GEO series

TP53 deficiency in AML induces resistance to T-cell engagers through an immunosuppressive secretome

GSE309028 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2025/09/29 Platform GPL30173

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)

Bispecific T-cell engagers (BiTEs) have transformed the treatment of B-cell malignancies, yet clinical activity in AML has been modest at best, irrespective of the target antigen. Resistance is driven in part by the genetic heterogeneity of AML, most notably TP53 mutations, present in 10-15% of de novo and up to 25% of therapy-related AML.

Read the full summary ↓

These mutations confer resistance to T-cell-based therapies, consistent with the dismal outcomes observed after allogeneic HSCT. Thus, we hypothesized that TP53 aberrations in AML contribute to cellintrinsic and extrinsic resistance against T-cell-based immunotherapy. To investigate this, we utilized the BiTE molecule AMG 330 (CD3×CD33) in co-cultures with primary AML cells (pAML) harboring TP53 deletions (DEL) and AML cell lines with a TP53 knockdown (KD). Cytotoxicity against TP53 DEL pAML and TP53 KD cell lines was reduced in co-cultures with T cells. In addition, T-cell proliferation and proinflammatory cytokine secretion was impaired in cocultures with TP53 KD cells. Transwell assays identified the secretome of TP53 KD AML cells as a key contributor to the observed immunosuppressive effect. Furthermore, proteomic analysis revealed TGF-β1 in TP53 KD co-cultures as a mediator of T-cell suppression. Lastly, RNA sequencing of T cells co-cultured with TP53 KD cells uncovered a transcriptional shift toward a senescent cell cycle profile. Our data collectively identify the immunosuppressive secretome of TP53-deficient AML as a key barrier to T-cell-engaging immunotherapies, underscoring an unmet clinical need for strategies able to restore T-cell function in TP53 KD AML.

This dataset

Open GSE309028 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 GSE309028_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 PRJNA1333809 and SRA study SRP626293. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

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

GSM9258437 Human T cells_TP53_WT3_rep1
GSM9258438 Human T cells_TP53_WT4_rep2
GSM9258439 Human T cells_TP53_KD3_rep3
GSM9258440 Human T cells_TP53_KD1_rep4
GSM9258441 Human T cells_TP53_WT2_rep5
GSM9258442 Human T cells_TP53_KD2_rep6
GSM9258443 Human T cells_TP53_KD6_rep7
GSM9258444 Human T cells_TP53_KD4_rep8
GSM9258445 Human T cells_TP53_KD5_rep9
GSM9258446 Human T cells_TP53_WT6_rep10
GSM9258447 Human T cells_TP53_WT1_rep11
GSM9258448 Human T cells_TP53_WT5_rep12

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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.