High WEE1 Expression Correlates with Poor Survival in Multiple Myeloma, Independent of Standard Prognostic Factors
Authors: Simhal Anish Kumar , Firestone Ross S , Oh Jung Hun , Norton Larry , Hultcrantz Malin , Usmani Saad Z. , Maclachlan Kylee H , Deasy Joseph O. 
Published: December 2024

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. PFS between the WEE1-high and WEE1-low groups was significantly different (p &lt;1e-9) and validated in the TT2 & TT3 datasets with statistical significance. Multivariate CPH modeling showed the prognostic effect of WEE1-expression to be independent of hyperdiploidy, IgH translocations, del17p, TP53 mutation, gain/amp 1q21, APOBEC mutational activity, and the complex structural variant chromothripsis. WEE1 expression was highly discriminatory; when restricting to either WEE1-high or WEE1-low cohorts, membership differentiated PFS outcomes by approximately 1.98 years. As a single measurement, WEE1 expression remains prognostic for PFS irrespective of induction therapy or use of transplant, with a mean difference in PFS of 1.91 years.
WEE1 expression alone has a comparable predictive value (c-index: 0.58 ± 0.04) as ISS (c-index: 0.61 ±0.03). In the overall cohort, as WEE1 expression increases, the relationship between WEE1 and interacting genes becomes dysregulated, quantified as 3.2x by RF analysis. Differential gene expression analysis identified the P53 pathway as the most overexpressed hallmark pathway in the WEE1-high cohort.
Conclusion
Our results show that WEE1 expression is prognostic independent of known biomarkers, differentiates outcomes associated with known markers, is upregulated independently of its interacting neighbor genes, and is associated with dysregulated P53 pathways. This suggests that WEE1 expression levels may have clinical utility in prognosticating outcomes in newly diagnosed MM and may support the application of WEE1 inhibitors to MM preclinical models. Determining the causes of abnormal WEE1 expression may uncover novel therapeutic targets.