GEO series

Cathepsin L as a dual-target to mitigate muscle wasting while enhancing anti-tumor efficacy of anti-PD-L1 [Bulk2]

GSE278396 Mus musculus (mouse) Single-cell RNA-seq 15 samples Submitted 2024/10/08 Platform GPL30215

At a glance

Single-cell RNA-seq · mouse · 15 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism: Mus musculus (mouse)
Samples: 15 samples: 5 × CON vs 4 × IgG vs 3 × anti-PD-L1 vs 3 × anti-CD8aGroups 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: Cathepsin L as a dual-target to mitigate muscle wasting while enhancing anti-tumor efficacy of anti-PD-L1Nature communications · 2025

Summary (written by the submitters)

Immune checkpoint inhibitors (ICIs) have revolutionized cancer therapy; however, their use is frequently associated with immune-related adverse events (irAEs). In this study, anti-PD-L1 therapy exacerbates muscle wasting in tumor-bearing male mice despite its anti-tumor efficacy, accompanied by an accumulation of CD8+ T cells in muscle.

Read the full summary ↓

Single-cell RNA sequencing identifies these cells as tissue-resident memory-like CD49a+ CD8+ T cells. While CD8+ T cell depletion prevents muscle wasting, it compromises the anti-tumor efficacy of anti-PD-L1. To resolve this paradox, we identify cathepsin L (CTSL) as a dual-target capable of suppressing both tumor progression and CD8+ T cell-mediated muscle wasting, through integrative transcriptomic analysis. Pharmacological inhibition of CTSL not only mitigates anti-PD-L1-induced muscle wasting but also further suppresses tumor growth, potentially via downregulation of BNIP3. Here, we show that CTSL is a dual-action target to uncouple anti-tumor efficacy from muscle-specific irAEs, offering a strategy to improve clinical outcomes of ICIs.

Published in

Cathepsin L as a dual-target to mitigate muscle wasting while enhancing anti-tumor efficacy of anti-PD-L1

Park SY, Son K, Kim J et al. · Nature communications 2025 · PMID 41315185 · doi:10.1038/s41467-025-64500-0

This dataset

Open GSE278396 in finder → Find more mouse 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 GSE278396_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1167182 and SRA study SRP535687. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41315185) ↗

Study design

5 × CON vs 4 × IgG vs 3 × anti-PD-L1 vs 3 × anti-CD8a

Supports a between-group comparison across 15 samples.

How we read this: 4 replicated groups read from 15 sample titles; they account for 15 of them. 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

GSM8545834 CON_1 CON
GSM8545835 CON_2 CON
GSM8545836 CON_3 CON
GSM8545837 CON_4 CON
GSM8545838 CON_5 CON
GSM8545839 IgG_1 IgG
GSM8545840 IgG_2 IgG
GSM8545841 IgG_3 IgG
GSM8545842 IgG_4 IgG
GSM8545843 anti-PD-L1_1 anti-PD-L1
GSM8545844 anti-PD-L1_2 anti-PD-L1
GSM8545845 anti-PD-L1_3 anti-PD-L1
GSM8545846 anti-CD8a_1 anti-CD8a
GSM8545847 anti-CD8a_2 anti-CD8a
GSM8545848 anti-CD8a_3 anti-CD8a

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GSE231355 Syndecan-1 inhibition promotes anti-tumor immune response and facilitates the efficacy of anti-PD1 checkpoint immunotherapy [kdSdc1_RNAseq] 12 samples shares anti-tumor, efficacy
GSE271312 Selpercatinib mitigates cancer cachexia independent of anti-tumor activity 12 samples shares anti-tumor, mitigate
GSE254619 Syndecan-1 inhibition promotes anti-tumor immune response and facilitates the efficacy of anti-PD1 checkpoint immunotherapy (kdSdc1 and mutant) 10 samples shares anti-tumor, efficacy
GSE328461 Osteopontin mediates acquired resistance to hypoxia-inducing antiangiogenics and promotes anti-PD-L1 refractoriness in breast cancer models 37 samples shares anti-pd-l1
GSE241539 Osteopontin links acquired resistance to hypoxia-inducing antiangiogenics with refractoriness to anti-PD-L1 agents in breast cancer 13 samples shares anti-pd-l1

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