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Mutational and neoantigen load predict clinical benefit of adoptive T cell therapy in melanoma

GSE100797 Homo sapiens (human) RNA-seq (gene expression) 25 samples Submitted 2017/12/04 Platform GPL11154
At a glance

RNA-seq (gene expression) · human · 25 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)
Topic
MelanomaThis series is part of our curated briefing on it.
Samples
25 samples
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)
Purpose: Utility of immunological treatment in cancer has increased; however, many patients do not respond to treatment. Identification of robust predictive biomarkers is required to correctly stratify patients. Although clinical trials based on adoptive T cell therapy (ACT) have yielded high response rates and many durable responses in melanoma, 50-60% of the patients have no clinical benefit.
Read the full summary ↓
Herein, we searched for predictive biomarkers to ACT in melanoma. Methods: Whole exome- and transcriptome sequencing, neoantigen prediction and immune cell signature analysis were applied to pre-treatment melanoma samples from 27 patients recruited to a clinical phase I/II trial of ACT in stage IV melanoma. All patients had previously been treated with other immunotherapies. Results: We found that clinical benefit was associated with significantly higher neoantigen load (P=0.025). High mutation and neoantigen load were significantly associated with improved progression-free and overall survival (P=8x10^-4 and P=0.001, respectively). Further, gene-expression analysis of pre-treatment biopsies showed that clinical benefit was associated with strong immune activation signatures including a high MHC-I antigen processing and presentation score. Conclusions: These results improve our understanding of clinical benefit of ACT in melanoma, which can lead to clinically useful predictive biomarkers to be used for selecting patients that benefit from these highly intensive treatment regimens.
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 GSE100797_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: 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 PRJNA393105. Searching any of these in the dataset finder brings you back here.

Samples in this study

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