Spatial subsetting enables integrative modeling of oral squamous cell carcinoma multiplex imaging data

Authors

• Jakob Einhaus, Stanford University School of Medicine
• Dyani K. Gaudilliere, Stanford University School of Medicine
• Julien Hedou, Stanford University School of Medicine
• Dorien Feyaerts, Stanford University School of Medicine
• Michael G. Ozawa, Stanford University School of Medicine
• Masaki Sato, Stanford University School of Medicine
• Edward A. Ganio, Stanford University School of Medicine
• Amy S. Tsai, Stanford University School of Medicine
• Ina A. Stelzer, Stanford University School of Medicine
• Karl C. Bruckman, Stanford University School of Medicine
• Jonas N. Amar, Stanford University School of Medicine
• Maximilian Sabayev, Stanford University School of Medicine
• Thomas A. Bonham, Stanford University School of Medicine
• Joshua Gillard, Stanford University School of Medicine
• Maïgane Diop, Stanford University School of Medicine
• Amelie Cambriel, Stanford University School of Medicine
• Zala N. Mihalic, Stanford University School of Medicine
• Tulio Valdez, Stanford University School of Medicine
• Stanley Y. Liu, Stanford University School of Medicine
• Leticia Feirrera, University of the Pacific Arthur A. Dugoni School of DentistryFollow
• David K. Lam, University of the Pacific Arthur A. Dugoni School of DentistryFollow
• John B. Sunwoo, Stanford University School of Medicine

Document Type

Article

Publication Title

iScience

Department

Oral and Maxillofacial Surgery

ISSN

25890042

Volume

26

Issue

12

DOI

10.1016/j.isci.2023.108486

First Page

1

Last Page

19

Publication Date

12-15-2023

Abstract

Oral squamous cell carcinoma (OSCC), a prevalent and aggressive neoplasm, poses a significant challenge due to poor prognosis and limited prognostic biomarkers. Leveraging highly multiplexed imaging mass cytometry, we investigated the tumor immune microenvironment (TIME) in OSCC biopsies, characterizing immune cell distribution and signaling activity at the tumor-invasive front. Our spatial subsetting approach standardized cellular populations by tissue zone, improving feature reproducibility and revealing TIME patterns accompanying loss-of-differentiation. Employing a machine-learning pipeline combining reliable feature selection with multivariable modeling, we achieved accurate histological grade classification (AUC = 0.88). Three model features correlated with clinical outcomes in an independent cohort: granulocyte MAPKAPK2 signaling at the tumor front, stromal CD4⁺ memory T cell size, and the distance of fibroblasts from the tumor border. This study establishes a robust modeling framework for distilling complex imaging data, uncovering sentinel characteristics of the OSCC TIME to facilitate prognostic biomarkers discovery for recurrence risk stratification and immunomodulatory therapy development.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Recommended Citation

Einhaus, Jakob; Gaudilliere, Dyani K.; Hedou, Julien; Feyaerts, Dorien; Ozawa, Michael G.; Sato, Masaki; Ganio, Edward A.; Tsai, Amy S.; Stelzer, Ina A.; Bruckman, Karl C.; Amar, Jonas N.; Sabayev, Maximilian; Bonham, Thomas A.; Gillard, Joshua; Diop, Maïgane; Cambriel, Amelie; Mihalic, Zala N.; Valdez, Tulio; Liu, Stanley Y.; Feirrera, Leticia; Lam, David K.; and Sunwoo, John B., "Spatial subsetting enables integrative modeling of oral squamous cell carcinoma multiplex imaging data" (2023). Pacific Faculty Work. 160.
https://scholarlycommons.pacific.edu/all-faculty/160