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A p53 score derived from TP53 CRISPR/Cas9 HMCLs predicts survival and reveals major role of BAX in BH3 mimetics response [2]

GSE245162 Homo sapiens (human) Single-cell RNA-seq 96 samples Submitted 2024/04/04 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 96 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Homo sapiens (human)
Topic
Multiple myelomaThis series is part of our curated briefing on it.
Samples
96 samples: 36 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
A p53 score derived from TP53 CRISPR/Cas9 HMCLs predicts survival and reveals a major role of BAX in the response to BH3 mimeticsBlood · 2024
Summary (written by the submitters)
In order to establish a strict p53-dependent gene expression profile, p53 negative clones were derived from two TP53+/+ and two TP53-/mut t(4;14) human myeloma cell lines (HMCLs) using CRISPR/Cas9 technology. From the 17 dysregulated genes shared between the 6 clones from the two TP53+/+ myeloma cell lines, we established a 13-gene functional p53 score, involving genes downregulated on p53-silencing.
Read the full summary ↓
This functional score allowed us to segregate clones, myeloma cell lines and patients’ samples according to the presence or absence of a single or double hit in TP53 gene i.e., chromosomal deletion and/or mutation. The score also distinguished 1,162 cell lines from hematological and solid cancers according to hits in TP53. Moreover, the 13-gene score was predictive of overall survival in two independent cohorts of patients. Among the 13 genes, we showed that p53-regulated BAX expression correlated to, and directly impacted, the MCL1 BH3 mimetic S63845 sensitivity of myeloma cells by modulating the amount of MCL1-BAX complexes. Resistance to cell death induced by p53-silencing was overcome by combining S63845 with venetoclax, although the synergy was significantly lower in p53-deficient versus -proficient clones and patients’ samples. Finally, by using scRNAseq analysis, we further showed in patients’ bone marrow samples that myeloma cells surviving to the BH3 mimetic combination had an about 3-fold lower p53 score. This data established a functional p53 score and showed that p53-mediating BAX expression greatly impacts mitochondria-mediated cell death in myeloma cells.
Published in
A p53 score derived from TP53 CRISPR/Cas9 HMCLs predicts survival and reveals a major role of BAX in the response to BH3 mimetics
Durand R, Descamps G, Bellanger C et al. · Blood 2024 · PMID 38096363 · doi:10.1182/blood.2023021581
This dataset
Open GSE245162 in finder → Find more human RNA-seq datasets → Multiple myeloma landscape →
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 GSE245162_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: 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 PRJNA1027285 and SRA study SRP466098. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38096363) ↗
Study design
36 conditions, mostly in triplicate
JIM3_control_bulk_treated with 10 µM Nutlin3a… ×3 JIM3_control_bulk_untreated ×3 JIM3_KO_clone_1_treated with 10 µM Nutlin3a f… ×3 JIM3_KO_clone_2_treated with 10 µM Nutlin3a f… ×3 JIM3_KO_clone_3_treated with 10 µM Nutlin3a f… ×3 JIM3_KO_clone_1_untreated ×3 JIM3_KO_clone_2_untreated ×3 JIM3_KO_clone_3_untreated ×3 +28 more

Supports a between-group comparison across 96 samples.

How we read this: 36 replicated groups read from 96 sample titles; they account for 96 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

+ 56 more — browse all 96 samples with per-sample file links →

More Multiple myeloma 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.