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T cell populations correlate with response and resistance to anti-PD-1 plus lenvatinib combination therapy in hepatocellular carcinoma

GSE235863 Homo sapiens (human) Single-cell RNA-seq 69 samples Submitted 2024/12/17 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 69 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Homo sapiens (human)
Topic
Hepatocellular carcinomaThis series is part of our curated briefing on it.
Samples
69 samples: 69 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
Contrasting cytotoxic and regulatory T cell responses underlying distinct clinical outcomes to anti-PD-1 plus lenvatinib therapy in cancerCancer cell · 2025
Summary (written by the submitters)
The combination of immune checkpoint blockade and tyrosine kinase inhibitors has shown promising clinical benefits, but the underlying mechanisms driving response and resistance are poorly understood. Here, we performed single-cell RNA- and TCR-sequencing, together with a HBV-peptide-barcoded single-cell RNA-sequencing approach, on paired tumor and blood samples to track the dynamic changes of T cells in HBV+ hepatocellular carcinoma (HCC) patients before or after anti-PD-1 plus lenvatinib combination therapy.
Published in
Contrasting cytotoxic and regulatory T cell responses underlying distinct clinical outcomes to anti-PD-1 plus lenvatinib therapy in cancer
Guo X, Nie H, Zhang W et al. · Cancer cell 2025 · PMID 39889705 · doi:10.1016/j.ccell.2025.01.001
This dataset
Open GSE235863 in finder → Find more human RNA-seq datasets → Hepatocellular carcinoma landscape →
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 GSE235863_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: 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 PRJNA987720. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39889705) ↗
Study design
69 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 69 sample titles: 69 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

+ 29 more — browse all 69 samples with per-sample file links →

More Hepatocellular carcinoma datasets

Search all human RNA-seq datasets in GEO →

Share this dataset

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.