GEO series

Tumor-intrinsic kinome landscape of pancreatic cancer reveals new therapeutic approaches

GSE278757 Homo sapiens (human) RNA-seq (gene expression) 243 samples Submitted 2024/11/01 Platform GPL24676Platform GPL18573

At a glance

RNA-seq (gene expression) · human · 243 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: Pancreatic cancerThis series is part of our curated briefing on it.
Samples: 243 samples: 15 conditions, mostly with about 6 replicates eachGroups 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: Tumor-Intrinsic Kinome Landscape of Pancreatic Cancer Reveals New Therapeutic ApproachesCancer discovery · 2025

Summary (written by the submitters)

Effective therapies for pancreatic ductal adenocarcinoma (PDAC) have been largely elusive. Prior proteogenomic studies of PDAC have been limited to bulk samples limiting the ability to define tumor-intrinsic targets and understanding tumor heterogeneity. Here, we perform Multiplexed kinase Inhibitor Beads and Mass Spectrometry (MIB-MS) on 102 patient derived xenografts (PDX) derived from 14 unique primary PDAC to define the tumor-intrinsic kinome landscape.

Read the full summary ↓

Our findings uncover three kinome subgroups making up two dominant tumor-intrinsic kinome subtypes that we call kinotypes. The kinotypes show enrichment of different kinase classes and explain the biological differences in previously described molecular subtypes, basal-like and classical. The kinotype characterizing basal-like tumors shows enrichment of receptor tyrosine kinases, whereas the kinotype characterizing classical tumors is enriched in understudied kinases involved in Wnt signaling and immune pathways. We validate our findings in two clinical trials and show that only patients with basal-like kinotype tumors, who are thought to be refractory to chemotherapy, derive significant benefit from EGFR inhibitors. Our results provide a comprehensive tumor-intrinsic kinome landscape of PDAC that strongly supports actionable kinotype specific kinase targets, potential for sensitive quantitation of patient samples, and provides a roadmap for the use of kinase inhibitors for the treatment of PDAC.

Published in

Tumor-Intrinsic Kinome Landscape of Pancreatic Cancer Reveals New Therapeutic Approaches

Xu Y, Peng XL, East MP et al. · Cancer discovery 2025 · PMID 39632628 · doi:10.1158/2159-8290.CD-23-1480

This dataset

Open GSE278757 in finder → Find more human RNA-seq datasets → Pancreatic cancer landscape →

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 GSE278757_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 PRJNA1168601 and SRA study SRP536524. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39632628) ↗

Study design

15 conditions, mostly with about 6 replicates each

RASH_sample ×85 ACCEPT_sample ×70 PDAC PDX, Sample 6, ×13 PDAC PDX, Sample 8, ×12 PDAC PDX, Sample 10, ×12 PDAC PDX, Sample 1, ×9 PDAC PDX, Sample 4, ×9 PDAC PDX, Sample 2, ×6 +7 more

Supports a between-group comparison across 243 samples.

How we read this: 15 replicated groups read from 243 sample titles; they account for 243 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

GSM8554363 PDAC PDX, Sample 1, replicate 1 PDAC PDX, Sample 1,
GSM8554364 PDAC PDX, Sample 1, replicate 2 PDAC PDX, Sample 1,
GSM8554365 PDAC PDX, Sample 1, replicate 3 PDAC PDX, Sample 1,
GSM8554366 PDAC PDX, Sample 2, replicate 2 PDAC PDX, Sample 2,
GSM8554367 PDAC PDX, Sample 3, replicate 1 PDAC PDX, Sample 3,
GSM8554368 PDAC PDX, Sample 3, replicate 2 PDAC PDX, Sample 3,
GSM8554369 PDAC PDX, Sample 3, replicate 3 PDAC PDX, Sample 3,
GSM8554370 PDAC PDX, Sample 4, replicate 1 PDAC PDX, Sample 4,
GSM8554371 PDAC PDX, Sample 4, replicate 2 PDAC PDX, Sample 4,
GSM8554372 PDAC PDX, Sample 4, replicate 3 PDAC PDX, Sample 4,
GSM8554373 PDAC PDX, Sample 5, replicate 1 PDAC PDX, Sample 5,
GSM8554374 PDAC PDX, Sample 5, replicate 2 PDAC PDX, Sample 5,
GSM8554375 PDAC PDX, Sample 5, replicate 3 PDAC PDX, Sample 5,
GSM8554376 PDAC PDX, Sample 6, replicate 1 PDAC PDX, Sample 6,
GSM8554377 PDAC PDX, Sample 6, replicate 2 PDAC PDX, Sample 6,
GSM8554378 PDAC PDX, Sample 6, replicate 3 PDAC PDX, Sample 6,
GSM8554379 PDAC PDX, Sample 7, replicate 1 PDAC PDX, Sample 7,
GSM8554380 PDAC PDX, Sample 7, replicate 2 PDAC PDX, Sample 7,
GSM8554381 PDAC PDX, Sample 7, replicate 3 PDAC PDX, Sample 7,
GSM8554382 PDAC PDX, Sample 8, replicate 1 PDAC PDX, Sample 8,
GSM8554383 PDAC PDX, Sample 8, replicate 2 PDAC PDX, Sample 8,
GSM8554384 PDAC PDX, Sample 8, replicate 3 PDAC PDX, Sample 8,
GSM8554385 PDAC PDX, Sample 9, replicate 1 PDAC PDX, Sample 9,
GSM8554386 PDAC PDX, Sample 9, replicate 2 PDAC PDX, Sample 9,
GSM8554387 PDAC PDX, Sample 9, replicate 3 PDAC PDX, Sample 9,
GSM8554388 PDAC PDX, Sample 10, replicate 1 PDAC PDX, Sample 10,
GSM8554389 PDAC PDX, Sample 10, replicate 2 PDAC PDX, Sample 10,
GSM8554390 PDAC PDX, Sample 10, replicate 3 PDAC PDX, Sample 10,
GSM8554391 PDAC PDX, Sample 1, replicate 4 PDAC PDX, Sample 1,
GSM8554392 PDAC PDX, Sample 1, replicate 5 PDAC PDX, Sample 1,
GSM8554393 PDAC PDX, Sample 1, replicate 6 PDAC PDX, Sample 1,
GSM8554394 PDAC PDX, Sample 11, replicate 2 PDAC PDX, Sample 11,
GSM8554395 PDAC PDX, Sample 11, replicate 3 PDAC PDX, Sample 11,
GSM8554396 PDAC PDX, Sample 7, replicate 4 PDAC PDX, Sample 7,
GSM8554397 PDAC PDX, Sample 7, replicate 5 PDAC PDX, Sample 7,
GSM8554398 PDAC PDX, Sample 7, replicate 6 PDAC PDX, Sample 7,
GSM8554399 PDAC PDX, Sample 2, replicate 4 PDAC PDX, Sample 2,
GSM8554400 PDAC PDX, Sample 2, replicate 5 PDAC PDX, Sample 2,
GSM8554401 PDAC PDX, Sample 2, replicate 6 PDAC PDX, Sample 2,
GSM8554402 PDAC PDX, Sample 12, replicate 1 PDAC PDX, Sample 12,

+ 203 more — browse all 243 samples with per-sample file links →

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