PSG and Other Candidate Genes as Potential Biomarkers of Therapy Resistance in B-ALL: Insights from Chromosomal Microarray Analysis and Machine Learning

TL;DR

This study identifies specific genomic regions, including the PSG gene family, linked to treatment resistance in B-cell acute lymphoblastic leukemia.

Contribution

The study introduces PSG and other genes as potential biomarkers for therapy resistance in B-ALL using CMA and machine learning.

Findings

A cnLOH cluster in the 19q13.2–19q13.31 region was significantly enriched in MRD-positive B-ALL patients.

Deletions in 7p22.3 and 16q13 were linked to chromosomal instability and poor treatment response.

High-resolution CMA was shown to improve risk stratification and diagnostics in B-ALL.

Abstract

Chromosomal microarray analysis (CMA) was performed for 40 patients with B-ALL undergoing treatment according to the ALL-2016 protocol to investigate the copy number alterations (CNAs) and copy neutral loss of heterozygosity (cnLOH) associated with minimal residual disease (MRD)-positive remission. Aberrations involving over 20,000 genes were identified, and a random forest approach was applied to isolate a subset of genes whose CNAs and cnLOH are significantly associated with poor therapeutic response. We have assembled the triple matched healthy population data and used that data as a reference, but not as a matched control. We identified a recurrent cluster of cnLOH in the 19q13.2–19q13.31 region, significantly enriched in MRD-positive patients (70% vs. 47% in the reference group vs. 16% in MRD-negative patients). This region includes the pregnancy-specific glycoprotein (PSG) gene family and the oncogene ERF, suggesting a potential role in leukemic persistence and treatment resistance. Additionally, we observed significant deletions involving 7p22.3 and 16q13, often as part of large-scale losses affecting almost the entire chromosomes 7 and 16, indicative of global chromosomal instability. These findings highlight specific genomic regions potentially involved in therapy resistance and may contribute to improved risk stratification in B-ALL. Our findings emphasize the value of high-resolution CMA in diagnostics and risk stratification and suggest that PSG genes and other candidate genes could serve as biomarkers for predicting treatment outcomes.

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