Prognostic value of tumor-infiltrating lymphocytes in melanoma: a systematic review and meta-analysis

Fu, Qiangqiang; Chen, Nan; Ge, Chunlei; Li, Ruilei; Li, Zhen; Zeng, Baozhen; Li, Chunyan; Wang, Ying; Xue, Yingwei; Song, Xin; Li, Heng; Li, Gaofeng

doi:10.1080/2162402x.2019.1593806

Cited by 193 publications

(181 citation statements)

References 78 publications

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“…T-cell infiltration was associated with a better prognosis in all three of the tumors we examined. This was consistent with prior reports in melanoma and HNSCC 19,60 , but to our knowledge this is a novel finding in GBM that was likely missed by previous studies because T-cells are so rare in GBM.…”

Section: Discussionsupporting

confidence: 93%

“…During the past two decades numerous studies have revealed interactions between malignant cells and the stroma that promote diverse tumor functions including angiogenesis 4,5 , metastasis 6 , and immunosuppression 7,8 . Stromal cells differ between patients and tumor types [9][10][11][12][13][14][15] and the abundance of certain stromal cell populations are used in the clinic as biomarkers [16][17][18][19] and therapeutic targets [20][21][22][23][24][25] . These studies motivate direct measurements of cell types and the interactions between them in human subjects.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian cell-type deconvolution and gene expression inference reveals tumor-microenvironment interactions

Chu

Danko

2020

Preprint

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Understanding the dynamic interactions between malignant cells and the tumor stroma is a major goal of cancer research. Here we developed a Bayesian model that jointly infers both cellular composition and gene expression in each cell type, including heterogeneous malignant cells, from bulk RNA-seq using scRNA-seq as prior information. We conducted an integrative analysis of 85 single-cell and 1,412 bulk RNA-seq datasets in primary human glioblastoma, head and neck squamous cell carcinoma, and melanoma. We identified cell types correlated with clinical outcomes and explored regional heterogeneity in tumor state and stromal composition. We redefined common molecular subtypes using gene expression in malignant cells, after excluding confounding non-malignant cell types. Finally, we identified genes whose expression in malignant cells correlated with infiltration of macrophages, T-cells, fibroblasts, and endothelial cells across multiple tumor types. Our work provides a new lens that we used to measure cellular composition and expression in a statistically powered cohort of three primary human malignancies. Results Bayesian inference of cell type composition and tumor expressionTED uses a scRNA-seq reference dataset to infer two parameters of interest from bulk RNA-seq data: (i) the proportion of cell types in the bulk population and (ii) the average expression profiles of each cell type. TED describes the proportion and expression profiles of each cell type as latent variables that it infers from the data ( Fig. 1a, Supplementary Fig. 1, Supplementary Note 1). TED makes the key simplifying assumption that each non-malignant cell type shares a common gene expression profile across patients, as observed in the cases analyzed to date 28,30,31 .Critically, each bulk RNA-seq sample is then assumed to have a unique tumor expression profile that we infer from the data.Expression in the reference and bulk RNA-seq data often differ substantially due to batch effects or tumor heterogeneity. To account for uncertainty in the reference cell type expression matrix, TED implements a fully Bayesian inference of tumor composition. First, TED uses Gibbs sampling to estimate the posterior joint distribution of cell type composition, θ0, and gene expression profiles, Z, i.e. P(θ0, Z | φ, X; α) ( Fig. 1a, red, top). Second, to account for tumor cells, or other cell types which cannot be observed in the reference dataset, TED infers a maximum likelihood estimate (MLE) for tumor expression profiles, ψtum (Fig. 1a, red, mid). During this step TED also infers the maximum a posterior estimate (MAP) of the expression profile of non-malignant stromal cells, ψstr, to correct for batch effects between bulk and single cell RNAseq platforms. Last, TED uses the updated expression profile for each patient and cell type to resample the posterior distribution of cell type composition, θ, i.e. P(θ | ψtum , ψstr ,X; α) ( Fig. 1a, red, bottom). Optionally, TED has an additional mode which can be used to learn common patterns of expression heterogeneit...

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Section: Discussionsupporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Bayesian cell-type deconvolution and gene expression inference reveals tumor-microenvironment interactions

Chu

Danko

2020

Preprint

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“…We did not observe similar effects of protein expression in our second validation cohort, which might be due to much smaller sample size. It is difficult to exclude the possibility that a significant component of the MX2 gene expression signal in the Leeds Melanoma Cohort is derived from TILs which themselves are a favorable prognostic marker in melanoma (Fu et al, ). The expression of MX2 in tumor cells did, however, correlate with the amount of immune cell infiltration implying that IFN secretion by TILs leads to induction of MX2 and other IFN‐dependent genes like MX1 .…”

Section: Discussionmentioning

confidence: 99%

MX 2 is a novel regulator of cell cycle in melanoma cells

Juraleviciute

Poźniak

Nsengimana

et al. 2019

Pigment Cell Melanoma Res

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MX2 protein is a dynamin‐like GTPase2 that has recently been identified as an interferon‐induced restriction factor of HIV‐1 and other primate lentiviruses. A single nucleotide polymorphism (SNP), rs45430, in an intron of the MX2 gene, was previously reported as a novel melanoma susceptibility locus in genome‐wide association studies. Functionally, however, it is still unclear whether and how MX2 contributes to melanoma susceptibility and tumorigenesis. Here, we show that MX2 is differentially expressed in melanoma tumors and cell lines, with most metastatic cell lines showing lower MX2 expression than primary melanoma cell lines and melanocytes. Furthermore, high expression of MX2 RNA in primary melanoma tumors is associated with better patient survival. Overexpression of MX2 reduces in vivo proliferation partially through inhibition of AKT activation, suggesting that it can act as a tumor suppressor in melanoma. However, we have also identified a subset of melanoma cell lines with high endogenous MX2 expression where downregulation of MX2 leads to reduced proliferation. In these cells, MX2 downregulation interfered with DNA replication and cell cycle processes. Collectively, our data for the first time show that MX2 is functionally involved in the regulation of melanoma proliferation but that its function is context‐dependent.

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“…Patients with tumors that are poorly oxygenated have an increased risk of mortality (22). In addition, the presence of tumor-infiltrated lymphocytes (TILs) can be used as a relevant prognostic indicator (23)(24)(25). A notorious hallmark associated with BCa intratumoral oxygen deprivation is alteration in the cancer cell survival metabolism to function through the hypoxia-inducible factor-1α (HIF-1α) regulatory pathway (26)(27)(28).…”

Section: Introductionmentioning

confidence: 99%

Bioengineering a novel 3D in-vitro model to recreate physiological oxygen levels and tumor-immune interactions

Bhattacharya

Calar

Evans

et al. 2019

Preprint

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Oxygen deprivation within tumors is one of the most prevalent causes of resilient cancer cell survival and increased immune evasion in breast cancer (BCa). Current in vitro models do not adequately mimic physiological oxygen levels relevant to breast tissue and its tumor-immune interactions. Here, we propose an approach to engineer a three-dimensional (3D) model (named 3D engineered oxygen, 3D-O) that supports growth of BCa cells and generates physio-and pathophysiological oxygen levels. Low oxygen-induced changes within the 3D-O model supported known tumor hypoxia characteristics such as reduced BCa cell proliferation, increased extracellular matrix protein expression, increased extracellular vesicle secretion and enhanced immune surface marker expression on BCa cells. We further demonstrated that low oxygeninduced changes mimicked tumor-immune interactions leading to immune evasion mechanisms. CD8+ T cell infiltration was significantly impaired under pathophysiological oxygen levels andwe were able to establish that hypoxia inhibition re-sensitize BCa cells to cytotoxic CD8+ T cells.Therefore, our novel 3D-O model could serve as a promising platform for the evaluation of immunological events and as a drug-screening platform tool to overcome hypoxia-driven immune evasion.

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Prognostic value of tumor-infiltrating lymphocytes in melanoma: a systematic review and meta-analysis

Cited by 193 publications

References 78 publications

Bayesian cell-type deconvolution and gene expression inference reveals tumor-microenvironment interactions

Bayesian cell-type deconvolution and gene expression inference reveals tumor-microenvironment interactions

MX 2 is a novel regulator of cell cycle in melanoma cells

Bioengineering a novel 3D in-vitro model to recreate physiological oxygen levels and tumor-immune interactions

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