GEO series

Improved Prediction of Venetoclax and Azacitidine Efficacy in Acute Myeloid Leukemia through Machine Learning

GSE289785 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2025/02/18 Platform GPL24676

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: 6 conditions, mostly in duplicateGroups 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: 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 GSE289785 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 GSE289785_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 PRJNA1224776 and SRA study SRP564375. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41265442) ↗

Study design

6 conditions, mostly in duplicate

MOLM13 cells, shControl, ×2 MOLM13 cells, shPINK1, sh1, ×2 MOLM13 cells, shPINK1, sh2, ×2 OA3 cells, shControl, ×2 OA3 cells, shPINK1, sh1, ×2 OA3 cells, shPINK1, sh2, ×2

Supports a between-group comparison across 12 samples.

How we read this: 6 replicated groups read from 12 sample titles; they account for 12 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

GSM8798564 MOLM13 cells, shControl, Replicate 1 MOLM13 cells, shControl,
GSM8798565 MOLM13 cells, shControl, Replicate 2 MOLM13 cells, shControl,
GSM8798566 MOLM13 cells, shPINK1, sh1, Replicate 1 MOLM13 cells, shPINK1, …
GSM8798567 MOLM13 cells, shPINK1, sh1, Replicate 2 MOLM13 cells, shPINK1, …
GSM8798568 MOLM13 cells, shPINK1, sh2, Replicate 1 MOLM13 cells, shPINK1, …
GSM8798569 MOLM13 cells, shPINK1, sh2, Replicate 2 MOLM13 cells, shPINK1, …
GSM8798570 OA3 cells, shControl, Replicate 1 OA3 cells, shControl,
GSM8798571 OA3 cells, shControl, Replicate 2 OA3 cells, shControl,
GSM8798572 OA3 cells, shPINK1, sh1, Replicate 1 OA3 cells, shPINK1, sh1,
GSM8798573 OA3 cells, shPINK1, sh1, Replicate 2 OA3 cells, shPINK1, sh1,
GSM8798574 OA3 cells, shPINK1, sh2, Replicate 1 OA3 cells, shPINK1, sh2,
GSM8798575 OA3 cells, shPINK1, sh2, Replicate 2 OA3 cells, shPINK1, sh2,

Datasets on a similar topic

GSE289786 Improved Prediction of Venetoclax and Azacitidine Efficacy in Acute Myeloid Leukemia through Machine Learning [AML patients] 142 samples shares azacitidine, prediction, venetoclax, improved, efficacy, leukemia, learning, myeloid
GSE269669 Phase Ib/II Study of Azacitidine, Venetoclax and Magrolimab in Adult Patients with Newly Diagnosed and Relapsed Refractory Acute Myeloid Leukemia 27 samples shares azacitidine, venetoclax, leukemia, myeloid
GSE282741 Menin inhibitor DS-1594b drives differentiation and induces synergistic lethality in combination with Venetoclax in acute myeloid leukemia cells with rearranged MLL1 and NPM1 mutations 36 samples shares venetoclax, leukemia, myeloid
GSE261921 Synergistic Activity of Adefovir Dipivoxil and Venetoclax to Target the Bioenergetic Metabolism of Acute Myeloid Leukemia 24 samples shares venetoclax, leukemia, myeloid
GSE267885 Targeting SUMOylation sensitizes Acute myeloid leukemia to Venetoclax treatment 8 samples shares venetoclax, leukemia, myeloid
GSE306605 Venetoclax Enhances gamma-deltaT cell Antileukemia Activity via Fatty Acid Oxidation in Relapsed Acute Myeloid Leukemia 6 samples shares venetoclax, leukemia, myeloid
GSE252529 Establishment of interpretable cytotoxicity prediction models using machine learning analysis of transcriptome features 15 samples shares prediction, learning
GSE302692 Enhanced efficacy of a specific HDAC3 inhibitor in combination with 5-Azacitidine against diffuse large B-cell lymphoma [MD901_RNAseq_Day5] 12 samples shares azacitidine, efficacy

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