GEO series

Subclonal evolution revealed by single-cell profiles of expressed mutations identifies adaptation mechanisms to immunotherapy in melanoma [scRNA-seq]

GSE215960 Mus musculus (mouse) Single-cell RNA-seq 192 samples Submitted 2024/10/25 Platform GPL24247

At a glance

Single-cell RNA-seq · mouse · 192 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: 192 samples: 192 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: Stochastic modeling of single-cell gene expression adaptation reveals non-genomic contribution to evolution of tumor subclonesCell systems · 2025

Summary (written by the submitters)

Intratumoral heterogeneity (ITH) arises from distinct subclonal expansion following genetic or epigenetic alterations, and profoundly influences how tumors response to their immune microenvironment. Tumor progression trees based on single-cell mutational profiles have made it possible to trace subclonal evolution; however, conventional tree-building methods can incorporate only a limited number of cells and mutations, restricting their application to larger single-cell data.

Read the full summary ↓

To investigate the effect of ITH on the therapeutic response of melanoma, we have developed Trisicell (https://trisicell.rtfd.io), a computational toolkit for scalable inference of mutational ITH through assessment of single-cell genomic variant data. By applying Trisicell to genetically matched mouse melanoma datasets, we found that expressed mutations are sufficient to effectively drive subclonal evolution. On single-cell, full-length RNA sequencing data of mouse melanoma from preclinical immune checkpoint blockade (ICB) studies, the analysis showed that the subtree-seeding mutations in the trees identified distinct subclones associated with a specific developmental state and neural crest lineage markers. Using the tree to trace cell lineages, we found that neoantigens depleted by ICB were predominantly expressed in minor subclones, suggesting that post-treatment recurrence is driven by immunoediting. Moreover, these neoantigens were enriched with those derived from frameshift mutations and mutated nuclear genes. Importantly, recurrently mutated genes in ICB-responding human melanoma exhibited the same features. We next used Trisicell to analyze single-cell, full-length RNA data of brain metastases (BM) from melanoma patients treated with ICB, and discovered that relapsing BM from ICB-responding patients exhibited subclones that also expressed a higher fraction of frameshift mutations and were associated with elevated levels of infiltrated T cells. Notably, they also exhibited more mutated HLA genes and expressed high level of the novel immune checkpoint HLA-G, a putative local immunosuppressive mechanism. In summary, applying Trisicell to single-cell transcriptomic analyses allowed us to identify novel features of ICB-responding melanoma neoantigens and distinct mechanisms for adapting to immunotherapy at different sites. These results have important implications for both melanoma evolution and target identification for immunotherapies, including ICB and cancer vaccines. This is the first study to trace subclonal evolution of neoantigens at single-cell resolution, demonstrating that Trisicell and our datasets represent important resources for the field.

Published in

Stochastic modeling of single-cell gene expression adaptation reveals non-genomic contribution to evolution of tumor subclones

Hirsch MG, Pal S, Rashidi Mehrabadi F et al. · Cell systems 2025 · PMID 39701099 · doi:10.1016/j.cels.2024.11.013

This dataset

Open GSE215960 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 GSE215960_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 PRJNA891348 and SRA study SRP403101. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39701099) ↗

Study design

192 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 192 sample titles: 192 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

GSM6651433 C1_1 [1_P01_A01]
GSM6651434 C1_2 [2_P01_B01]
GSM6651435 C1_3 [3_P01_C01]
GSM6651436 C1_4 [4_P01_D01]
GSM6651437 C1_5 [5_P01_E01]
GSM6651438 C1_6 [6_P01_F01]
GSM6651439 C1_7 [7_P01_G01]
GSM6651440 C1_8 [8_P01_H01]
GSM6651441 C2_1 [9_P01_A02]
GSM6651442 C2_2 [10_P01_B02]
GSM6651443 C2_3 [11_P01_C02]
GSM6651444 C2_4 [12_P01_D02]
GSM6651445 C2_5 [13_P01_E02]
GSM6651446 C2_6 [14_P01_F02]
GSM6651447 C2_7 [15_P01_G02]
GSM6651448 C2_8 [16_P01_H02]
GSM6651449 C3_1 [17_P01_A03]
GSM6651450 C3_2 [18_P01_B03]
GSM6651451 C3_3 [19_P01_C03]
GSM6651452 C3_4 [20_P01_D03]
GSM6651453 C3_5 [21_P01_E03]
GSM6651454 C3_6 [22_P01_F03]
GSM6651455 C3_7 [23_P01_G03]
GSM6651456 C4_1 [24_P01_A04]
GSM6651457 C4_2 [25_P01_B04]
GSM6651458 C4_3 [26_P01_C04]
GSM6651459 C4_4 [27_P01_D04]
GSM6651460 C4_5 [28_P01_E04]
GSM6651461 C4_6 [29_P01_F04]
GSM6651462 C4_7 [30_P01_H04]
GSM6651463 C5_1 [31_P01_A05]
GSM6651464 C5_2 [32_P01_B05]
GSM6651465 C5_3 [33_P01_C05]
GSM6651466 C5_4 [34_P01_D05]
GSM6651467 C5_5 [35_P01_E05]
GSM6651468 C5_6 [36_P01_F05]
GSM6651469 C5_7 [37_P01_G05]
GSM6651470 C5_8 [38_P01_H05]
GSM6651471 C6_1 [39_P01_A06]
GSM6651472 C6_2 [40_P01_B06]

+ 152 more — browse all 192 samples with per-sample file links →

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