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Impact of immune repertoire in Muscle – Invasive Bladder Cancer (MIBC) on predicting response and survival after neoadjuvant cisplatin-based chemotherapy (NAC)

GSE247185 Homo sapiens (human) RNA-seq (gene expression) 13 samples Submitted 2024/08/16 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 13 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
Bladder cancerThis series is part of our curated briefing on it.
Samples
13 samples: 13 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
Impact of intra-tumoral immunity on predicting response and survival after neoadjuvant cisplatin-based chemotherapy in patients with muscle invasive bladder cancerCancer medicine · 2024
Summary (written by the submitters)
Purpose: Neoadjuvant cisplatin-based Chemotherapy (NAC) followed by cystectomy is the standard of care for patients with muscle-invasive bladder cancer (MIBC). Unfortunately, only a third of patients achieve pathologic complete response (pCR). The aim of this study is to deeply investigate the role played by peripheral immunogenicity and the Tumor and Immune Microenvironment (TIME) in patients undergoing NAC to identify predictive and pharmacodynamic biomarkers associated with treatment response or treatment resistance.
Read the full summary ↓
Experimental design: N= 19 patients with MIBC received NAC and were classified as pCR (n=10) or non-pCR (n=9). Bulk RNA-seq was performed on 13 formalin-fixed paraffin-embedded (FFPE) tumor biopsies collected before treatment with NAC and immune protein evaluations using GeoMx Digital Spatial Profiling (DSP) technology were performed on matched FFPE tumor biopsies collected before and after treatment with NAC. Matching Peripheral blood collected prior treatment initiation and during cystectomy was assessed for lymphocytes and neutrophils counts. Kaplan–Meier analyses and Cox PH regression models were used for survival analyses (OS). Results: Genes associated with immune signaling and DNA repair signaling correlated with treatment outcome of patients with MIBC undergoing NAC. At baseline, gene expression analysis and digital special profile highlighted an abundance of B cells and a reduced neuroendocrine phenotype in the TME of pCR vs. no-pCR. Increased protein expression of immune checkpoints molecules (e.g. LAG3, ICOS, PD-L1) was also observed in the TME of patients with no-pCR. In peripheral blood, a significant increase of lymphocytes and decrease of neutrophils counts, respectively, was observed in pCR vs no-pCR following NAC. Conclusions: When confirmed on a larger cohort, our results might lead to novel therapeutic strategies and to novel biomarkers predicting patients likely to respond to NAC.
Published in
Impact of intra-tumoral immunity on predicting response and survival after neoadjuvant cisplatin-based chemotherapy in patients with muscle invasive bladder cancer
Mendoza-Valderrey A, Choe J, Kessler DM et al. · Cancer medicine 2024 · PMID 39119802 · doi:10.1002/cam4.70088
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 GSE247185_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 PRJNA1036594 and SRA study SRP470568. Searching any of these in the dataset finder brings you back here.

Study design
13 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 13 sample titles: 13 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 Bladder cancer datasets

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