GEO series

Hypoxia shapes both therapeutic response and resistance in metastatic clear cell renal cell carcinoma [scRNAseq]

GSE294109 Homo sapiens (human) Single-cell RNA-seq 25 samples Submitted 2026/04/23 Platform GPL34281Platform GPL24676

At a glance

Single-cell RNA-seq · human · 25 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: 25 samples: 20 distinct titles, 3 of them repeatedGroups 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 GSE294109 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 GSE294109_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 PRJNA1248398 and SRA study SRP655815. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42242232) ↗

Study design

20 distinct titles, 3 of them repeated

ccRCC tumor, Ipilimumab/Nivolumab treated, sc ×3 ccRCC tumor, Axitinib/Pembrolizumab treated, … ×3 ccRCC tumor, Lenvatinib/Pembrolizumab treated… ×2

How we read this: Read from 25 sample titles: 20 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

GSM8898802 ccRCC tumor, untreated, sc02, center
GSM8898803 ccRCC tumor, untreated, sc02, lower
GSM8898804 ccRCC tumor, untreated, sc02, upper
GSM8898805 ccRCC tumor, untreated, sc03, center
GSM8898806 ccRCC tumor, untreated, sc03, far
GSM8898807 ccRCC tumor, untreated, sc03, near
GSM8898808 ccRCC tumor, Ipilimumab/Nivolumab treated, sc06, center
GSM8898809 ccRCC tumor, Ipilimumab/Nivolumab treated, sc06, far
GSM8898810 ccRCC tumor, Ipilimumab/Nivolumab treated, sc06, near
GSM8898811 ccRCC tumor, Ipilimumab/Nivolumab treated, sc08, center
GSM8898812 ccRCC tumor, Ipilimumab/Nivolumab treated, sc08, far
GSM8898813 ccRCC tumor, Ipilimumab/Nivolumab treated, sc08, near
GSM8898814 ccRCC tumor, Ipilimumab/Nivolumab treated, sc10, center
GSM8898815 ccRCC tumor, Ipilimumab/Nivolumab treated, sc10, lateral
GSM8898816 ccRCC tumor, Ipilimumab/Nivolumab treated, sc10, medial
GSM8898817 ccRCC tumor, Ipilimumab/Nivolumab treated, sc16
GSM8898818 ccRCC tumor, Cabozantinib treated, sc21
GSM8898819 ccRCC tumor, Axitinib/Pembrolizumab treated, sc24.
GSM8898820 ccRCC tumor, Axitinib/Pembrolizumab treated, sc30
GSM8898821 ccRCC tumor, Axitinib/Pembrolizumab treated, sc44
GSM8898822 ccRCC tumor, Ipilimumab/Nivolumab treated, sc46
GSM8898823 ccRCC tumor, Axitinib/Pembrolizumab treated, sc48
GSM8898824 ccRCC tumor, Lenvatinib/Pembrolizumab treated, sc50
GSM8898825 ccRCC tumor, Lenvatinib/Pembrolizumab treated, sc58
GSM8898826 ccRCC tumor, Ipilimumab/Nivolumab treated, sc60

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