# Intratumoral microbiota and immune predictors of response to immunotherapy in lung cancer

> **NIH NIH R01** · BAYLOR COLLEGE OF MEDICINE · 2024 · $684,367

## Abstract

ABSTRACT
Although immunotherapy, especially immune checkpoint inhibitors (ICIs), has emerged as a powerful cancer
treatment, less than half of advanced Non-Small Cell Lung Cancer (NSCLC) patients have responded. There is
a crucial need for biomarkers to enable better prediction before ICI therapy and to overcome ICI resistance.
Among the contributing factors to ICI therapy resistance, the immunosuppressive nature of the tumor
microenvironment (TME) is one of the most challenging. Despite the important roles of both intratumoral
microbiota and tumor-infiltrating immune cells, there are huge gaps in linking specific tumor-residing microbiota
changes with immune cell subpopulations in NSCLCs treated with ICIs.
We hypothesize that an integrative prediction model that incorporates intratumoral microbiota and
immune predictors has the potential to substantially distinguish ICI responders from non-responders,
and improve the selection of patients that are most likely to benefit from ICI therapy. We propose to capitalize
on existing prospectively collected pre-ICI tumors or biopsies in ICI-treated NSCLCs from three cohorts (n =
500): Baylor College of Medicine cohort, Boston Lung Cancer Survival cohort, and Moffitt Cancer Center cohort.
Patients will be classified as ICI responders (defined as complete/partial response and stable disease) and non-
responders (defined as progression), using the modified Response Evaluation Criteria in Solid Tumors
(mRECIST) criteria. To identify new predictors that distinguish responders from non-responders before ICI
therapy, we propose the following three Specific Aims: 1) To identify intratumoral microbiome profiles predictive
of ICI response, using Whole-Metagenome Sequencing (WMS); 2) To characterize spatial immune signatures
predictive of ICI response, using single-cell Imaging Mass Cytometry (IMC); and 3) To develop integrated
predictive models of ICI response incorporating clinical, microbial, and immunological data using deep learning
approaches.
Integration of tumor microbiome and its interactions with immune infiltrate within TME is a relatively new field of
investigation and challenging endeavor, which has not been reported in NSCLCs. This proposed study will build
upon archived formalin-fixed, paraffin-embedded specimen repositories (pre-treatment tumor resection and core
biopsies), mature clinical treatment response and survival data from three independent cohorts, experience with
the proposed experimental microbiome and spatial immune profiling (WMS and IMC) approaches, institutional
core facilities, and the multidisciplinary research team, including cancer epidemiologists, immunologists,
microbiologists, oncologists and bioinformatics experts.

## Key facts

- **NIH application ID:** 10981747
- **Project number:** 1R01CA285882-01A1
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** David C Christiani
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $684,367
- **Award type:** 1
- **Project period:** 2024-08-01 → 2029-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10981747

## Citation

> US National Institutes of Health, RePORTER application 10981747, Intratumoral microbiota and immune predictors of response to immunotherapy in lung cancer (1R01CA285882-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10981747. Licensed CC0.

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