GEO series

EGFR-mutant transformed small cell lung cancer harbors intratumoral heterogeneity targetable with MEK inhibitor combination therapy

GSE310962 Homo sapiens (human) Single-cell RNA-seq 8 samples Submitted 2025/11/25 Platform GPL11154

At a glance

Single-cell RNA-seq · human · 8 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism: Homo sapiens (human)
Topic: Small cell lung cancerThis series is part of our curated briefing on it.
Samples: 8 samples: 8 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: EGFR-mutant transformed small cell lung cancer harbors intratumoral heterogeneity targetable with MEK inhibitor combination therapyJCI insight · 2026

Summary (written by the submitters)

Small cell lung cancer (SCLC) transformation is an incompletely characterized mechanism of resistance to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) in EGFR mutant cancers, limiting development of optimal treatment approaches. Through single-cell RNA sequencing of malignant pleural effusions from patients who underwent SCLC transformation, we identified heterogeneity and diversity including distinct neuroendocrine (NE) and mesenchymal non-NE cancer cell subsets, which was maintained in patient-derived cell lines.

Read the full summary ↓

We demonstrate that EZH2 regulates EGFR expression in NE cells where EGFR expression is silenced at baseline. Although neither epigenetic derepression nor exogenous overexpression of mutant EGFR sensitized the cells to EGFR inhibition, non-NE cells exhibited selective sensitivity to MEK inhibitors. Combined MEK inhibitor and chemotherapy effectively inhibited growth of both NE and non-NE cells in vitro and in vivo. Our findings demonstrate that EGFR mutant SCLC is composed of mixed cell states with distinct therapeutic vulnerabilities and offer a novel therapeutic strategy to target tumor heterogeneity in highly plastic and treatment-resistant malignancies such as SCLC transformed cancers.

Published in

EGFR-mutant transformed small cell lung cancer harbors intratumoral heterogeneity targetable with MEK inhibitor combination therapy

Ogino A, Vajdi A, Mu XJ et al. · JCI insight 2026 · PMID 41574603 · doi:10.1172/jci.insight.197008

This dataset

Open GSE310962 in finder → Find more human RNA-seq datasets → Small cell lung 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 GSE310962_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1367631 and SRA study SRP647600. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41574603) ↗

Study design

8 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 8 sample titles: 8 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

GSM9313305 DFCI163ad
GSM9313306 DFCI190ad
GSM9313307 DFCI190F
GSM9313308 DFCI283ad
GSM9313309 DFCI283F
GSM9313310 DFCI112_scRNA
GSM9313311 DFCI190_scRNA
GSM9313312 DFCI283_scRNA

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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.