# Identifying Candidate Gene Drivers Associated with Relapse in Pediatric T-Cell Acute Lymphoblastic Leukemia Using a Gene Co-Expression Network Approach

## TL;DR

This study identifies genes linked to relapse in pediatric T-cell leukemia using gene network analysis and could help develop new treatments or biomarkers.

## Contribution

The study introduces a novel approach combining gene co-expression networks and discriminant analysis to identify relapse-associated genes in pediatric T-ALL.

## Key findings

- Relapse-associated hub genes were identified using hdWGCNA and sPLS-DA from paired diagnosis–relapse samples.
- Three model gene signatures were found to efficiently identify patients with lower survival rates in the AALL0434 cohort.
- Key gene functions include antigen presentation, cytoskeleton remodeling, and immune responses.

## Abstract

Non-genetic transcriptomic plasticity plays a pivotal role in cancer cell resistance to treatments. This study aims to elucidate the molecular mechanisms underlying the transcriptomic evolution from diagnosis to relapse in pediatric T-cell acute lymphoblastic leukemia (T-ALL). We conducted single-cell RNA sequencing analysis on paired diagnosis–relapse samples to address this question. Using hdWGCNA, we constructed gene co-expression networks to identify relapse-associated networks, modules, and hub genes, potentially indicative of leukemic drivers. Through combining results from three pairs of patient samples, we identified the most discriminating genes between diagnosis and relapse using sPLS-DA. A Cox analysis revealed their potential to identify patients with lower survival in the AALL0434 cohort. These relapse hub genes are promising as future therapeutic targets or relapse markers.

Pediatric T-cell Acute Lymphoblastic Leukemia (T-ALL) relapses are still associated with a dismal outcome, justifying the search for new therapeutic targets and relapse biomarkers. Using single-cell RNA sequencing (scRNAseq) data from three paired samples of pediatric T-ALL at diagnosis and relapse, we first conducted a high-dimensional weighted gene co-expression network analysis (hdWGCNA). This analysis highlighted several gene co-expression networks (GCNs) and identified relapse-associated hub genes, which are considered potential driver genes. Shared relapse-expressed genes were found to be related to antigen presentation (HLA, B2M), cytoskeleton remodeling (TUBB, TUBA1B), translation (ribosomal proteins, EIF1, EEF1B2), immune responses (MIF, EMP3), stress responses (UBC, HSP90AB1/AA1), metabolism (FTH1, NME1/2, ARCL4C), and transcriptional remodeling (NF-κB family genes, FOS-JUN, KLF2, or KLF6). We then utilized sparse partial least squares discriminant analysis to select from a pool of 481 unique leukemic hub genes, which are the genes most discriminant between diagnosis and relapse states (comprising 44, 35, and 31 genes, respectively, for each patient). Applying a Cox regression method to these patient-specific genes, along with transcriptomic and clinical data from the TARGET-ALL AALL0434 cohort, we generated three model gene signatures that efficiently identified relapsed patients within the cohort. Overall, our approach identified new potential relapse-associated genes and proposed three model gene signatures associated with lower survival rates for high-score patients.