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The Proteostasis Network is a Therapeutic Target in Acute Myeloid Leukemia

GSE277715 Homo sapiens (human) RNA-seq (gene expression) 36 samples Submitted 2025/10/20 Platform GPL16791
At a glance

RNA-seq (gene expression) · human · 36 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
Acute myeloid leukemiaThis series is part of our curated briefing on it.
Samples
36 samples: 12 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.
Summary (written by the submitters)
Oncogenic growth typically places great strain and dependence on protein homeostasis (proteostasis) pathways. This has made proteostasis pathways attractive therapeutic targets in cancer, but efforts to drug components of these pathways have yielded largely disappointing clinical outcomes. One notable exception is proteasome inhibitors, which are approved for frontline treatment of multiple myeloma.
Read the full summary ↓
Despite this success, proteasome inhibitors are largely ineffective in the treatment of other cancers, including acute myeloid leukemia (AML), although the reasons for these cancer-specific differences are unknown. Here, we determined that proteasome inhibitors are ineffective in AML due to their inability to disrupt proteostasis. In response to proteasome inhibition, human AML cells activated heat shock factor 1 (HSF1) and increased autophagic flux to preserve proteostasis. Genetic inactivation of HSF1 sensitized multiple human AML cell lines to proteasome inhibition, marked by accumulation of unfolded protein and activation of the PERK-mediated integrated stress response. This response was associated with severe reductions in protein synthesis, proliferation and cell survival in vitro and significant slowing of disease progression and extension of survival in AML xenografts in vivo. Similarly, pharmacological inhibition of autophagy sensitized human AML cells to proteasome inhibition by disrupting proteostasis. Combined autophagy and proteasome inhibition suppressed proliferation and synergistically killed human AML cells in vitro, and significantly reduced AML burden and extended survival in vivo. Furthermore, combined autophagy and proteasome inhibition preferentially suppressed protein synthesis and induced apoptosis in primary patient AML cells, including CD34+ AML stem and progenitor cells, without severely affecting normal hematopoietic stem and progenitor cells. Effects of combined autophagy and proteasome inhibition were associated with induction of a terminal integrated stress response, which, surprisingly, was not driven by a canonical unfolded protein response, but rather by Protein kinase R (PKR). Overall, these studies unravel how proteostasis pathways are co-opted to promote AML growth, progression and drug resistance, and reveal that disabling the proteostasis network is a promising strategy to therapeutically target AML.
This dataset
Open GSE277715 in finder → Find more human RNA-seq datasets → Acute myeloid leukemia 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 GSE277715_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 PRJNA1163424 and SRA study SRP535324. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
12 conditions, mostly in triplicate
OCI-AML3 cells, Control, ×3 OCI-AML3 cells, Carfilzomib, ×3 OCI-AML3 cells, Lys05, ×3 OCI-AML3 cells, Carfilzomib + Lys05, ×3 HSF1+/+ TF-1a cells, Control ×3 HSF1+/+ TF-1a cells, Carfilzomib ×3 HSF1-/- TF-1a cells, Control ×3 HSF1-/- TF-1a cells, Carfilzomib ×3 +4 more

Supports a between-group comparison across 36 samples.

How we read this: 12 replicated groups read from 36 sample titles; they account for 36 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
More Acute myeloid leukemia datasets

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