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Identifying Senescence and Immune Biomarkers Predictive of Benefit to Combined CDK4/6 and PD-1 Blockade in Dedifferentiated Liposarcoma: Single-Cell RNA-seq of Serial Tumor Biopsies

GSE320212 Homo sapiens (human) Single-cell RNA-seq 23 samples Submitted 2026/04/08 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 23 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)
Topic
Soft tissue sarcomaThis series is part of our curated briefing on it.
Samples
23 samples: 23 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
Tumor and Immune Dynamics Following Sequential CDK4/6 and PD-1 Inhibition: Results from a Phase 2 Study in Dedifferentiated LiposarcomaCancer research communications · 2026
Summary (written by the submitters)
This dataset comes from an exploratory correlatives sub-study embedded within NCT04438824, a single-arm, open-label phase II trial evaluating the combination of the CDK4/6 inhibitor palbociclib and the PD-1 inhibitor INCMGA00012 in patients with advanced well-differentiated or dedifferentiated liposarcoma. The dbGaP submission is limited to the first 12 patients enrolled on the trial, for whom protocol-mandated tumor biopsies were collected for single-cell RNA sequencing.
Read the full summary ↓
Single-cell RNASeq was performed to characterize cellular composition and transcriptional states within the tumor microenvironment in this pilot cohort. The dataset includes processed single-cell expression profiles and a restricted set of associated clinical variables such as demographics and response data abstracted from the parent study.
Published in
Tumor and Immune Dynamics Following Sequential CDK4/6 and PD-1 Inhibition: Results from a Phase 2 Study in Dedifferentiated Liposarcoma
Rosenbaum E, Gularte-Mérida R, Seffar E et al. · Cancer research communications 2026 · PMID 41325133 · doi:10.1158/2767-9764.CRC-25-0334
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 GSE320212_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 PRJNA1427154. Searching any of these in the dataset finder brings you back here.

Study design
23 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 23 sample titles: 23 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
More Soft tissue sarcoma datasets

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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.