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Improved Prediction of Venetoclax and Azacitidine Efficacy in Acute Myeloid Leukemia through Machine Learning [AML patients]

GSE289786 Homo sapiens (human) RNA-seq (gene expression) 142 samples Submitted 2025/10/23 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 142 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
142 samples
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
Precision prediction of venetoclax-azacitidine treatment efficacy in acute myeloid leukemia via integrative drug screening and machine learningCell reports. Medicine · 2025
Summary (written by the submitters)
Despite the transformative impact of venetoclax-azacitidine in treating acute myeloid leukemia (AML), reliable markers for accurately predicting patient responses remain urgently needed. To address this challenge, we employed a multidisciplinary approach that combined transcriptomic profiling, ex vivo drug sensitivity testing, functional assays, and clinical data to identify robust predictors of venetoclax-azacitidine response.
Read the full summary ↓
We pinpointed a set of core genes linked to both ex vivo and in vivo drug responsiveness, validated through CRISPR-Cas9 screens in the setting of both venetoclax and azacitidine therapies. In particular, silencing BCL2L1 and PINK1 preferentially enhanced response to the venetoclax-azacitidine treatment. Building on these insights, we further developed and validated an 8-gene random forest model (RF8) that demonstrated high specificity and sensitivity in four independent cohorts comprising 498 patients. This model was capable of distilling downstream effects of genetic alterations to assist in predicting treatment response and outperformed existing genetic mutation-based signatures. Furthermore, the RF8 score demonstrated a nearly monotonic relationship with venetoclax-azacitidine response probabilities and patient outcomes, enabling precise stratification of patients. These findings illustrated the feasibility of translating integrated transcriptomic and drug-response profiling data into more refined risk stratification approaches, offering a new avenue for optimizing clinical decision-making in AML.
Published in
Precision prediction of venetoclax-azacitidine treatment efficacy in acute myeloid leukemia via integrative drug screening and machine learning
Jin P, Wang D, Shen J et al. · Cell reports. Medicine 2025 · PMID 41265442 · doi:10.1016/j.xcrm.2025.102461
This dataset
Open GSE289786 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 GSE289786_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 PRJNA1224775. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41265442) ↗
Study design
A cohort of 142 individual samples

The grouping that defines any comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Sample titles run LJR0003 … LJR0179 and carry no group information.

Samples in this study

+ 102 more — browse all 142 samples with per-sample file links →

More Acute myeloid leukemia 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.