GEO series

Immunopeptidome-guided personalized peptide vaccines induce potent T cell response in chronic lymphocytic leukemia

GSE305113 Homo sapiens (human) RNA-seq (gene expression) 24 samples Submitted 2026/08/01 Platform GPL24676

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: Chronic lymphocytic leukemiaThis series is part of our curated briefing on it.
Samples: 24 samples: 24 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)

T cell immunity is central for the control of malignant disease. Chronic lymphocytic leukemia (CLL) lacks mutation-derived T cell neoantigens, yet harbours frequent tumor-associated antigens as targets for therapeutic vaccine design. iVAC-XS15-CLL01 is a warehouse-based personalized muli-peptide vaccine of immunopeptidome-defined CLL-associated antigens.

Read the full summary ↓

We report the results of an open-label phase I trial evaluating the safety, immunogenicity and first efficacy of iVAC-XS15-CLL01 in 20 CLL patients who achieved partial remission under bruton’s tyrosine kinase inhibitor treatment. Vaccination was well tolerated and induced potent T cell response in 95% of patients, targeting multiple vaccine peptides. CLL-specific T cell responses were mediated by polyfunctional T helper 1 CD4+ and CD8+ T cells and persisted until 6 month follow up. Minimal residual disease reduction (median 50%) was observed in 90% of patients and correlated with vaccine-induced T cell response, providing first evidence of iVAC-XS15-CLL01’s clinical efficacy, warranting its further evaluation in an upcoming phase II trial.

This dataset

Open GSE305113 in finder → Find more human RNA-seq datasets → Chronic lymphocytic leukemia 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 GSE305113_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 PRJNA1304449 and SRA study SRP608085. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

24 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 24 sample titles: 24 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

GSM9162393 T cells CLL, patient 3 and 8, unstimulated, Day1, VDJ Library
GSM9162394 T cells CLL, patient 3 and 8, IVE stimulated, Day1, VDJ Library
GSM9162395 T cells CLL, patient 3 and 8, unstimulated, Month3, VDJ Library
GSM9162396 T cells CLL, patient 3 and 8, IVE stimulated, Month3, VDJ Library
GSM9162397 T cells CLL, patient 5 and 17, unstimulated, Day1, VDJ Library
GSM9162398 T cells CLL, patient 5 and 17, IVE stimulated, Day1, VDJ Library
GSM9162399 T cells CLL, patient 5 and 17, unstimulated, Month3, VDJ Library
GSM9162400 T cells CLL, patient 5 and 17, IVE stimulated, Month3, VDJ Library
GSM9162401 T cells CLL, patient 3 and 8, unstimulated, Day1, GEX Library
GSM9162402 T cells CLL, patient 3 and 8, IVE stimulated, Day1, GEX Library
GSM9162403 T cells CLL, patient 3 and 8, unstimulated, Month3, GEX Library
GSM9162404 T cells CLL, patient 3 and 8, IVE stimulated, Month3, GEX Library
GSM9162405 T cells CLL, patient 5 and 17, unstimulated, Day1, GEX Library
GSM9162406 T cells CLL, patient 5 and 17, IVE stimulated, Day1, GEX Library
GSM9162407 T cells CLL, patient 5 and 17, unstimulated, Month3, GEX Library
GSM9162408 T cells CLL, patient 5 and 17, IVE stimulated, Month3, GEX Library
GSM9162409 T cells CLL, patient 3 and 8, unstimulated, Day1, Hash Library
GSM9162410 T cells CLL, patient 3 and 8, IVE stimulated, Day1, Hash Library
GSM9162411 T cells CLL, patient 3 and 8, unstimulated, Month3, Hash Library
GSM9162412 T cells CLL, patient 3 and 8, IVE stimulated, Month3, Hash Library
GSM9162413 T cells CLL, patient 5 and 17, unstimulated, Day1, Hash Library
GSM9162414 T cells CLL, patient 5 and 17, IVE stimulated, Day1, Hash Library
GSM9162415 T cells CLL, patient 5 and 17, unstimulated, Month3, Hash Library
GSM9162416 T cells CLL, patient 5 and 17, IVE stimulated, Month3, Hash Library

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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.