A Novel and Personalized Method Using Simulation for Predicting Effective Therapeutics for Waldenströms Macroglobulinemia

Background: The concept of treatment personalization in cancer often means associating a drug with an actionable mutation. One limitation of this approach is that most tumors have multiple aberrations, which are not actionable. Here we present a predictive simulation based approach which models the molecular architecture and resulting physiology of advanced stage Waldenströms Macroglobulinemia (WM, a B-lymphoplasmacytic lymphoma) as well as the design of novel personalized treatments using existing drug agents for rapid clinical translation with validations.

Methods: We used the human WM cell line, RPCI-WM1, as a surrogate model of refractory, advanced patient sub-groups. Publically available as well as proprietary genomic and cytogenetic data was utilized for the creation of an avatar of RPCI-WM1, which through simulation identified the salient and prominently dysregulated cellular pathways. Importantly, illustrating these pathways highlights common convergence points on increased proliferation and viability. These convergence points were then directly and indirectly targeted by simulated testing of a library of FDA approved drugs and those impacting these dysregulated pathways were shortlisted. Importantly, this simulation avatar approach not only looks for agents acting on the specific gene mutation, but also predicts the convergence points to be attacked. The personalized simulation avatar technology is a comprehensive functional proteomics representation of WM physiology network. A standardized library of equations models all the biological reactions such as enzymatic reactions, allosteric binding and protein modulation by phosphorylation, de-phosphorylation, ubiquitination, acetylation, prenylation and others. A library of over 150 digital functional library of FDA approved drug agents and those in clinical study has been developed and was simulated individually and in combination on the RPCI-WM1 (advanced stage WM patient) avatar.

Results: Overall, there were 272 gene aberrations used to create the RPCI-WM1 simulation avatar. Importantly, presence of MYD88_L265P mutation, absence of CXCR4 mutations and additional chromosomal aberrations (derivatives, translocations, deletions and amplifications of chromosomes 3, 6, 9, 13, 18 and 19) were taken into account.

The RPCI-WM1 patient avatar predicted increased IRAK1/4 engagement due to MYD88 mutation and high copy number (CN) of IL18, to increased NFkB via TAK1, and increased ERK signaling through IRAK1/4 mediated down regulation of DUSP1. The RPCI-WM1 model also indicated high AKT due to indirect convergence of multiple aberrations. Another key characteristic of the genomic aberrations driving proliferative phenotype was low CN of RB1, FOXO3, CDKN2A, CDKN2B, and CEBPa.

Modeling predicted sensitivity to the aurora kinase (AURKA) inhibitor, tozasertib and resistance to cell cycle cyclin D-CDK4/6 pathway inhibitors., Although there was no CN variation in AURKA, as per simulation its expression and activity was upregulated due to high CN of ETS1 and PAK1 and increased activation of NFkB, HIF1A and STAT5 in the disease network. Despite high proliferation, LEE011 (representative CDK4/6 inhibitor) was predicted to exert no cytotoxic effect, due to the presence of an RB1 deletion. Through phosphorylation of RB1, the CDK4-CyclinD1 complex aids in release of E2F1 from the RB1 sequestered complex, to drive proliferation. However, predictive modeling suggested that with an RB1 deletion present, this regulation becomes irrelevant and therefore the inhibition of the CDK4/6-CyclinD1 complex would be ineffective.

The simulation predictions were experimentally validated. As predicted, AURKA inhibition with tozasertib significantly inhibited viability and proliferation of RPCI-WM1 cells (IC₅₀ ~7nM) whereas inhibition of CDK4/6 with LEE011 had no effect on tumor cell survival.

Conclusions: This study demonstrates the utility of a novel technology for rapid translation of a (WM) genetic signature towards a personalized therapeutic strategy. This simulation avatar based approach holistically integrates all genomic aberration information to design personalized therapies architected from FDA approved and/or clinical drug agents with therapeutic potential for WM patients based on unique genetic signatures.