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CD8+ T cells in the tumor immune microenvironment modulate resistance to endocrine therapy in hormone receptor-positive breast cancer

GSE297605 Homo sapiens (human) RNA-seq (gene expression) 174 samples Submitted 2025/10/07 Platform GPL21290
At a glance

RNA-seq (gene expression) · human · 174 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
174 samples: 66 × preSens vs 65 × postSens vs 23 × postRest vs 20 × preRestGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper
CD8+ T cells in the tumor microenvironment modulate the response to endocrine therapy in breast cancerThe Journal of clinical investigation · 2026
Summary (written by the submitters)
Enhanced immune infiltration in the tumor immune microenvironment (TIME) is associated with better survival outcomes in patients with triple negative and HER2+ breast cancers. In the hormone-dependent hormone receptor-positive (HR+) subtype, previous studies have identified a subgroup with enhanced immune infiltration. However, the biological significance and role of the TIME in modulating the response to anti-estrogen therapy in breast cancer are not well understood.
Read the full summary ↓
Herein, using cyclic immunofluorescence and spatial transcriptomics (ST), we dissected the TIME in primary breast cancers that were sensitive or resistant to estrogen deprivation (ED) induced by the aromatase inhibitor letrozole. Stromal tumor-infiltrating lymphocytes (TILs) were increased in ED-resistant vs. ED-sensitive tumors. RNA-sequencing from on-treatment tumors showed differentially regulated pathways, including immune-related gene sets, such as upregulation of IFNɣ and IFNα signaling and allograft rejection. Spatial transcriptomics using GeoMx identified increased antigen processing and immune gene expression signatures in both cancer cells and neighboring immune cells in ED-resistant vs. ED-sensitive tumors. Using CIBERSORT, we uncovered a differential distribution of immune cell subtypes, with CD8+ T cells being enriched in ED-resistant tumors and Tregs in ED-sensitive tumors. The expression of chemokine genes involved in CD8+ T cell recruitment, CXCL9, CXCL10, and CXCL11, was upregulated in ED-resistant primary tumors before and after letrozole treatment. Additionally, CXCL11 levels were higher in media conditioned from HR+ breast cancer cells co-cultured with CD8+ T cells. Both recombinant CXCL11 and co-culture with CD8+ T cells stimulated the growth of HR+ breast cancer cells when deprived of estrogen, akin to the scenario of patients treated with letrozole. Taken together, these data suggest that CD8+T cells in the TIME modulate the response of HR+ breast cancer cells to therapeutic estrogen suppression.
Published in
CD8+ T cells in the tumor microenvironment modulate the response to endocrine therapy in breast cancer
Napolitano F, Wang Y, Sudhan DR et al. · The Journal of clinical investigation 2026 · PMID 41364532 · doi:10.1172/JCI188458
This dataset
Open GSE297605 in finder → Find more human RNA-seq datasets →
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE297605_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1265246 and SRA study SRP586398. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41364532) ↗
Study design
66 × preSens vs 65 × postSens vs 23 × postRest vs 20 × preRest

Supports a between-group comparison across 174 samples.

How we read this: 4 replicated groups read from 174 sample titles; they account for 174 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study

+ 134 more — browse all 174 samples with per-sample file links →

Datasets on a similar topic

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