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Radiation and Dual Checkpoint Blockade Activates Non-Redundant Mechanisms in Cancer

GSE65503 Mus musculus (mouse) Expression microarray 14 samples Submitted 2015/03/16 Platform GPL6246Platform GPL16570

At a glance

Expression microarray · mouse · 14 samples

What it is: Expression microarrayGene activity read on a chip (the older technology that RNA-seq replaced).
Organism: Mus musculus (mouse)
Samples: 14 samples
Data you can get: A processed expression table (series matrix) and the array files, from NCBI.

Summary (written by the submitters)

Response to immune checkpoint inhibitors may be improved through combinations with each other and other therapies, raising questions about non-redundancy and resistance. We report results from parallel studies of melanoma patients and mice treated with anti-CTLA4 and radiation (RT). Although combined treatment improved responses, resistance was common.

Read the full summary ↓

Computational analyses of immune and transcriptomic profiles (provided here) revealed that resistance in mice was due to upregulation of tumor PD-L1 that drives T cell exhaustion. Accordingly, optimal response requires RT, anti-CTLA4, and anti-PD-L1. Anti-CTLA4 inhibits Tregs, RT diversifies and shapes the TCR repertoire, and anti-PD-L1 reinvigorates exhausted T cells. Together, all three therapies promote the expansion of clonotypes with distinct TCR traits. Similar to mice, patients with melanoma showing high PD-L1 did not respond to RT + anti-CTLA4, demonstrated persistent T cell exhaustion, and rapidly progressed. Thus, the combination of RT, anti-CTLA4, and anti-PD-L1 promotes response through distinct mechanisms.

This dataset

Open GSE65503 in finder → Find more mouse microarray datasets →

Download · which file do I need?

Direct links to NCBI, 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.

All supplementary files ↗

3 · The original raw files

Whole study · RAW.tar

Everything the authors deposited as one archive (GSE65503_RAW.tar), including the raw array or instrument files. Can be large; the picker in step 2 takes just the samples you need.

Whole study · RAW.tar ↓

What the files usually look like: For arrays the series matrix is normally the normalised expression table you want; the raw array files (CEL and similar) are in the RAW.tar.

Also filed as BioProject PRJNA274233. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 25754329) ↗

Samples in this study

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+ 14 more — browse all 14 samples with per-sample file links →

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