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Late-stage tertiary lymphoid structures in hepatocellular carcinoma treated with neoadjuvant immune checkpoint blockade [scRNA-seq]

GSE272347 Homo sapiens (human) Single-cell RNA-seq 17 samples Submitted 2024/09/13 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 17 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Homo sapiens (human)
Samples
17 samples: 17 samples, each with a distinct title — no replicated groupsGroups 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
Immunotherapy response induces divergent tertiary lymphoid structure morphologies in hepatocellular carcinomaNature immunology · 2024
Summary (written by the submitters)
Tertiary lymphoid structures (TLS) are associated with improved response in solid tumors treated with immune checkpoint blockade (ICB), but understanding of their clinical significance and the circumstances of their resolution remains incomplete. Here, we found that in hepatocellular carcinoma (HCC) treated with neoadjuvant immunotherapy, high intratumoral TLS density at the time of surgery is associated with pathologic response and improved relapse free survival.
Read the full summary ↓
In areas of tumor regression, we further identified a non-canonical involuted morphology of TLS marked by dispersion of the B cell follicle, persistence of a T cell zone enriched for T cell-mature dendritic cell interactions, and increased expression of T cell memory markers. Collectively, these data suggest that TLS may serve be both a prognostic and predictive marker of response to immunotherapy in HCC and suggest a functional role for late-stage TLS T cell memory formation after elimination of viable tumor.
Published in
Immunotherapy response induces divergent tertiary lymphoid structure morphologies in hepatocellular carcinoma
Shu DH, Ho WJ, Kagohara LT et al. · Nature immunology 2024 · PMID 39455893 · doi:10.1038/s41590-024-01992-w
This dataset
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.

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 GSE272347_RAW.tar (it can run to tens of gigabytes).

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.

What the files usually look like: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1136494. Searching any of these in the dataset finder brings you back here.

Study design
17 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 17 sample titles: 17 distinct titles with little repetition. 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
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