← BioTransfer GEO Dataset Finder
GEO series

Insights into the mechanisms of serplulimab: a distinctive anti-PD-1 monoclonal antibody, in combination with a TIGIT or LAG3 inhibitor in preclinical tumor immunotherapy studies

GSE263274 Mus musculus (mouse) RNA-seq (gene expression) 45 samples Submitted 2024/10/01 Platform GPL30215
At a glance

RNA-seq (gene expression) · mouse · 45 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
Mus musculus (mouse)
Samples
45 samples: 15 conditions, mostly in triplicateGroups 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
Insights into the mechanisms of serplulimab: a distinctive anti-PD-1 monoclonal antibody, in combination with a TIGIT or LAG3 inhibitor in preclinical tumor immunotherapy studiesmAbs · 2024
Summary (written by the submitters)
Background: Anti-PD-1 therapy has become a cornerstone of tumor immunotherapy. Over a dozen of anti-PD-1/PD-L1 antibodies have currently been marketed. However, these agents exhibit notable disparities in their characteristics and clinical performance. For instance, in the field of small cell lung cancer (SCLC) where the majority of anti-PD-1 antibodies yielded limited success, Serplulimab produced impressive survival improvements and has been approved by NMPA.
Read the full summary ↓
The marketing authorization application has also been validated by EMA. Nevertheless, the molecular mechanism underpinning Serplulimab’s superiority over its competitors remains elusive. Methods: We characterized the difference of Serplulimab with approved PD-1/PD-L1 inhibitors such as Pembrolizumab and Nivolumab on their antibody binding features and funcations in vitro and anti-tumor activity in vivo. We also investigated the potential cellular pathways underlying the efficacy of Serplulimab in combination with other immune checkpoint inhibitors. Results: In comparison to competitors, Serplulimab robustly induces PD-1 receptor endocytosis while fostering weaker PD-1-CD28 cis interactions. This phenomenon could mitigate the dephosphorylation of CD28 by SHP2, thereby facilitating sustained and robust T cell activation. Notably, Serplulimab and Pembrolizumab exhibited similar performance in both in vitro Mixed Lymphocyte Reaction assays and in vivo efficacy studies. However, upon co-administration with anti-TIGIT or anti-LAG3, the Serplulimab-containing combination consistently demonstrated superior tumor killing efficacy compared to the Pembrolizumab-based combination. Moreover, our mechanistic investigations have unveiled that the Serplulimab combination effectively reduces tumor microenvironment Treg cell populations, augments effector and memory T cell populations, and more potently modulates genes associated with diverse facets of the immune system, surpassing the effects of the Pembrolizumab combination. Conclusions: In summary, our data underscore Serplulimab as a highly differentiated PD-1 monoclonal antibody with best-in-class therapeutic potential
Published in
Insights into the mechanisms of serplulimab: a distinctive anti-PD-1 monoclonal antibody, in combination with a TIGIT or LAG3 inhibitor in preclinical tumor immunotherapy studies
Zhang Y, Wei R, Song G et al. · mAbs 2024 · PMID 39497266 · doi:10.1080/19420862.2024.2419838
This dataset
Open GSE263274 in finder → Find more mouse RNA-seq datasets →
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.

Series matrix ↓
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 GSE263274_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
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.

Raw reads on ENA ↗

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 PRJNA1096616 and SRA study SRP499903. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39497266) ↗
Study design
15 conditions, mostly in triplicate
E4421-T2106, Vehicle, ×3 E4421-T2106, Pembrolizumab(10mg/kg) + Tiragol… ×3 E4421-T2106, HLX53(10mg/kg), ×3 E4421-T2106, Serplulimab(10mg/kg), ×3 E4421-T2106, HLX53(5mg/kg) + Serplulimab(10mg… ×3 E4421-T2106, HLX53(10mg/kg) + Serplulimab(10m… ×3 E4421-T2215, Vehicle, ×3 E4421-T2215, Pembrolizumab(10mg/kg), ×3 +7 more

Supports a between-group comparison across 45 samples.

How we read this: 15 replicated groups read from 45 sample titles; they account for 45 of them. 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

+ 5 more — browse all 45 samples with per-sample file links →

Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

Share this dataset

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.