Ghobrial, Irene M.

Summary: The multiple myeloma treatment landscape has changed dramatically. This change, paralleled by an increase in scientific knowledge, has resulted in significant improvement in survival. However, heterogeneity remains in clinical outcomes, with a proportion of patients not benefiting from current approaches and continuing to have a poor prognosis. A significant proportion of the variability in outcome can be predicted on the basis of clinical and biochemical parameters and tumor-acquired genetic variants, allowing for risk stratification and a more personalized approach to therapy. This article discusses the principles that can enable the rational and effective development of therapeutic approaches for high-risk multiple myeloma.

Abstract Background Multiple myeloma (MM) evolves from monoclonal gammopathy of undetermined significance (MGUS), a clinically detectable but asymptomatic premalignant phase seen in ~3% of the general population 50 years of age or older. The prevalence of MGUS has not been described in a population at high risk of developing MM, specifically Black/African American (AA) individuals or first-degree relatives of patients with hematologic malignancies (HM). In 2019, we launched the first nationwide US screening study for individuals at high risk of MM to help better identify what population would benefit most from screening and early intervention for precursor MM stages. We aim to assess the prevalence of MGUS in a population at high risk of MM and characterize clinical variables of individuals who screen positive. Here, we report interim screening data on the first 2,960 participants. Methods Individuals aged 40 or older with an additional MM risk factor are eligible to be screened in the PROMISE Study. High-risk individuals include Black/AAs and those with a first-degree relative diagnosed with a hematologic malignancy or a precursor condition to MM. Blood from all participants was analyzed via serum protein electrophoresis, immunofixation, and Optilite® to measure the serum free light chains (sFLC), IgG, IgA and IgM. Results were returned to all participants, and those who tested positive for a monoclonal gammopathy (MGUS/SMM) were referred to a hematologist for clinical follow-up and invited to periodically complete epidemiologic exposure and psychosocial questionaries, including a 4-item cancer worry questionnaire and the RAND 36-item Short Form Survey (SF-36). To investigate the use of the higher-sensitivity matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) with the Optilite® IgG, IgA, IgM and sFLC results as a screening test for all participants, we rescreened 1,092 samples from PROMISE. The Binding Site Group proprietary software was used for the analysis of the combined MS/Optilite® results, allowing for the detection and quantification of M-protein. Heavy-Chain MGUS (HC-MGUS) was defined by the presence of one or more paired heavy and light chain monoclonal peaks detected by MS. Pairing was based on mass to charge ratio of identified peaks. To enrich the PROMISE cohort with Black/AA individuals, we identified and screened 1,868 Black/AA additional individuals from the Mass General Brigham (MGB) Biobank who met the PROMISE enrollment criteria. Screening was performed by MS/Optilite®, and results have not been returned to participants. Results We screened 2,960 participants with the combined MS/Optilite® approach. We report here the prevalence of HC-MGUS and plan on presenting the estimated rate of light chain MGUS in our cohort, at the meeting. We detected HC-MGUS in 9.6% (95% CI: 8.6-11%) of our cohort, with a prevalence of 10% (95% CI: 8.3-12%) in the PROMISE cohort and 9.4% (95% CI: 8.1-11%) in the MGB cohort (Table 1 and Figure 1). HC-MGUS prevalence increased with age in high-risk individuals from 4.9% (CI: 3.3-6.9%) for participants aged 40-49 to 13% (CI: 10-17%) in the 70-79 range (P < 1.2E-5). Among monoclonal HC-MGUS, we found 65% IgG, 18% IgM, and 18% IgA. M-spike was quantified in 97% of samples. Median M-spike concentration was, 0.058g/dL (max. 2.6g/dL) for IgG, 0.0043g/dL (max. 0.6g/dL) for IgM, and 0.067g/dL (max. 0.8g/dL) for IgA. In the Promise cohort, no significant change in cancer worry was observed across the pre- and post-screening interval among participants who screened positive (P = 0.52). Health-related quality of life, as measured by the SF-36, was not significantly different in screen-positive vs. screen-negative individuals for any of the eight subscales (all P > 0.20). Conclusions We present the largest dataset on monoclonal gammopathy prevalence and screening in individuals at high risk for MM, and more specifically the largest cohort of Black/AA, using a novel high-sensitivity testing approach. Our results confirm that older adults who are Black/AA or have a first-degree relative with an HM have a high prevalence MGUS and may benefit from precision screening approaches to allow for early detection and clinical intervention. Preliminary data on cancer worry and quality of life indicates that the psychosocial burden of screening in this population is likely minimal. Figure 1 Figure 1. Disclosures Sakrikar: The Binding Site: Current Employment. Krause: The Binding Site: Current Employment. Barnidge: The Binding Site: Current Employment. Bustoros: Takeda: Consultancy, Honoraria; Janssen, Bristol Myers Squibb: Honoraria, Speakers Bureau. Perkins: The Binding Site: Current Employment. Harding: The Binding Site: Current Employment, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties. Kapoor: Ichnos Sciences: Research Funding; Amgen: Research Funding; Pharmacyclics: Consultancy; Takeda: Research Funding; Sanofi: Consultancy; Regeneron Pharmaceuticals: Research Funding; AbbVie: Research Funding; BeiGene: Consultancy; Sanofi: Research Funding; Karyopharm: Research Funding; Glaxo SmithKline: Research Funding; Karyopharm: Consultancy; Cellectar: Consultancy. Mo: Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees; GSK: Consultancy, Membership on an entity's Board of Directors or advisory committees; BMS: Membership on an entity's Board of Directors or advisory committees; Epizyme: Consultancy; Eli Lilly: Consultancy; Janssen: Honoraria; Karyopharm: Honoraria, Membership on an entity's Board of Directors or advisory committees. Murray: The Binding Site: Patents & Royalties: Potential Royalties for use of mass spectrometry in M-protein detection. Getz: Scorpion Therapeutics: Consultancy, Current holder of individual stocks in a privately-held company, Membership on an entity's Board of Directors or advisory committees; IBM, Pharmacyclics: Research Funding. Marinac: JBF Legal: Consultancy; GRAIL Inc: Research Funding. Ghobrial: AbbVie, Adaptive, Aptitude Health, BMS, Cellectar, Curio Science, Genetch, Janssen, Janssen Central American and Caribbean, Karyopharm, Medscape, Oncopeptides, Sanofi, Takeda, The Binding Site, GNS, GSK: Consultancy.

Abstract Introduction: Multiple Myeloma (MM) is an incurable plasma cell malignancy commonly preceded by the asymptomatic stage smoldering multiple myeloma (SMM). MM is characterized with significant genomic heterogeneity of chromosomal gains and losses (CNVs), translocations, and point mutations (SNVs); alterations that are also observed in SMM patients. However, current SMM risk models rely solely on clinical markers and do not accurately capture progression risk. While incorporating some genomic biomarkers improves prediction, using all MM genomic features to comprehensively stratify patients may increase risk stratification precision in SMM. Methods: We obtained a total of 214 patient samples at SMM diagnosis. We performed whole-exome sequencing on 166 tumors; of these, RNA sequencing was performed on 100. Targeted capture was done on 48 additional tumors. Upon binarization of DNA features, we performed consensus non-negative matrix factorization to identify distinct molecular clusters. We then trained a random forest classifier on translocations, SNVs, and CNVs. The predicted clinical outcomes for the molecular subtypes were further validated in an independent SMM cohort of 74 patients. Results: We identified six genomic subtypes, four with hyperdiploidy (>48 chromosomes, HMC, HKR, HNT, HNF) and two with IgH translocations (FMD, CND) (Table 1). In multivariate analysis accounting for IMWG (20-2-20) clinical risk stages, high-risk (HMC, FMD, HKR) and intermediate-risk (HNT, HNF) genetic subtypes were independent predictors of progression (Hazards ratio [HR]: 3.8 and 5.5, P = 0.016 and 0.001, respectively). The low-risk, CND subtype harboring translocation (11;14) was enriched for the previously defined CD-2 MM signature defined by the B cell markers CD20 and CD79A (FDR = 0.003 ), showed upregulation of CCND1, E2F1, and E2F7 (FDR = 0.01, 0.0004, 0.08), and was enriched for G2M checkpoint, heme metabolism, and monocyte cell signature (FDR = 0.003, 0.003, 0.003, respectively). The FMD subtype with IgH translocations (4;14) and (14;16) was enriched for P53, mTORC1, unfolded protein signaling pathways and plasmacytoid dendritic cell signatures (FDR = 0.01, 0.005, 0.008, respectively). The HKR tumors were enriched for inflammatory cytokine signaling, MYC target genes, T regulatory cell signature, and the MM proliferative (PR) signatures (FDR = 0.02, 0.03, 0.007, 0.02, respectively). The APOBEC mutational signature was enriched in HMC and FMD tumors (P = 0.005), while there was no statistical difference across subtypes in the AID signature. The median follow-up for the primary cohort is 7.1 years. Median TTP for patients in HMC, FMD, and HKR was 3.8, 2.6, and 2.2 years, respectively; TTP for HNT and HNF was 4.3 and 5.2, respectively, while it was 11 years in CND patients (P = 0.007). Moreover, by analyzing the changes in MM clinical biomarkers over time, we found that patients from high-risk subgroups had higher odds of developing evolving hemoglobin and monoclonal protein levels over time (P = 0.01 and 0.002, respectively); Moreover, the absolute increase in M-protein was significantly higher in patients from the high-risk genetic subtypes at one, two, and five years from diagnosis (P = 0.001, 0.03, and 0,01, respectively). Applying the classifier to the external cohort replicated our findings where intermediate and high-risk genetic subgroups conferred increased risk of progression to MM in multivariate analysis after accounting for IMWG staging (HR: 5.5 and 9.8, P = 0.04 and 0.005, respectively). Interestingly, within the intermediate-risk clinical group in the primary cohort, patients in the high-risk genetic subgroups had increased risk of progression (HR: 5.2, 95% CI 1.5 - 17.3, P = 0.007). In the validation cohort, these patients also had an increased risk of progression to MM (HR: 6.7, 95% CI 1.2 - 38.3, P = 0.03), indicating that molecular classification improves the clinical risk-stratification models. Conclusion: We identified and validated in an independent dataset six SMM molecular subgroups with distinct DNA alterations, transcriptional profiles, dysregulated pathways, and risks of progression to active MM. Our results underscore the importance of molecular classification in addition to clinical evaluation in better identifying high-risk SMM patients. Moreover, these subgroups may be used to identify tumor vulnerabilities and target them with precision medicine efforts. Figure 1 Figure 1. Disclosures Bustoros: Janssen, Bristol Myers Squibb: Honoraria, Speakers Bureau; Takeda: Consultancy, Honoraria. Casneuf: Janssen: Current Employment. Kastritis: Amgen: Consultancy, Honoraria, Research Funding; Takeda: Honoraria; Pfizer: Consultancy, Honoraria, Research Funding; Genesis Pharma: Honoraria; Janssen: Consultancy, Honoraria, Research Funding. Walker: Bristol Myers Squibb: Research Funding; Sanofi: Speakers Bureau. Davies: Takeda: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Roche: Consultancy, Honoraria; Janssen: Consultancy, Honoraria. Dimopoulos: Amgen: Honoraria; BMS: Honoraria; Takeda: Honoraria; Beigene: Honoraria; Janssen: Honoraria. Bergsagel: Genetech: Consultancy, Honoraria; Oncopeptides: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Patents & Royalties: human CRBN mouse; GSK: Consultancy, Honoraria; Celgene: Consultancy, Honoraria. Yong: BMS: Research Funding; Autolus: Research Funding; Takeda: Honoraria; Janssen: Honoraria, Research Funding; Sanofi: Honoraria, Research Funding; GSK: Honoraria; Amgen: Honoraria. Morgan: BMS: Membership on an entity's Board of Directors or advisory committees; Jansen: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; GSK: Membership on an entity's Board of Directors or advisory committees. Getz: IBM, Pharmacyclics: Research Funding; Scorpion Therapeutics: Consultancy, Current holder of individual stocks in a privately-held company, Membership on an entity's Board of Directors or advisory committees. Ghobrial: AbbVie, Adaptive, Aptitude Health, BMS, Cellectar, Curio Science, Genetch, Janssen, Janssen Central American and Caribbean, Karyopharm, Medscape, Oncopeptides, Sanofi, Takeda, The Binding Site, GNS, GSK: Consultancy.

AbstractThe development of novel agents has transformed the treatment paradigm for multiple myeloma, with minimal residual disease (MRD) negativity now achievable across the entire disease spectrum. Bone marrow–based technologies to assess MRD, including approaches using next-generation flow and next-generation sequencing, have provided real-time clinical tools for the sensitive detection and monitoring of MRD in patients with multiple myeloma. Complementary liquid biopsy–based assays are now quickly progressing with some, such as mass spectrometry methods, being very close to clinical use, while others utilizing nucleic acid–based technologies are still developing and will prove important to further our understanding of the biology of MRD. On the regulatory front, multiple retrospective individual patient and clinical trial level meta-analyses have already shown and will continue to assess the potential of MRD as a surrogate for patient outcome. Given all this progress, it is not surprising that a number of clinicians are now considering using MRD to inform real-world clinical care of patients across the spectrum from smoldering myeloma to relapsed refractory multiple myeloma, with each disease setting presenting key challenges and questions that will need to be addressed through clinical trials. The pace of advances in targeted and immune therapies in multiple myeloma is unprecedented, and novel MRD-driven biomarker strategies are essential to accelerate innovative clinical trials leading to regulatory approval of novel treatments and continued improvement in patient outcomes.

Abstract Introduction: The bone marrow (BM) niche promotes multiple myeloma (MM) growth, survival and drug resistance. Therapies targeting both cancer cells and the microenvironment are highly effective. We were interested in identifying novel signaling pathways supporting MM pathogenesis through MM-BM crosstalk. The transmembrane receptor Roundabout 1 (ROBO1) plays a role in growth and dissemination of solid tumors, however its function in MM is unknown. Material and Methods: We analyzed ROBO1 expression in cell lines and primary samples via western blot, immunohistochemistry (IHC) and gene expression profiling. We used short hairpin RNA and CRISPR-Cas9 for ROBO1 knock down (KD) and knock out (KO), respectively. For protein structure-function and rescue studies, we stably expressed full-length (FL) or mutant ROBO1 devoid of extracellular (Cyt) or intracellular domain (DeltaCyt), including patient-derived G674* truncation, with a C-terminus FLAG tag. We used a 3D hydrogel encapsulation system to study proliferation; FLAG immunoprecipitation (IP) followed by mass spectrometry to identify ROBO1 interacting partners; and immunofluorescence to detect ROBO1 localization. To study tumor growth in vivo, we performed PET-CT of mice inoculated subcutaneously or intramedullary with WT or ROBO1 KO MM cells and retrieved tumors for RNA sequencing. To study dissemination and homing, KO and FL addback MM cells were injected intravenously in SCID mice. Mice were monitored for development of tumors or hindlimb paralysis and femora/tumors harvested once mice reached endpoint. Results: ROBO1 is highly expressed in MM cell lines and primary cells but low/absent in normal plasma cells and other hematologic cancer cell lines. ROBO1 KD is specifically cytotoxic for MM cells and ROBO1 KO decreases proliferation, a phenotype fully rescued by FL ROBO1. Compared to WT, ROBO1 KO significantly decreases intramedullary (mean tumor volume (MTV): 1323 vs 457 mm3, p value= 0.02) and extramedullary (MTV: 2684 vs 823 mm3, p value= 0.001) tumor growth in vivo. We further discovered that ROBO1 KO decreases adhesion of MM to BM endothelial and BMSC, which is fully rescued by FL ROBO1. In a disseminated mouse model, ROBO1 KO cells generate bone plasmacytoma with reduced BM invasion, as compared to the extensive BM infiltration observed with ROBO1 FL cells. Consistently, in primary samples from patients, we detected ROBO1 expression only in 1 out of 10 solitary plasmacytoma (dim staining) as compared to 14 out of 14 MM bone marrow biopsy samples tested (11 strong, 3 dim, p value= 0.0001). Mechanistically, we show for the first time that ROBO1 C-terminus is cleaved in a ligand-independent fashion; translocates to the nucleus; and is necessary and sufficient to rescue ROBO1 KO proliferative defect. Viceversa, mutants lacking the cytoplasmic domain, including the G674* truncation, act dominantly negative. Interactomic and RNA sequencing studies point to a previously unknown function of ROBO1 in RNA processing, setting the bases for future studies. Conclusions: We show for the first time that ROBO1 is necessary for MM growth and homing to the BM. Cleaved ROBO1 cytosolic domain translocates to the nucleus and is necessary and sufficient to rescue ROBO1 KO proliferative defect, possibly by participating in RNA processing. These data suggest that ROBO1 C-terminus may be a novel molecular target in MM. Citation Format: Giada Bianchi, Peter G. Czarnecki, Matthew Ho, Aldo M. Roccaro, Antonio Sacco, Yawara Kawano, Annamaria Gulla, Anil Aktas Samur, Tianzeng Cheng, Kenneth Wen, Yu-Tzu Tai, Maria Moscvin, Xinchen Wu, Gulden Camci-Unal, Matteo Claudio Da VIa, Niccolo Bolli, Ruben D. Carrasco, Irene M. Ghobrial, Kenneth C. Anderson. ROBO1 promotes homing, dissemination, and survival of multiple myeloma within the bone marrow microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr LB217.

Abstract The bone marrow (BM) microenvironment actively promotes multiple myeloma pathogenesis, and therapies targeting both cancer cells and the niche are highly effective. We were interested in identifying novel signaling pathways supporting multiple myeloma–BM cross-talk. Mutations in the transmembrane receptor Roundabout 1 (ROBO1) were recently identified in patients with multiple myeloma; however, their functional consequences are uncertain. Through protein structure–function studies, we discovered that ROBO1 is necessary for multiple myeloma adhesion to BM stromal and endothelial cells and that ROBO1 knockout (KO) compromises BM homing and engraftment in a disseminated mouse model. ROBO1 KO significantly decreases multiple myeloma proliferation in vitro and intra- and extramedullary tumor growth in vivo. Mechanistically, the ROBO1 C-terminus is cleaved in a ligand-independent fashion and is sufficient to promote multiple myeloma proliferation. Vice versa, mutants lacking the cytoplasmic domain, including the human-derived G674* truncation, act dominantly negative. Interactomic and RNA-sequencing studies suggest that ROBO1 may be involved in RNA processing, supporting further studies. Significance: ROBO1 is highly expressed in t(4;14) multiple myeloma and supports homing and dissemination to the BM niche. ROBO1 knockout causes reduced tumor growth in intramedullary and extramedullary myeloma animal models, while the ROBO1 C-terminus is cleaved in multiple fragments and it is necessary and sufficient to sustain myeloma proliferation.

e14640 Background: MicroRNAs (miRNAs) are non-coding RNAs that act as negative regulators of gene expression. They have been described to play roles in solid tumors and hematologic malignancies. However the role of miRNAs in multiple myeloma (MM) has not been yet fully described. Methods: We performed miRNA-profiling of primary bone marrow-derived CD138+ MM cells, compared to their normal cellular counterparts and validated data by qRT-PCR. In vitro and in vivo functional studies were performed on miRNA-15a- and -16–1-precursors-transfected MM cells. Effect of miRNA-15a and -16–1 on signaling cascades have been evaluated by western blot and immunofluorescence. NF-kB activity has been studied using the Active Motif TransAM kit. In vivo MM cell growth has been evaluated by either using an in vivo imaging model or bioluminescence. Angiogenesis has been studied both in vitro and in vivo using the chorioallantoic membrane model. Results: We identified a MM-specific miRNA signature characterized by down-expression of miRNA-15a, -16–1 and over-expression of miRNA-222/-221/-382/-181a/-181b (P<0.01). Data were confirmed by qRT-PCR on matched samples. Predicted targets for the decreased miRNAs in MM patients included pro-angiogenic cytokines, oncogenes, cell cycle regulators, NFkB activators. Conversely, predicted targets for the increased miRNAs included cell cycle inhibitors, suppressors of cytokine signaling, and pro-apoptotic factors. We demonstrated that miRNA-15a and - 16–1 regulate proliferation and growth of MM cells. Indeed, transfected cells showed decreased DNA synthesis; decreased cyclinD1/cyclinD3/CDC25a/pRb protein expression; phase G1 cell cycle arrest. Moreover, transfected cells showed inhibition of NFkB pathway as shown by reduced p65-/p50-/p52-NFkB activities; downregulation of p-p65/p50/p52 nuclear protein level; upregulation of cttosolic phospho-IkB; and inhibited translocation of p-p65 from the cytolplasm to the nucleus. Similarly, inhibition of MM cell growth was confirmed in vivo; and anti-angiogenic properties of miRNA-15a and -16–1 were demonstrated both in vitro and in vivo. Conclusions: These data indicate that miRNAs play a pivotal role in the biology of MM; providing the basis for the development of new miRNA-based targeted therapies in this disease. No significant financial relationships to disclose.

PURPOSE Smoldering multiple myeloma (SMM) is a precursor condition of multiple myeloma (MM) with a 10% annual risk of progression. Various prognostic models exist for risk stratification; however, those are based on solely clinical metrics. The discovery of genomic alterations that underlie disease progression to MM could improve current risk models. METHODS We used next-generation sequencing to study 214 patients with SMM. We performed whole-exome sequencing on 166 tumors, including 5 with serial samples, and deep targeted sequencing on 48 tumors. RESULTS We observed that most of the genetic alterations necessary for progression have already been acquired by the diagnosis of SMM. Particularly, we found that alterations of the mitogen-activated protein kinase pathway ( KRAS and NRAS single nucleotide variants [SNVs]), the DNA repair pathway (deletion 17p, TP53, and ATM SNVs), and MYC (translocations or copy number variations) were all independent risk factors of progression after accounting for clinical risk staging. We validated these findings in an external SMM cohort by showing that patients who have any of these three features have a higher risk of progressing to MM. Moreover, APOBEC associated mutations were enriched in patients who progressed and were associated with a shorter time to progression in our cohort. CONCLUSION SMM is a genetically mature entity whereby most driver genetic alterations have already occurred, which suggests the existence of a right-skewed model of genetic evolution from monoclonal gammopathy of undetermined significance to MM. We identified and externally validated genomic predictors of progression that could distinguish patients at high risk of progression to MM and, thus, improve on the precision of current clinical models.

Abstract Prior studies have demonstrated that MM cells with PTEN mutation and high AKT activity are more sensitive to inhibitors of the PI3K/Akt/mTOR pathway. However, the molecular mechanisms that regulate the differential response to these agents are not well characterized. The objective of this study was to determine proteins that are different between MM cell lines with higher AKT activity (OPM2) and lower AKT activity (MM.1S) in response to the AKT inhibitor, perifosine. Methods: MM cell lines (MM.1S and OPM2) were treated with serial concentration of perifosine (KRX-0401, Keryx, NY, NY, provided by the NCI). Proteomic analysis using the nanoscale BD Clontech antibody-based protein microarray technique was performed using cells treated with perifosine (10uM for 16 hrs) or vehicle (sterile water) as control. Apoptosis was determined using Annexin V/PI FACS analysis at 24 and 48 hrs. The treatment time and concentration were chosen so that it did not induce more than 25% apoptosis to ensure adequate analysis of changes in signaling pathways. The antibody microarray is a technique that detects differences in protein abundance between the treated and control sample with each experiment by hybridizing fluorescently labeled (Cy3 and Cy5) protein mixtures onto slides spotted with 512 human monoclonal antibodies. Two microarray slides were used for each experiment. The slides were scanned using the Axon GenePix 4000B scanner. Two ratios were generated from the spot images for each protein target. The mean of the ratios of Cy5/Cy3 of both slides were analyzed using Clontech software and used to calculate an Internally Normalized Ratio (INR = (Ratio1/Ratio2, ratios 1 and 2 correspond to slides 1 and 2) for each spot on the array. The INR values were input into GeneSpring 6.0 software (Silicon Genetics, CA). The data was normalized to the mean INR of the two cell lines. Proteins whose expression level changed relative to control greater than 1.3 fold were determined. Unsupervised clustering demonstrated a different protein signature between MM.1S and OPM2 in response to perifosine. There were 144 proteins differentially expressed by 1.3 fold between MM.1S and OPM2. Proteins that were downregulated in OPM2 as compared to MM.1S included those in the PI3K pathway and cell cycle regulation such as PTEN, p70S6Kinase, the AKT substrate GSK-3, eEF-2 kinase, eIF-4g, Ku-80, cyclin A, E2F-2, CDK2, CDK7, and c-myc; proteins involved in apoptosis such as p21WAF, caspase 4 and 8, FADD, and PARP; kinases such as PKAc, PKA RI, ERK2, and JNK1; and other proteins regulating apoptosis and proliferation including p53, the NF-kB inhibitor IkB, and the heat shock protein HSP70. The nanoscale protein array is a useful and rapid technique that may be used to identify differences between resistant and sensitive cells to novel therapeutic agents. We identified proteins that are differentially expressed between MM cells sensitive and relatively resistant to the AKT inhibitor perifosine. Further analysis of the role of these proteins in the mechanism of resistance/sensitivity to perifosine is being performed. Future use of inhibitors of NF-kB (bortezomib) or heat shock protein inhibitors in conjunction with perifosine may overcome resistance induced by these proteins in MM cells with low AKT activity. Supported in part by an ASH scholar award and an MMRF grant.

Abstract The objective of this study was to determine the molecular alterations that occur at the protein level in patients with PTLD in order to identify novel targets of therapy, determine new markers of prognosis, and begin to delineate the pathogenesis of PTLD. Six tumor samples were obtained after informed consent from adult patients with PTLD post solid organ transplant. Four benign lymph nodes were obtained from age-matched controls. Three of the samples and their two controls were obtained from frozen tissue and the other three with two matching controls were obtained form paraffin embedded tissue. Immunohistochemistry using anti-CD19+ antibodies was performed and laser microdissection or slide scraping was used to obtain the tissue. Deparaffinization was performed using xylene and ethanol serial dilutions. Protein quantification was performed and 1ug of protein was obtained for each sample and its control. The nanoscale protein micorarray technique (BD Clontech, CA) was used to measure changes in the patterns of protein expression between PTLD samples and control lymphocytes. This is a new technique that detects differences in protein abundance between the disease and control samples by hybridizing fluorescently labeled (Cy3 and Cy5) protein mixtures onto slides spotted with 512 human monoclonal antibodies. It requires minute amounts of proteins. Two microarray slides were used for each experiment and a control experiment of control versus control was performed for normalization of the data. The slides were scanned using the Axon GenePix 4000B scanner. Two ratios were generated from the spot images for each protein target. The mean of the ratios of Cy5/Cy3 of both slides were analyzed using Clontech software and used to calculate an Internally Normalized Ratio (INR = Ratio1/Ratio2) for each spot on the array. The INR values were input into GeneSpring 6.0 software (Silicon Genetics, Redwood City CA). The data were normalized to the mean INR of the control samples. Proteins whose expression fold change relative to control was greater than 1.3 fold were determined. All cases represented monomorphic PTLD. Five were confirmed EBV positive (EBNA positive). The tissues were obtained from multiple organs including liver, gastric, cerebral, renal and lymph nodes. There were 22 proteins upregulated in the frozen sections as compared to the control and 157 proteins upregulated by 1.3 fold in the paraffin embedded sections as compared to control. Most of these proteins were common between the paraffin embedded and frozen sections. Proteins that were dysregulated included: proteins in the PI3K pathway and cell cycle regulation such as p70S6K, VHR, the AKT substrate GSK-3, cyclin C, MCM6, eIF-4E, CDK1, CDK7, eE-F-2 kinase; kinases such as JNKK1, PKC-lambda, RhoA kinase ROKalpha, ERK1, PKAc, PKArIIa and PKCbeta; apoptosis related proteins such as caspase 8, NF-kB, and MDM2; TGF signaling protein p300, and the heat shock protein HSP90. These results provide insight into pathways that are dysregulated in PTLD and can be targeted in future clinical trials with specific inhibitors such as PI3K, PKC, NF-kB or HSP90 inhibitors.