Data from: The melanocytic transcriptomic state independently associates with poor survival in long term follow up of metastatic melanoma patients
Data files
Jun 22, 2026 version files 35.03 MB
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ITM_log2CPM.csv
35.02 MB
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README.md
2.34 KB
Abstract
Melanomas display distinct transcriptomic states, but it remains unclear how they associate with clinical outcomes. We performed digital spatial RNA profiling (DSP-RNA) of metastatic tumors from patients to investigate how transcriptomic states correlate with melanoma-specific survival (MSS) and acral melanoma (AM) status. We performed DSP- RNA across a tissue microarray constructed from 111 patients with in-transit metastatic melanoma (ITM) diagnosed from 1990-2020. Data quality control, noise correction, and normalization yielded high quality profiles from 105 patients, including 30 (36%) who received immune checkpoint inhibitors, and 20 (24%) with AM. We performed principal component analysis (PCA) and correlated the results with published gene signatures: the PC1 axis differentiated transitory from undifferentiated melanoma, PC2 reflected immune cell infiltration, PC3 corresponded to stromal cells and neural crest-like melanoma, and PC4 associated with melanocytic melanoma. Across a cohort of treatment-naïve ITM, high expression of the melanocytic state conferred a median MSS difference of 7.72 years (melanocytic ‘high’=5.16 years vs ‘low’=12.88 years, log-rank p=0.0061) and independently associated with poor survival in multivariate analysis. AMs showed higher melanocytic state gene expression compared to non-acral cases. These findings were validated in external datasets, supporting that the melanocytic state predicts poor prognosis. The melanocytic state is associated with poor prognosis and may be enriched in AM, implying that identifying patients with melanocytic melanoma may be important for therapeutic decisions. Unlike other gene expression predictors proposed for prognostic stratification, the melanocytic state characterizes a biological subtype, suggesting that it may have specific therapeutic vulnerabilities.
Dataset DOI: 10.5061/dryad.kh18932pf
Description of the data and file structure
Spatial transcriptomics was performed on the TMA using the Nanostring GeoMx Digital Spatial Profiler (DSP) with the human Whole Transcriptome Atlas (WTA) probe panel. The TMA underwent staining for CD20 (B cells), CD3 (T cells), and DNA to guide region of interest (ROI) selection. ROIs were drawn to maximize tumor content within each tissue spot on the TMA. Full ROIs were profiled without the use of segmentation to avoid inadequate cell counts. The DSP plates were dried down and rehydrated in 10uL nuclease-free water. Libraries were prepared and pooled as specified in the protocol for WTA RNA samples. The final pool was assessed for quality using the Tapestation D1000 assay and Qubit. Sequencing was performed using NovaSeq 6000 S4 flowcell run as 27x8x8x27 bp (Illumina). The GeoMx DSP Analysis Pipeline version 2.3.3.10 performed trimming, alignment, and deduplication of UV-cleaved probe barcodes. A tabulated matrix of probe counts for each AOI was exported from the DSP server for subsequent analysis.
Files and variables
File: ITM_log2CPM.csv
Description: The normalized gene expression matrix (ITM_log2CPM) containing 170 Areas of Interest (AOIs)
File: Code_stgemox_itm_mel_tma-submission-v1.zip (Zenodo)
Description: The code used for analysis and figure generation
Code/software
The data analysis was performed using R version 4.4.1.
Code Availability: Script used for analysis and figure generation has been deposited on Zenodo and Github.
Zenodo DOI: https://zenodo.org/records/20058020
GitHub Repository: https://github.com/ziyinhuang96/stgemox_itm_mel_tma/releases/tag/submission-v2
Access information
Other publicly accessible locations of the data:
- The raw data have been deposited in GEO under accession number GSE325123.
Human subjects data
We confirm tha all direct identifiers were removed and samples were assigned de-identified study codes prior to sharing.
