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Transcription Factor Subtype Governs Response and Resistance to DLL3-Directed T-Cell Engagement in Small Cell Lung Cancer [scRNA-seq]

GSE324849 Homo sapiens (human) Single-cell RNA-seq 1 samples Submitted 2026/10/20 Platform GPL34281
At a glance

Single-cell RNA-seq · human · 1 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Homo sapiens (human)
Samples
1 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)
Although small cell lung cancer (SCLC) comprises transcription factor (TF)-defined molecular subtypes (ASCL1, NEUROD1, POU2F3), the extent to which these subtypes predict response to effective therapy—and whether therapy can select for subtype switching—remains unknown. The recent approval of the DLL3×CD3 bispecific T-cell engager tarlatamab represents one of the first meaningful advances in relapsed small cell lung cancer (SCLC) in decades, yet responses remain heterogeneous and resistance is inevitable.
Read the full summary ↓
Here, we profiled cell-free circulating chromatin from prospectively collected patient plasma (46 patients; 167 samples) and applied APEX to infer tumor gene expression at single-gene resolution, enabling interrogation of response and acquired resistance to tarlatamab. Parallel development of the first immunocompetent syngeneic mouse model to study tarlatamab response and resistance enabled functional validation. Across species, findings converged on a central principle: TF subtype governs both initial response and acquired resistance. Therapeutic response was significantly associated with ASCL1⁺ tumors, whereas NEUROD1⁺ tumors exhibited inferior responses and POU2F3⁺ tumors were uniformly resistant, consistent with DLL3 being a direct ASCL1 transcriptional target and most highly expressed in ASCL1⁺ tumors. Strikingly, one mode of acquired resistance converged on therapeutic selection for a NEUROD1-high state with concomitant DLL3 downregulation. Some tumors also exhibited enrichment of regulatory and exhausted T-cell programs underscoring tarlatamab’s dual-targeting mechanism of action. Together, these results reveal that under tarlatamab pressure, tumors select against ASCL1-driven cell states, driving resistance to an antigen intrinsically linked to ASCL1 activity. These findings establish the clinical relevance of TF-defined molecular subtypes in human SCLC. More broadly, they highlight the power of integrating longitudinal in vivo plasma transcriptional profiling in patients with functional mouse modeling to uncover clinical and biological mechanisms of response and resistance to cell-surface-targeted therapies.
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 GSE324849_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: 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 PRJNA1437917. Searching any of these in the dataset finder brings you back here.

Samples in this study

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