Immune-Cold Resistance Landscape of NSCLC Driven by STK11/KEAP1 Mutations

1. Overview

Scientific rationale & importance
Non–small cell lung cancer (NSCLC) often harbors STK11 and/or KEAP1 alterations, which are linked to reduced immune response and poor outcomes with PD-(L)1 therapy. This project will leverage TCGA transcriptomic data to investigate how these alterations shape gene expression and the tumor immune microenvironment. The goal is to produce a clear, reproducible baseline analysis that characterizes immune-related differences in LUAD and LUSC subgroups.

Funding, facilities, ethics
Analyses will be performed in R on the national HPC system, which provides both storage and computational resources. The requested $5,000 RSC budget will support compute resources, data handling, and publication costs. Only publicly available, de-identified data (TCGA/GEO) will be used, so additional ethics approval is not required.

Estimated timeline (8–10 weeks)

Weeks 1–2: Data collection and quality control.
Weeks 3–5: Differential expression analysis (DEG) and immune deconvolution.
Weeks 6–7: Survival analysis and figure preparation.
Weeks 8–10: Draft manuscript preparation and submission to a preprint server.
Data sharing commitment
The study will be preregistered on ResearchHub. All analysis scripts and processed data will be shared publicly on GitHub and archived with a DOI on Zenodo to ensure transparency and reproducibility.

2. Introduction

Non-small cell lung cancer (NSCLC) accounts for ~85% of all lung cancer cases and remains a leading cause of cancer-related mortality worldwide (1). While immune checkpoint inhibitors (ICI) have revolutionized NSCLC treatment, only a subset of patients experiences durable clinical benefit (2).

Mutations in STK11 and KEAP1 have emerged as important drivers of tumor progression and immune-cold phenotypes. STK11 loss is associated with suppressed cGAS–STING/IFN pathways, leading to reduced T-cell infiltration, while KEAP1 mutations activate NRF2-driven pathways that blunt antigen presentation and immune recognition (3–5).

This pilot study aims to characterize transcriptomic and immune features of NSCLC tumors harboring STK11 and/or KEAP1 alterations, using bulk RNA-seq data from TCGA. Findings from this analysis will provide a foundation for future multi-omic and integrative studies.

Hypotheses

H1: STK11-mutant tumors exhibit reduced cytotoxic immune signatures compared to wild-type tumors.
H2: KEAP1-mutant tumors show increased immunosuppressive cell populations.
H3: Immune profiles differ between LUAD and LUSC subtypes, though detailed subtype-specific modeling will be reserved for future work.

3. 1. Participants / Sample

The study will include publicly available genomic and transcriptomic datasets from The Cancer Genome Atlas (TCGA) for lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). Samples with annotated STK11 and KEAP1 mutational status will be included. Samples missing mutation calls or clinical annotations will be excluded. Clinical metadata (age, sex, smoking history, and overall survival) will be integrated for downstream analyses.

2. Materials and Procedures

Data acquisition: RNA-seq (HTSeq counts) and mutation (MAF) files will be downloaded using TCGAbiolinks in R.
Group classification: Samples will be stratified into:
- STK11-mutant
- KEAP1-mutant
- Any mutation (STK11 or KEAP1)
- Wild-type controls
Differential expression: DEG analysis will be performed with DESeq2.
Functional enrichment: Significant DEGs will undergo GO and KEGG enrichment.
Immune deconvolution: Immune cell composition will be estimated using CIBERSORT with the LM22 signature matrix.
Survival analysis: Kaplan–Meier curves and log-rank tests will compare mutation-positive vs. wild-type groups.
Contingency plan:
- IF HPC access is unavailable for more than one week, THEN analyses will be executed on a reduced LUAD subset locally to maintain timelines.

3. Planned Analyses

Preprocessing: Normalize and quality-control RNA-seq data in R.
Differential expression: Identify significant DEGs (adjusted p-value < 0.05) between mutant and wild-type groups.
Functional enrichment: Perform pathway enrichment analyses (GO/KEGG).
Immune deconvolution: Estimate immune cell proportions with CIBERSORT and compare across groups.
Survival analysis: Conduct basic univariable survival modeling with Kaplan–Meier and log-rank tests.

Visualization: Heatmaps, volcano plots, and immune cell bar plots.

Future Work

Future efforts will expand this analysis by:

Conducting more advanced multivariable survival models.
Performing external validation using GEO datasets.

4. Budget

Personnel

Graduate Research Assistant (data analysis): €2,500
Bioinformatics Supervisor (advisory role, not salaried by this project): €0
Dissemination
- Publication fees (open access journal, estimate): €2,500

5. References

Skoulidis, F. et al. STK11/LKB1 mutations and PD-1 inhibitor resistance in KRAS-mutant lung adenocarcinoma. Cancer Discov. 8, 822–835 (2018).
Koyama, S. et al. STK11/LKB1 deficiency promotes neutrophil recruitment and resistance to immune checkpoint blockade. Cancer Discov. 6, 146–159 (2016).
Romero, R. et al. Keap1 loss promotes Kras-driven lung cancer and results in dependence on glutaminolysis. Nat Med. 23, 1362–1368 (2017).
Chen, N. et al. Genomic and immune profiling of STK11/LKB1- and KEAP1-mutant lung adenocarcinoma. Clin Cancer Res. 28, 1412–1425 (2022).
Love, M.I. et al. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Newman, A.M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods 12, 453–457 (2015).

Immune-Cold Resistance Landscape of NSCLC Driven by STK11/KEAP1 Mutations

Immune-Cold Resistance Landscape of NSCLC Driven by STK11/KEAP1 Mutations

Funding Progress

Related Funding

Request for Proposals - Bioinformatics & Genomics Research

Nonprofit Recipient

Topics

DOI