GEO series

Human scFv based, ALPPL2-specific CAR expressing T cells induce potent antitumor activity in an IL15-dependent manner

GSE284686 Homo sapiens (human) RNA-seq (gene expression) 18 samples Submitted 2026/03/13 Platform GPL34281

At a glance

RNA-seq (gene expression) · human · 18 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)
Samples: 18 samples: 4 × ABBz baseline, vs 4 × 15.ABBz round 2, vs 4 × 15.ABBz baseline, vs 3 × ABBz round 2,Groups 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: Coexpression of IL15 Promotes Effector Differentiation and Sustained Proliferative Capacity in ALPPL2-Specific Human CAR T CellsCancer immunology research · 2026

Summary (written by the submitters)

Chimeric antigen receptor (CAR) T cells have robust antitumor activity against hematologic malignancies and have the potential to benefit patients with solid tumors. Immune recognition of murine proteins expressed in adoptively transferred T cells and lack of homeostatic in the tumor microenvironment can limit expansion and persistence of CAR T cells.

Read the full summary ↓

CARs generated only from human sequences reduce the risk of immune mediated rejection and Interleukin-15, which promotes T cell survival and fitness can improve the expansion and persistence of T cells. In this study, we report a CAR T construct (ABBz) assembled from only human sequences including an scFv specific to ALPPL2 selected through an unbiased, high-throughput screen of human antibody-based, phage-displayed scFv library based on binding specificity, stringency, and low dissociation constant. We demonstrate specificity to the antigen, effective cytolytic function, and cytokine production induction in ABBz T cells. We detail key transcriptomic, phenotypic and functional changes specific to IL15 coexpression in ABBz T cells. Lastly, we demonstrate that ABBz CAR T cells have robust anti-tumor activity which is further enhanced through IL15 co-expression resulting in increased cytolytic capacity and superior expansion secondary to reduced apoptosis of CAR T cells.

Published in

Coexpression of IL15 Promotes Effector Differentiation and Sustained Proliferative Capacity in ALPPL2-Specific Human CAR T Cells

Cunningham D, Liu J, Cevher Zeytin I et al. · Cancer immunology research 2026 · PMID 41860794 · doi:10.1158/2326-6066.CIR-25-0609

This dataset

Open GSE284686 in finder → Find more human 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 GSE284686_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 PRJNA1200046 and SRA study SRP552459. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41860794) ↗

Study design

4 × ABBz baseline, vs 4 × 15.ABBz round 2, vs 4 × 15.ABBz baseline, vs 3 × ABBz round 2,

Supports a between-group comparison across 15 samples.

How we read this: 4 replicated groups read from 18 sample titles; they account for 15 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

GSM8690517 ABBz baseline, rep 1 ABBz baseline,
GSM8690518 15.ABBz round 2, rep 1 15.ABBz round 2,
GSM8690519 ABBz round 2, rep 2 ABBz round 2,
GSM8690520 15.ABBz round 2, rep 2 15.ABBz round 2,
GSM8690521 15.ABBz round 2, rep 3 15.ABBz round 2,
GSM8690522 ABBz round 2, rep 3 ABBz round 2,
GSM8690523 15.ABBz round 2, rep 4 15.ABBz round 2,
GSM8690524 ABBz round 2, rep 4 ABBz round 2,
GSM8690525 15.ABBz baseline, rep 1 15.ABBz baseline,
GSM8690526 ABBz baseline, rep 2 ABBz baseline,
GSM8690527 15.ABBz baseline, rep 2 15.ABBz baseline,
GSM8690528 ABBz baseline, rep 3 ABBz baseline,
GSM8690529 15.ABBz baseline, rep 3 15.ABBz baseline,
GSM8690530 ABBz baseline, rep 4 ABBz baseline,
GSM8690531 15.ABBz baseline, rep 4 15.ABBz baseline,
GSM8690532 ABBz round2, rep 1
GSM8690533 Capan1, baseline, rep 1
GSM8690534 Nontransduced, baseline, rep 1

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