GEO series

XIAP-coordinated PKC signaling overcomes immunotherapy resistance via immunogenic rewiring to empower therapeutic rechallenge [scRNA-seq]

GSE303943 Mus musculus (mouse) Single-cell RNA-seq 2 samples Submitted 2026/05/16 Platform GPL24247

At a glance

Single-cell RNA-seq · mouse · 2 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Mus musculus (mouse)
Samples: 2 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)

Immune-checkpoint blockade (ICB) against PD-1/PD-L1 has transformed cancer care, yet primary and acquired resistance limit its reach. By profiling substrate-based protein kinase C (PKC) activity, rather than pan-PKC expression, we uncover a superior biomarker predicting survival and anti-PD-1 responsiveness in non-small cell lung cancer (NSCLC).

Read the full summary ↓

Critically, pharmacological pan-PKC inhibition overcomes anti-PD-1 resistance across diverse preclinical models through a coordinated immunologic program. Specifically, PKC blockade triggers Caspase-3/GSDME-dependent immunogenic cell death (ICD) with ATP/HMGB1 release. It concurrently reprograms the tumor microenvironment (TME) by enriching CD8+ T cells and regulatory T cells (Tregs) in a CCR5-dependent manner, while inducing tumor-intrinsic PD-L1 degradation to sustain CD8+ T cell function. Mechanistically, targeting PKC destabilizes XIAP, whose degradation not only unleashes caspase-mediated ICD and PD-L1 degradation but also licenses CCL4 secretion for CCR5-centric TME rewiring. Importantly, PKC blockade synergizes exclusively with anti-CTLA4 by selectively depleting intratumoral Tregs, providing a rational rechallenge regimen for anti-PD-1-refractory malignancies. Our findings establish PKC activity as a crucial biomarker and validate PKC inhibition as a translatable strategy to dismantle ICB resistance.

This dataset

Open GSE303943 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 GSE303943_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 PRJNA1298268 and SRA study SRP604330. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Samples in this study

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+ 2 more — browse all 2 samples with per-sample file links →

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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.