Single-cell RNA-seq of melanoma ecosystems reveals sources of T cells exclusion linked to immunotherapy clinical outcomes
Immune checkpoint inhibitors (ICI) produce durable responses in some melanoma patients, but many patients derive no clinical benefit, and the molecular underpinnings of such resistance remain elusive. Here, we leveraged single-cell RNA-seq (scRNA-seq) from 31 melanoma tumors and novel computational methods to interrogate malignant cell states that promote immune evasion. We identified a resistance program expressed by malignant cells that is associated with T cell exclusion and immune evasion. T...
Provenance — who produced it, who reused it
Linked to 54 papers in the literature. Roles are inferred factual signals (who deposited the data vs who reused it), with counts — never a judgement about any author.
- ReactomeGSA - Efficient Multi-Omics Comparative Pathway Analysis 2020 · 375 cites
- Integrated analysis of single-cell and bulk RNA sequencing data... 2022 · 280 cites
- A cellular hierarchy in melanoma uncouples growth and metastasis 2022 · 249 cites
- A gene expression signature of TREM2hi macrophages and γδ T cell... 2020 · 246 cites
- BIDCell: Biologically-informed self-supervised learning for segm... 2024 · 72 cites
49 further papers cite this accession but reuse could not be confirmed.
Deep data QC
metadata only · no data-level QC for this typeStandardized, field-standard QC computed by touching the data — every metric states how it was obtained
No quantitative QC rubric exists for this data type yet, so it is deliberately left unscored — this is an honest "not applicable", not a poor rating.
measured = computed from the data · extrapolated/reported = derived or from the repository · dq-1.0 · provisional — verify independently
Scientific quality
Based on hands-on reproduction of the papers that use this dataset. A reproducible paper that stands on this data is positive evidence; a flagged one is a prompt to look closer — never a verdict on the dataset itself without the evidence.