GEO series

Microbial cancer immunotherapy reprograms hematopoiesis to enhance myeloid-driven anti-tumor immunity [Human_Mulitome_Cohort1]

GSE295277 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 14 samples Submitted 2025/04/22 Platform GPL24676

At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 14 samples

What it is: ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism: Homo sapiens (human)
Samples: 14 samples: 14 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: Microbial cancer immunotherapy reprograms hematopoiesis to enhance myeloid-driven anti-tumor immunityCancer cell · 2025

Summary (written by the submitters)

Mycobacterium bovis Bacillus Calmette-Guérin (BCG) is the vaccine against tuberculosis and an immunotherapy for bladder cancer. When administered intravenously, BCG reprograms bone marrow hematopoietic stem and progenitor cells (HSPCs), leading to heterologous protection against infections. Whether HSPC reprogramming contributes to the anti-tumor effects of BCG administered into the bladder is unknown.

Read the full summary ↓

We demonstrate that BCG administered in the bladder colonizes the bone marrow and, in both mice and humans, reprograms HSPCs to alter and amplify myelopoiesis. BCG-reprogrammed HSPCs are sufficient to confer augmented anti-tumor immunity through production of neutrophils, monocytes, and dendritic cells that broadly remodel the tumor microenvironment, drive T cell-dependent anti-tumor responses, and synergize with checkpoint blockade. We conclude that bladder BCG acts systemically through hematopoiesis, highlighting the broad potential of HSPC reprogramming to enhance the innate drivers of T cell-dependent tumor immunity.

Published in

Microbial cancer immunotherapy reprograms hematopoiesis to enhance myeloid-driven anti-tumor immunity

Daman AW, Antonelli AC, Redelman-Sidi G et al. · Cancer cell 2025 · PMID 40446799 · doi:10.1016/j.ccell.2025.05.002

This dataset

Open GSE295277 in finder → Find more human RNA-seq datasets →

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 GSE295277_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1254106 and SRA study SRP580421. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40446799) ↗

Study design

14 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 14 sample titles: 14 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

GSM8944726 PBMC_Pie_Pre_RNA
GSM8944727 PBMC_Pie_Pre_ATAC
GSM8944728 PBMC_Pool_Pre_RNA
GSM8944729 PBMC_Pool_Pre_ATAC
GSM8944730 PBMC_Post_RNA
GSM8944731 PBMC_Post_ATAC
GSM8944732 PBMC_Pre_RNA
GSM8944733 PBMC_Pre_ATAC
GSM8944734 PIE_Post_RNA
GSM8944735 PIE_Post_ATAC
GSM8944736 PIE_Post_1_RNA
GSM8944737 PIE_Post_1_ATAC
GSM8944738 PIE_Post_2_RNA
GSM8944739 PIE_Post_2_ATAC

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