Phase I trial of intravenous VCN-01 oncolytic adenovirus and durvalumab in patients with head and neck metastatic squamous cell carcinoma refractory to immunotherapy
RNA-seq (gene expression) · human · 35 samples
- What it is
- RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
- Organism
- Homo sapiens (human)
- Samples
- 35 samples: 28 distinct titles, 7 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.
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE333537_RAW.tar (it can run to tens of gigabytes).
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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.
Also filed as BioProject PRJNA1471265 and SRA study SRP704266. Searching any of these in the dataset finder brings you back here.
How we read this: Read from 35 sample titles: 28 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.
- GSM9768122 1S01_SCR
- GSM9768123 2S04_SCR
- GSM9768124 2S08_SCR
- GSM9768125 2S09_SCR
- GSM9768126 2S11_SCR
- GSM9768127 1S21_SCR
- GSM9768128 2S13_SCR
- GSM9768129 1S22_SCR
- GSM9768130 1S25_SCR
- GSM9768131 1S01_D15
- GSM9768132 2S04_D15
- GSM9768133 2S08_D15
- GSM9768134 2S09_D15
- GSM9768135 2S11_D15
- GSM9768136 1S21_D15
- GSM9768137 2S13_D15
- GSM9768138 1S22_D15
- GSM9768139 1S25_D15
- GSM9768140 1S01_D7
- GSM9768141 2S04_D7
- GSM9768142 2S08_D7
- GSM9768143 2S11_D7
- GSM9768144 1S21_D7
- GSM9768145 2S13_D7
- GSM9768146 1S22_D7
- GSM9768147 1S03_SCR
- GSM9768148 1S18_SCR
- GSM9768149 1S24_SCR
- GSM9768150 1S03_D7
- GSM9768151 1S18_D7
- GSM9768152 1S24_D7
- GSM9768153 2C04_SCR
- GSM9768154 2C10_SCR
- GSM9768155 2C04_D8
- GSM9768156 2C10_D8
- GSE239676 Atlas of metastatic gastric cancer links ferroptosis to disease progression and immunotherapy response 68 samples shares immunotherapy, metastatic
- GSE243223 The oncolytic adenovirus Delta-24-RGD in combination with ONC201 displays a potent antitumor effect in pediatric high-grade glioma models [RNA-seqI] 52 samples shares adenovirus, oncolytic
- GSE271689 Enhancing Immunotherapy Outcomes: Spatial Multi-Omics Predictive Models for Non-Small Cell Lung Cancer 586 samples shares immunotherapy
- GSE235093 Pre-existing skin-resident CD8 and γδ T cell circuits mediate immune response in Merkel cell carcinoma and Predict immunotherapy efficacy 333 samples shares immunotherapy
- GSE296826 Raw RNA-sequencing data for deriving BCR sequences from the NIH U01 project: Finding the optimal balance of immunotherapy efficacy and toxicity. 256 samples shares immunotherapy
- GSE235090 Pre-existing skin-resident CD8 and γδ T cell circuits mediate immune response in Merkel cell carcinoma and Predict immunotherapy efficacy [scRNA_tissue] 192 samples shares immunotherapy
- GSE307931 Circulating transcriptome analysis finds gene signatures and immune cell activation and abundance predict response to immunotherapy in bladder cancer 164 samples shares immunotherapy
- GSE310957 DeCAF redefines fibroblast states uncovering multidimensional tumor-stroma relationships driving clinical tumor progression and immunotherapy response [RNA-Seq] 129 samples shares immunotherapy
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.