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Sensitive CAR T cells redefine targetable CD70 expression in solid tumors [RNA-seq RCC]

GSE306609 Homo sapiens (human) RNA-seq (gene expression) 55 samples Submitted 2026/02/26 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 55 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)
Topic
Kidney cancerThis series is part of our curated briefing on it.
Samples
55 samples: 10 conditions, mostly in triplicateGroups 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
Sensitive CAR T cells redefine targetable CD70 expression in solid tumorsScience (New York, N.Y.) · 2026
Summary (written by the submitters)
Overcoming tumor antigen heterogeneity in solid tumors is a major challenge for cancer immunotherapies including chimeric antigen receptor (CAR) T cells. Unlike CD19 for B-cell malignancies, no target with pan-cellular expression in solid tumors and absence in normal vital cells has been identified. CD70 is a potential candidate, confined to immune cell subsets and aberrantly expressed in many solid tumors, albeit heterogeneously.
Read the full summary ↓
We find that CD70 expression in heterogeneous tumors is not binary but ranges from high to very low, appearing negative. We show that CD70-heterogeneous tumors are efficiently eliminated by highly sensitive CD70 receptors where prototypic CAR T cells fail. We further identify an epigenetic signature that predicts targetable expression. These findings provide a potential strategy to treat a broad range of solid tumors.
Published in
Sensitive CAR T cells redefine targetable CD70 expression in solid tumors
Hanina SA, Park T, Lopez M et al. · Science (New York, N.Y.) 2026 · PMID 41747043 · doi:10.1126/science.adv7378
This dataset
Open GSE306609 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 GSE306609_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 PRJNA1311463 and SRA study SRP613501. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41747043) ↗
Study design
10 conditions, mostly in triplicate
RNA-seq, K5, KIDNEY, CD70LO, D0, ×3 RNA-seq, K5, KIDNEY, CD70LO, D8, ×3 RNA-seq, K5, KIDNEY, CD70HI, D0, ×3 RNA-seq, K5, LUNG, CD70HI, D0, ×3 RNA-seq, K5, IN VITRO, ×3 RNA-seq, K7, KIDNEY, CD70LO, D0, ×3 RNA-seq, K7, KIDNEY, CD70LO, D8, ×3 RNA-seq, K7, KIDNEY, CD70LO, D2, ×3 +27 more

Supports a between-group comparison across 30 samples.

How we read this: 10 replicated groups read from 55 sample titles; they account for 30 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

+ 15 more — browse all 55 samples with per-sample file links →

More Kidney cancer datasets

Search all human RNA-seq datasets in GEO →

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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.