High WEE1 Expression Correlates with Poor Survival in Multiple Myeloma, Independent of Standard Prognostic Factors

• Published in: Blood 2024-11, Vol.144 (Supplement 1), p.6827-6827
• Main Authors: Simhal, Anish Kumar, Firestone, Ross S, Oh, Jung Hun, Norton, Larry, Hultcrantz, Malin, Usmani, Saad Z., Maclachlan, Kylee H, Deasy, Joseph O.
• Format: Article
• Language: English
• ISSN: 0006-4971; 1528-0020
• DOI: 10.1182/blood-2024-205838

Purpose Multiple myeloma (MM) is a hematological malignancy associated with a malignant proliferation of plasma cells. Although the disease is usually responsive to upfront therapies, MM still remains incurable. Current prognostic scores in MM (International Staging System; ISS, revised-ISS; R-ISS) rely on disease burden and a limited set of genomic alterations. Several prognostication methods have been proposed but with marginal predictive power of progression-free survival (PFS), producing a concordance index (c-index) of ~60% and therefore leaving room for improvement. The tyrosine kinase WEE1 is a critical cell cycle regulator during cell division. Abnormal WEE1 expression has been implicated in multiple cancers including breast, ovarian, and gastric cancers, with WEE1 inhibitors currently in clinical trials. In MM, preclinical studies have shown promising results when inhibiting WEE1 both alone and in drug combinations. Here, we examine the relationship between WEE1 expression and survival outcomes in MM as an emerging prognostic marker. Methods Multiple bioinformatic and machine learning-based methods were applied to three MM datasets to examine the role of WEE1. RNA-seq data was downloaded from the Multiple Myeloma Research Foundation's (MMRF) CoMMpass database, version 19 (N=659). Gene expression profiling (GEP) data was obtained from the University of Arkansas's Total Therapy 2 (TT2, N=341) and Total Therapy 3 (TT3, N=214) trials. For each dataset, patients'WEE1 expression values were sorted, with the top tertile labeled as WEE1-high and the bottom tertile as WEE1-low. Multivariate Cox proportional hazards (CPH) models determined the effect of WEE1 relative to genomic risk factors. Random survival forests (RSF) determined the prognostic value of WEE1 from its expression. To quantify the relative change in expression levels of genes known to interact with WEE1 between the cohorts, we used random forest (RF) regression models to predict WEE1 expression, and feature importances were computed via permutation importance. Differential gene expression analysis was conducted using DESeq2, and dysregulated pathways were labeled using the hallmark gene set. Results The mean age of individuals in the MMRF dataset was 62.5 ± 10.7 years; 60% were male, the ISS distribution was 35/35/30%, and 53% received an autologous stem cell transplant. For TT2, the mean age was 56.3 ± 9.8 years and 57% were male; for TT3, the mean age was 58.6 ± 8.8 years and 67% were male