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Combination of a SOS1-KRAS interaction inhibitor with a KRASG12C inhibitor combination can address intrinsic and acquired resistance leading to stronger and more durable response (Quant-Seq 2)

GSE250032 Homo sapiens (human) RNA-seq (gene expression) 65 samples Submitted 2024/05/03 Platform GPL18573
At a glance

RNA-seq (gene expression) · human · 65 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
65 samples: 16 conditions, mostly with about 4 replicates eachGroups 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
Co-targeting SOS1 enhances the antitumor effects of KRAS(G12C) inhibitors by addressing intrinsic and acquired resistanceNature cancer · 2024
Summary (written by the submitters)
To investigate the transcriptomic changes associated with KRAS G12C inhibitor Adagrasib resistance and Adagrasib + SOS1 inhibitor (BI-3406) or Adagrasib + EGFR inhibitor (Cetuximab) combination treatment to overcome resistance. We performed differential gene expression analysis using RNA-seq generated from cell line derived xenograft (CDX) in vivo models. We compared different treatment conditions with DMSO treated condition.
Published in
Co-targeting SOS1 enhances the antitumor effects of KRAS(G12C) inhibitors by addressing intrinsic and acquired resistance
Thatikonda V, Lyu H, Jurado S et al. · Nature cancer 2024 · PMID 39103541 · doi:10.1038/s43018-024-00800-6
This dataset
Open GSE250032 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 GSE250032_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 PRJNA1051636 and SRA study SRP477629. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39103541) ↗
Study design
16 conditions, mostly with about 4 replicates each
NCI-H2122 cells, Vehicle_A ×5 NCI-H2122 cells, BI3406_50mpk_4h_C ×4 NCI-H2122 cells, BI3406_50mpk_24h_B ×4 NCI-H2122 cells, BI3406_50mpk_48h_D ×4 NCI-H2122 cells, Adagrasib_100mpk_4h_F ×4 NCI-H2122 cells, Adagrasib_100mpk_24h_E ×4 NCI-H2122 cells, Adagrasib_100mpk_48h_G ×4 NCI-H2122 cells, TNO155_10mpk_4h_I ×4 +8 more

Supports a between-group comparison across 65 samples.

How we read this: 16 replicated groups read from 65 sample titles; they account for 65 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

+ 25 more — browse all 65 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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