Biosimulation Using the Cellworks Computational Omics Biology Model (CBM) Identifies Genomic and Molecular Markers for Decitabine (DAC) Plus Valproic-Acid (VPA) Treatment Response in Patients with Myelodysplastic Syndromes (MDS)

Background: DNA methyltransferase inhibition (DNMTi) with hypomethylating agents (HMA), azacitidine (AZA) or decitabine (DAC), remains the mainstay of therapy for most high-risk Myelodysplastic syndrome (MDS) patients. However, only 40-50% of MDS patients achieve clinical improvement with DNMTi. Previously, combinations of HMA and histone deacetylase (HDAC) inhibitors have been explored in MDS with varying clinical outcomes. However, the heterogeneity of genomic aberrations in MDS portend widely divergent responses from HDAC inhibition, implying that a predictive clinical decision support tool could select patients most likely to benefit from this combination. We explored the molecular basis of observed clinical response in a group of patients treated with DAC and Valproic-Acid (VPA).

Method: 16 MDS patients with known clinical responses to DAC + VPA were selected for study from the Cellworks patient repository. The aberration and copy number variations from individual cases served as input into the Computational Omics Biology Model, a computational multi-omic biology software model largely created using literature sourced from PubMed, to generate a patient-specific protein network map. Disease biomarkers unique to each patient were identified within these maps. The Cellworks Biosimulation Platform has the capacity to biosimulate disease phenotypic behavior and was used to create a patient-specific disease model. Biosimulations were then conducted on each patient-specific disease model to measure the effect of DAC + VPA according to a cell growth score. This score was comprised of a composite of cell proliferation, viability, apoptosis, metastasis, and other cancer hallmarks. Biosimulation of drug response was conducted to identify and predict therapeutic efficacy.

Results: In the biosimulation, VPA is a relatively weak HDAC inhibitor, but it also inhibits GSK3B and in turn increases beta-catenin (CTNNB1) levels. Additionally, monosomy 7 associated with loss of CAV1, HIPK2, and TRRAP also causes high CTNNB1, thereby further contributing to drug resistance. Biosimulation correctly identified that 7 of 8 patients with these genomic findings were clinical non-responders (NR) to VPA, indicating that CTNNB1 status is likely to predict treatment failure from the VPA + HMA combination in this disease. Notably, CTNNB1 levels have been reported to foster an immune-evasive tumor microenvironment resistant to CTL activation.

By contrast, high levels of c-MYC predict response to VPA + HMA combination. VPA inhibits MYC transcription and thereby reduces MYC-induced downregulation of p21 through CKS1B. Additionally MYC is a transcriptional regulator of DNMT1 which is degraded after hyperacetylation induced by HDAC3 inhibition suggesting that VPA also enhances DNMT1 turnover. One patient analyzed had trisomy 8 resulting in c-MYC over-expression and responded to HMA + VPA. Additionally, other aberrations enhancing c-MYC transcription such as copy number variant (CNV) loss of MXI1, HHEX, FBXW7, SMAD7 or CNV gain of BRD4, BCL7B led to high clinical response to the combination (Table 1). By comparison to the CTNNB1-driven subset, the impact of VPA on CTNNB1 in the MYC-dominant disease network did not negate the benefit of VPA for these patients. Additionally, the inhibition of GSK3B by VPA leading to diminished FBXW7 and less ubiquitin-mediated turnover of c-MYC was not sufficient to overcome the inhibition of MYC transcription and HDAC3i-mediated turnover.

Immune activation has become a recognized mechanism of responsiveness to HMA. However, among patients with upregulated CTNNB1, VPA is likely to further decrease response to treatment. By contrast, among MYC-driven cancers that are typically immune-evasive, VPA appears to be a vital mechanism of overcoming MYC-driven immune evasion.

Conclusion: Signaling pathway consequences related to CTNNB1 and c-MYC upregulation predict response to DAC + VPA. Although HMA plus HDAC inhibition can be generally beneficial for MDS, variable mechanisms of action among various HDAC inhibitors and unique patient disease characteristics should be considered for optimal treatment selection. Finally, CTNNB1 emerged from the Cellworks biosimulations as a therapeutically relevant target in MDS that determines whether VPA synergizes or antagonizes the effect of other agents in this challenging subtype of MDS.

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