High WEE1 expression is independently linked to poor survival in multiple myeloma

Abstract

Current prognostic scores in multiple myeloma (MM) currently rely on disease burden and a limited set of genomic alterations. Some studies have suggested gene expression panels may predict clinical outcomes, but none are presently utilized in clinical practice. We therefore analyzed the MMRF CoMMpass dataset (N=659) and identified a high-risk group (top tertile) and a low-risk group ( bottom tertile) based on WEE1 expression sorted in descending order. The tyrosine kinase WEE1 is a critical cell cycle regulator during the S-phase and G2M-checkpoint. Abnormal WEE1 expression has been implicated in multiple cancers including breast, ovarian, and gastric cancers, but has not until this time been implicated in MM. PFS was significantly different (p <1e-9) between the groups, which was validated in two independent microarray gene expression profiling (GEP) datasets from the Total Therapy 2 (N=341) and 3 (N=214) trials. Our results show WEE1 expression is prognostic independent of known biomarkers, differentiates outcomes associated with known markers, is upregulated independently of its interacting neighbors, 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 pathways.

Figure 1.. Prognostic value of WEE1 expression from RNA-seq and GEP data.

A) Progression free survival (PFS) based on CoMMpass RNA-seq data showing the two-year difference in median PFS with a p-value of less than 1e-9. B & C) Event free survival of the Total Therapy 2 and Total Therapy 3 cohorts gene expression profiling (GEP) data, respectively, showing diverging outcomes with a P<0.05.

Figure 2.. Cox proportional hazards (CPH) modeling of MM markers and WEE1 expression.

A) Coefficients of the multivariate CPH model show WEE1 to be the most significant prognosticator. B & C) Within the WEE1-high and WEE1-low cohorts, none of the markers are significant for PFS after FDR-BH correction. TP53 aberration status — 0 = diploid, 1 = either deletion or mutation, 2 = biallelic loss. Certain markers not available for all subjects.

Figure 3.. Kaplan Meyer curves stratified by MM markers show the prognostic signal in WEE1 expression.

WEE1 expression defines prognosis regardless of marker type. The top row represents the cohort with a given feature, and the bottom row represents the cohort without the given feature. In both cases, WEE1 defined low-risk and high-risk groups as separate outcomes with a median PFS difference of two years.

Figure 4.. Kaplan Meyer curves show the effect of WEE1 expression on treatment type.

The top row is the cohort that received a treatment type, and the bottom row is the cohort that did not receive the treatment type.

Figure 5.. Random forest feature importance plots.

RF modeling of WEE1 expression in the WEE1-high cohort is 3.2x more inaccurate than WEE1 expression modeling in the WEE1-low cohort. A) Feature importance plot showing the informative features for predicting WEE1 RNA-seq in the WEE1-low group. B) Feature importance plot showing the informative features for predicting WEE1 RNA-seq in the WEE1-high group.