GEO series
Clinical evaluation of a functional combinatorial precision medicine platform to predict treatment outcomes and enhance combination therapy design in soft tissue sarcomas
GSE282752
Homo sapiens (human)
RNA-seq (gene expression)
24 samples
Submitted 2025/03/06
Platform GPL20301
At a glance
RNA-seq (gene expression) · human · 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
- Homo sapiens (human)
- Topic
- Soft tissue sarcomaThis series is part of our curated briefing on it.
- 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.
- Paper
- Functional combinatorial precision medicine for predicting and optimizing soft tissue sarcoma treatmentsNPJ precision oncology · 2025
Summary (written by the submitters)
Background Soft tissue sarcomas (STS) are a highly heterogeneous collection of tumors that arise from primitive mesenchymal cells. Survival outcomes for advanced STS patients remain poor, with effective agents limited primarily to cytotoxic chemotherapies, achieving responses below 15% as the standard of care. Although efforts to understand sarcomagenesis have identified potential biomarkers, the development of effective targeted therapies has been hindered by the diversity of STS subtypes and interpatient tumour heterogeneity.
Read the full summary ↓
Given the uniquely individualized nature of STS, we hypothesized that the application of an ex vivo drug sensitivity platform, Quadratic Phenotypic Optimization Platform (QPOP), in primary STS patient samples can improve identification of effective drug regimens on a per-patient basis. In this study, we evaluated clinical concordance of QPOP to predict treatment outcomes in an STS cohort, and explored QPOP’s ranking function for drug combination discovery. Methods Freshly dissociated tumor samples were treated with a predesigned array of 155 test combinations to rank and compare all possible therapeutic combinations from a 12-drug set comprising STS standard of care, FDA approved drugs and promising investigational drugs. Concordance analysis was performed by comparing QPOP-defined outcomes with clinical outcomes from either QPOP-guided therapies or prior lines of treatment. Efficacy of the most frequently top-ranked drug combination was evaluated in both cell line and patient-derived models, and potential mechanisms were investigated using transcriptomic, ChIP-qPCR and in vivo studies. Results Across a total of 45 patient samples, QPOP demonstrated a total predictive value of 72.4% and an AUCROC of 74%, highlighting its discriminative ability in predicting treatment response in patients. We identified BETi and pazopanib as most frequently top-ranked in 67.8% of all patient samples, outperforming standard of care ifosfamide and doxorubicin. Validation in a panel of established patient lines and in vivo models supported its synergistic interaction, which shows repressed oncogenic MYC and related pathways. Conclusions Work here provides preliminary clinical evidence for QPOP to predict STS treatment outcomes and scientific rationale for an effective combinatorial therapeutic strategy validated in vivo that may offer new therapeutic options for sarcoma patients.
Published in
Chan SPY, Rashid MBMA, Lim JJ et al.
· NPJ precision oncology 2025
· PMID 40121334
· doi:10.1038/s41698-025-00851-7
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 GSE282752_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 PRJNA1190368 and SRA study SRP547385. Searching any of these in the dataset finder brings you back here.
Study design
8 conditions, mostly in triplicate
LPS141 cells, untreated, ×3
LPS141 cells, AZD5153, ×3
LPS141 cells, Pazopanib, ×3
LPS141 cells, Combo, ×3
hSC40 cells, untreated, ×3
hSC40 cells, AZD5153, ×3
hSC40 cells, Pazopanib, ×3
hSC40 cells, Combo, ×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
- GSM8649945 LPS141 cells, untreated, replicate 1 LPS141 cells, untreated,
- GSM8649947 LPS141 cells, untreated, replicate 2 LPS141 cells, untreated,
- GSM8649948 LPS141 cells, untreated, replicate 3 LPS141 cells, untreated,
- GSM8649950 LPS141 cells, AZD5153, replicate 1 LPS141 cells, AZD5153,
- GSM8649952 LPS141 cells, AZD5153, replicate 2 LPS141 cells, AZD5153,
- GSM8649953 LPS141 cells, AZD5153, replicate 3 LPS141 cells, AZD5153,
- GSM8649955 LPS141 cells, Pazopanib, replicate 1 LPS141 cells, Pazopanib,
- GSM8649957 LPS141 cells, Pazopanib, replicate 2 LPS141 cells, Pazopanib,
- GSM8649959 LPS141 cells, Pazopanib, replicate 3 LPS141 cells, Pazopanib,
- GSM8649960 LPS141 cells, Combo, replicate 1 LPS141 cells, Combo,
- GSM8649962 LPS141 cells, Combo, replicate 2 LPS141 cells, Combo,
- GSM8649964 LPS141 cells, Combo, replicate 3 LPS141 cells, Combo,
- GSM8649965 hSC40 cells, untreated, replicate 1 hSC40 cells, untreated,
- GSM8649967 hSC40 cells, untreated, replicate 2 hSC40 cells, untreated,
- GSM8649969 hSC40 cells, untreated, replicate 3 hSC40 cells, untreated,
- GSM8649971 hSC40 cells, AZD5153, replicate 1 hSC40 cells, AZD5153,
- GSM8649972 hSC40 cells, AZD5153, replicate 2 hSC40 cells, AZD5153,
- GSM8649974 hSC40 cells, AZD5153, replicate 3 hSC40 cells, AZD5153,
- GSM8649976 hSC40 cells, Pazopanib, replicate 1 hSC40 cells, Pazopanib,
- GSM8649978 hSC40 cells, Pazopanib, replicate 2 hSC40 cells, Pazopanib,
- GSM8649979 hSC40 cells, Pazopanib, replicate 3 hSC40 cells, Pazopanib,
- GSM8649981 hSC40 cells, Combo, replicate 1 hSC40 cells, Combo,
- GSM8649983 hSC40 cells, Combo, replicate 2 hSC40 cells, Combo,
- GSM8649984 hSC40 cells, Combo, replicate 3 hSC40 cells, Combo,
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Share this dataset
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.