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Patient-derived organoids as a platform to study cervical cancer and viral infection

GSE168244 Homo sapiens (human) RNA-seq (gene expression) 36 samples Submitted 2021/03/05 Platform GPL18573
At a glance

RNA-seq (gene expression) · human · 36 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
Cervical cancerThis series is part of our curated briefing on it.
Samples
36 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)
Cervical cancer is a common gynecological malignancy, often caused by high-risk human papillomavirus. There is a paucity of human-derived culture systems to study cervical epithelium and the cancers derived thereof. Here, we describe a long-term culturing protocol for ecto- and endocervical epithelia, which generates 3D organoids that stably recapitulate the two tissues of origin.
Read the full summary ↓
In the example of successful HSV-1 infection, we show that the organoid-based cervical models serve as promising platforms for studying sexually transmitted pathogens. Starting from Pap-brush material, a small biobank of patient-derived tumoroids was established that retained the causative HPV genomes. One of these uniquely carried the poorly characterized HPV30 subtype, implying a potential role in carcinogenesis. The tumoroids displayed differential responses to common chemotherapeutics and grew as xenografts in mice. This study describes an experimental platform for cervical (cancer) research and for future personalized medicine approaches. Purpose: Transcriptome analysis was performed on organoids/tissues derived from human ecto- and endocervix as well as associated malignancies to characterize the new organoid-based platform. Methods: Ectocervical organoids and tumoroid lines were cultured in M7 media, whereas endocervical organoids in M7+4 media prior harvesting for RNA extraction (see paper for details). Additionally, RNA was extracted from healthy ecto-and endocervical tissue biopsies as well as from primary cells collected via Pap brush method from cervical cancer patients. The libraries were prepared by the Utrecht Sequencing Facility (USEQ) based on polyA enrichment. Sequencing was performed on an Illumina NextSeq500 by using 75-bp single-end or paired-end sequencing. Reads were aligned to the human reference genome (GRCh37) using Burrows-Wheeler Aligner (BWA) (v0.5.9). Viral sequences of different HPV subtypes were detected in the RNA-seq data of the different tumoroid lines and tissues by using VirusSeq-CLI, a wrapper around the VirusSeq pipeline. Results: Ectocervical and endocervical organoids display distinct transcriptomic profiles that can be maintained in cultures for long term. Similarily, the the gene expression profile of tumor organoids clusters apart from healthy counterparts and shows evident tumor-like characteristics. Tumor organoids show expression of causative HPV oncogenes.
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 GSE168244_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 PRJNA706665 and SRA study SRP309299. Searching any of these in the dataset finder brings you back here.

Samples in this study

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