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Senolytic approaches enhance chemotherapy immunogenicity in prostate cancer

GSE316754 Mus musculus (mouse) RNA-seq (gene expression) 24 samples Submitted 2026/01/16 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 24 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
Mus musculus (mouse)
Samples
24 samples: 8 conditions, mostly in triplicateGroups 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)
Cellular senescence is a common outcome of many standard-of-care cytostatic and cytotoxic cancer therapies. Whereas is some contexts therapy-induced senescence can drive tumor growth suppression and anti-tumor immune surveillance through the senescence-associated secretory phenotype (SASP), in others it can promote tumor progression, metastatic dissemination, and immune suppression.
Read the full summary ↓
How the heterogeneous senescent phenotypes induced by different classes of cancer therapies directly impact tumor and immune responses, and whether understanding those mechanisms could be leveraged for effective combination therapies, has yet to be evaluated in a systematic manner. Here we compared the effects of various CDK inhibitor combinations, chemotherapies, and other DNA damaging agents commonly used in the treatment of advanced prostate cancer on senescence phenotypes in human and mouse prostate tumor models. We observed that while CDK inhibitors could induce senescence in MYC-driven prostate tumor cells, DNA-damaging chemotherapies and Aurora kinase inhibitors mediated a more robust senescence-mediated growth arrest and SASP program, which was paradoxically enhanced in the context of p53 and Rb tumor suppressor losses. The SASP produced by chemotherapy-induced senescence, and in particular with docetaxel, contained not only inflammatory factors that could influence innate immune responses, but also ECM remodeling factors that could facilitate tumor cell dissemination. Though docetaxel treatment could induce senescence and reduce tumor growth in syngeneic prostate cancer mouse models, it was not sufficient to activate anti-tumor immune surveillance or prevent metastatic dissemination. Use of senolytic therapies targeting anti-apoptotic BCL2 and IAP pathways upregulated after docetaxel-induced senescence could effectively clear senescent tumor cells. SMAC mimetic LCL-161 also induced immunogenic cell death capable to remodeling the inflammatory SASP to confer CD8+ T cell activation to further reduce tumor growth and even prevent metastasis in vivo. Our results suggest that combining senescence-inducing chemotherapies with senolytic agents can enhance the immunogenicity and anti-metastatic potential of standard-of-care chemotherapy used for treating prostate cancer patients.
This dataset
Open GSE316754 in finder → Find more mouse RNA-seq datasets →
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 GSE316754_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 PRJNA1404142 and SRA study SRP663594. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
8 conditions, mostly in triplicate
DMSO ×3 PF-06873600 ×3 Alisertib ×3 Docetaxel ×3 T/P ×3 Etoposide ×3 Nutlin-3 ×3 Palbociclib ×3

Supports a between-group comparison across 24 samples.

How we read this: 8 replicated groups read from 24 sample titles; they account for 24 of them. 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
Datasets on a similar topic

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