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Engineered cytokine/antibody fusion proteins improve delivery of IL-2 to pro-inflammatory cells and promote antitumor activity

GSE273415 Homo sapiens (human) RNA-seq (gene expression) 14 samples Submitted 2024/07/30 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 14 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
14 samples: 14 samples, each with a distinct title — no replicated groupsGroups 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.
Paper
Engineered cytokine/antibody fusion proteins improve IL-2 delivery to pro-inflammatory cells and promote antitumor activityJCI insight · 2024
Summary (written by the submitters)
Progress in cytokine engineering is driving therapeutic translation by overcoming these proteins’ inherent limitations as drugs. The interleukin-2 (IL-2) cytokine harbors great promise as an immune stimulant for cancer treatment. However, the cytokine’s concurrent activation of both pro- inflammatory immune effector cells and anti-inflammatory regulatory T cells, toxicity at high doses, and short serum half-life limit clinical application.
Read the full summary ↓
One promising approach to improve the selectivity, safety, and longevity of IL-2 is complexation with anti-IL-2 antibodies that bias the cytokine towards activation of immune effector cells. Although this strategy shows therapeutic potential in preclinical cancer models, clinical translation of a cytokine/antibody complex is complicated by challenges in formulating a multi-protein drug and concerns about complex stability. Here, we introduce a versatile approach to designing intramolecularly assembled single- agent fusion proteins (immunocytokines, ICs) comprising IL-2 and a biasing anti-IL-2 antibody that directs the cytokine’s activities towards immune effector cells. We establish the optimal IC construction and further engineer the cytokine/antibody affinity to improve immune biasing function. We demonstrate that our IC preferentially activates and expands immune effector cells, leading to superior antitumor activity compared to natural IL-2, both alone and in combination with immune checkpoint inhibitors. Moreover, therapeutic efficacy is observed without inducing toxicity, as measured by weight loss, pulmonary edema, cytokine secretion, and liver damage markers. This work presents a roadmap for the design and translation of cytokine/antibody fusion proteins.
Published in
Engineered cytokine/antibody fusion proteins improve IL-2 delivery to pro-inflammatory cells and promote antitumor activity
Leonard EK, Tomala J, Gould JR et al. · JCI insight 2024 · PMID 39115939 · doi:10.1172/jci.insight.173469
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 GSE273415_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 PRJNA1141569 and SRA study SRP522987. Searching any of these in the dataset finder brings you back here.

Study design
14 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 14 sample titles: 14 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.

Samples in this study
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