AI Knockout Screen Reveals GSTP1 as a Driver of Lung Cancer Immunotherapy Resistance

Immunotherapy has transformed the treatment of non-small cell lung cancer, turning a once uniformly fatal diagnosis into a disease that many patients can live with for years. Yet the hard truth remains that a large fraction of patients derive little or no benefit from checkpoint inhibitors, and clinicians have had no reliable way to predict who will respond before committing to therapy. A new study published in the Journal of Translational Medicine by researchers at Sun Yat-Sen University Cancer Center and collaborating institutions in China now offers both a computational roadmap and a concrete biological target. Using a transformer-based artificial intelligence model to simulate the deletion of individual genes across an unprecedented atlas of 109 distinct cell types, the team identified 110 genes tied to immunotherapy efficacy and pinpointed one enzyme, glutathione S-transferase Pi 1, or GSTP1, as a leading culprit behind treatment failure in lung cancer.

The study’s methodological core is as notable as its biological findings. The researchers began with single-cell RNA sequencing datasets from patients with non-small cell lung cancer who had undergone immunotherapy, drawing on three publicly available cohorts catalogued in the Gene Expression Omnibus under the accession numbers GSE207422, GSE179994, and GSE176021. After careful annotation against a large-scale pancancer atlas and established hallmarks of cancer biology, they resolved the tumor microenvironment into 109 distinct cell types, a granularity that captures not only malignant cells but also the intricate cast of immune and stromal players that surround them, from conventional dendritic cells and tumor-infiltrating myeloid cells to cancer-associated fibroblasts.

What sets the work apart is how the team interrogated this cellular landscape. Rather than relying solely on conventional differential-expression analysis, which can only report correlations, they deployed Geneformer, a transformer-based foundation model trained on large corpora of single-cell transcriptomic data. Geneformer allows what the authors call in-silico single-gene knockouts: the computational deletion of a gene across every cell type in the dataset, followed by prediction of how that deletion reshapes the cell’s transcriptional state. By systematically performing these virtual perturbations across all 109 cell types, the researchers could estimate each gene’s functional influence on immunotherapy outcome, a task that would be experimentally prohibitive if attempted gene by gene in the laboratory.

This virtual screen converged with more traditional approaches to yield a ranked list of 110 genes associated with immunotherapy efficacy. Of these, 25 were linked to better treatment outcomes, while 85 were associated with worse outcomes, a skew that reflects the biological reality that many tumor programs actively suppress immune attack. Crucially, the analysis did not treat the tumor as a uniform mass. The team mapped precisely which cell types each gene acted in, revealing that the same gene can exert opposing or complementary effects depending on whether it is expressed in malignant cells, cytotoxic lymphocytes, or stromal compartments. Hierarchical clustering then organized these genes into functional modules, and variance-partitioning statistics quantified how much of the variation in immunotherapy response between patients each gene could explain.

To translate this catalog into something clinically usable, the researchers built an immune scoring system based on all 110 genes, and then distilled it into a leaner model called CancerCellScore, built on just seven genes. GSTP1 emerged as the top contributor among those seven. The scoring system was verified to distinguish patients who respond to immunotherapy from those who do not, offering a potential decision-support tool for oncologists weighing whether checkpoint blockade is the right first move. To make the tool accessible, the team deployed a website at immunotherapy.live/ResisGenes, where the 110 genes can be visualized and real-time sample scores can be computed, an unusual degree of transparency and practicality for a discovery-stage biomarker study.

The mechanistic story that follows is where the study acquires its therapeutic punch. GSTP1 encodes glutathione S-transferase Pi 1, a detoxification enzyme best known for conjugating electrophilic compounds to glutathione and thereby shielding cells from oxidative damage. The researchers found that GSTP1 expression was negatively associated with immunotherapy response: the more of it a tumor expressed, the less likely the patient was to benefit from anti-PD-1 therapy. This connection is not arbitrary. GSTP1 sits squarely within the antioxidant machinery that determines whether a cancer cell succumbs to ferroptosis, an iron-dependent form of regulated cell death driven by the accumulation of lipid peroxides, a process in which the lipid peroxidation marker 4-hydroxynonenal serves as a classic readout.

By keeping glutathione levels high and lipid peroxidation in check, GSTP1 effectively buffers tumor cells against the oxidative stress that ferroptosis-inducing immune pressure generates. The team validated this logic in the laboratory using RNA interference approaches, including short hairpin RNAs and small interfering RNAs, and confirmed the regulatory circuitry with chromatin immunoprecipitation and quantitative polymerase chain reaction, tracing GSTP1’s activity to antioxidant response elements in its regulatory regions. The most striking result, however, came from in vivo experiments: when anti-GSTP1 strategies were combined with anti-PD-1 therapy, tumor growth was significantly inhibited compared with either approach alone. In other words, disarming GSTP1 appeared to re-sensitize tumors to checkpoint blockade, plausibly by lowering the ferroptosis threshold and allowing the immune-stimulated oxidative assault to push cancer cells over the edge into death.

The implications for patients with non-small cell lung cancer are twofold. First, the seven-gene CancerCellScore model, with GSTP1 at its head, could serve as a predictive biomarker, identifying before treatment begins which patients are likely to resist PD-1 inhibitors and might instead be candidates for combination regimens or clinical trials. Second, GSTP1 itself becomes a druggable target. Glutathione S-transferases have long interested pharmacologists because of their role in chemotherapy detoxification, and the present study reframes that pharmacology in the immunotherapy era: inhibiting GSTP1 is not merely a way to make tumors more chemosensitive, but a way to make them more visible and vulnerable to the immune system. The study also received ethical approval from the Sun Yat-sen University Cancer Center Animal Care and Use Committee, underscoring that the in vivo findings rested on formally reviewed animal work.

There are, of course, the usual caveats that separate a compelling translational study from bedside practice. The scoring system was developed and verified in retrospective cohorts, and prospective validation in independent patient populations will be needed before it can guide treatment decisions. The combination of GSTP1 targeting with anti-PD-1 therapy, while dramatically effective in preclinical models, must clear the familiar hurdles of pharmacology, safety, and tumor delivery before human trials can be designed with confidence. The authors themselves frame the work as a discovery and prioritization engine rather than a finished therapeutic protocol, and the open-access release of the model, the gene list, and the live scoring website invites the wider community to stress-test the findings against their own datasets.

Even with those caveats, the study stands as a vivid demonstration of how large language models adapted to biology are changing the pace and scale of target discovery. A transformer trained on single-cell data can, in silico, delete thousands of genes across more than a hundred cell types and rank their contributions to a clinical outcome, compressing years of bench work into a computational pass that then directs focused experiments. The result here is a complete arc, from machine-learning perturbation screens through multi-omic prioritization to mechanistic validation and a candidate combination therapy, all centered on a single enzyme that lung tumors use to shield themselves from both oxidative death and immune destruction. If GSTP1 inhibition fulfills its promise in the clinic, the path that led there, virtual knockouts across 109 cell types converging on ferroptosis as the Achilles heel of immunotherapy resistance, may prove as influential as the target itself.

Subject of Research: GSTP1-mediated resistance to anti-PD-1 immunotherapy in non-small cell lung cancer identified by transformer-based single-cell perturbation modeling

Article Title: GSTP1 is associated with NSCLC immunotherapy resistance: multi-omic discovery via transformer-based perturbation across 109 cell types

Article References: GSTP1 is associated with NSCLC immunotherapy resistance: multi-omic discovery via transformer-based perturbation across 109 cell types. (n.d.). https://doi.org/10.1186/s12967-026-08949-7

Image Credits: AI Generated

DOI: 10.1186/s12967-026-08949-7

Keywords: non-small cell lung cancer, immunotherapy resistance, GSTP1, single-cell RNA sequencing, Geneformer, in-silico knockout, anti-PD-1, ferroptosis, biomarker, tumor microenvironment, transformer model, Journal of Translational Medicine