Background: Current tumor profiling analytics provide some insight into the various molecular abnormalities and their individual consequences on oncogenic signaling. However, these analyses are limited by their lack of integration where the combined effect of individual mutations, gene copy number variations and chromosomal aberrations are not consolidated to create the global molecular architecture that supports neoplastic growth, particularly in the context of drug resistance. Consequentially, identities of the preferential oncogenic pathway(s) tumor cells employ to oppose the effects of targeted therapies remain cryptic and unactionable. Here we present a simulation-based method, which not only replicates the molecular architecture of ibrutinib-resistant Waldenstroms Macroglobulinemia (WM, for which ibrutinib is the only FDA-approved agent) in silico, but also predicts cell sensitivity towards existing drugs, which we validated experimentally for potential clinical translation.
Materials: We used the newly established human WM cell line, RPCI-WM1/IR, as a surrogate model of ibrutinib-refractory WM. Genomic data including whole exome sequencing (WES) and copy number analysis (CNA) was utilized for the creation of an avatar of RPCI-WM1/IR, 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 nominated. 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 the 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.
Results: Several genomic aberrations were used to create the RPCI-WM1/IR simulation avatar. Functional activity (based on mutation or copy number alteration) of several ibrutinib targets or transcription factors associated with BTK activity such as FYN, SP1, BMX and FRK were predicted to be lost. Increased expression of CAV1, which also inhibits BTK mediated signaling, was increased. An increase in CSNK2B, which activates PU.1- a transcriptional target of BTK, was also observed. Of note, no CXCR4 mutations, which have been shown to impact ibrutinib response, were observed. Next, the cytotoxic potential of over 150 FDA approved drug (and some in experimental stages) were simulated individually and in combination on the RPCI-WM1/IR avatar. In silico modeling predicted aberrant activity of aurora kinase A (AURKA) and its associated signaling partners, which could be disrupted with the (AURKA) inhibitor, tozasertib. AURKA activation was predicted as upregulated due to alterations in several genes: RASA1 loss and SOS1 increase --> increased ERK --> increased ETS1 --> increased AURKA. High beta-catenin signaling (high CTNNB1 and FZD1/4 and low AXIN1 and GSK3B) was also shown to increase AURKA. The simulation predictions were experimentally validated in vitro where AURKA inhibition with tozasertib significantly inhibited proliferation of RPCI-WM1/IR cells (IC50~14nM) as well as inducing apoptosis (48hr, 20nM treatment) and cell-cycle arrest.
Conclusions: Our data demonstrates the potential of in silico modeling in predicting novel drug targets, allowing guidance in 1.) Delineating operational oncogenic circuits in an ibrutinib-resistant state by reanimation of the molecular architecture in silico, 2.) Calculating the impact of individual genomic abnormalities and their collective influence on maintaining tumor survival and 3.) Performing a rapid in-silico drug-sensitivity screen directed by the pathway analyses, which can be validated experimentally using standard assays. This novel approach holds tremendous potential in creating highly personalized therapies for ibrutinib-refractory WM patients based on unique genetic signatures.
Vali:Cellworks Group, Inc.: Employment, Equity Ownership. Kumar:Cellworks Group, Inc.: Employment. Singh:Cellworks Group, Inc.: Employment. Abbasi:Cellworks Group, Inc.: Employment, Equity Ownership.
Asterisk with author names denotes non-ASH members.