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Therapy Induced Senescence Promotes Immunogenicity in Acute Myeloid Leukemia through Reduced EZH2 Activity (RNA-seq AraC)

GSE229736 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2026/07/24 Platform GPL18573
At a glance

RNA-seq (gene expression) · human · 12 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)
Samples
12 samples: 12 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.
Summary (written by the submitters)
Chemotherapy resistance and disease relapse are major determinants of treatment failure in acute myeloid leukemia (AML). Therapy-induced senescence (TIS) is one outcome of chemotherapy, but its immunological consequences in AML remain unclear. Here we show that ex vivo chemotherapy induces senescence in a subset of therapy-naïve AML samples.
Read the full summary ↓
TIS is marked by elevated interferon signaling, upregulation of human leukocyte antigen (HLA) class I and II molecules, and increased presentation of leukemia- and senescence-associated peptides, conferring AML cells antigen-presenting cell-like features. These changes enhance autologous CD4+ and CD8+ T cell responses against AML, both ex vivo and in patient-derived xenograft models. TIS also restores AML sensitivity to immune checkpoint blockade therapy. Mechanistically, we identify reduced Polycomb Repressive Complex 2 (PRC2) activity as central to TIS induction and its immunogenicity. PRC2 inhibition reactivates senescence-related genes and HLA expression in non-senescent AML cells, enabling T cell activation. These findings uncover a senescence-driven immune mechanism with potential to improve therapy outcomes in AML.
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 GSE229736_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: 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 PRJNA955730 and SRA study SRP432716. Searching any of these in the dataset finder brings you back here.

Study design
12 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 12 sample titles: 12 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
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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.