Activation vs. Organization: Prognostic Implications of T and B Cell Features of the PDAC Microenvironment

Elliot Gray, Shannon Liudahl, Shamilene Sivagnanam, Courtney Betts, Jason Link, Dove Keith, Brett Sheppard, Rosalie Sears, Guillaume Thibault, Joe W. Gray, Lisa M. Coussens, Young Hwan Chang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Pancreatic ductal adenocarcinoma (PDAC) patients, who often present with stage III or IV disease, face a dismal prognosis as the 5-year survival rate remains below 10%. Recent studies have revealed that CD4+ T, CD8+ T, and/or B cells in specific spatial arrangements relative to intratumoral regions correlate with clinical outcome for patients, but the complex functional states of those immune cell types remain to be incorporated into prognostic biomarker studies. Here, we developed an interpretable machine learning model to analyze the functional relationship between leukocyte-leukocyte or leukocyte-tumor cell spatial proximity, correlated with clinical outcome of 46 therapy-naïve PDAC patients following surgical resection. Using a multiplex immunohistochemistry imaging data set focused on profiling leukocyte functional status, our model identified features that distinguished patients in the fourth quartile from those in the first quartile of survival. The top ranked important features identified by our model, all of which were positive prognostic stratifiers, included CD4 T helper cell frequency among CD45+ immune cells, frequency of Granzyme B-positivity among CD4 and CD8 T cells, as well as the frequency of PD-1 positivity among CD8 T cells. The spatial proximity of CD4 T- to B cells, and between CD8 T cells and epithelial cells, were also identified as important prognostic features. While spatial proximity features provided valuable prognostic information, the best model required both spatial and phenotypic information about tumor infiltrating leukocytes. Our analysis links the immune microenvironment of PDAC tumors to outcome of patients, thus identifying features associated with more progressive disease.

Original languageEnglish (US)
Title of host publicationMathematical and Computational Oncology - Second International Symposium, ISMCO 2020, 2020, Proceedings
EditorsGeorge Bebis, Max Alekseyev, Heyrim Cho, Jana Gevertz, Maria Rodriguez Martinez
PublisherSpringer Science and Business Media Deutschland GmbH
Pages44-55
Number of pages12
ISBN (Print)9783030645106
DOIs
StatePublished - 2020
Externally publishedYes
Event2nd International Symposium on Mathematical and Computational Oncology, ISMCO 2020 - San Diego, United States
Duration: Oct 8 2020Oct 10 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12508 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Symposium on Mathematical and Computational Oncology, ISMCO 2020
CountryUnited States
CitySan Diego
Period10/8/2010/10/20

Keywords

  • Machine learning
  • Multiplexed imaging
  • Pancreatic cancer

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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