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Modeling Lung Adenocarcinoma Metastases Using Patient-Derived Organoids

GSE276387 Homo sapiens (human) RNA-seq (gene expression) 26 samples Submitted 2024/09/12 Platform GPL24676Platform GPL16791
At a glance

RNA-seq (gene expression) · human · 26 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
Lung cancerThis series is part of our curated briefing on it.
Samples
26 samples: 16 distinct titles, 1 of them repeatedGroups 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
Modeling lung adenocarcinoma metastases using patient-derived organoidsCell reports. Medicine · 2024
Summary (written by the submitters)
Approximately 50% of patients with surgically resected early-stage lung cancer develop distant metastasis. At present, there is no in vivo metastasis model to investigate the biology of human lung cancer metastases. Using well-characterized patient-derived organoids (PDOs) from patients with lung adenocarcinoma (LUAD), we establish an in vivo metastasis model that preserves the biologic features of human LUAD metastases.
Read the full summary ↓
Results of whole-genome and RNA sequencing performed in this study establish that our in vivo PDO metastasis model can be used to study clonality and tumor evolution and to identify biomarkers related to organotropism. Investigation of the response of KRASG12C PDOs to Sotorasib demonstrates that the model can examine the efficacy of treatments to suppress metastasis and identify mechanisms of drug resistance. Finally, our PDO model cocultured with autologous peripheral blood mononuclear cells can potentially be used to determine the optimal immune-priming strategy for individual patients with LUAD.
Published in
Modeling lung adenocarcinoma metastases using patient-derived organoids
Liu Y, Lankadasari M, Rosiene J et al. · Cell reports. Medicine 2024 · PMID 39413736 · doi:10.1016/j.xcrm.2024.101777
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 GSE276387_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 PRJNA1157185 and SRA study SRP530581. Searching any of these in the dataset finder brings you back here.

Study design
16 distinct titles, 1 of them repeated
Patient derived organoid from sample ×11

How we read this: Read from 26 sample titles: 16 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
More Lung cancer datasets

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.