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Phenotypic plasticity and increased tissue infiltration of TREM1+ mono-macrophages following radiotherapy in rectal cancer. [RNA-Seq]

GSE278405 Homo sapiens (human) RNA-seq (gene expression) 16 samples Submitted 2024/11/20 Platform GPL29480
At a glance

RNA-seq (gene expression) · human · 16 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
16 samples: 16 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
Phenotypic plasticity and increased infiltration of peripheral blood-derived TREM1(+) mono-macrophages following radiotherapy in rectal cancerCell reports. Medicine · 2025
Summary (written by the submitters)
Our previously reported phase II and phase III trials have demonstrated that short-course radiotherapy (SCRT) combined with neoadjuvant immunochemotherapy (SIC) has led to clinical benefits in locally advanced rectal cancer (LARC). Characterization of the molecular mechanisms underlying responses to SIC may lead to improved treatment strategies.
Read the full summary ↓
Here, we prospectively collected and applied multi-omic analyses to paired pre- and post-treatment LARC specimens undergoing SIC. The TREM1+ mono-macrophages subsets that display a pro-inflammatory phenotype are identified and correlate with complete response to SIC. Mechanically, ionizing radiation (IR) induces peripheral TREM1+ mono-macrophages expansion in tumors. Following IR, loss of TREM1 in mono-macrophages undermines antitumor immunity by altering mono-macrophages differentiation and inhibiting CD8+ T cell infiltration and activation. The TREM1+ mono-macrophages response may rely on activation of key inflammatory pathways, including NF-κB signaling and Toll-like receptor pathway. Pharmacological inhibition of TREM1 abolishes IR-induced immunoactivation and reduces combined IR and/or anti-PD-1 treatment. Thus, we establish a crucial role of a mono-macrophages state in mediating effective cancer therapy.
Published in
Phenotypic plasticity and increased infiltration of peripheral blood-derived TREM1(+) mono-macrophages following radiotherapy in rectal cancer
Wang H, Zhai M, Li M et al. · Cell reports. Medicine 2025 · PMID 39793571 · doi:10.1016/j.xcrm.2024.101887
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 GSE278405_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 PRJNA1167199. Searching any of these in the dataset finder brings you back here.

Study design
16 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 16 sample titles: 16 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
More Colorectal cancer datasets

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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.