In vitro drug screening of RAF/MEK/ERK Inhibitors in combination with Autophagy inhibition in the context of two PDA Cell Lines
RNA-seq (gene expression) · human · 136 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
- 136 samples: 136 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.
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 GSE275078_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 PRJNA1149068 and SRA study SRP526937. 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 136 sample titles: 136 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.
- GSM8465262 ASPC1PSA_PS01ULAASPC1PSAS6_TCCTTTGCC_L003_R1_001, ASPC1, VSP34I-Alone
- GSM8465263 ASPC1PSA_PS01ULAASPC1PSAS18_CCCAATAGC_L003_R1_001, ASPC1, VSP34I-Alone
- GSM8465264 ASPC1PSA_PS01ULAASPC1PSAS30_AGATTCCTG_L003_R1_001, ASPC1, VSP34I-Alone
- GSM8465265 ASPC1PSA_PS01ULAASPC1PSAS42_TCTATCTCC_L003_R1_001, ASPC1, Hydroxychloroquine-Alone
- GSM8465266 ASPC1PSA_PS01ULAASPC1PSAS54_ACCCTCTCA_L003_R1_001, ASPC1, Hydroxychloroquine-Alone
- GSM8465267 ASPC1PSA_PS01ULAASPC1PSAS66_CCAATACAC_L003_R1_001, ASPC1, Hydroxychloroquine-Alone
- GSM8465268 ASPC1PSA_PS01ULAASPC1PSAS7_CGAGTAGAG_L003_R1_001, ASPC1, AZD0364-Hydroxychloroquine
- GSM8465269 ASPC1PSA_PS01ULAASPC1PSAS9_GTCTTAAGC_L003_R1_001, ASPC1, Ulixertinib-Hydroxychloroquine
- GSM8465270 ASPC1PSA_PS01ULAASPC1PSAS10_CTTTCACCT_L003_R1_001, ASPC1, Dabrafenib-VSP34I
- GSM8465271 ASPC1PSA_PS01ULAASPC1PSAS11_TGGAAGTGT_L003_R1_001, ASPC1, Refamatinib-VSP34I
- GSM8465272 ASPC1PSA_PS01ULAASPC1PSAS19_TAGTTCGGG_L003_R1_001, ASPC1, Binimetinib-Hydroxychloroquine
- GSM8465273 ASPC1PSA_PS01ULAASPC1PSAS20_TATGTCAGC_L003_R1_001, ASPC1, PLX8394-Hydroxychloroquine
- GSM8465274 ASPC1PSA_PS01ULAASPC1PSAS23_GCCAAACGA_L003_R1_001, ASPC1, SCH772984-VSP34I
- GSM8465275 ASPC1PSA_PS01ULAASPC1PSAS32_ACGGTAGAC_L003_R1_001, ASPC1, RAF709-Hydroxychloroquine
- GSM8465276 ASPC1PSA_PS01ULAASPC1PSAS33_CTGATGTCT_L003_R1_001, ASPC1, Sorafenib-VSP34I
- GSM8465277 ASPC1PSA_PS01ULAASPC1PSAS35_CTCCCCTTT_L003_R1_001, ASPC1, Selumetinib-VSP34I
- GSM8465278 ASPC1PSA_PS01ULAASPC1PSAS43_CATTGTTGG_L003_R1_001, ASPC1, Cobimetinib-Hydroxychloroquine
- GSM8465279 ASPC1PSA_PS01ULAASPC1PSAS44_GTGGCCAAA_L003_R1_001, ASPC1, Ravoxertinib-Hydroxychloroquine
- GSM8465280 ASPC1PSA_PS01ULAASPC1PSAS46_TTAACCCCG_L003_R1_001, ASPC1, GDC0879-VSP34I
- GSM8465281 ASPC1PSA_PS01ULAASPC1PSAS47_TCCAGACTA_L003_R1_001, ASPC1, Trametinib-VSP34I
- GSM8465282 ASPC1PSA_PS01ULAASPC1PSAS55_TTTTCTGCG_L003_R1_001, ASPC1, Dabrafenib-Hydroxychloroquine
- GSM8465283 ASPC1PSA_PS01ULAASPC1PSAS56_GCTTCGACA_L003_R1_001, ASPC1, Refamatinib-Hydroxychloroquine
- GSM8465284 ASPC1PSA_PS01ULAASPC1PSAS57_TATGGCGAT_L003_R1_001, ASPC1, AZD0364-VSP34I
- GSM8465285 ASPC1PSA_PS01ULAASPC1PSAS59_CACCTACAA_L003_R1_001, ASPC1, Ulixertinib-VSP34I
- GSM8465286 ASPC1PSA_PS01ULAASPC1PSAS68_CTCGTTACC_L003_R1_001, ASPC1, SCH772984-Hydroxychloroquine
- GSM8465287 ASPC1PSA_PS01ULAASPC1PSAS69_ACTGCATGG_L003_R1_001, ASPC1, Binimetinib-VSP34I
- GSM8465288 ASPC1PSA_PS01ULAASPC1PSAS70_ATTGCGACT_L003_R1_001, ASPC1, PLX8394-VSP34I
- GSM8465289 ASPC1PSA_PS01ULAASPC1PSAS78_GGGCTTCAA_L003_R1_001, ASPC1, Sorafenib-Hydroxychloroquine
- GSM8465290 ASPC1PSA_PS01ULAASPC1PSAS80_TGAAGCATC_L003_R1_001, ASPC1, Selumetinib-Hydroxychloroquine
- GSM8465291 ASPC1PSA_PS01ULAASPC1PSAS82_AAAACGGCG_L003_R1_001, ASPC1, RAF709-VSP34I
- GSM8465292 ASPC1PSA_PS01ULAASPC1PSAS91_GTCATGTAG_L003_R1_001, ASPC1, GDC0879-Hydroxychloroquine
- GSM8465293 ASPC1PSA_PS01ULAASPC1PSAS92_AAGCCTGAC_L003_R1_001, ASPC1, Trametinib-Hydroxychloroquine
- GSM8465294 ASPC1PSA_PS01ULAASPC1PSAS93_TAACCCGTC_L003_R1_001, ASPC1, Cobimetinib-VSP34I
- GSM8465295 ASPC1PSA_PS01ULAASPC1PSAS94_TGTTGATGC_L003_R1_001, ASPC1, Ravoxertinib-VSP34I
- GSM8465296 ASPC1PSB_PS02ULAASPC1PSBS102_TCCGCAGAA_L003_R1_001, ASPC1, VSP34I-Alone
- GSM8465297 ASPC1PSB_PS02ULAASPC1PSBS114_CATGAAGTC_L003_R1_001, ASPC1, VSP34I-Alone
- GSM8465298 ASPC1PSB_PS02ULAASPC1PSBS126_TCCGGTTGA_L003_R1_001, ASPC1, VSP34I-Alone
- GSM8465299 ASPC1PSB_PS02ULAASPC1PSBS138_TCCAACACG_L003_R1_001, ASPC1, Hydroxychloroquine-Alone
- GSM8465300 ASPC1PSB_PS02ULAASPC1PSBS150_AAAGCGTCT_L003_R1_001, ASPC1, Hydroxychloroquine-Alone
- GSM8465301 ASPC1PSB_PS02ULAASPC1PSBS162_TAGTCCGTA_L003_R1_001, ASPC1, Hydroxychloroquine-Alone
+ 96 more — browse all 136 samples with per-sample file links →
- 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
- 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, inhibition
- 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
- GSE256123 Lactylation of LSD1 is an acquired epigenetic vulnerability of BRAFi/MEKi-resistant melanoma [ChIP-seq] 14 samples shares raf, mek
- GSE281506 Combining Menin and MEK inhibition to target poor prognosis KMT2A-rearranged RAS pathway-mutant acute myeloid leukemia 42 samples shares mek, inhibition
- GSE319664 RNA-seq analysis of tumors derived from a xenograft model treated with the KDM5B inhibitors JB-157 or JB-161. 29 samples shares raf, inhibitors
- GSE312764 VEGF/ERK activation and PI3K inhibition together drive a vein-to-artery transition in an in vitro model of human angiogenesis 21 samples shares erk, inhibition
- GSE318418 Immunosuppressive monocytes are enriched in hepatocellular carcinoma patients with liver dysfunction in a phase II trial of combination sorafenib and nivolumab [scRNA-Seq] 16 samples shares raf, combination
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.