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Abstract 601 A paucity of validated kinase targets in human multiple myeloma (MM) has delayed clinical deployment of kinase inhibitors in treatment strategies. We therefore conducted a kinome-wide small interfering RNA (siRNA) lethality study in MM tumor lines bearing common t(4;14), t(14;16) and t(11;14) translocations to identify critically vulnerable kinases in MM tumor cells without regard to preconceived mechanistic notions. Primary screening was performed in duplicate using an 1800-oligo siRNA library in a single-siRNA-per-well format. siRNA were transfected at low concentration (13nM) to minimize off-target effects using conditions that resulted in transfection of >95% cells and Disclosures: Perkins: MMRC: Employment. Reeder:Celgene: Research Funding; Millennium: Research Funding. Fonseca:Otsuka: Consultancy; BMS: Consultancy; Amgen: Consultancy; Medtronic: Consultancy; Genzyme: Consultancy.

Abstract 2801 Poster Board II-777 To provide a rationale basis for targeted drug development for multiple myeloma patients, we have conducted high-throughput genome-scale RNA interference (RNAi) synthetic lethality studies in human myeloma cell lines (HMCL) to generate a comprehensive map of critical genes and molecular vulnerabilities in human myeloma tumor cells. KMS11 human myeloma cells were screened with a 13,982-oligo library targeting the ‘druggable' genome (6,991 genes) using optimized conditions that resulted in >95% transfection efficiency. Each gene was screened with 2 or more distinct oligos, in duplicate, using a single-siRNA-per-well format, testing >34,000 wells. Replicate high throughput experiments yielded highly reproducible results (R2=0.82). Viability was measured at 96h by ATP-dependent luminescence. Universally lethal siRNA and non-targeted siRNA were employed as controls. The specificity (FDR) of lethal RNAi was evaluated by custom-developed statistical methods based on RNAi result concordancy. From screening, 5.8% of druggable genome siRNA caused statistically relevant reductions in HMCL viability (greater than three standard deviations from control samples treated with non-silencing siRNA, compared with an anticipated rate due to chance of only 0.135%). Of these, two hundred and nineteen genes, targeted by the most lethal siRNA, were forwarded to validation studies. Validation high-throughput RNAi studies, using 4 oligos per gene, were conducted in order to verify target gene vulnerability. Ultimately, seventy two genes were validated as highly critical for myeloma cell survival (each with multiple concordant siRNA hits plus high reproducibility of R2 =0.94). Among top-ranked lethal molecular vulnerabilities in KMS11 myeloma cells we recurrently identified the proteasome (8 PSM subunits were independently identified as highly vulnerable); and BCL2 family member MCL1; both known therapeutic targets, validating a functional genomics approach to target discovery. In addition, a number of novel, equally lethal, molecular vulnerabilities were identified in KMS11 including WEE1, kinetochore complex component KNTC2, the aurora kinases, polo-like kinase 1, a previously uncharacterized DNA methyl transferase, ribonucleotide reductase, ribosomal protein L38, leptin receptor overlapping transcript LEPROT and SLC25A23 (a carrier responsible for mitochondrial adenine nucleotide flux). Various ubiquitous cellular proteins involved in RNA or protein processing (e.g. SF3a, SNW1, SNRPA1, EIF3s8, amongst others) were also identified as non redundant and critical for cellular viability. Thirty nine top-ranked molecular vulnerabilities in KMS11 were evaluated for comparative vulnerability in a second myeloma cell line, JJN3, and in A549 and 293 epithelial cells. A majority were vulnerable in JJN3. While 25 genes proved equally lethal on silencing in A549 and 293 epithelial cells, several, notably including MCL1 and three proteasome subunits, appear differentially susceptible in myeloma cells versus epithelial cells. From this genome scale study, MCL1 ranks third as a target capable of inducing cell death in myeloma cells, highlighting the potent vulnerability of myeloma cell viability to alterations in this anti-apoptosis regulator. Moreover, from active comparative studies of molecular vulnerability in myeloma versus epithelial cell lines, MCL1 as an RNAi target demonstrates perhaps the greatest cytotoxic selectivity between myeloma and epithelial cell lines of all targets examined. Gene expression studies indicate that MCL1 is highly expressed in myeloma tumor cells but is absent or only weakly expressed in human primary somatic tissues. Overall, therefore, from an unsupervised genome-scale screening approach, our data support MCL1 as an optimal Achilles heel molecular target in multiple myeloma, potentially offering greater specificity and deeper cytotoxic effect than proteasome inhibition. Disclosures: No relevant conflicts of interest to declare.

Abstract 602 The molecular target(s) which co-operate with proteasome inhibition in inducing drug sensitivity or resistance in Multiple Myeloma (MM) remain unknown. We therefore conducted a genome scale small interfering RNA (siRNA) lethality study in KMS11 MM cells in the presence or absence of bortezomib without regard to pre-conceived mechanistic notions. Primary screening was performed in a single-siRNA-per-well format with the human druggable genome siRNA set V4 comprising 13,984 siRNA targeting 6,992 genes and comprising two RNAi per gene. siRNA were transfected at low concentration (13nM) to minimize off-target effects using conditions that resulted in transfection of >95% cells and Disclosures: No relevant conflicts of interest to declare.

• Hypomethylating agents like 5-Azacytidine (5Aza) have become an effective therapy for myelodysplastic syndromes (MDS) and show promise in acute myeloid leukemia (AML). In AML, complimentary mechanisms including epigenetic silencing of growth controlling genes, i.e. tumor suppressors, and activation of kinases contribute to malignant transformation. In order to enhance the therapeutic potential of epigenetic therapies, we developed a high-throughput RNA interference (HT-RNAi) platform for large-scale transient gene silencing in acute myeloid leukemia cells. This assay allows for the first time to individually silence hundreds or thousands of genes in combination with 5Aza to identify molecular targets whose inhibition enhances the anti-leukemic effect of hypomethylating agents. As part of assay development for HT-RNAi, ten AML cell lines were used to determine the median inhibitory concentration (IC50) of 5Aza for each AML cell lines. Furthermore, the ten cell lines were tested with a panel of cationic lipid transfection reagents at varying weight to volume (wt:vol) ratios to determine the optimal siRNA transfection conditions. Results from these studies identified two AML cell lines TF1 and ML4, which were advanced into kinome-epigenetic RNAi screens. Using a lipid-based method, cells were reverse transfected for 48hrs with 2 different siRNA sequences per gene targeting a total of 572 kinases. After 48hrs, 5Aza at the calculated IC25 was added for an additional 72 hrs and cell proliferation was measured using a luminescence-based assay. Data was background corrected and analyzed using the B-score method to report the strength and statistical significance of growth inhibition compared to controls. A B-score of 95% confidence); a B-score 87% confidence and was used as lowest cutoff given that screens are focused and contain validated siRNA to kinases. Analysis of two independent RNAi kinome screens, one in TF1 and the other in ML4, in combination with 5Aza, identified six and eleven kinases respectively whose silencing by two different siRNA sequences (2× coverage) potentiated the effects of 5Aza at B-score In summary, initial kinome RNAi screens in myeloid cells identified specific kinases as potential sensitizing targets to hypomethylating agents. Moreover, functional genomic RNAi screens provide a fast and attractive approach to identify molecular targets in AML for the rational development of combination therapies with hypomethylating agents as well as other drug classes.

The Multiple Myeloma Research Consortium (MMRC) Genomics Initiative is a three-year program to analyze tumor tissue from hundreds of multiple myeloma (MM) patients via gene expression profiling (GEP), comparative genomic hybridization (aCGH), and exon re-sequencing. In addition, RNAi knockdown of selected genes in MM tumor cell lines is being evaluated to identify potential new targets. All genomic data generated is scheduled for placement in an open-access Multiple Myeloma Genomics Portal pre-publication and in near real-time (www.broad.mit.edu/mmgp). Additionally, samples are also destined for drug validation and correlative science on clinical protocols as this study moves forward. This comprehensive project is spearheaded by the MMRC and conducted via collaboration with the Eli and Edythe L. Broad Institute of MIT and Harvard, the Translational Genomics Research Institute (TGen), Mayo Clinic Arizona, and The Dana-Farber Cancer Center. The study is supported by the collection from member institutions of the MMRC of bone marrow aspirates and matched peripheral blood samples from over 1000 patients. Specific genomic technologies that are currently being employed across this sample set include GEP using Affymetrix Human Genome U133A 2.0 Plus Arrays, and, in parallel, efforts to identify regions of genomic gain and loss are using Agilent Human Genome CGH arrays. In contrast to other large-scale genomic projects based on exon-sequencing of targeted gene sets, this project will be the first to perform genome-scale single molecule sequencing (SMS) of DNA from patient specimens. Results will be targeted against candidate classes of genes (e.g. kinases, phosphatases, known oncogenes and tumor suppressors), and genes from GEP or within candidate regions of copy gain or loss identified by the aCGH experiments. Mutations will be further validated in an independent set of patient specimens. Finally we will attempt to identify points of vulnerability of MM through systematic loss-of-function screens in myeloma cell lines using high-throughput RNA interference (using both shRNA and siRNA platforms). Importantly, data generated from this genomics initiative will ultimately be made public pre-publication through the established MMRC Multiple Myeloma Genomics Portal. Data from all aspects of this project (sample collection and analyte isolation, GEP, aCGH, SMS, RNAi and bioinformatics) will be described in this presentation. The power of this study is the comprehensive collection of gene expression, CGH, and genome sequencing on a single reference set of clinically annotated samples. The addition of RNAi screens makes this a very important and unique data resource, which we hope will help expedite the discovery of novel targeted agents for MM scientific community.

Treatment of AML is guided by cytogenetics, age, patients’ medical condition and increasingly, new molecular markers. However, no definitive algorithms and biomarkers predicting response to induction chemotherapy exist. We recently showed that distinct protein profiles of AML correlate with cytogenetics, FAB classes and treatment outcome for a class/group of patients as a whole (Tibes, ASH 2005; Kornblau, ASH 2006 and manuscript in submission). To further individualize therapy, we applied a set-based decision modeling methodology based on the Rough Set Theory (RST) to our initial proteomic dataset of 34 (phospho)-proteins for 96 samples from 73 patients (Tibes, ASH 2005). The first model was built based on partitioning discretized protein expression levels of proteins for the 96 samples into 2 outcome categories: complete remission (CR) vs. refractory to treatment; we generated reducts for all proteins and computed pseudo-cores. Several proteins (incl. phospho-NPM1, PTEN, phospho-AKT, p53, cyclin D1 etc.) were able to distinguish outcomes in terms of CR and refractoriness. A second model based on discretized clinical condition attributes (e.g., blast %, FAB, cytogenetics, age, sex, blood counts etc.) yielded the most important clinical reducts. In a third model, protein expression and clinical attributes were combined to generate decision rules to predict outcome to induction therapy for each patient. For proof-of-concept purposes, the data set was divided into a training set and a test set. Prediction accuracy for CR vs. refractory state was in the 75% range for individual patients based on their pre-treatment clinical and proteomic attributes. In conclusion, predictive models obtained with RST yield reducts corresponding to a small but significant number of proteins (6–8) and clinical parameters which can further be used to derive association rules, capable of accurately predicting a patient’s response to induction therapy. The availability of several dozens of distinct decisions rules within each group (CR vs. refractory) based on protein expression and clinical characteristics, allows an individualized approach for each patient. This preliminary study is now being expanded on an independent proteomic dataset of 52 proteins for 258 AML samples (Kornblau, ASH 2006) to be presented at the Annual Meeting. The clinical utility lies in the fact that measuring expression levels of our protein biomarker profiles can be adopted to routine clinical testing (e.g., immunohistochemistry on diagnosis marrows) and potentially help to direct patients towards standard chemotherapy, upfront treatment on a clinical trial or evaluation for early transplant depending on the chance of response and relapse. Lastly, this model can be applied to predict other clinical outcomes (relapse vs. continuous CR), as well as to derive predictive signatures of gene expression datasets.

We have conducted systematic small interfering RNA (siRNA) lethality screening of the kinome and “druggable genome” in human myeloma cell lines (HMCL) to functionally generate a comprehensive map of critical genes and vulnerabilities in human myeloma tumors. KMS11 human myeloma cells were screened with both an 1800 oligo (639 gene) siRNA library targeting the kinome and with a 13,984 oligo (6,791 gene) library targeting the druggable genome in duplicate using optimized conditions that resulted in >95% transfection efficiency and 50 somatic tissues in which they are absent or only weakly expressed. The extent to which the kinome vulnerabilities identified in KMS11 occur in other HMCL was assessed by tertiary siRNA screening and by lentiviral shRNA techniques. More than 75% of kinases vulnerable in KMS11 cells are recurrently vulnerable in other myeloma tumor lines. Secondary validation studies of all genomic vulnerabilities identified from the much larger siRNA lethality screen of the druggable genome in myeloma cells is ongoing, however, to date 134 genes have been validated as essential myeloma survival factors during primary screening by concordant lethality of 2/2 independent siRNA (compared with an expected rate due to chance of n

Bortezomib and related proteasome inhibitors have profound pre-clinical and clinical activity in Multiple Myeloma (MM). However, the critical molecular target(s) modulated by proteosome inhibition remains unknown. The overall response rate to bortezomib in relapsed and refractory MM patients is consistently 35%–50%, suggesting specific molecular targets or resistance mechanisms are inherent in patients. None of the known effects of bortezomib has been shown to correlate with clinical responses in patients or to explain the unique sensitivity of MM (as opposed to other malignancies) to these reagents. Therefore, in this study, we have conducted a small interfering RNA (siRNA) screen of the druggable genome in human myeloma cells to identify genes that modulate sensitivity to bortezomib. KMS11 human myeloma cells were reverse transfected with 13,984 synthetic short interfering RNAs (siRNA) against each of 6992 druggable targets (2 siRNA/gene with a single-siRNA per well/384 well format). At 24 hours post transfection, bortezomib was added at IC10, 30, 60 and 90. Cell viability was measured by ATP-dependent luminescence assay 72 hours post drug treatment. The resulting data from primary screening was evaluated for multiple quality control metrics and found to exceed all expected performance parameters with >99% global transfection efficiency

Chromosome 13 abnormalities (Δ13), monosomy or deletion, are seen in greater than 50% in patients with Multiple Myeloma (MM) and are associated with poor survival and reduced response to therapy. Δ13 is suggestive of a tumor suppressor model. In this study, we use a comprehensive, high-throughput approach involving array-based technologies to identify genes on chromosome 13 that are important in the pathogenesis and/or progression of MM. Array-based Comparative Genomic Hybridization (aCGH) is being conducted to identify a minimum region of 13q loss in a series of MM cell lines and patient samples. DNA is isolated, labeled and hybridized with a differentially labeled normal DNA reference to determine gene/genomic copy number changes. Arrays are analyzed to search for the minimum region of loss based upon single copy loss for a series of nearby mapping transcripts in each cell line. Data will be cross-referenced to determine the true minimum region of 13q loss in MM. Also, to discover genes harboring potential inactivating mutations, we are using inhibition of nonsense-mediated RNA decay (NMD) coupled with cDNA microarray. This modification of the Gene Identification by NMD Inhibition (GINI) method, first introduced by Noensie and Dietz (2001), is used to identify genes on chromosome 13 that may be inactivated due to premature truncating codons (PTCs). This method is based upon a cell’s natural ability to survey for and degrade RNA species that contain PTCs by activation of the nonsense mediated RNA decay (NMD) pathway. Treating cells with the drug Emetine can inhibit NMD and increase the expression of genes likely harboring truncating mutations. Preliminary data have revealed several candidate genes, with increased ratios after Emetine treatment, which map to chromosome 13. Upon defining a minimum region of loss based upon aCGH data, we will prioritize genes from our NMD study for mutational analysis. It our hope to also perform an array-based CpG island screen to simultaneously analyze all CpG islands on chromosome 13 to detect genes possibly inactivated by hypermethylation. This comprehensive, organized approach incorporating high-throughput techniques for the detection of genomic loss, mutation, and methylation is the first of its kind to our knowledge and may maximize the potential for the identification of a 13q tumor suppressor in MM.