# Construction of A Lung Cancer Preclinical Model Cross-comparison Platform

> **NIH NIH R01** · UT SOUTHWESTERN MEDICAL CENTER · 2024 · $627,833

## Abstract

Project Summary
Preclinical models of lung cancer are essential tools for researchers to understand cancer biology and develop
therapeutic strategies. Choosing the most cost-effective preclinical model to answer a specific scientific question
requires careful study of the existing molecular and pathological characterization of different models and human
tumors, as molecular profiling reveals the orchestration of biological processes and pathological characterization
informs the spatial composition of the tumor microenvironment. While preclinical models of lung cancer have
been extensively characterized, the molecular data remain scattered, the pathology data have rarely been
deposited, and no tool exists to evaluate the molecular and pathological agreement between preclinical models
and human tumors. We have previously built a lung cancer explorer, which provides user-friendly integrative
analytical tools to explore gene expression and clinical data from over 6,700 patients in 56 published datasets.
Leveraging patient lung tumor pathology image archives, we developed algorithms and pipelines to perform
histopathology digital staining and feature extraction from H&E images and identified interesting pathology
features that predict outcome and response to targeted therapy. Extending these efforts to preclinical models,
this proposal aims to develop an informatics platform integrating molecular and pathology data from various lung
cancer preclinical models and patient tumors to assess preclinical model fidelity through comparative analyses.
Specific Aim 1 will harmonize molecular profiling datasets from various lung cancer preclinical models.
Statistical methods for cross-study validation and quality control will be implemented to ensure computational
compatibility and to select appropriate datasets for analysis. Model-specific web applications will be built to
support data exploration and analysis.
Specific Aim 2 will perform histopathological and spatial transcriptomic characterization of tumors from in vivo
models. We will network with lung cancer preclinical model investigators to solicit contributions of pathology
images and samples for establishing a public image archive and for spatial molecular profiling experiments.
Effective algorithms and pipelines for preclinical model pathology image analyses will be established.
Specific Aim 3 will integrate data collected and harmonized in Aims 1 and 2 to construct an informatics platform
for cross-model comparison and alignment to human tumors. This platform will allow users to review and
download our processed molecular and pathology datasets and compare molecular and pathology profiles of
preclinical models and patient tumors from multiple facets. We will share these resources with the lung cancer
research community and solicit feedback to improve our platform.
The successful implementation of this project will assemble the scattered molecular datasets, establish a large-
scale public pathology ima...

## Key facts

- **NIH application ID:** 10774776
- **Project number:** 1R01CA285336-01
- **Recipient organization:** UT SOUTHWESTERN MEDICAL CENTER
- **Principal Investigator:** Ling Cai
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $627,833
- **Award type:** 1
- **Project period:** 2024-09-01 → 2029-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10774776

## Citation

> US National Institutes of Health, RePORTER application 10774776, Construction of A Lung Cancer Preclinical Model Cross-comparison Platform (1R01CA285336-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10774776. Licensed CC0.

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