GEO series

Molecular signatures of T cells targeting multiple myeloma

GSE242883 Homo sapiens (human) RNA-seq (gene expression) 19 samples Submitted 2026/06/30 Platform GPL24676

At a glance

RNA-seq (gene expression) · human · 19 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: Multiple myelomaThis series is part of our curated briefing on it.
Samples: 19 samples: 19 samples, each with a distinct title — no replicated groupsGroups 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)

Immunotherapeutic approaches including cell therapy are believed to be the next generation of paradigm-changing treatment options in hematological malignancies. Cell therapy using CAR-T cells or expanded autologous T cells (TILs) has shown promising results in non-solid and solid malignancies. However, acquired resistance to immunotherapy or rapid disease progression represent major clinical challenges in hematological cancers.

Read the full summary ↓

Transgenic T cell receptors (TCRs) redirect patient-autologous lymphocytes to tumor antigens and can induce regression of refractory solid tumors. However, a bone fide population of myeloma reactive TCRs has yet to be identified. Here, we have established a multiplexed optical barcoding assay that allows for ex vivo profiling of single bone marrow-resident T cells in order to identify sequences and transcriptional signatures of patient-individual tumor-reactive lymphocytes. By integrating antigen specificity and the transcriptional profile of single myeloma-associated T cells using the CDR3 sequence as clone-specific barcode, we found that tumor-reactivity results in a specific gene expression state of bone marrow-resident CD8+ T cells. Non-tumor-reactive clones were either CD4+ or enriched for viral specificities, while myeloma reactive TCRs demonstrated an increased clonality within the overall lymphocyte repertoire. We furthermore describe a subset of CD29 (ITGB1)-expressing effector-memory CD8+ T cells that is associated with response to induction immunochemotherapy. Our results demonstrate antitumor reactivity in a subset of bone marrow-resident T cells against malignant plasma cells and provide the rationale for future personalized TCR-transgenic cell therapy approaches in newly diagnosed multiple myeloma patients. More broadly, the generated resources from this project might contribute to identifying and monitoring tumor reactive T cell responses targeting hematological neoplasias.

This dataset

Open GSE242883 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 GSE242883_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 PRJNA1015223. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

19 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 19 sample titles: 19 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

GSM7774340 Pt-01_BMNC sort CD3, T NK cell subset bmref
GSM7774341 Pt-02_BMNC sort CD3, T NK cell subset bmref
GSM7774342 Pt-03_BMNC sort CD3, T NK cell subset bmref
GSM7774343 Pt-03_PBMC sort CD3, T NK cell subset bmref
GSM7774344 Pt-04_BMNC sort CD3, T NK cell subset bmref
GSM7774345 Pt-05_BMNC sort CD3, T NK cell subset bmref
GSM7774346 Pt-06_BMNC sort CD3, T NK cell subset bmref
GSM7774347 Pt-07_BMNC sort CD3, T NK cell subset bmref
GSM7774348 Pt-07_PBMC sort CD3, T NK cell subset bmref
GSM7774349 Pt-08_BMNC sort CD3, T NK cell subset bmref
GSM7774350 Pt-08_PBMC sort CD3, T NK cell subset bmref
GSM7774351 Pt-09_BMNC sort CD3, T NK cell subset bmref
GSM7774352 Pt-09_PBMC sort CD3, T NK cell subset bmref
GSM7774353 Pt-10_BMNC sort CD3, T NK cell subset bmref
GSM7774354 Pt-10_PBMC sort CD3, T NK cell subset bmref
GSM7774355 Pt-11_BMNC sort CD3, T NK cell subset bmref
GSM7774356 Pt-11_PBMC sort CD3, T NK cell subset bmref
GSM7774357 Pt-12_BMNC sort CD3, T NK cell subset bmref
GSM7774358 Pt-12_PBMC sort CD3, T NK cell subset bmref

More Multiple myeloma datasets

GSE155631 Oncogenic mechanisms of DIS3 mutations in Multiple Myeloma 62 samples same disease briefing · shares multiple, myeloma
GSE161195 Single cell RNA-seq of relapsed and refractory multiple myeloma patients defines new resistance pathways and targeted treatments 213 samples same disease briefing · shares multiple, myeloma
GSE167969 ATAC-seq and RNA-Seq of Multiple Myeloma 46 samples same disease briefing · shares multiple, myeloma
GSE148715 “Direct to Drug” screening as a precision medicine tool in multiple myeloma 38 samples same disease briefing · shares multiple, myeloma
GSE167042 Splicing modulation increases BCL2 dependence and sensitizes Multiple Myeloma cells to Venetoclax 4 samples same disease briefing · shares multiple, myeloma
GSE178442 Genome-wide maps of transcription in DEAF1 overexpression(OE) and wildtype(WT) cells in Multiple Myeloma 4 samples same disease briefing · shares multiple, myeloma
GSE167968 RNA-seq of Multiple Myeloma 33 samples same disease briefing · shares multiple, myeloma
GSE179453 Expression data from control-knockdown (CTR-KD) and MAST4-KD human multiple myeloma (MM) cell lines 6 samples same disease briefing · shares multiple, myeloma

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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.