Machine learning analyses of highly-multiplexed immunofluorescence identifies distinct tumor and stromal cell populations in primary pancreatic tumors.

ABSTRACT:

Background

Pancreatic ductal adenocarcinoma (PDAC) is a formidable challenge for patients and clinicians.

Objective

To analyze the distribution of 31 different markers in tumor and stromal portions of the tumor microenvironment (TME) and identify immune cell populations to better understand how neoplastic, non-malignant structural, and immune cells, diversify the TME and influence PDAC progression.

Methods

Whole slide imaging (WSI) and cyclic multiplexed-immunofluorescence (MxIF) was used to collect 31 different markers over the course of nine distinctive imaging series of human PDAC samples. Image registration and machine learning algorithms were developed to largely automate an imaging analysis pipeline identifying distinct cell types in the TME.

Results

A ra

SUBMITTER: Vance K 

PROVIDER: S-EPMC9278645 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature