GEO series

NRAS knockdown reverses monocytic cell state and resensitizes resistant FLT3-AML cells to gilteritinib

GSE338135 Homo sapiens (human) RNA-seq (gene expression) 75 samples Submitted 2026/07/13 Platform GPL24676

At a glance

RNA-seq (gene expression) · human · 75 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: 75 samples: 75 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)

While the FLT3 inhibitor gilteritinib is initially effective in patients with FLT3-mutated acute myeloid leukemia (AML), patients invariably relapse within months of treatment. Gilteritinib resistance is commonly driven by the emergence of NRAS mutations and a shift towards a more monocytic cell state. We hypothesized that directly targeting and depleting NRAS protein would reverse these adaptive differentiation changes and restore therapeutic sensitivity.

Read the full summary ↓

To test this, we utilized a mutation-agnostic antisense oligonucleotide (ASO) to selectively knock down NRAS expression across gilteritinib-resistant AML cell lines, in vivo cell-line-derived xenografts, and primary patient samples. NRAS ASO successfully resensitized gilteritinib resistant cells with multiple distinct NRAS mutations, displaying superior efficacy compared to downstream MEK inhibition. This therapeutic efficacy was independent of NRAS mutant variant allele frequency, suggesting that wild-type NRAS may also contribute to resistance. Comprehensive multi-omic profiling (transcriptomics, proteomics, phosphoproteomics) revealed that NRAS knockdown consistently reversed monocytic phenotype, shifting cells back toward a more primitive cell state. Monocytic differentiation and NRAS mutations are established drivers of resistance to diverse targeted regimens in AML, including FLT3, IDH, and BCL2 inhibitors, so we also tested venetoclax resistant primary cells with NRAS mutations. Resistant cells were resensitized to venetoclax after NRAS knockdown, suggesting that NRAS knockdown may be more broadly applicable in overcoming monocytic cell state and drug resistance in AML.

This dataset

Open GSE338135 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 GSE338135_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 PRJNA1492600 and SRA study SRP716694. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

75 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 75 sample titles: 75 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

GSM9867477 12L, Control, Y, 4hr
GSM9867478 6L, NRAS, Y, 4hr
GSM9867479 10L, No ASO, N, 0hr
GSM9867480 8L, NRAS, N, 0hr
GSM9867481 5L, NRAS, Y, 4hr
GSM9867482 11L, NRAS, Y, 4hr
GSM9867483 7L, NRAS, Y, 24hr
GSM9867484 9L, NRAS, Y, 4hr
GSM9867485 7L, Control, N, 0hr
GSM9867486 11L, Control, Y, 4hr
GSM9867487 6L, Control, N, 0hr
GSM9867488 12L, NRAS, Y, 4hr
GSM9867489 8L, Control, Y, 24hr
GSM9867490 9L, No ASO, N, 0hr
GSM9867491 11L, No ASO, Y, 24hr
GSM9867492 10L, NRAS, Y, 24hr
GSM9867493 7L, NRAS, N, 0hr
GSM9867494 9L, Control, N, 0hr
GSM9867495 12L, No ASO, Y, 24hr
GSM9867496 9L, Control, Y, 4hr
GSM9867497 7L, Control, Y, 4hr
GSM9867498 6L, NRAS, Y, 24hr
GSM9867499 9L, NRAS, N, 0hr
GSM9867500 12L, No ASO, N, 0hr
GSM9867501 MOLM14, No ASO, Y, 24hr
GSM9867502 11L, No ASO, Y, 4hr
GSM9867503 9L, NRAS, Y, 24hr
GSM9867504 12L, NRAS, Y, 24hr
GSM9867505 10L, No ASO, Y, 24hr
GSM9867506 9L, No ASO, Y, 24hr
GSM9867507 9L, No ASO, Y, 4hr
GSM9867508 6L, Control, Y, 4hr
GSM9867509 7L, No ASO, Y, 24hr
GSM9867510 6L, No ASO, N, 0hr
GSM9867511 8L, No ASO, Y, 24hr
GSM9867512 6L, NRAS, N, 0hr
GSM9867513 11L, No ASO, N, 0hr
GSM9867514 12L, NRAS, N, 0hr
GSM9867515 6L, No ASO, Y, 24hr
GSM9867516 MOLM14, No ASO, Y, 4hr

+ 35 more — browse all 75 samples with per-sample file links →

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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.