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Background: Pediatric AML (pAML) treatment outcomes can vary due to genomic heterogeneity. Thus, selecting the right drugs for a given patient is challenging. There is a need for a priori means of predicting treatment responses based on tumor "omics". Computational biology modeling (CBM) is a precision medicine approach by which biological pathways of tumorigenesis are mapped using mathematical principles to yield a virtual, interactive tumor model. This model can be customized based on a patient's omics and analyzed virtually for response to therapies. Aim: To define prediction values of a CBM precision medicine approach in matching clinical response to ADE therapy in a cohort of pAML patients. Methods: Thirty pAML patients that were treated ADE chemotherapy were utilized with information on the clinical, genomic (cytogenetics, mutations) and protein expression data from this cohort of pAML patients used for the CBM. From cytogenetics results, gene copy number variations were coded as either knocked-down (KD) or over-expressed (OE). From NGS results (2 gene panel - CEBPA, NPM1), gene mutations were coded as either loss or gain of function (LOF or GOF). For protein expression data, proteins that were >2sigma from the mean were coded as KD if their value was 0. Proteins with values Clinical outcome data for these pAML patients treated with ADE was compared with CBM predictions. Clinical response was defined as complete response at the end of consolidation therapy as per International Working Group 2006 criteria. Results: Assessment was made for 30 patients, 14 female, median age 14 years, all of which achieved CR, with predictions made for all but one which lacked sufficient genomic inputs. CBM accurately predicted the clinical outcomes of 28 of 29 responders, with an accuracy and positive predictive value of 96 %. Multivariate analysis of predictive score with age at diagnosis, DFS and OS is positively correlated with Pearson coefficient of 0.22, 0.54 and 0.49. Analysis of the individual drug responses of each patient indicated that some of the drugs were predicted to be non-responsive based on the patient-disease pathway characteristics, and could have been eliminated from the treatment, thus reducing the overall adverse impact of the very intensive therapy regimen. There were profiles in which AraC was a responder due to decreased mismatch repair pathway in the disease network resulting from presence of aberrations such as KMT2A-AFDN, RUNX1-RUNX1T1, CEBPA LOF, KDM1A OE, MSH2 KD etc., while Daunorubicin and Etoposide were predicted as non-responders due to presence of an intact homologous DNA repair pathway as a result of absence of aberrations in HR pathway genes. CBM analysis of patient "omics" driven disease characteristics could have eliminated additional drugs for such patients. Conclusion: The CBM prediction of ADE in pAML patients based on genomic, proteomic and clinical data showed high predictive accuracy of 96.55%. CBM analysis of patients' genomics and proteomics driven disease characteristics and individual drug response prediction indicated that the intensive therapy regimen can be tailored for each patient to minimize toxicity by removing non-responsive drugs. The study validates the approach to a priori predict response and identify optimal therapy option for the patient. Disclosures Cogle: Celgene: Other: Steering Committee Member of Connect MDS/AML Registry. Abbasi:Cell Works Group Inc.: Employment. Singh:Cellworks Research India Private Limited: Employment. Sauban:Cellworks Research India Private Limited: Employment. Raman:Cellworks Research India Private Limited: Employment. Vidva:Cellworks Research India Private Limited: Employment. Tyagi:Cellworks Research India Private Limited: Employment. Talawdekar:Cellworks Research India Private Limited: Employment. Das:Cellworks Research India Private Limited: Employment. Vali:Cell Works Group Inc.: Employment.

Background: Racial disparities in incidence and prevalence in multiple myeloma (MM) are well known with black (B) individuals having 2-3 fold increased risks compared to white non-Hispanic (WNH) populations. An ECOG review (Baker, Blood 2013) and a Mayo Clinic study (Greenberg, Blood Cancer Journal 2015) found a lower frequency of IgH translocations in B patients (pts). However a Veterans Health Administration study (Blue, BJH 2017) did not identify racial differences by classical karyotype procedures. Paulus (ASH 2016) using the COMMPASS database also failed to identify cytogenetic differences by race or ethnicity among approximately 1000 patients but did identify gene expression profiling , gene copy number variations, and gene single nucleotide variations. Our group presented a single institution review (Velez ASCO 2017) of 482 pts and noted that WNH pts had more IgH rearrangements (similar to Mayo) and t(11;14) than minorities. We sought to extend these observations by reviewing the Cota database containing diagnostic information drawn from 12 cancer centers (85 hematologists-oncologists) within the northeast USA. Methods: The Cota database contains diagnostic, clinical and outcome information abstracted and enriched from the electronic health records. The database is purely observational and has been de-identified for secondary research purposes. Cytogenetics and FISH studies were performed at various laboratories per physician discretion and not all pts were tested for all genomic abnormalities. Fisher-exact test comparisons were calculated. Results: The electronic health records of 935 pts with MM diagnosed between January 1, 2012 and May 1, 2017 were reviewed. FISH genomic data on 819 pts obtained at the time of initial diagnosis was available including 581 (71%) WNH, 66 (8%) White Hispanic (H), 138 (17%) B, and 34 (4%) Asian/Indian (AI) pts. 454 (55%) were male with median age at initial presentation of 65 years. Subtypes included IgG kappa NHW: 36%, H: 38% B: 38% AI: 32%; IgG lambda NHW: 23%, H: 18% B: 27% AI: 35%; IgA kappa NHW: 14%, H: 15% B: 9% AI: 6%; kappa light chain NHW: 10%, H: 11% B:12% AI: 21%; IgA lambda NHW: 7%, H: 11% B: 5% AI 6%; lambda light chain NHW: 7%, H: 4% B: 4% AI: 6%; other subtypes all 1% or less. Durie-Salmon stage I, II, III was NHW: 8%, 13%, 78%; H: 6%, 8%, 86%; B: 5%, 9%, 86%; AI: 12%, 0%, 88%. As detailed in the Table, FISH identified abnormalities were similar across race and ethnicity, with the exception of higher rates of t(4:14) in minorities and higher rates of 1q amplifications in Asian/Indians (p Conclusions: Unlike the Mayo/ECOG experiences, but similar to the COMMPASS and Veteran's reports, we found no significant differences in the frequency of IgH translocations. We also noted no differences in R-ISS defining high risk abnormality frequencies by racial or ethnicity groupings. Thus differences in incidence or severity of MM appear unlikely to be explained by genomics at the FISH level. Table. Table. Disclosures Biran: Merck: Research Funding; Amgen: Consultancy, Speakers Bureau; Celgene: Consultancy, Honoraria, Speakers Bureau; Takeda: Consultancy, Speakers Bureau; BMS: Research Funding. Norden:Cota Inc: Employment. Siegel:Merck: Consultancy, Honoraria, Speakers Bureau; Novartis: Honoraria, Speakers Bureau; Amgen: Consultancy, Honoraria, Speakers Bureau; Karyopharm: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Speakers Bureau; Celgene: Consultancy, Honoraria, Research Funding, Speakers Bureau; Takeda: Consultancy, Honoraria, Speakers Bureau; BMS: Consultancy, Honoraria, Speakers Bureau. Goldberg:COTA Inc.: Employment, Equity Ownership.

Background: Outcomes in multiple myeloma (MM) have improved significantly over the years but disparities in outcomes exist among MM patients (pts) belonging to different racial subgroups. Previous studies have explored healthcare access and utilization disparities for pts of different races as potential causes, but genomic differences, which could alter disease biology and clinical behavior, have not been studied. We utilized the MMRF CoMMpass database, which prospectively captures clinical and molecular characteristics of MM patients to explore race-based differences at the genomic level. Methods: The publically available CoMMpass trial database (data release version IA8) was utilized. Pts with race category of White (total n=698) and non-White (total n=223, comprising 129 Blacks, 19 Asians and 45 others) were analyzed for genomics including cytogenetics (fluorescent in situ hybridization; FISH), tumor diploidy (conventional karyotyping), gene expression profiling (GEP), gene copy number variations (CNV), gene single nucleotide variations (SNV) and genomic translocations analysis focused on genes from the FoundationOne Heme panel¨ (402 genes). GEP was considered significantly different between categories for more than or equal to a 2-fold change for a given gene (p Results: GEP clustering analysis uncovered 67 genes differentially expressed between Whites vs. non-Whites (Fig 1A). Baseline cytogenetics aberrations between both race cohorts showed statistically insignificant differences. Multiple cytogenetic abnormalities were seen most commonly in both race groups (~31%) with the distribution of others (in descending order) as such: Del13 > No abnormalities > t(11;14) > 1q amplification > Del17p or p53 > t(4;14) > Del1p > t(14;16) > t(14;20). Aneuploidy status showed insignificant differences between race groups with ~34% of patients from both groups having no aneuploidy. SNV data was present for 90% of Whites and 57% of non-Whites. Overall, 84% of genes had>1 SNV (84%), 2% translocations only and 14% had neither SNVs or translocations. SNVs were classified as Non-specific Non-Synonymous coding (NNSCs), Splice-site (SS) or Tier 1 (T1, codon deletion, insertion, frame-shift, etc.). A total of 880 NNSCs SNVs were observed: 670 in Whites across 227 genes, 210 in non-Whites across 120 genes. In whites, 57% of NNSC SNVs consisted of transition-type mutations and 43% as transversions. In non-Whites, transition mutations comprised 64% of all NNSC SNVs and transversions in the remaining 36%. A total of 38 SS SNVs were observed, 30 in Whites and 8 in non-Whites observed in 25 and 6 genes, respectively. In Whites, 57% of SS SNVs were transition type and remaining 43% were transversion. In non-Whites, an equal % of SS transition and transversion mutations were noted. A total of 240 T1 SNVs were noted; 182 in Whites seen in 82 genes; 58 in non-Whites seen 6 genes. In both Whites and non-Whites, frameshifts (41% in Whites, 47% in non-Whites) and stop-gained (46% in Whites, 45% in non-Whites) SNVs were most common. A total of 17 genes with>10 distinct SNVs were identified in Whites; 5 genes in non-Whites and 4 genes commonly seen in both cohorts (Fig 1B). In Whites, 17 genes were found to have translocations (intrachromosomal TRAF3 and PIK3R2 most common); in non-Whites, 5 genes had translocations (intrachromosomal CIC and EMSY most common). CNV analysis demonstrated 21 genes that were significantly altered with either copy number gain or loss between Whites vs. non-Whites (Fig 1C). Conclusions: We present the first race-based comprehensive genomic analysis utilizing the public interface of the CoMMpass trial. We discovered genomic differences between Whites and non-Whites at several levels including GEP, SNV and CNV, but not at the level of cytogenetics, which are in fact the most commonly performed clinical genomic analysis. Correlation of these variations with MM pt outcomes would be invaluable in understanding disease biology and hopefully mitigating some of the outcome disparities by pt race. As the database grows and becomes more enriched, separate analyses for Blacks, Asians and Hispanics would be attempted. Acknowledgments: We would like to thank the MMRF, Ms. Mary Derome and Dr. Jonathan Keats for providing scientific overview and technical support. Disclosures Fonseca: Millennium, a Takeda Company: Consultancy; Millennium, a Takeda Company: Consultancy; AMGEN: Consultancy; AMGEN: Consultancy; Patent: Patents & Royalties: Prognostication of MM based on genetic categorization of FISH of the disease; Patent: Patents & Royalties: Prognostication of MM based on genetic categorization of FISH of the disease; Patent Pending: Patents & Royalties: The use of calcium isotopes as biomarkers for bone metabolisms; Patent Pending: Patents & Royalties: The use of calcium isotopes as biomarkers for bone metabolisms; Celgene: Consultancy; BMS: Consultancy; Bayer: Consultancy; Novartis: Consultancy; Sanofi: Consultancy; Celgene: Consultancy; Janssen: Consultancy; Millennium, a Takeda Company: Consultancy; BMS: Consultancy; AMGEN: Consultancy; Bayer: Consultancy; Patent: Patents & Royalties: Prognostication of MM based on genetic categorization of FISH of the disease; Novartis: Consultancy; Patent Pending: Patents & Royalties: The use of calcium isotopes as biomarkers for bone metabolisms; Sanofi: Consultancy; Janssen: Consultancy; Millennium, a Takeda Company: Consultancy; AMGEN: Consultancy; Patent: Patents & Royalties: Prognostication of MM based on genetic categorization of FISH of the disease; Patent Pending: Patents & Royalties: The use of calcium isotopes as biomarkers for bone metabolisms. Ailawadhi:Takeda Oncology: Consultancy; Amgen Inc: Consultancy; Novartis: Consultancy; Pharmacyclics: Consultancy.

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.

A strong correlation between trisomy 13 and AML1 mutation has been recently reported in AML, particularly in M0 FAB subtype (Dicker et al, Silva et al). Whereas the exact mechanism of this coincidence has not been completely clarified, this correlation suggests a probable cooperation of these two events in leukemogenesis. FLT3, located on the 13q12 region, has been proposed to be implicated by over expression induced by gene dosage effect. However, trisomy 13 remains a rare event in AML and its association with AML1 mutations is not constant. To better document this association, we studied, a cohort of patients with trisomy 13 by array-CGH, which is a powerful tool to detect cryptic alterations as small size gene copy number variations (CNV). In addition we searched for AML1 mutations and performed the correlation with FLT3 alterations. We studied 12 AML with trisomy 13 (7 of which were M0 FAB subtype) and 6 additional patients affected by non-AML hematological malignancies (2 T-ALL, 1 NHL and 3 MPD). We performed direct sequencing of the exons 3 to 8 of AML1. Array-CGH was performed on 10/12 AML patients using Agilent CGH-Array 185k with pooled normal DNA as reference. GEP was performed using HG-U133 plus 2.0 Affymetrix Array on 5 patients characterized by trisomy 13 and AML1 mutations. AML1 mutations were observed in 8/18 patients including 6 of the 7 M0 AML, which represents a particularly high rate even in this subtype (86%). On the contrary, none of the non-AML patients harbored AML1 gene mutation. FLT3 locus analysis in the 12 AML patients showed the following results: None FLT3-ITD was observed whereas 5 FLT3-D835 mutations were detected (42%). FLT3 over expression was observed by GEP on 4/5 analyzed patients all without FLT3-D835 mutation. The remaining patient presented a FLT3-D835 mutation. CGH study confirmed trisomy 13 in 9/10 AML cases with weak amplitude for 4 patients, strongly suggesting that trisomy 13 was a sub-clonal anomaly, in accordance with caryotype results. Only one other recurrent abnormality was observed by CGH in 3 AML patients: 2 gains and one deletion affecting chromosome 21, all including AML1 locus. Moreover, CNV incidence was strong in these patients traducing a great genomic instability: there were 34 CNV gains and 23 deletions equally distributed all along the genome with a size extending from 300kb to complete chromosome. Among the interstitial CNV loci were included the following genes (CD68, CDH1, CDX2, CTNNA3, DOCK2, EIF3S9, EIF4A1, ERG, GNA12, IGF2, KLRC3, MAD1L1, MAFA, MKL2, VAV2) that could perhaps play a role in leukemogenesis. Here we confirmed the strong correlation between AML1 mutation and trisomy 13. Trisomy 13 is often sub-clonal indicating this alteration could be an additional event contributing to leukemia progression. In accordance with previous reported data, FLT3 over expression or FLT3-D835 mutation could cooperate with AML1 mutation. However, alteration of genes located on other CNV could not be excluded.