Kumar, Ansu

Abstract Background: Multiple myeloma (MM) is characterized by the invasion of malignant plasma cells into the bone marrow. While first line treatment options result in significant clinical benefit to patients, spatiotemporal clonal evolution results in disease relapse and mortality. Advances in genomics have armed clinicians with unprecedented insight into the molecular architecture of MM cells, however, the clinical benefit derived by genomics-guided intervention has been limited. We present a novel computational biology modelling (CBM) tool, which takes into account the combined effect of individual mutations, gene copy number abnormalities and large scale chromosomal changes in order to predict the salient molecular pathways utilized by the MM cell for survival. By reverse-engineering MM cell architecture in silico, the CBM tool is able to predict drug response and resistance mechanisms. Thus, our aim was to determine the accuracy of the CBM tool in predicting treatment response of relapsed/refractory MM patients for future management of their disease, in a more individualized manner. Methods: Cytogenetics and somatic mutations (by targeted NGS) for 15 MM patients were input into the CBM model to predict responses to different therapeutic combinations. All patients were relapsed to prior treatment. CBM uses PubMed and other online resources to generate patient-specific protein network maps of activated and inactivated disease pathways. We simulated the specific combinations of the drugs per patient and measured the quantitative drug effect on a composite MM disease inhibition score (i.e., cell proliferation, viability, apoptosis and paraproteins). The actual clinical outcome of the treatments was compared with predicted outcomes. Results: Fifteen patients were analysed using CBM for prediction of treatment response after NGS was performed. 13/15 were clinically evaluable, of which 1 was a responder and 12 were non-responder. 6/13 patients were treated on clinical trial and 7/13 were on drug combinations per physician decision. CBM correctly predicted 1 responder and 11 non-responder with a PPV of 50%, NPV 100%, specificity 91.67%, sensitivity 100%. The accuracy of CBM prediction was 92.30%. CBM also predicted the response of prior drug therapies for its non-response at relapse. For prior drug treatment options, 14 patients were evaluable. All the 14 patients were clinically non-responders and CBM correctly predicted for 13 patients with NPV 100%, Specificity 92.85% and overall accuracy of 92.85%. The majority of patients did not respond to therapies recommended at relapse. As an example, the operative molecular pathways from 2 patients who did not respond to combination treatment, either pre-NGS or post-NGS profiling, are shown in Fig. 1 and Table 1. CBM identified amplification (AMP) of chromosome (chr) 1 (WNT3A, IL6R, CKS1B, MCL1, PIK3C2B, USF1), chr 3 (HES1, PIK3CA, CTNNB1, WNT7A, FANCD2), chr 5 (IL6ST, IRF1, GLRX, SKP2), chr 7 (CDK5, EZH2, IL6, CAV1, ABCB1), chr 9 (NOTCH1, HSPA5, FANCC, FANCG), chr 15 (DLL4, FANCI, ALDH1A2), chr 19 (ERCC1, ERCC2, USF2); deletion(DEL) of chr 13 (CUL4A) , chr 16 (AXIN1, CDH1) and TP53 mutation in different combinations, which confer resistance to therapies at relapse. Conclusions: The CBM technology represents a potential means to identify therapeutic options for MM patients based on the patients individual tumor-genome profile and which can also be deployed for uncovering drug resistance mechanisms. This tool may aid clinicians in decision making for recommending the most appropriate therapy based on standard of care agents or clinical trials; thus improving patient outcomes and reducing unnecessary costs or drug-related toxicities. Disclosures Singh: Cellworks Research India Private Limited: Employment. Sauban:Cellworks Research India Private Limited: Employment. Husain:Cellworks Research India Private Limited: Employment. Kumar:Cellworks Research India Private Limited: Employment. Kumari:Cellworks Research India Private Limited: Employment. Tyagi:Cellworks Research India Private Limited: Employment. Abbasi:Cell Works Group Inc.: Employment. Vali:Cell Works Group Inc.: Employment. Ailawadhi:Pharmacyclics: Research Funding; Takeda: Consultancy; Celgene: Consultancy; Amgen: Consultancy; Janssen: Consultancy.

Abstract 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. Disclosures Vali: Cellworks Group, Inc.: Employment, Equity Ownership. Kumar:Cellworks Group, Inc.: Employment. Singh:Cellworks Group, Inc.: Employment. Abbasi:Cellworks Group, Inc.: Employment, Equity Ownership.

Abstract Background: The observation that B-cell receptor (BCR) signaling play a critical role in the survival of malignant Waldenstrom macroglobulinemia (WM) cells is highlighted by clinical efficacy of the BTK-inhibitor, ibrutinib in WM patients. BCR signaling leads to downstream activation of several intermediary components whose activity results in increased cell proliferation. Although disruption of this axis at the proximal end (BCR) has proved successful in WM, the exact role and impact of targeting the distal intermediaries remains unknown, particularly in aggressive forms of the disease. This is important as induction of resistance to ibrutinib is clearly documented which can either be mediated through mutational changes in BTK or other members within the BCR signalosome. Using a novel simulation-based approach, we reverse-engineered the WM cell molecular architecture to uncover the role of oncogenic intermediaries distal to the BCR complex in advanced-stage WM and conducted a virtual drug-screen targeting these pathways with in vitro validation. Methods: The human WM cell line, RPCI-WM1, was used in simulation and validation experiments. RPCI-WM1 was established from a highly drug-refractory advanced disease stage patient refractory to both rituximab and bortezomib. Publically available as well as proprietary genomic and cytogenetic data was utilized for the creation of the RPCI-WM1 patient avatar, 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. 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 FDA approved drug agents (n~150) 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: MYD88L265P mutation, absence of CXCR4 mutations and additional chromosomal aberrations (derivatives, translocations, deletions and amplifications of chromosomes 3, 6, 9, 13, 18 and 19), which are notable features of RPCI-WM1 cells were configured and modeled in silico. The RPCI-WM1 patient avatar was predicted to have increased IRAK1/4 engagement due to MYD88 mutation and high copy number (CN) of IL18. Downregulation of DUSP1 through MYD88-mediated signaling was noted to result in high ERK activity. Increase gene copy number of both FOS and ETS1, which are downstream of ERK, were noted. Notably, FOS is a key regulator of the AP1 complex, whose activity is regulated upstream by ERK. The transcription factor, ETS1 was also predicted as over expressed and ERK-mediated phosphorylation of ETS1 regulates the activity of ETS1. The RPCI-WM1 patient avatar model also indicated high AKT due to indirect convergence of multiple aberrations. Drug screening revealed sensitivity to the MEK inhibitor, binimetinib. Although no copy number variation in MEK-ERK pathway genes were detected, per simulation, MYD88 mutation through inactivation of DUSP1 activated ERK and downstream associated survival pathways. The simulation predictions were experimentally validated. As predicted, binimetinib significantly inhibited proliferation and viability of RPCI-WM1 cells (IC50 <100nM). Conclusions: Our study demonstrates the functional role and impact of MEK1/2 - an oncogenic component distal to the BCR, and whose activity can be targeted with binimetinib to elicit a lethal effect in advanced-stage WM cells. We also show the utility of a novel technology, which is capable of integrating genome-wide molecular data points to emulate the salient genomic drivers of a given tumor cell and test its sensitivity to numerous drugs in a high-throughput manner for truly personalized therapy. Disclosures Vali: Cellworks Group, Inc.: Employment, Equity Ownership. Kumar:Cellworks Group, Inc.: Employment. Singh:Cellworks Group, Inc.: Employment. Usmani:3Cellworks Research India Limited: Employment. Grover:3Cellworks Research India Limited: Employment. Abbasi:Cellworks Group, Inc.: Employment, Equity Ownership.

Abstract 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 MYD88L265P 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. Disclosures Vali: CellWorks: Employment. Kumar:CellWorks: Employment. Singh:CellWorks: Employment. Kapoor:Cellworks Research India Limited: Employment. Abbasi:CellWorks: Employment, Equity Ownership.