Clinico-Genomic Characterization of AML Patients Based on IL2RA (CD25) Expression Uncovers an Association with Stem Cell Signatures and FLT3-ITD Status and Informs Drug Combinations

Background

Acute myeloid leukemia (AML) is a heterogeneous disease with poor outcomes, thus there remains a need to integrate molecular information to identify patients most likely to respond to drugs and combinations. Disease segmentation based on transcriptomics has provided valuable insight into disease risk and the likelihood of response to targeted compounds. IL2RA(CD25) is a receptor expressed on both AML leukemic and immune cells, and has shown initial promise as a potential dual target of leukemic blasts and regulatory T (Treg) cells in pre-clinical models (Pousse et al. Front. Oncol. 2023). We used a systems approach based on a recently described transcriptomic classifier for AML (Hamidi et al. ASH 2021) and other molecular tools to characterize the association between IL2RA expression levels and AML genomic markers, clinical features and patient outcomes. In addition, we evaluated IL2RA in relation to ex vivo drug sensitivity, to identify patient segments who are most likely to benefit from CD25-targeting drugs.

Methods

BEAT-AML (NCT01728402) RNAseq data from patient samples, associated ex vivo drug sensitivity data (N=283), was VOOM normalized. Patients were binned into quartiles based on IL2RA expression. Gene signature scoring was performed using xCell cell type enrichment algorithm and GSVA for Hallmark pathways and scRNAseq signature based on Van Galen et al. Cell 2019. Associations were performed with clinical outcome (log-rank test), gene signatures (Spearman correlation), mutations (Wilcoxon test) and ex vivo drug sensitivity (Kruskal Wallis test).

Results

We characterized the BeatAML dataset and found that elevated IL2RA expression associates with inferior overall survival (p=0.014) and high-risk features. In addition, we identified a strong association between IL2RA expression and FLT3-ITD status (Figure), as well as other genetic alterations. Using correlation analyses, we established an association between IL2RA expression levels and “primitive” AML signatures (leukemic stem cell (LSC), R=0.4, p=6.8x10-12, hematopoietic stem cell (HSC)-like, R=0.43, p=6.8x10-14) and Tregs (R=0.47, p=1.7x10-15), and an anti-correlation with promonocytic signatures (R=-0.23, p=1.1x10-4). Interestingly, HSC-like and Treg signatures were also correlated (R=0.19, p=0.00087), consistent with an association between stem cell abundance and a repressive immune microenvironment. Finally, using a multivariate model adjusting for the effect of Tregs and LSCs, the prognostic value of IL2RA remained significant (p=0.029).

We previously used unsupervised machine learning clustering based on consensus non-negative factorization (cNMF) to discover novel transcription-based classification (Hamidi ASH 2021). Using this methodology, we identified a strong correlation between IL2RA expression in cNMF subtypes (p=1.6x10-10). Moreover, this method uncovered patient subtypes in which IL2RA correlates with LSC (cNMF 6.3), Treg (6.4, 6.6) or both signatures (6.1, 6.2). This differential association was independent of prognostic category, maturation state or venetoclax sensitivity (Table).

Transcription-based classifiers have been shown to be highly predictive of ex vivo drug sensitivity. Consistent with an association between IL2RA expression and FLT3-ITD status, we identified strong correlations between IL2RA and sensitivity to FLT-3 and other tyrosine kinase inhibitors, providing a rationale for combining these compounds with CD25-targeting therapeutics. In contrast, there was no correlation between IL2RA levels and venetoclax AUC (p=0.25), while the cNMF classification system similarly revealed that patient segments with increased IL2RA expression had heterogeneous venetoclax sensitivity (Table), supporting CD25 targeting in patients who may not respond to venetoclax.

Conclusion

AML patients with elevated IL2RA expression have inferior prognosis, enrichment of stem-like and Treg signatures and FLT3-ITD alterations. Transcriptomics-based clustering could be used to guide combination therapies based on potential impact of CD25 targeting on Tregs, leukemic cells, or both populations. Ex vivo drug sensitivity analyses support combinations of CD25-targeting agents with FLT3 inhibitors, as well as venetoclax. Additional work to evaluate these and other combinations using functional assays is warranted.