Understanding heterogeneous tumor microenvironment in metastatic melanoma.

Understanding heterogeneous tumor microenvironment in metastatic melanoma.

Publication date: Dec 18, 2018

A systemic analysis of the tumor-immune interactions within the heterogeneous tumor microenvironment is of particular importance for understanding the antitumor immune response. We used multiplexed immunofluorescence to elucidate cellular spatial interactions and T-cell infiltrations in metastatic melanoma tumor microenvironment. We developed two novel computational approaches that enable infiltration clustering and single cell analysis-cell aggregate algorithm and cell neighborhood analysis algorithm-to reveal and to compare the spatial distribution of various immune cells relative to tumor cell in sub-anatomic tumor microenvironment areas. We showed that the heterogeneous tumor human leukocyte antigen-1 expressions differently affect the magnitude of cytotoxic T-cell infiltration and the distributions of CD20+ B cells and CD4+FOXP3+ regulatory T cells within and outside of T-cell infiltrated tumor areas. In a cohort of 166 stage III melanoma samples, high tumor human leukocyte antigen-1 expression is required but not sufficient for high T-cell infiltration, with significantly improved overall survival. Our results demonstrate that tumor cells with heterogeneous properties are associated with differential but predictable distributions of immune cells within heterogeneous tumor microenvironment with various biological features and impacts on clinical outcomes. It establishes tools necessary for systematic analysis of the tumor microenvironment, allowing the elucidation of the “homogeneous patterns” within the heterogeneous tumor microenvironment.

Open Access PDF

Yan, Y., Leontovich, A.A., Gerdes, M.J., Desai, K., Dong, J., Sood, A., Santamaria-Pang, A., Mansfield, A.S., Chadwick, C., Zhang, R., Nevala, W.K., Flotte, T.J., Ginty, F., and Markovic, S.N. Understanding heterogeneous tumor microenvironment in metastatic melanoma. 22865. 2018 PLoS One (14):6.

Concepts Keywords
Algorithm Tumor
Antigen Heterogeneous tumor
CD20 Systematic tumor
CD4 Immune heterogeneous tumor
Clustering Medicine
Cohort Cancer
Cytotoxic T Cell Medical specialties
Differential Oncology
FOXP3 T cells
Homogeneous Cancer pathology
Immune Response Tumor
Immunofluorescence Tumor microenvironment
Leukocyte Regulatory T cell
Magnitude Metastasis
Melanoma Treatment of cancer
Multiplexed Cancer treatments
Regulatory T Cell
T Cell
Tumor

Semantics

Type Source Name
gene UNIPROT CTBP1
gene UNIPROT IDS
gene UNIPROT THOP1
disease MESH metastasis
gene UNIPROT SAFB
gene UNIPROT SLC22A18
disease DOID gist
gene UNIPROT DEPP1
gene UNIPROT GOPC
gene UNIPROT NANS
gene UNIPROT TSPAN31
gene UNIPROT PSMB8
gene UNIPROT POC1A
gene UNIPROT SGSM3
disease MESH visual
gene UNIPROT FOXP3
gene UNIPROT CD4
gene UNIPROT TRIM37
gene UNIPROT STK26
gene UNIPROT MARK1
gene UNIPROT S100A1
gene UNIPROT S100B
gene UNIPROT GKN1
gene UNIPROT RPL26
gene UNIPROT MS4A1
gene UNIPROT KRT20
gene UNIPROT FURIN
gene UNIPROT CD8A
gene UNIPROT SRL
gene UNIPROT LARGE1
gene UNIPROT TLR1
disease DOID noma
disease MESH noma
drug DRUGBANK Formaldehyde
gene UNIPROT PTPRF
disease MESH multiple
gene UNIPROT PDCD1
gene UNIPROT SNCA
gene UNIPROT SPATA2
disease MESH death
gene UNIPROT MENT
drug DRUGBANK Trestolone
drug DRUGBANK Spinosad
gene UNIPROT CRYGD
drug DRUGBANK Factor IX Complex (Human)
pathway BSID Reproduction
gene UNIPROT TNFSF13
gene UNIPROT ANP32B
gene UNIPROT PDGFB
drug DRUGBANK L-Leucine
gene UNIPROT BTG3
disease MESH neighborhood
gene UNIPROT TBATA
pathway BSID Melanoma
disease DOID melanoma
disease MESH melanoma
disease MESH tumor

Original Article

Leave a Comment

Your email address will not be published. Required fields are marked *