An in vivo screening platform using PDX models reveals KEAP1, USP22 and other drivers of chemoresistance in small cell lung cancer
RNA-seq (gene expression) · human · 67 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
- Small cell lung cancerThis series is part of our curated briefing on it.
- Samples
- 67 samples: 16 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
- In vivo functional screens reveal KEAP1 loss as a driver of chemoresistance in small cell lung cancerScience advances · 2025
Read the full summary ↓
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE255095_RAW.tar (it can run to tens of gigabytes).
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.
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 PRJNA1073511. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 65 samples.
How we read this: 16 replicated groups read from 67 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.
- GSM8063971 H14_KEAP1sg1_SAL1
- GSM8063972 H14_KEAP1sg1_CE1
- GSM8063973 H14_Ctrlsg1_CE1 H14_Ctrlsg1_CE
- GSM8063974 H14_Ctrlsg1_CE2 H14_Ctrlsg1_CE
- GSM8063975 H14_Ctrlsg1_SAL1 H14_Ctrlsg1_SAL
- GSM8063976 H14_KEAP1g1_SAL2 H14_KEAP1g1_SAL
- GSM8063977 H14_Ctrlsg1_CE3 H14_Ctrlsg1_CE
- GSM8063978 H14_KEAP1g1_SAL3 H14_KEAP1g1_SAL
- GSM8063979 H14_KEAP1g1_CE2 H14_KEAP1g1_CE
- GSM8063980 H14_Ctrlsg1_SAL2 H14_Ctrlsg1_SAL
- GSM8063981 H14_Ctrlsg1_SAL3 H14_Ctrlsg1_SAL
- GSM8063982 H14_KEAP1g1_CE3 H14_KEAP1g1_CE
- GSM8063983 H14_Ctrlsg1_CE4 H14_Ctrlsg1_CE
- GSM8063984 H14_Ctrlsg1_CE5 H14_Ctrlsg1_CE
- GSM8063985 H14_Ctrlsg1_CE6 H14_Ctrlsg1_CE
- GSM8063986 H14_Ctrlsg1_SAL4 H14_Ctrlsg1_SAL
- GSM8063987 H14_Ctrlsg1_SAL5 H14_Ctrlsg1_SAL
- GSM8063988 H14_Ctrlsg1_SAL6 H14_Ctrlsg1_SAL
- GSM8063989 H14_KEAP1g1_SAL4 H14_KEAP1g1_SAL
- GSM8063990 H14_KEAP1g1_CE4 H14_KEAP1g1_CE
- GSM8063991 H14_KEAP1g1_SAL6 H14_KEAP1g1_SAL
- GSM8063992 H14_KEAP1g1_CE5 H14_KEAP1g1_CE
- GSM8063993 H14_KEAP1g1_CE6 H14_KEAP1g1_CE
- GSM8063994 FHSC14_Ctrlsg1_SAL1 FHSC14_Ctrlsg1_SAL
- GSM8063995 FHSC14_Ctrlsg1_SAL2 FHSC14_Ctrlsg1_SAL
- GSM8063996 FHSC14_Ctrlsg1_SAL3 FHSC14_Ctrlsg1_SAL
- GSM8063997 FHSC14_Ctrlsg2_SAL4 FHSC14_Ctrlsg2_SAL
- GSM8063998 FHSC14_Ctrlsg2_SAL5 FHSC14_Ctrlsg2_SAL
- GSM8063999 FHSC14_Ctrlsg2_SAL6 FHSC14_Ctrlsg2_SAL
- GSM8064000 FHSC14_Ctrlsg1_CE1 FHSC14_Ctrlsg1_CE
- GSM8064001 FHSC14_Ctrlsg1_CE2 FHSC14_Ctrlsg1_CE
- GSM8064002 FHSC14_Ctrlsg1_CE3 FHSC14_Ctrlsg1_CE
- GSM8064003 FHSC14_Ctrlsg2_CE4 FHSC14_Ctrlsg2_CE
- GSM8064004 FHSC14_Ctrlsg2_CE5 FHSC14_Ctrlsg2_CE
- GSM8064005 FHSC14_Ctrlsg2_CE6 FHSC14_Ctrlsg2_CE
- GSM8064006 FHSC14_USP22sg1_SAL1 FHSC14_USP22sg1_SAL
- GSM8064007 FHSC14_USP22sg1_SAL2 FHSC14_USP22sg1_SAL
- GSM8064008 FHSC14_USP22sg1_SAL3 FHSC14_USP22sg1_SAL
- GSM8064009 FHSC14_USP22sg2_SAL4 FHSC14_USP22sg2_SAL
- GSM8064010 FHSC14_USP22sg2_SAL5 FHSC14_USP22sg2_SAL
+ 27 more — browse all 67 samples with per-sample file links →
- GSE296155 Loss of the USP22 deubiquitylase confers resistance to chemotherapy in small cell lung cancer [RNA-seq] 20 samples same disease briefing · shares usp22, other
- GSE296261 Loss of the USP22 deubiquitylase confers resistance to chemotherapy in small cell lung cancer 43 samples same disease briefing · shares usp22, other
- GSE296258 Loss of the USP22 deubiquitylase confers resistance to chemotherapy in small cell lung cancer [CUT&Tag] 23 samples same disease briefing · shares usp22, other
- GSE294644 Identification of the Notch ligand DLK1 as an immunotherapeutic target and regulator of tumor cell plasticity and chemoresistance in adrenocortical carcinoma 7 samples same disease briefing · shares chemoresistance, other
- GSE279100 Differential KEAP1/NRF2-mediated signaling widens the therapeutic window of redox-targeting drugs in SCLC therapy [seq] 50 samples same disease briefing · shares keap1
- GSE280643 Differential KEAP1/NRF2-mediated signaling widens the therapeutic window of redox-targeting drugs in SCLC therapy 74 samples same disease briefing · shares keap1
- GSE330338 Multi-Omics Integration Identifies Critical Molecular Drivers and Alternative Splicing Events in Small Cell Lung Cancer 16 samples same disease briefing · shares drivers
- GSE233820 PARP inhibitor radiosensitization enhances anti-PD-L1 immunotherapy through depression of chemokine translation in small cell lung cancer 12 samples same disease briefing · shares other
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.