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Hypoxia shapes both therapeutic response and resistance in metastatic clear cell renal cell carcinoma [bulk RNA-seq]

GSE293952 Homo sapiens (human) Single-cell RNA-seq 61 samples Submitted 2026/04/23 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 61 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
Kidney cancerThis series is part of our curated briefing on it.
Samples
61 samples: 44 × ccRCC, pretreatment tumor, TKI/IO treatment group, sample vs 17 × ccRCC, post-treatment tumor, TKI/IO treatment group, sampleGroups 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
Hypoxia shapes both therapeutic response and resistance in metastatic clear cell renal cell carcinomaCancer cell · 2026
Summary (written by the submitters)
Vascular endothelial growth factor receptor-targeting tyrosine kinase inhibitors (VEGFR-TKIs) and aPD1 combinations are effective in multiple solid tumors, particularly in clear cell renal cell carcinoma (ccRCC), due it’s characteristic pseudo-hypoxic, hyper-angiogenic state driven by biallelic VHL-loss. However, long-term durability is inferior to dual aPD1/aCTLA4 regimens, yet the mechanisms underlying these differences remain unclear.
Read the full summary ↓
Since tumor-associated macrophages (TAMs) are implicated in therapeutic resistance, we used scRNAseq to investigate TAM evolution following VEGFR-TKI, aPD1 and combined VEGFR-TKI/aPD1 treatment in a transgenic ccRCC mouse model. We identify hypoxia-responsive SPP1+ TAMs that are absent in baseline pseudo-hypoxic tumors. This proxy of true hypoxia tracks with successful response to VEGFR-TKI/aPD1 in mouse and human on-treatment samples, reflecting treatment-induced hypoxic necrosis. Paradoxically, pretreatment hypoxia predicted worse outcomes across multiple VEGFR-TKI/aPD1 trial and real-world cohorts and extended exposure to hypoxia-inducing VEGFR-TKIs and aPD1 exacerbated metastasis in mice, highlighting the dual implications of hypoxia in ccRCC disease trajectory.
Published in
Hypoxia shapes both therapeutic response and resistance in metastatic clear cell renal cell carcinoma
Vuong L, Cornish AE, Pfeil J et al. · Cancer cell 2026 · PMID 42242232 · doi:10.1016/j.ccell.2026.05.007
This dataset
Open GSE293952 in finder → Find more human RNA-seq datasets → Kidney cancer 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 GSE293952_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 PRJNA1247487 and SRA study SRP655821. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42242232) ↗
Study design
44 × ccRCC, pretreatment tumor, TKI/IO treatment group, sample vs 17 × ccRCC, post-treatment tumor, TKI/IO treatment group, sample

Supports a between-group comparison across 61 samples.

How we read this: 2 replicated groups read from 61 sample titles; they account for 61 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

+ 21 more — browse all 61 samples with per-sample file links →

More Kidney cancer datasets

Search all human RNA-seq datasets in GEO →

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