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Heterobifunctional proteomimetic polymers for targeted degradation of MYC and KRAS

GSE269664 Mus musculus (mouse) RNA-seq (gene expression) 33 samples Submitted 2025/12/01 Platform GPL21103Platform GPL20301
At a glance

RNA-seq (gene expression) · mouse · 33 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
33 samples: 11 conditions, mostly in triplicateGroups 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
Heterobifunctional proteomimetic polymers for targeted degradation of MYC and KRASNature communications · 2026
Summary (written by the submitters)
Targeted protein degradation has unlocked new strategies for modulating previously undruggable proteins; however, existing small molecule approaches are challenging to optimize and largely limited to targets with ligandable pockets. To overcome these challenges, we introduce the HYbrid DegRAding Copolymer (HYDRAC), a polymer-based platform that integrates target-binding peptides with peptide or small-molecule degrons for the selective degradation of disease-relevant proteins.
Read the full summary ↓
HYDRACs are synthesized easily, exhibit structural tunability, and facilitate the attachment of multivalent payloads. The modular payload capability accommodates diverse target-binding motifs and E3 ligase recruiters—such as VHL, KEAP1, and CRBN—broadening the design space. We apply HYDRACs against two historically intractable targets, MYC and KRAS, achieving robust degradation in vitro and sustained tumor inhibition in murine models. Notably, HYDRACs containing consensus RAS-binding motifs effectively degrade KRAS in cells harboring different alleles, suggesting pan-KRAS potential. We envision the HYDRAC platform as a generalizable approach, greatly expanding the armamentarium for TPD.
Published in
Heterobifunctional proteomimetic polymers for targeted degradation of MYC and KRAS
Wang MM, Truica MI, Gattis BS et al. · Nature communications 2026 · PMID 41735327 · doi:10.1038/s41467-026-68913-3
This dataset
Open GSE269664 in finder → Find more 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 GSE269664_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 PRJNA1123183 and SRA study SRP513585. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41735327) ↗
Study design
11 conditions, mostly in triplicate
P-shR, 24hours, ×3 HYDRAC, 24hours, ×3 Vehicle, 24hours, ×3 A549 HYDRAC 24hr ×3 A549 sHR 24hr ×3 MycCaP HYDRAC 24hr ×3 MycCaP sHR 24hr ×3 PC3 HYDRAC 24hr ×3 +3 more

Supports a between-group comparison across 33 samples.

How we read this: 11 replicated groups read from 33 sample titles; they account for 33 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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