Literature DB >> 30737470

Specific immune cell and lymphatic vessel signatures identified by image analysis in renal cancer.

Peter Schraml1, Maria Athelogou2, Thomas Hermanns3, Ralf Huss2, Holger Moch4.   

Abstract

Anti-angiogenic therapy and immune checkpoint inhibition are novel treatment strategies for patients with renal cell carcinoma. Various components and structures of the tumor microenvironment are potential predictive biomarkers and also attractive treatment targets. Macrophages, tumor infiltrating lymphocytes, vascular and lymphatic vessels represent an important part of the tumor immune environment, but their functional phenotypes and relevance for clinical outcome are yet ill defined. We applied Tissue Phenomics methods including image analysis for the standardized quantification of specific components and structures within the tumor microenvironment to profile tissue sections from 56 clear cell renal cell carcinoma patients. A characteristic composition and unique spatial relationship of CD68+ macrophages and tumor infiltrating lymphocytes correlated with overall survival. An inverse relationship was found between vascular (CD34) and lymphatic vessel (LYVE1) density. In addition, outcome was significantly better in patients with high blood vessel density in the tumors, whereas increased lymphatic vessel density in the tumors was associated with worse outcome. The Tissue Phenomics imaging analysis approach allowed visualization and simultaneous quantification of immune environment components, adding novel contextual information, and biological insights with potential applications in treatment response prediction.

Entities:  

Mesh:

Year:  2019        PMID: 30737470     DOI: 10.1038/s41379-019-0214-z

Source DB:  PubMed          Journal:  Mod Pathol        ISSN: 0893-3952            Impact factor:   7.842


  4 in total

1.  Profiles of tumor-infiltrating immune cells in renal cell carcinoma and their clinical implications.

Authors:  Gongmin Zhu; Lijiao Pei; Hubin Yin; Fan Lin; Xinyuan Li; Xin Zhu; Weiyang He; Xin Gou
Journal:  Oncol Lett       Date:  2019-09-20       Impact factor: 2.967

2.  Identification of a Five-Gene Signature and Establishment of a Prognostic Nomogram to Predict Progression-Free Interval of Papillary Thyroid Carcinoma.

Authors:  Mengwei Wu; Hongwei Yuan; Xiaobin Li; Quan Liao; Ziwen Liu
Journal:  Front Endocrinol (Lausanne)       Date:  2019-11-15       Impact factor: 5.555

Review 3.  Next-Generation Digital Histopathology of the Tumor Microenvironment.

Authors:  Felicitas Mungenast; Achala Fernando; Robert Nica; Bogdan Boghiu; Bianca Lungu; Jyotsna Batra; Rupert C Ecker
Journal:  Genes (Basel)       Date:  2021-04-07       Impact factor: 4.096

4.  Identification of a three-gene-based prognostic model in multiple myeloma using bioinformatics analysis.

Authors:  Ying Pan; Ye Meng; Zhimin Zhai; Shudao Xiong
Journal:  PeerJ       Date:  2021-06-28       Impact factor: 2.984

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.