Multiplexed Fluorescent Immunohistochemical Staining, Imaging, and Analysis in Histological Samples of Lymphoma

Guo, Hong; Shuangyi, Fan; Phyu, The; Maheshwari, Priyanka; Hoppe, Michal Marek; Hoang, Phuong Mai; Mel, Sanjay de; Poon, Michelle; Ng, Siok-Bian; Jeyasekharan, Anand D.

doi:10.3791/58711-v

JoVE

2019

DOI: 10.3791/58711-v

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Multiplexed Fluorescent Immunohistochemical Staining, Imaging, and Analysis in Histological Samples of Lymphoma

Hong Guo

Fan Shuangyi

The Phyu

et al.

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2020

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Tumor-Immune Partitioning and Clustering (TIPC) algorithm reveals distinct signatures of tumor-immune cell interactions within the tumor microenvironment

Lau

Borowsky²,

Väyrynen

et al. 2020

Preprint

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Growing evidence supports the importance of understanding tumor-immune spatial relationship in the tumor microenvironment in order to achieve precision cancer therapy.However, existing methods, based on oversimplistic cell-to-cell proximity, are largely confounded by immune cell density and are ineffective in capturing tumor-immune spatial patterns. Here we developed a novel computational algorithm, termed Tumor-Immune Partitioning and Clustering (TIPC), to offer an effective solution for spatially informed tumor subtyping. Our method could measure the extent of immune cell partitioning between tumor epithelial and stromal areas as well as the degree of immune cell clustering. Using a U.S. nation-wide colorectal cancer database, we showed that TIPC could determine tumor subtypes with unique tumor-immune spatial patterns that were significantly associated with patient survival and key tumor molecular features. We also demonstrated that TIPC was robust to parameter settings and readily applicable to different immune cell types. The capability of TIPC in delineating clinically relevant patient subtypes that encapsulate tumor-immune spatial relationship, immune density, and tumor morphology is expected to shed light on underlying immune mechanisms. Hence, TIPC can be a useful bioinformatics tool for effective characterization of the spatial composition of the tumor-immune microenvironment to inform precision immunotherapy.

show abstract

Tumor-Immune Partitioning and Clustering (TIPC) algorithm reveals distinct signatures of tumor-immune cell interactions within the tumor microenvironment

Lau

Borowsky²,

Väyrynen

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

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Multiplexed Fluorescent Immunohistochemical Staining, Imaging, and Analysis in Histological Samples of Lymphoma

Cited by 1 publication

References 0 publications

Tumor-Immune Partitioning and Clustering (TIPC) algorithm reveals distinct signatures of tumor-immune cell interactions within the tumor microenvironment

Tumor-Immune Partitioning and Clustering (TIPC) algorithm reveals distinct signatures of tumor-immune cell interactions within the tumor microenvironment

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