GEO series

Efficacy of Neoadjuvant Chemotherapy Combined with Anti-PD-1 Monoclonal Antibody in Locally Advanced Resectable Oral Squamous Cell Carcinoma: A Single-Arm Phase II Clinical Trial

GSE284162 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2026/08/01 Platform GPL29480Platform GPL24676

At a glance

RNA-seq (gene expression) · human · 12 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: Head and neck cancerThis series is part of our curated briefing on it.
Samples: 12 samples: 12 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.

Summary (written by the submitters)

Background: Neoadjuvant chemotherapy (NAC) combined with anti-PD-1 antibody is a potential treatment option for resectable oral squamous cell carcinoma (OSCC). It is important to be able to predict the effectiveness of NAC combined with anti-PD-1 antibody in clinical practice.

Read the full summary ↓

Methods: In total, 30 patients with locally advanced, resectable OSCC (T3–T4, N0–N2, M0) who received two cycles of neoadjuvant chemoimmunotherapy (NACI) consisting of albumin-bound paclitaxel (250 mg/m2) and cisplatin (75 mg/m2) with the anti-PD-1 monoclonal antibody sintilimab (200 mg) were enrolled in this single-arm phase II trial. Twenty-three patients underwent surgical resection and seven received nonsurgical therapy. The primary and secondary end points were pathological response and clinical response, respectively. We utilized artificial intelligence (AI)-powered nuclear segmentation and classification to preliminarily assess the level of inflammatory cell infiltration in patients before NACI. Transcriptome RNA sequencing (RNA-seq) analysis was used to identify differentially expressed genes and immune cell subtypes between patients with and without major pathological response (MPR). Results: The MPR rate was 39.1% among 23 patients who underwent surgical resection. The objective response rate (ORR) was 43.3%. The primary location of OSCC was associated with the pathological response. There were no significant differences in the overall level of inflammatory cell infiltration between patients with and without MPR. Immune infiltration analysis of RNA-seq data revealed significant differences in the proportions of M1- and M2-type macrophages between the MPR and non-MPR groups. Conclusions: This study reveals the potential efficacy of NAC combined with anti-PD-1 antibody in treating OSCC patients, with acceptable side effects. This strategy can effectively inhibit clinical progression of locally advanced OSCC. The pathological response may be related to the degree of M1 macrophage infiltration.

This dataset

Open GSE284162 in finder → Find more human RNA-seq datasets → Head and neck cancer landscape →

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 GSE284162_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 PRJNA1197480 and SRA study SRP551126. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

12 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 12 sample titles: 12 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

GSM8679114 Patient01,non-MPR
GSM8679115 Patient02,non-MPR
GSM8679116 Patient03,non-MPR
GSM8679117 Patient04,non-MPR
GSM8679118 Patient05,non-MPR
GSM8679119 Patient06, MPR
GSM8679120 Patient07,non-MPR
GSM8679121 Patient08, MPR
GSM8679122 Patient09,non-MPR
GSM8679123 Patient10, MPR
GSM8679124 Patient11,non-MPR
GSM8679125 Patient12, MPR

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Search all human RNA-seq datasets in GEO →

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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.