GEO series
TP53 Mutation is a Prognostic Biomarker for Patients with Advanced RAS Wild-Type Colorectal Adenocarcinoma Treated with Cetuximab and Pembrolizumab [dataset 1]
GSE302917
Homo sapiens (human)
RNA-seq (gene expression)
28 samples
Submitted 2025/12/03
Platform GPL24676
At a glance
RNA-seq (gene expression) · human · 28 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
- Colorectal cancerThis series is part of our curated briefing on it.
- Samples
- 28 samples: 28 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
- TP53 mutation is associated with improved disease control in patients with advanced RAS wild-type colorectal adenocarcinoma treated with cetuximab and pembrolizumabInternational journal of cancer · 2026
Summary (written by the submitters)
Immunotherapy with checkpoint inhibitors targeting the PD1/PD-L1 and CTLA4 pathways has limited activity in patients with microsatellite stable (MSS) colorectal adenocarcinoma (CRC). In a prior study, the combination of cetuximab and pembrolizumab failed to improve outcomes for patients with advanced RAS wild-type (RASwt) CRC. In this post hoc secondary analysis, we show that patients with TP53 mutant (p53mt) tumors had significantly higher progression-free survival (PFS) and decrease in tumor burden compared to patients with TP53 wild-type (p53wt) tumors.
Read the full summary ↓
The gene set enrichment analysis showed uniform upregulation in several metabolic gene sets and most immune gene sets including NK-mediated immunity and IL-12 pathway while the IL6 pathway was downregulated. While there were no overlapping transcriptional alterations between the p53mt and p53wt groups with treatment that remain constant despite the therapeutic intervention, we did identify functional overlap with treatment in both groups in proliferative, immune, and metabolic pathways. In the pre-treatment tumor samples, the number of PD-L1+ tumor cells was significantly higher in p53mt tumors while the number of exhausted-1 immune cells (defined as OX40-/AE1_AE3-/PD-L1- cells, positive for 1/3 exhaustion markers, LAG3 or CTLA4 or TIM3) was significantly higher in p53wt tumors. In conclusion, TP53 status was prognostic of improved disease control with cetuximab plus pembrolizumab in RASwt CRC. Future studies evaluating immune-oncology agents in patients with MSS, RASwt CRC should include TP53 as an integrated biomarker and evaluate its performance as a positive predictive biomarker (ClinicalTrials.gov NCT02713373).
Published in
Fountzilas C, Rosario S, Witkiewicz AK et al.
· International journal of cancer 2026
· PMID 41793309
· doi:10.1002/ijc.70434
This dataset
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.
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 GSE302917_RAW.tar (it can run to tens of gigabytes).
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.
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 PRJNA1292819. Searching any of these in the dataset finder brings you back here.
Study design
28 samples, each with a distinct title — no replicated groups
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 28 sample titles: 28 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.
Samples in this study
- GSM9113605 Patient 1 Baseline Transcriptomics, Male, Stable Disease
- GSM9113606 Patient 1 Post-Treatment Transcriptomics, Male, Stable Disease
- GSM9113607 Patient 12 Baseline Transcriptomics, Male, Stable Disease
- GSM9113608 Patient 12 Post-Treatment Transcriptomics, Male, Stable Disease
- GSM9113609 Patient 14 Baseline Transcriptomics, Male, Stable Disease
- GSM9113610 Patient 14 Post-Treatment Transcriptomics, Male, Stable Disease
- GSM9113611 Patient 17 Baseline Transcriptomics, Male, Stable Disease
- GSM9113612 Patient 17 Post-Treatment Transcriptomics, Male, Stable Disease
- GSM9113613 Patient 18 Baseline Transcriptomics, Male, Progressive Disease
- GSM9113614 Patient 18 Post-Treatment Transcriptomics, Male, Progressive Disease
- GSM9113615 Patient 19 Baseline Transcriptomics, Male, Stable Disease
- GSM9113616 Patient 19 Post-Treatment Transcriptomics, Male, Stable Disease
- GSM9113617 Patient 22 Baseline Transcriptomics, Male, Progressive Disease
- GSM9113618 Patient 22 Post-Treatment Transcriptomics, Male, Progressive Disease
- GSM9113619 Patient 35 Baseline Transcriptomics, Male, Stable Disease
- GSM9113620 Patient 35 Post-Treatment Transcriptomics, Male, Stable Disease
- GSM9113621 Patient 37 Baseline Transcriptomics, Female, Progressive Disease
- GSM9113622 Patient 37 Post-Treatment Transcriptomics, Female, Progressive Disease
- GSM9113623 Patient 39 Baseline Transcriptomics, Male, Stable Disease
- GSM9113624 Patient 39 Post-Treatment Transcriptomics, Male, Stable Disease
- GSM9113625 Patient LH-02 Baseline Transcriptomics, Male, Stable Disease
- GSM9113626 Patient LH-04 Baseline Transcriptomics, Male, Stable Disease
- GSM9113627 Patient LH-05 Baseline Transcriptomics, Male, Stable Disease
- GSM9113628 Patient LH-06 Baseline Transcriptomics, Male, Stable Disease
- GSM9113629 Patient 16 Baseline Transcriptomics, Female, Stable Disease
- GSM9113630 Patient 21 Baseline Transcriptomics, Male, Stable Disease
- GSM9113631 Patient 32 Baseline Transcriptomics, Male, Partial Regression
- GSM9113632 Patient 38 Baseline Transcriptomics, Male, Progressive Disease
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- GSE12032 Inherent gene signature of stage II and III colorectal cancer leads in individual recurrence 198 samples same disease briefing · shares colorectal
- GSE109308 Single-cell transcriptomic survey of adenomatous polyposis patients with germline APC mutations 97 samples same disease briefing
- GSE110009 Single cell transcriptome landscape of primary and metastatic tumor in colon cancer 76 samples same disease briefing
- GSE333925 Gene expression profile of 31 RAS/RAF-wild-type colorectal cancer (CRC) stem-cell (SC) spheroids and 7 FGFR-inhibitor resistant CSC-SC spheroids 38 samples shares ras, colorectal, wild-type
- GSE300200 The Lysine Demethylase Kdm4C is an Oncogenic Driver and Regulates ERK Activity in KRAS Mutant Pancreatic Ductal Adenocarcinoma 26 samples shares ras, adenocarcinoma
- GSE310252 Transcriptome-based classification of resected pancreatic ductal adenocarcinoma enhances prognostic modelling accuracy of overall survival following adjuvant treatment 118 samples shares adenocarcinoma, prognostic
- GSE307398 Phase II Study of Pembrolizumab Plus Capecitabine and Bevacizumab for Microsatellite Stable/Mismatch Repair-Proficient Metastatic Colorectal Cancer 12 samples shares pembrolizumab, colorectal
Share this dataset
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.