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Identification of four immune subtypes in locally advanced rectal cancer treated with neoadjuvant chemotherapy for predicting the efficacy of subsequent immune checkpoint blockade

GSE213331 Homo sapiens (human) RNA-seq (gene expression) 27 samples Submitted 2022/09/17 Platform GPL16791
At a glance

RNA-seq (gene expression) · human · 27 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
27 samples
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.
Summary (written by the submitters)
Introduction: Neoadjuvant chemoradiotherapy (nCRT) was the foundation treatment for locally advanced rectal cancer (LARC). The nCRT can improve the efficacy of immunotherapy on account of its in-situ vaccine effect. Objective: The aim is to identify stable and reliable transcriptome signatures to predict the efficacy of immune checkpoint blockade (ICB) in patients with LARC.
Read the full summary ↓
Methods: Immunophenotyping was established by using xCell immune cell infiltration abundance and consistent clustering in GSE39582 and verified in several data sets. The effects of immunophenotyping, follicular regulatory T cells, tumor associated fibroblasts (CAF) and tertiary lymphoid structures (TLS) signatures on the efficacy of ICB were analyzed by using IMvigor210, GSE91061 and an independent Daping Hospital (DPH) cohort. Results: There are four stable and repeatable immune subtypes in rectal cancer, among which C1 is low immune infiltration type, C2 is high interstitial infiltration type, C3 is high immune infiltration type and C4 is ion channel type. C2 is mainly characterized by high infiltration CAF, C3 is characterized by high infiltration of cytotoxic T lymphocytes, high expression of PD-L1 and TLS. In rectal cancer patients receiving nCRT, immunophenotyping was not significantly associated with pathological remission rate, but immunophenotyping was an independent prognostic factor of RFS. In IMvigor210 patients treatment with atezolizumab, the pathological remission rates of C1, C2, C3 and C4 were 23.86%, 10.94%, 33.33% and 23.08% respectively (χ2 = 8.981, P = 0.029), which were 11.76%, 50.00%, 42.86% and 0.0% respectively in the GSE91061 patients treatment with nivolumab (Fisher's exact probability, P = 0.018). Both follicular regulatory T cells and CAF showed a further impact on the ICB therapeutic efficacy of C2 and C3 subtype. In addition, both GSE91404 and DPH cohorts showed that nCRT treatment induced a significant increase in the expression of TNFRSF9 and abundance of macrophages in C3 subtype. Conclusion: Our data suggest that there are four immune types of rectal cancer, which are related to the prognosis of patients. Among them, C3 and some C2 subtypes represents the patients who may benefit from ICB after nCRT treatment.
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 GSE213331_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 PRJNA880566. Searching any of these in the dataset finder brings you back here.

Samples in this study

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