Patient-derived three-dimensional lung tumor models to evaluate response to therapy

Novel preclinical models that better mimic the in vivo tumor microenvironment are essential to advance understanding of tumor biology and resistance/response to therapy. Herein, we report development of a novel ex vivo patient-derived three-dimensional lung tumor model (3D-LTM) for use in evaluating response to therapy. With this model system that maintains cell-cell interactions and tissue architecture, we observed heterogeneity of response to immune checkpoint inhibitors (ICI), as noted in non-small cell lung cancer (NSCLC) patients, and defined gene signatures associated with response. Spatial transcriptomics identified positive correlation of CD8 + T cell populations, CD4 + memory T cells, mast cells, NK cells, naive B cells, endothelial cells and non-classical monocytes with response status, and negative correlation of macrophages with response status. Pathway analysis of gene expression showed that chemokine signaling related pathways were activated in responder 3D-LTM tissues, whereas suppression of antigen presentation-related pathways and activation of T reg differentiation-related pathways were associated with non-responder 3D-LTM tissues. Additionally, the abundance of dividing T cells and naive CD8 + T cells differentially correlated with T cell cytotoxicity gene signatures based on response status. Thus, this model may provide utility for rapid testing of therapeutic outcomes and biomarker development.

Authors

Institutions

Publication Details

Journal
npj Precision Oncology
Published
2026-06-16
DOI
https://doi.org/10.1038/s41698-026-01559-y
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Patient-derived three-dimensional lung tumor models to evaluate response to therapy

Jessy S. Deshane, S. Sivan, Selvarangan Ponnazhagan, Kenneth P. Hough et al.
npj Precision Oncology
Single-cell and spatial transcriptomics
article

Patient-derived three-dimensional lung tumor models to evaluate response to therapy

Jessy S. Deshane, S. Sivan, Selvarangan Ponnazhagan, Kenneth P. Hough, Kayla F. Goliwas, Sierra L. Single, Sanad Alhushki, Joel L. Berry, James Donahue, Sameer Deshmukh, Benjamin Wei, Aakash Desai, Maya Khalil, Yanis Boumber, Nithya Puritipati, Aroma G. Tom, Mohammad Athar
article en

Abstract

Novel preclinical models that better mimic the in vivo tumor microenvironment are essential to advance understanding of tumor biology and resistance/response to therapy. Herein, we report development of a novel ex vivo patient-derived three-dimensional lung tumor model (3D-LTM) for use in evaluating response to therapy. With this model system that maintains cell-cell interactions and tissue architecture, we observed heterogeneity of response to immune checkpoint inhibitors (ICI), as noted in non-small cell lung cancer (NSCLC) patients, and defined gene signatures associated with response. Spatial transcriptomics identified positive correlation of CD8 + T cell populations, CD4 + memory T cells, mast cells, NK cells, naive B cells, endothelial cells and non-classical monocytes with response status, and negative correlation of macrophages with response status. Pathway analysis of gene expression showed that chemokine signaling related pathways were activated in responder 3D-LTM tissues, whereas suppression of antigen presentation-related pathways and activation of T reg differentiation-related pathways were associated with non-responder 3D-LTM tissues. Additionally, the abundance of dividing T cells and naive CD8 + T cells differentially correlated with T cell cytotoxicity gene signatures based on response status. Thus, this model may provide utility for rapid testing of therapeutic outcomes and biomarker development.

npj Precision Oncology
University of Alabama at Birmingham (US), HistoGenetics (United States) (US)
LUNGevity Foundation, American Lung Association, Lung Cancer Research Foundation, Respiratory Health Association
Good health and well-being
Openalex Percentile: Top 12%
Single-cell and spatial transcriptomics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.