In vitro drug screening of RAF/MEK/ERK Inhibitors in two PDA Cell Lines
RNA-seq (gene expression) · human · 152 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
- 152 samples: 152 samples, each with a distinct title — no replicated groupsGroups 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
- Tumour-selective activity of RAS-GTP inhibition in pancreatic cancerNature · 2024
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 GSE252002_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 PRJNA1057073 and SRA study SRP480088. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 152 sample titles: 152 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.
- GSM7991053 ASPC1PSA_PS01ULAASPC1PSAS1_ACCAGTCCT_L003_R1_001
- GSM7991054 ASPC1PSA_PS01ULAASPC1PSAS3_GACGTATGG_L003_R1_001
- GSM7991055 ASPC1PSA_PS01ULAASPC1PSAS5_AGCACTGGA_L003_R1_001
- GSM7991056 ASPC1PSA_PS01ULAASPC1PSAS12_CAACGGAAG_L003_R1_001
- GSM7991057 ASPC1PSA_PS01ULAASPC1PSAS14_CGCTAAAGT_L003_R1_001
- GSM7991058 ASPC1PSA_PS01ULAASPC1PSAS15_GTAACCCTT_L003_R1_001
- GSM7991059 ASPC1PSA_PS01ULAASPC1PSAS16_TACGGTCAT_L003_R1_001
- GSM7991060 ASPC1PSA_PS01ULAASPC1PSAS17_CTACGTCCT_L003_R1_001
- GSM7991061 ASPC1PSA_PS01ULAASPC1PSAS24_TTAGCCGCT_L003_R1_001
- GSM7991062 ASPC1PSA_PS01ULAASPC1PSAS25_ACGCATGAT_L003_R1_001
- GSM7991063 ASPC1PSA_PS01ULAASPC1PSAS27_TGTCAGTTC_L003_R1_001
- GSM7991064 ASPC1PSA_PS01ULAASPC1PSAS28_GCGTCTTGA_L003_R1_001
- GSM7991065 ASPC1PSA_PS01ULAASPC1PSAS29_ACTGGCAAG_L003_R1_001
- GSM7991066 ASPC1PSA_PS01ULAASPC1PSAS36_TGCTACCTT_L003_R1_001
- GSM7991067 ASPC1PSA_PS01ULAASPC1PSAS37_GCAACTTCC_L003_R1_001
- GSM7991068 ASPC1PSA_PS01ULAASPC1PSAS38_CTAACGCAG_L003_R1_001
- GSM7991069 ASPC1PSA_PS01ULAASPC1PSAS41_GTCGAACCT_L003_R1_001
- GSM7991070 ASPC1PSA_PS01ULAASPC1PSAS48_CGAACTCTA_L003_R1_001
- GSM7991071 ASPC1PSA_PS01ULAASPC1PSAS50_TTGGTGAGC_L003_R1_001
- GSM7991072 ASPC1PSA_PS01ULAASPC1PSAS51_TACGTACGA_L003_R1_001
- GSM7991073 ASPC1PSA_PS01ULAASPC1PSAS53_TTTCCGGCA_L003_R1_001
- GSM7991074 ASPC1PSA_PS01ULAASPC1PSAS60_AGAGAGCAA_L003_R1_001
- GSM7991075 ASPC1PSA_PS01ULAASPC1PSAS61_CGCAGTAAG_L003_R1_001
- GSM7991076 ASPC1PSA_PS01ULAASPC1PSAS62_TCATGATGG_L003_R1_001
- GSM7991077 ASPC1PSA_PS01ULAASPC1PSAS64_AACGCGAAC_L003_R1_001
- GSM7991078 ASPC1PSA_PS01ULAASPC1PSAS65_TGTGCATAC_L003_R1_001
- GSM7991079 ASPC1PSA_PS01ULAASPC1PSAS72_CATTTGGGC_L003_R1_001
- GSM7991080 ASPC1PSA_PS01ULAASPC1PSAS73_CGATTTAGC_L003_R1_001
- GSM7991081 ASPC1PSA_PS01ULAASPC1PSAS74_CAATGCCAC_L003_R1_001
- GSM7991082 ASPC1PSA_PS01ULAASPC1PSAS75_AATGGGCTG_L003_R1_001
- GSM7991083 ASPC1PSA_PS01ULAASPC1PSAS77_TACCTTCGT_L003_R1_001
- GSM7991084 ASPC1PSA_PS01ULAASPC1PSAS83_ATGTGCTGG_L003_R1_001
- GSM7991085 ASPC1PSA_PS01ULAASPC1PSAS84_TCTATGCGA_L003_R1_001
- GSM7991086 ASPC1PSA_PS01ULAASPC1PSAS86_AAGACGTGC_L003_R1_001
- GSM7991087 ASPC1PSA_PS01ULAASPC1PSAS87_CGGTTGTAT_L003_R1_001
- GSM7991088 ASPC1PSA_PS01ULAASPC1PSAS88_GCATTATGC_L003_R1_001
- GSM7991089 ASPC1PSA_PS01ULAASPC1PSAS95_GAATCGAGA_L003_R1_001
- GSM7991090 ASPC1PSA_PS01ULAASPC1PSAS96_TCCAGGTTT_L003_R1_001
- GSM7991091 ASPC1PSB_PS02ULAASPC1PSBS97_CCGACTAAA_L003_R1_001
- GSM7991092 ASPC1PSB_PS02ULAASPC1PSBS99_TGAATCCGA_L003_R1_001
+ 112 more — browse all 152 samples with per-sample file links →
- GSE275078 In vitro drug screening of RAF/MEK/ERK Inhibitors in combination with Autophagy inhibition in the context of two PDA Cell Lines 136 samples shares raf, mek, erk, pda, inhibitors, screening, vitro, lines
- GSE272460 Transcriptome analysis of pancreatic progenitor organoids (POs) carrying PDAC related mutations, treated with ERK inhibitors and depleted of AP-1 factors 40 samples shares erk, pda, inhibitors
- GSE280160 In vitro arrayed OVOL2 overexpression in PDA Cell Lines (bulk RNA-Seq) 51 samples shares pda, vitro, lines
- GSE277629 RNA-seq of AsPC-1 cell line-derived xenograft mouse tumors treated with a MEK-RAF molecular glue, IK-595 24 samples shares raf, mek
- GSE306221 Transient inhibition of MEK/ERK and WNT pathways enhances direct differentiation of primed hPSCs into functional trophoblast stem cells 18 samples shares mek, erk
- GSE256123 Lactylation of LSD1 is an acquired epigenetic vulnerability of BRAFi/MEKi-resistant melanoma [ChIP-seq] 14 samples shares raf, mek
- GSE161368 TF overexpression assay in PDA cell lines (Bulk RNA-Seq) 69 samples shares pda, lines
- GSE319124 Next-GenerationPediatric Cancer Models: Patient-Matched Orthotopic Xenografts, 3D Organoids, 2D Cultures, and Cell Lines for Identification of Therapeutic Vulnerabilities in Pediatric Solid Tumors [scRNAseq] 59 samples shares raf, lines
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.