# Statistical models for intratumor heterogeneity of tumor-infiltrated leukocytes in lung cancer

> **NIH NIH R03** · UNIVERSITY OF TX MD ANDERSON CAN CTR · 2022 · $81,000

## Abstract

ABSTRACT
Although studies about tumor-infiltrated leukocytes (TILs) have attracted substantial attention to understand
tumor microenvironment and related immune response, considerable methodological gaps remain for evaluating
intratumor heterogeneity of TILs in multi-region omics data. The proposed study is directly motivated by our
collaborations with lung cancer medical oncologists in the investigation of intratumor heterogeneity and lung
cancer patients' treatment outcome. The primary objective of this proposal is to develop accurate statistical
models to quantify TILs by combining multi-region omics data and the prior knowledge about leukocytes. In this
project, (Aim 1) we propose a Bayesian modeling approach to estimate intratumor heterogeneity of TILs from
multi-region transcriptomics data. The model overcomes the limitations in existing works and specifically
addresses the correlations within the same tumor and the variability at the patients' level. We will further
generalize the approaches to account for different data distributions to address the estimations in the multi-omics
setting (Aim 2). We will apply the proposed methods to the MD Anderson Cancer Center Intra-Tumor
Heterogeneity (MDACC-ITH) project for lung cancer patients and the TCGA datasets. From an application
perspective, our proposed methods of maximizing the use of existing multi-region omics data and incorporating
complex data structure is cost-effective and may directly improve our understanding of TILs and their relationship
with patient outcomes. Although motivated by lung cancer research, the statistical methods will be useful for
estimating intratumor heterogeneity of TILs in other cancer types. All software for statistical tools developed in
this project, once validated, will be made available to the broader research community.

## Key facts

- **NIH application ID:** 10435087
- **Project number:** 1R03CA270725-01
- **Recipient organization:** UNIVERSITY OF TX MD ANDERSON CAN CTR
- **Principal Investigator:** Ziyi Li
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $81,000
- **Award type:** 1
- **Project period:** 2022-04-18 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10435087

## Citation

> US National Institutes of Health, RePORTER application 10435087, Statistical models for intratumor heterogeneity of tumor-infiltrated leukocytes in lung cancer (1R03CA270725-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10435087. Licensed CC0.

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