GEO series

Tissue-resident memory features are linked to the magnitude of cytotoxic T cell responses in human lung cancer [NSCLC]

GSE90728 Homo sapiens (human) RNA-seq (gene expression) 68 samples Submitted 2017/06/19 Platform GPL16791

At a glance

RNA-seq (gene expression) · human · 68 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: 68 samples
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.

Summary (written by the submitters)

Therapies that boost the anti-tumor responses of cytotoxic lymphocytes (CTLs) have shown promise in the clinic. However, clinical responses to currently available immunotherapeutic agents vary considerably, for which the molecular basis is unclear. To date, unbiased transcriptomic studies of CTLs in human cancers have been performed in whole tumors or cells obtained from peripheral blood or metastatic sites of heavily pre-treated patients.

Read the full summary ↓

We performed global transcriptional profiling of CTLs in tumors and adjacent non-tumor tissue from treatment-naïve patients with early stage lung cancer to define the molecular features associated with robustness of anti-tumor immune responses. We observed major differences in the transcriptional program of tumor-infiltrating CTLs that are shared across tumor subtypes. Pathway analysis revealed enrichment of genes in cell cycle, T cell receptor (TCR) activation and co-stimulation pathways, indicating tumor-driven expansion of presumed tumor antigen-specific CTLs. We also observed marked heterogeneity in the expression of molecules associated with TCR activation and immune checkpoints such as 4-1BB, PD1, TIM3, and their expression was positively correlated with the density of tumor-infiltrating CTLs. Interestingly, transcripts linked to tissue-resident memory cells (TRM), such as CD103, were enriched in tumors containing a high density of CTLs, and CTLs from CD103high tumors displayed features of enhanced cytotoxicity, implying better anti-tumor activity. In an independent cohort of 689 lung cancer patients, we confirmed that patients with CD103high (TRM rich) tumors survived significantly longer. In summary, we define the molecular fingerprint of tumor-infiltrating CTLs at the site of primary tumor and identify a number of novel targets that may be important in modulating the magnitude and specificity of anti-tumor immune responses in lung cancer.

This dataset

Open GSE90728 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 GSE90728_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 PRJNA355638 and SRA study SRP094414. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 28628092) ↗

Samples in this study

The sample list for this study is not cached yet. Press Load samples and it will be fetched from NCBI.

+ 68 more — browse all 68 samples with per-sample file links →

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.