Cellular adaptation to cancer therapy along a resistance continuum
ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 93 samples
- What it is
- ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
- Organism
- Homo sapiens (human)
- Samples
- 93 samples: 84 distinct titles, 2 of them repeatedGroups 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
- Cellular adaptation to cancer therapy along a resistance continuumNature · 2024
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 GSE247691_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.
Also filed as BioProject PRJNA1040167 and SRA study SRP381781. Searching any of these in the dataset finder brings you back here.
How we read this: Read from 93 sample titles: 84 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.
- GSM6243786 Kura_C
- GSM6243787 Kura_T1
- GSM6243788 Kura_T2.5
- GSM6243789 Kura_T5
- GSM6243790 Kura_T10
- GSM6243791 Kura_T20
- GSM6243792 Kura_T40
- GSM6243793 Kura_T80
- GSM6243794 Kura_T160
- GSM6243795 Kura_T320
- GSM7898872 a375_pool_run1_C_to_T4,sequencing run 1,scRNA-Seq
- GSM7898873 a357_pool_run2_C_to_T4,sequencing run 2,scRNA-Seq
- GSM7898874 a375_pool_run1_multiplex_CMO_C_to_T4,sequencing run 1,scRNA-Seq
- GSM7898875 a375_pool_run2_multiplex_CMO_C_to_T4,sequencing run 2,scRNA-Seq
- GSM7898876 pc9_pool_C_to_T1.2_P1.2,scRNA-Seq
- GSM7898877 pc9_pool_multiplex_CMO_C_to_T1.2_P1.2,scRNA-Seq
- GSM7898878 kuramochi_C,replicate 1,ATAC-Seq
- GSM7898879 kuramochi_C,replicate 2,ATAC-Seq
- GSM7898880 kuramochi_T5,replicate 1,ATAC-Seq
- GSM7898881 kuramochi_T5,replicate 2,ATAC-Seq
- GSM7898882 kuramochi_T10,replicate 1,ATAC-Seq
- GSM7898883 kuramochi_T10,replicate 2,ATAC-Seq
- GSM7898884 kuramochi_T20,replicate 1,ATAC-Seq
- GSM7898885 kuramochi_T20,replicate 2,ATAC-Seq
- GSM7898886 kuramochi_T40,replicate 1,ATAC-Seq
- GSM7898887 kuramochi_T40,replicate 2,ATAC-Seq
- GSM7898888 kuramochi_T80,replicate 1,ATAC-Seq
- GSM7898889 kuramochi_T80,replicate 2,ATAC-Seq
- GSM7898890 kuramochi_T160,replicate 1,ATAC-Seq
- GSM7898891 kuramochi_T160,replicate 2,ATAC-Seq
- GSM7898892 kuramochi_T320,replicate 1,ATAC-Seq
- GSM7898893 kuramochi_T320,replicate 2,ATAC-Seq
- GSM7898894 kuramochi_P10,replicate 1,ATAC-Seq
- GSM7898895 kuramochi_P10,replicate 2,ATAC-Seq
- GSM7898896 kuramochi_P320,replicate 1,ATAC-Seq
- GSM7898897 kuramochi_P320,replicate 2,ATAC-Seq
- GSM7898898 kuramochi_T320_nodrug,replicate 1,ATAC-Seq
- GSM7898899 kuramochi_T320_nodrug,replicate 2,ATAC-Seq
- GSM7898900 kuramochi_T320_drug,replicate 1,ATAC-Seq
- GSM7898901 kuramochi_T320_drug,replicate 2,ATAC-Seq
+ 53 more — browse all 93 samples with per-sample file links →
- GSE235863 T cell populations correlate with response and resistance to anti-PD-1 plus lenvatinib combination therapy in hepatocellular carcinoma 69 samples shares resistance, cellular, therapy
- GSE211434 Diverse immune suppressing cancer communications with myeloid cells prevent T cell activation and recruitment and provide resistance to cell cycle inhibitor therapy 81 samples shares resistance, therapy
- GSE309004 Molecular determinants of neoadjuvant chemotherapy resistance in breast cancer: An analysis of gene expression and tumor microenvironment 54 samples shares resistance, therapy
- GSE262796 Gene expression changes associated with acquired resistance to anti-EGFR therapy (cetuximab) in colorectal cancer cells 18 samples shares resistance, therapy
- GSE273127 Comparative single-cell analysis of esophageal cancer subtypes reveals cellular distinctions explaining varied immunotherapy responses 11 samples shares cellular, therapy
- GSE273829 GABA promotes resistance to immunotherapy of patients with TLS-positive tumors [RNA-seq] 92 samples shares resistance, therapy
- GSE286827 4-1BB+ Tregs and inhibitory progenitor exhausted T cells confer resistance to anti-PD-L1 and anti-CTLA-4 combination therapy 87 samples shares resistance, therapy
- GSE285963 Transcriptome profile of hepatocellular carcinoma in patients with atezolizumab plus bevacizumab therapy. 84 samples shares cellular, therapy
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.