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Novel CRISPR-Cas9 BAP1 Knockout Pre-Clinical Tumor Model Recapitulates Human Melanoma Tumorigenesis and Immune Evolution

GSE318166 Mus musculus (mouse) Single-cell RNA-seq 19 samples Submitted 2026/03/12 Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 19 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
19 samples: 16 distinct titles, 1 of them repeatedGroups 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.
Summary (written by the submitters)
BAP1-deficient melanocytic tumors exhibit strong immunosuppressive features and poor prognosis. Currently, no immune-competent preclinical models exist to study their tumor-immune interactions or test new immunotherapies. This limitation hinders progress in understanding how BAP1 loss drives tumor aggressiveness and immune evasion. To address this, we generated a syngeneic BAP1 knockout melanocyte tumor line using CRISPR-Cas9.
Read the full summary ↓
We then evaluated its functional and immunological impact in immune-competent mice, including its ability to recapitulate metabolic and immunosuppressive features of human BAP1-deficient melanomas. The selected knockout clone exhibited hallmarks of aggressive skin and intraocular melanomas, including epithelioid morphology, in vivo tumorigenic potential, rapid growth, and key immunosuppressive features, mirroring those observed in human BAP1-deficient melanomas. Cross-species single-cell transcriptome analysis demonstrated strong molecular overlap between BAP1 knockout mouse tumors and high-risk (class 2) human uveal melanomas, highlighting shared pathways in lipid metabolism, transmembrane receptor signaling, and immune modulation. Gene Set Enrichment Analysis confirmed that lipid metabolic reprogramming, previously described in human tumors, is also a key feature of our model, validating its ability to recapitulate human disease biology. This study introduces a syngeneic preclinical model that mimics the immunosuppressive landscape of BAP1-deficient melanocytic tumors, enabling the development and optimization of new combination immunotherapies.
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 GSE318166_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: 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 PRJNA1416836 and SRA study SRP671927. Searching any of these in the dataset finder brings you back here.

Study design
16 distinct titles, 1 of them repeated
Whole-exome sequencing of Melan-a BAP1 knocko… ×4

Supports a case/control comparison: 12 samples read as cases, 3 as controls.

How we read this: Read from 19 sample titles: 16 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
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