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Multi-selective RAS(ON) Inhibition Targets Oncogenic RAS Mutations and Overcomes RAS/MAPK-Mediated Resistance to FLT3 and BCL2 Inhibitors in Acute Myeloid Leukemia

GSE301645 Homo sapiens (human) RNA-seq (gene expression) 24 samples Submitted 2025/07/04 Platform GPL16791
At a glance

RNA-seq (gene expression) · human · 24 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
24 samples: 24 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)
Aberrant activation of the RAS/MAPK signaling limits the clinical efficacy of several targeted therapies in acute myeloid leukemia (AML). In FLT3-mutant AML, the selection of clones harboring heterogeneous RAS mutations drives resistance to FLT3 inhibitors (FLT3i). RAS activation is also associated with resistance to other AML targeted therapies, including the BCL2 inhibitor venetoclax.
Read the full summary ↓
Despite the critical need to inhibit RAS/MAPK signaling in AML, no targeted therapies have demonstrated clinical benefit in RAS-driven AML. To address this unmet need, we investigated the preclinical activity of RMC-7977, a multi-selective inhibitor of GTP-bound active [RAS(ON)] isoforms of mutant and wild-type RAS in AML models. RMC-7977 exhibited potent antiproliferative and pro-apoptotic activity across AML cell lines with MAPK-activating signaling mutations. In Molm-14 and OCIAML-3 cell lines, RMC=7977 downregulated expression of genesets involved in RAS/MAPK and PI3K/Akt/mTOR signaling, as well as MYC targets and cell cycle regulators.
This dataset
Open GSE301645 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 GSE301645_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 PRJNA1285887 and SRA study SRP598095. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
24 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 24 sample titles: 24 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
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.