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Development and Preclinical Evaluation of Tetraspecific Antibodies in Combination with Ex Vivo Expanded NK Cells Infusion for Improved Control of Esophageal Cancer

GSE341700 Homo sapiens (human) RNA-seq (gene expression) 15 samples Submitted 2026/07/28 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 15 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
Esophageal cancerThis series is part of our curated briefing on it.
Samples
15 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)
Background: Esophageal squamous cell carcinoma (ESCC) is highly heterogeneous, leading to limited drug effectiveness and resistance. Antibody therapies offer limited benefits due to low response rates. multispecific antibodies, targeting multiple tumor antigens and NK cell receptors, may overcome these challenges, but their effectiveness and mechanisms in ESCC therapy are not yet understood.
Read the full summary ↓
Methods: Whole transcriptome sequencing was performed on tumor tissues from 32 ESCC patients to identify tumor antigens. Tetraspecific antibodies, referred as NK cell engagers (NKCEs), were designed and constructed to target two ESCC antigens and two surface markers of NK cells. AlphaFold was used to predict the spatial structure of tetraspecific NKCEs and its binding properties with antigens. Fast protein liquid chromatography and surface plasmon resonance were applied for quality control of NKCEs. Using multiple human ESCC cell lines, chemoresistant ESCC cell lines and NK cells from different donors, we carried out flow cytometry to assess NK cell cytotoxicity and activation. Immunofluorescence assays were used to confirm NKCEs binding and colocalization. Transcriptome sequencing, flow cytometry and western blot were performed to explore molecular mechanisms. Subcutaneous and metastatic xenograft tumor models were established in nude mice to evaluate the in vivo anti-tumor efficacy of NKCEs combined with adoptive transfer of NK cells. Results: We found that EGFR was consistently highly expressed in all ESCC samples while MET was specifically upregulated in recurrent ESCC. Tetraspcific NKCEs were successfully engineered and could specifically bind to EGFR, MET, CD16, NKp30 or NKp46. The tetraspecific NKCEs significantly enhanced the cytotoxicity of NK cells against ESCC cell lines as well as paclitaxel-resistant ESCC cell lines in vitro, compared with the commercial antibody Amivantamab. Transcriptomic analysis followed by functional validation revealed that tetraspecific NKCEs triggered immune activation pathways and increased the expression of TNF-α, IFN-γ, and granzyme B in NK cells, but also induced NK cell exhaustion. In vivo studies demonstrated that tetraspecific NKCEs combined with NK cell infusions exerted better antitumor efficacy against esophageal tumors in different stages. The combination therapy was well tolerated, with enhanced NK cell infiltration in tumor tissues. Conclusions: We developed tetraspecific NKCEs targeting two tumor antigens (EGFR, MET) and two NK-cell activating receptors (CD16, NKp30 or NKp46) to effectively bridge ESCC cells and NK cells. The tetraspecific NKCEs exert the potential to enhance antitumor activity of NK cells against ESCC. This study supports the clinical translation of multispecific NKCEs for NK cell-based immunotherapy for ESCC.
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 GSE341700_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 PRJNA1503503 and SRA study SRP722048. Searching any of these in the dataset finder brings you back here.

Samples in this study

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+ 15 more — browse all 15 samples with per-sample file links →

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