC [abstract]. In: Proceedings of the AACR Special Conference on the Hippo Pathway: Signaling, Cancer, and Beyond; 2019 May 8-11; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Res 2020;18(8_Suppl):Abstract nr B18.

Profiles of immune cell infiltration and immune-related genes in the tumor microenvironment of colorectal cancer

Purposetumor-infiltrating immune cells are highly relevant to the progression and prognosis of colorectal cancer (CRC). The aim of this study is to explore the immune cells and immune-related gene expression in tumor microenvironment of CRC. MethodsCIBERSORT, a deconvolution algorithm, was used to analyze the infiltration of 22 immune cell types in the tumor microenvironment and immune-related gene expression in 404 CRC and 40 adjacent non-tumorous tissues. Resultsa wide heterogeneity of immune cells among different paired tissues and in tumor stages was uncovered. M0 macrophages, M1 macrophages and CD4 memory activated T cells were infiltrated significantly more in CRC compared with normal tissues in both TCGA and GEO cohorts. CRC with T1–2 tumor stage showed increased CD4 memory activated T cells compared with T3–4 tumors. M0 macrophages were the highest in stage N1 tumors. Significant immune-related genes were identified to build prognostic models by Cox regression analysis. The concordance index of the prognostic model for TNM stage I–II was 0.69, and 0.71 for stage III–IV. The AUC values for 1-, 3-, and 5-year survivals were 0.674, 0.773, 0.812 for TNM stage I–II, respectively, and 0.764, 0.782, 0.803 for stage III–IV, respectively. Conclusionthese results could assist clinicians in selecting targets for immunotherapies and individualize treatment strategies for patients with CRC.

Soluble Form of T Cell Ig Mucin 3 Is an Inhibitory Molecule in T Cell-Mediated Immune Response

T cell Ig mucin 3 (Tim-3) has been found to play an important role in Th1-mediated auto- and alloimmune responses, but the function of soluble form of Tim-3 (sTim-3) remains to be elucidated. In this study, we report the inhibitory effect of sTim-3 on T cell-mediated immune response. In this study, sTim-3 mRNA was found, among different tissues and organs, only in splenic cells, and the activation of splenocytes resulted in up-regulated production of both sTim-3 mRNA and protein. We constructed a eukaryotic expression plasmid, psTim-3, which expresses functional murine sTim-3. In C57BL/6 mice inoculated with B16F1 melanoma cells, the growth of tumor was facilitated by the expression of this plasmid in vivo. Furthermore, sTim-3 inhibited the responses of T cells to Ag-specific stimulation or anti-CD3 mAb plus anti-CD28 mAb costimulation and the production of cytokines IL-2 and IFN-gamma in vitro. In tumor rejection model, sTim-3 significantly impaired T cell antitumor immunity, evidenced by decreased antitumor CTL activity and reduced amount of tumor-infiltrating lymphocytes in tumor. Real-time PCR analysis of gene expression in tumor microenvironment revealed the decreased expression of Th1 cytokine genes and the unchanged profile of the genes related to T regulatory cell function, suggesting that the inhibitory effect of sTim-3 on the generation of Ag-specific T cells in vivo is dominated by T effector cells rather than T regulatory cells. Our studies thus define sTim-3 as an immunoregulatory molecule that may be involved in the negative regulation of T cell-mediated immune response.