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Targeted KRAS G12V degradation elicits efficient and durable lung adenocarcinoma regression in vivo [R_invivo_invitro]

GSE301620 Mus musculus (mouse) RNA-seq (gene expression) 23 samples Submitted 2026/05/14 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 23 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
Mus musculus (mouse)
Samples
23 samples: 8 × T_R_InVivo_BL6 vs 6 × T_R_InVitro vs 6 × T_R_InVivo_NSG vs 3 × UT_InVitroGroups 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)
Recent drug discovery breakthroughs led to the approval of KRASG12C inhibitors in lung adenocarcinoma (LUAD). Unfortunately, clinical responses are often hampered by the rapid emergence of resistance. Proteolysis-targeting chimeras (PROTACs) have emerged as promising alternatives to traditional inhibition. However, there is limited mechanistic understanding of KRAS degradation in vivo.
Read the full summary ↓
Here, we developed a preclinical LUAD mouse model and demonstrated that targeted oncogenic KRAS degradation induces rapid tumor regression primarily due to cancer cell-intrinsic mechanisms. Yet, transcriptional, histological, and immunophenotypic analyses revealed a substantial remodeling of the tumor microenvironment. Notably, disease relapse observed during prolongued degrader treatment stems from proteolysis machinery dysregulation, indicating resistance mechanisms distinct from those reported upon KRAS inhibition. Collectively, our findings highlight the therapeutic potential of KRAS degradation in LUAD, providing insights into both cell-intrinsic and -extrinsic mechanisms that accompany antitumor responses and support the continued clinical development of this approach.
This dataset
Open GSE301620 in finder → Find more mouse 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 GSE301620_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 PRJNA1285811 and SRA study SRP598061. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
8 × T_R_InVivo_BL6 vs 6 × T_R_InVitro vs 6 × T_R_InVivo_NSG vs 3 × UT_InVitro

Supports a between-group comparison across 23 samples.

How we read this: 4 replicated groups read from 23 sample titles; they account for 23 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
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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.