ALBERT

Description: Antigen-presenting cells are localized in essentially every tissue, where they operate at the interface of innate and acquired immunity by capturing pathogens and presenting pathogen-derived peptides to T cells. C-type lectins are important pathogen recognition receptors and the C-type lectin, dendritic cell–specific intercellular adhesion molecule 3-grabbing nonintegrin (DC-SIGN), is unique in that, in addition to pathogen capture, it regulates adhesion processes such as DC trafficking and T-cell synapse formation. We have isolated a murine homologue of DC-SIGN that is identical to the previously reported murine homologue mSIGNR1. mSIGNR1 is more closely related to the human DC-SIGN homologue L-SIGN than to DC-SIGN itself because mSIGNR1 is specifically expressed by liver sinusoidal endothelial cells, similar to L-SIGN, and not by DCs. Moreover, mSIGNR1 is also expressed by medullary and subcapsular macrophages in lymph nodes and by marginal zone macrophages (MZMs) in the spleen. Strikingly, these MZMs are in direct contact with the bloodstream and efficiently capture specific polysaccharide antigens present on the surface of encapsulated bacteria. We have investigated the in vivo function of mSIGNR1 on MZMs in spleen. We demonstrate here that mSIGNR1 functions in vivo as a pathogen recognition receptor on MZMs that capture blood-borne antigens, which are rapidly internalized and targeted to lysosomes for processing. Moreover, the antigen capture is completely blocked in vivo by the blocking mSIGNR1-specific antibodies. Thus, mSIGNR1, a murine homologue of DC-SIGN, is important in the defense against pathogens and this study will facilitate further investigations into the in vivo function of DC-SIGN and its homologues.

Description: Binding of anti-phospholipid antibodies to circulating platelets and its consequences on platelet activation and aggregation was investigated in 11 patients with anti-phospholipid antibodies. Seven patients had mild thrombocytopenia. Nine healthy donors served as controls. Binding to platelets was investigated by performing enzyme- linked immunosorbent assays (ELISAs) with phospholipids as antigen on platelet eluates. Platelet activation was measured by flow cytofluorometry using monoclonal antibodies to an activation-specific lysosomal membrane protein. Findings in ELISA were compared with results of a conventional immunofluorescence method to detect platelet autoantibodies. In seven patients antibodies to negatively charged phospholipids were present in platelet eluates. In all thrombocytopenic patients and controls the platelets were not activated and aggregation was not impaired. There was a positive concordance of 50% between the results of immunofluorescence and ELISA. No apparent relation was found between the results of ELISA or immunofluorescence and platelet counts. It is concluded that anti-phospholipid antibodies can bind to circulating platelets. This binding is not associated with measurable aggregation abnormalities nor with platelet activation characterized by exposure of lysosomal membrane proteins. More studies are necessary to determine the exact role of anti-phospholipid antibodies in the pathogenesis of thrombocytopenia and thrombosis.

Description: Introduction Gene Expression Profiling studies have resulted in signatures capable of providing robust prognosis for Multiple Myeloma (MM) patients, such as the EMC92 [SKY92, Kuiper et al. Leukemia 2012]. Recently, data from 4720 MM patients from the HOVON-65/GMMG-HD4, UAMS-TT2, UAMS-TT3, MRC-IX, APEX and IFM trials were employed to assessed the majority of currently identified prognostic markers and their combinations (GEP, FISH, and biochemistry data, [Kuiper et al ASH 2014]). The "SKY92 + ISS" was identified and validated as the statistically most optimal (i.e. most significant and robust) prognostic marker combination for MM patients. The combination of the EMC92 (=SKY92) and ISS prognostic features proves to be a very powerful, unprecedented and straightforward prognostic system. It identifies four prognostic risk classes: low risk (ISS I-SKY92 standard risk (SR)), intermediate-low (ISS II-SKY92 SR), intermediate-high (ISS III-SKY92 SR) and high risk (ISS I-III, SKY92 high risk). Previously, the SKY92 has been validated on the 91 MM cases in the MMGI cohort [Van Beers et al. ASH 2013]. Here we present an extension of that validation with the SKY92 + ISS combination on the 78 MM cases for whom both GEP and ISS is available, by both assessing as the "4 risk group" model defined above, but also a "3 risk group" model (the two intermediate groups combined) as this may be more relevant and useful for clinical application. Materials and Methods A public untreated MM dataset (Multiple Myeloma Genomics Initiative, MMGI) had n=78 cases for which OS, GEP, and ISS were available for analysis. The prognostic markers SKY92 and ISS were applied as proposed [Kuiper et al ASH 2014] to classify cases into the risk categories. Results The risks for the 4 group classification model are shown in Table 1 and Figure 1, and the 3 risk group model is shown in Table 2 and Figure 1. In the 4 group model, the intermediate-low group (SKY92 standard + ISS II) was a small and not significantly different (p=0.79) from the low risk. The intermediate-high group (SKY92 standard + ISS III) was also a small group, with significantly worse outcome compared with the low risk group (p=0.012). Although stratification into four groups was statistically superior in the training and validation data [Kuiper et al ASH 2014] the interpretability benefits from aggregation of the middle two groups [Fig 1 right]. Table 1. Classification results for the four risk groups (Fig 1 left) Risk group n % Median OS HR vs low risk SKY92 High Risk regardless of ISS 19 24 28.4 m 10.8 SKY92 standard risk + ISS-III 10 13 63.0 m 4.1 SKY92 standard risk + ISS-II 16 21 NR (0.8) SKY92 standard risk + ISS-I (low risk) 33 42 NR NA NR= Not Reached at 96 months, HR= hazard ratio Table 2. Classification results for the three risk groups (Fig 1 right) Risk group n % Median OS HR vs low risk SKY92 High Risk regardless of ISS 19 24 28.4 m 10.1 SKY92 standard risk + ISS-II/III 26 34 78.5 m (1.8) SKY92 standard risk + ISS-I 33 42 NR NA NR= Not Reached at 96 months, HR= hazard ratio, () not significant Conclusions By applying the SKY92 + ISS risk stratification model in an independent validation cohort of newly diagnosed Multiple Myeloma patients, besides a high risk group of 19 patients (24%), a group of 33 patients (42%) with superior prognosis could be predicted (SKY92 standard risk and ISS I) that translated into 62% OS at 96 months. The 19 high risk (SKY92 high risk) cases had very poor prognosis (median survival of 28 months). The combination of SKY92 Standard Risk and ISS II and III seems useful for definition of "intermediate risk" although sample size currently is insufficient for significance compared to low risk. The intention is to also perform this validation on the relapsed samples from the MMGI cohort, once OS data has been collected. This validated risk model could play a role in the design of future treatment strategies for high and low risk MM patients. Figure 1. Kaplan Meier curves on the 78 MMGI cases, split into four (left) or three (right) risk groups based on the combination of SKY92 + ISS. Hazard Ratios (HR) are from a Cox proportional Hazards model comparing a particular group to the low risk group. Figure 1. Kaplan Meier curves on the 78 MMGI cases, split into four (left) or three (right) risk groups based on the combination of SKY92 + ISS. Hazard Ratios (HR) are from a Cox proportional Hazards model comparing a particular group to the low risk group. Disclosures van Beers: SkylineDx: Employment. van Vliet:SkylineDx: Employment. de Best:SkylineDx: Employment. Chari:Novartis: Consultancy, Research Funding; Millennium/Takeda: Consultancy, Research Funding; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Biotest: Other: Institutional Research Funding; Array Biopharma: Consultancy, Other: Institutional Research Funding, Research Funding; Onyx: Consultancy, Research Funding. Jagganath:Millennium: Honoraria; Celgene: Honoraria. Jakubowiak:Sanofi-Aventis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Onyx: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Karyopharm: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Bristol-Myers Squibb: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: institutional funding for support of clinical trial conduct, Speakers Bureau; SkylineDx: Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Karyopharm: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; SkylineDx: Membership on an entity's Board of Directors or advisory committees; Onyx: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Sanofi-Aventis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Millennium: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Millennium: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Kumar:Celgene, Millenium, Sanofi, Skyline, BMS, Onyx, Noxxon,: Other: Consultant, no compensation,; Janssen: Research Funding; AbbVie: Research Funding; Sanofi: Research Funding; Millenium/Takeda: Research Funding; Celgene: Research Funding; Onyx: Research Funding; Skyline, Noxxon: Honoraria. Lebovic:Onyx: Speakers Bureau; Celgene: Speakers Bureau. Lonial:Millennium: Consultancy, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Onyx: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; Celgene: Consultancy, Research Funding. Reece:Onyx: Honoraria; Novartis: Honoraria; Millennium: Research Funding; Merck: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; BMS: Research Funding. Richardson:Millennium Takeda: Membership on an entity's Board of Directors or advisory committees; Celgene Corporation: Membership on an entity's Board of Directors or advisory committees; Jazz Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Gentium S.p.A.: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees. Siegel:Celgene Corporation: Consultancy, Speakers Bureau; Amgen: Speakers Bureau; Takeda: Speakers Bureau; Novartis: Speakers Bureau; Merck: Speakers Bureau. Stewart:SkylineDx: Membership on an entity's Board of Directors or advisory committees. Vij:Onyx: Consultancy, Honoraria, Research Funding, Speakers Bureau; Celgene: Consultancy, Honoraria, Research Funding, Speakers Bureau; Millennium: Honoraria, Speakers Bureau; BMS: Consultancy; Takeda: Consultancy, Research Funding; Novartis: Consultancy; Sanofi: Consultancy; Janssen: Consultancy; Merck: Consultancy. Zimmerman:Celgene: Honoraria, Speakers Bureau; Millennium: Honoraria, Speakers Bureau; Onyx: Honoraria; Amgen: Honoraria, Speakers Bureau. Fonseca:Onyx/Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties, Research Funding; BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties, Research Funding; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties, Research Funding; Bayer: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties, Research Funding; Binding Site: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties, Research Funding; Millennium: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties, Research Funding; Applied Biosciences: Membership on an entity's Board of Directors or advisory committees; Sanofi: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties, Research Funding; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties, Research Funding.

Description: Introduction Multiple Myeloma (MM) is a heterogeneous disease with a strong need for robust markers for prognosis. Frequently occurring chromosomal abnormalities, such as t(4;14), gain(1q), and del(17p) etc. have some prognostic power, but lack robustness across different cohorts. Alternatively, gene expression profiling (GEP) studies have developed specific high risk signatures such as the SKY92 (EMC92, Kuiper et al. Leukemia 2012), which has shown to be a robust prognostic factor across five different clinical datasets. Moreover, studies comparing prognostic markers have indicated that the SKY92 signature outperforms all other markers for identifying high risk patients, both in single and multivariate analyses. Similarly, when assessing the prognostic value of combinations of various prognostic markers, the SKY92 combined with ISS was the top performer, and also enables detection of a low risk group (Kuiper et al. ASH 2014). Here, we present a further validation of the low and high risk groups identified by the SKY92 signature in combination with ISS on two additional cohorts of patients with diverse treatment backgrounds, containing newly diagnosed, previously treated, and relapsed/refractory MM patients. Materials and Methods The SKY92 signature was applied to two independent datasets. Firstly, the dataset from the Total Therapy 6 (TT6) trial, which is a phase 2 trial for symptomatic MM patients who have received 1 or more prior lines of treatment. The TT6 treatment regime consists of VTD-PACE induction, double transplant with Melphalan + VRD-PACE, followed by alternating VRD/VMD maintenance. Affymetrix HG-U133 Plus 2.0 chips were performed at baseline for n=55 patients, and OS was made available previously (Gene Expression Omnibus identifier: GSE57317). However, ISS was not available for this dataset. Secondly, a dataset of patients enrolled at two hospitals in the Czech Republic, and one in Slovakia (Kryukov et al. Leuk&Lymph 2013). Patients of all ages, and from first line up to seventh line of treatment were included (treatments incl Bort, Len, Dex). For n=73 patients Affymetrix Human Gene ST 1.0 array, OS (n=66), and ISS (n=58) was made available previously (ArrayExpress accession number: E-MTAB-1038). Both datasets were processed from .CEL files by MAS5 (TT6), and RMA (Czech), followed by mean variance normalization per probeset across the patients. The SKY92 was applied as previously described (Kuiper et al. Leukemia 2012), and identifies a High Risk and Standard Risk group. In conjunction with ISS, the SKY92 Standard Risk group is then further stratified into low and intermediate risk groups (Kuiper et al. ASH 2014). Kaplan-Meier plots were created, and the Cox proportional hazards model was used to calculate Hazard Ratios (HR), and associated 1-sided p-values that assess whether the SKY92 High Risk group has worse survival than SKY92 Standard Risk group (i.e. HR〉1). Results Figure 1 shows the Kaplan Meier plots of the SKY92 High Risk and Standard Risk groups on the TT6 and Czech cohorts. On the TT6 dataset, the SKY92 signature identifies 11 out of 55 patients (20%) as High Risk. In both datasets, the SKY92 High Risk group has significantly worse overall survival, HR=10.3, p=7.4 * 10-6 (TT6), and HR=2.6, p=2.2 * 10-2 (Czech). In addition, the combination of SKY92 with ISS on the Czech dataset identifies a low risk group of 14 out of 61 patients (23%), with a five year overall survival estimate of 100% versus 28.7% in the SKY92 High Risk group (HR=inf). Robustness of the SKY92 signature is further demonstrated by the fact that it validates on both datasets, despite different microarray platforms being used. Conclusions The SKY92 high risk signature has been successfully validated on two independent datasets generated using different microarray platforms. In addition, on the Czech data, the low risk group (SKY92 Standard Risk combined with ISS 1) has been successfully validated. Together, this signifies the robust nature of the SKY92 signature for high and low risk prediction, across treatments, and with applicability in newly diagnosed, treated, and relapsed/refractory MM patients. Figure 1. Kaplan-Meier plots showing a significantly poorer overall survival in patients identified as SKY92 High Risk (red curves), relative to SKY92 Standard Risk, on both the TT6 (left), and Czech (middle) datasets, as well as a low risk group by SKY92 & ISS1 on the Czech dataset (green curve, right). Figure 1. Kaplan-Meier plots showing a significantly poorer overall survival in patients identified as SKY92 High Risk (red curves), relative to SKY92 Standard Risk, on both the TT6 (left), and Czech (middle) datasets, as well as a low risk group by SKY92 & ISS1 on the Czech dataset (green curve, right). Disclosures van Vliet: SkylineDx: Employment. Ubels:SkylineDx: Employment. de Best:SkylineDx: Employment. van Beers:SkylineDx: Employment. Sonneveld:Celgene: Honoraria, Research Funding; Amgen: Honoraria, Research Funding; Karyopharm: Research Funding; SkylineDx: Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Research Funding.

Description: Abstract 445 Background. In newly diagnosed myeloma patients, bortezomib treatment induces high rates of complete response (CR) and very good partial response (VGPR). Recently, we published the clustering of gene expression profiles in 320 MM patients, who were included in a large prospective, randomized, phase III transplantation trial with bortezomib (PAD) versus conventional vincristine (VAD) based induction treatment (HOVON65/GMMG-HD4). We identified 12 distinct subgroups CD-1, CD-2, MF, MS, PR, HY, LB, Myeloid, including three novel defined subgroups NFκB, CTA, and PRL3 and a subgroup with no clear gene expression profile (NP). Aim. To look at the prognostic impact of these 12 clusters in the trial and group clusters together into a high risk (HR) and low risk (LR) group in the different treatment arms. Furthermore, to define a high risk signature to identify the patients at increased risk of disease progression. Methods. Gene expression profiles of myeloma cells obtained at diagnosis of 320 HOVON65/GMMG-HD4 patients were available. Response, progression free survival (PFS) and overall survival (OS) data were available for the first 628 patients, resulting in analysis of gene expression in relation to prognosis in 229 patients. The prognostic impact of the genetic subgroups separately and grouped into high and low risk were evaluated using Kaplan Meier and Cox regression analysis using exhaustive search (R). For the high risk gene signature the HOVON65 gene expression data was used as training set with PFS as outcome measure. Two independent myeloma datasets with survival data were used as an external validation, UAMS (GSE2658) and APEX (GSE9782)). The signature was generated by a Cox proportional hazard model in combination with LASSO (Least Absolute Shrinkages and Selection Operator) for simultaneous parameter estimation and variable selection using the R package glmnet. ISS stage was implemented by adjusting the individual covariant penalization factors of the LASSO. Results. The highest CR+nCR rates were found in the PRL3 and NP clusters, i.e. 78% and 86%, respectively (VAD), and 100% (PAD). The lowest CR+nCR rate was 17% in the CD1 cluster (PAD) and 0% in the CD2, MF and PR clusters (VAD). Based on the impact of clusters on PFS and OS in the VAD arm, the MS, MF, PR and CTA clusters were included into a High Risk (HR) group. This HR group showed a median PFS of 13 months and OS of 21 months vs. the Low Risk (LR) group consisting of the remainder of clusters with a median PFS of 31 months and a median OS not reached (P

Description: Background: Although T cell immunotherapy is considered a promising therapeutic approach in B cell malignancies, autologous T cell based therapy proved to be far less effective in CLL than in more aggressive B cell malignancies. This has been attributed to an acquired state of T cell dysfunction. Disturbances in conventional (αβ-)T cells include expansion of CD4+ and CD8+ T cells, increased expression of exhaustion markers and impaired cytotoxicity and cytokine production. Vγ9Vδ2-T cells are a conserved subset of cytotoxic T lymphocytes with potent antitumor activity, due to recognition of phosphoantigen-induced changes in CD277 in tumor cells. Aminobisphosphonate (ABP) treatment leads to intracellular accumulation of phosphoantigens and increased Vγ9Vδ2 antitumor responses. Vγ9Vδ2-T cells have been shown to effectively kill malignant B cell lines in vitro. Moreover, in clinical trials Vγ9Vδ2-T cells have been shown to recognize and kill B cell lymphomas. Whether Vγ9Vδ2-T cells could be exploited for CLL immunotherapy has not yet been explored. The aim of this study is to investigate the phenotype and function of Vγ9Vδ2-T cells in CLL patients, in order to determine whether Vγ9Vδ2-T cells can effectively kill CLL cells. Results: Frequencies of Vγ9Vδ2-T cells do not differ between untreated CLL patients (n=46) and age-matched healthy controls (HC) (n=20) as assessed by flow cytometry. Vγ9Vδ2-T cell subpopulations are skewed towards effector type (CD27- CD45RA-) in CLL patients, while numbers of naïve (CD27+ CD45RA+) Vγ9Vδ2-T cells are decreased. Expression of exhaustion markers PD-1 and BTLA is comparable between CLL and HC, as is expression of CD16, mediating antibody-dependent cellular cytotoxicity. Next, we compared the functionality of Vγ9Vδ2-T cells from CLL patients and HC. We first examined cytokine production and CD107a expression, a marker of degranulation. Production of TNFα and IFNγ upon PMA/ionomycin stimulation was significantly diminished in CLL Vγ9Vδ2-T cells as compared to HC Vγ9Vδ2-T cells. Similarly, CD107a expression was significantly reduced. Overnight coculture with primary CLL cells or the Vγ9Vδ2-T cell sensitive Daudi lymphoma cell line also induced expression of TNFα, IFNγ and CD107a. However, upon co-culture, HC Vγ9Vδ2-T cells expressed significantly more TNFα, IFNγ and CD107 than CLL Vγ9Vδ2-T cells. Subsequently, we compared cytotoxicity of Vγ9Vδ2-T cells towards Daudi cells. HC-derived Vγ9Vδ2-T cells killed Daudi cells 3-4 times more effectively at 1:5 and 1:2.5 effector:target ratios. Although ABP pretreatment of Daudi cells increased both CLL-derived and HC-derived Vγ9Vδ2-mediated killing, differences between CLL and HC could not be overcome. We then looked at Vγ9Vδ2-T cell cytotoxicity towards CLL cells. Vγ9Vδ2-T cells from HCs effectively recognized and killed primary CLL cells, irrespective of ABP pretreatment. CLL-derived Vγ9Vδ2-T cells killed allogeneic CLL cells significantly less efficiently. Finally, we investigated whether the Vγ9Vδ2-T cell dysfunction in CLL patients was reversible upon ex vivo activation without the presence of leukemic B cells. Purified Vγ9Vδ2-T cells were cocultured with mature monocytic-derived dendritic cells in the presence of ABP for 8 days. Following these culture conditions, no difference was observed in production of TNFα, IFNγ and IL-4 upon PMA/ionomycin stimulation between HC- and CLL-derived activated Vγ9Vδ2-T cells. Likewise, there was no difference in CD107a expression. The activated Vγ9Vδ2-T cells of HCs and CLL patients were equally effective at killing Daudi cells. Conclusion: Vγ9Vδ2-T cells are capable of recognizing and killing CLL cells. Yet, CLL-derived Vγ9Vδ2-T cells are functionally impaired in terms of cytokine production and cytotoxic capacity in comparison to age-matched HCs. Functional impairments of Vγ9Vδ2-T cells are reversible upon ex vivo activation. If dysfunction can be overcome effectively, the antileukemic properties of autologous Vγ9Vδ2-T cells can be efficiently employed. Disclosures No relevant conflicts of interest to declare.

Description: Introduction: Multiple myeloma (MM) is a genetically heterogeneous disease regarding both chromosomal abnormalities (CA) and dysregulated gene expression. Lately, several gene mutations (mut) have been identified further contributing to the genetic complexity. However, data on parallel assessment of morphologic, cytogenetic and molecular genetic parameters is scarce. Aim: Integration of morphological and extensive genetic information in an MM cohort to improve understanding of the disease biology and classification, providing a basis for evaluation of the most suitable therapy for MM patients (pts) by this holistic approach for future studies. Methods: We investigated 99 newly diagnosed MM pts (46 female, 53 male; median age 69 years, range 43 - 88). Plasma cell (PC) in bone marrow by cytomorphology ranged from 10 to 96% (median 54%). PC were enriched by magnetic-activated cell sorting targeting CD138 (median purity 95%) before interphase FISH was performed to detect hyperdiploidy, del(13q), del(17p), t(4;14), t(11;14), t(14;16), t(14;20), t(6;14), 1q gain, del(12p) and MYC rearrangements. Purified samples were further analyzed by next generation sequencing (NGS) using a comprehensive 36-gene panel targeting genes previously described mutated in MM. Library was prepared by TSCA-LI Multiple Myeloma Panel (Illumina, San Diego, CA). Gene expression profiling (GEP) was performed using Affymetrix HG U133 Plus 2.0 arrays. The MMprofiler assay algorithms were used to calculate the SKY92 signature classification into standard/high risk groups (Kuiper et al., 2012). Results: The frequencies of CA detected by FISH were consistent with published data. According to R-ISS high risk (hr) CA was defined by del(17p), t(4;14) and/or t(14;16) (28/98 pts, 29%). All other cases (71%) were standard risk (sr) (Palumbo et al., 2015). First, GEP were analyzed in relation to the CA risk groups. Cluster analysis revealed the majority of hr CA pts clustering together with overexpression of genes including ROBO1, CCNB2, FGFR3, WHSC1, DSG2 and PBX1, consistent with prior publications on hr GEP signature (Shaugnessy et al., 2007; Zhan et al., 2006). However, 9 pts assigned hr by CA clustered together with sr CA cases. Thus, in 10% of our pts GEP clusters would not be concordant with the risk classification by CA. In addition, the expression data were also analyzed based on the SKY92 signature. In consistency with published data this analysis assigned 16 pts (17%) as hr (Kuiper et al., 2012). Interestingly, out of the 9 hr CA pts mentioned above which clustered with sr CA 8 pts were also assigned sr by SKY92 classifier. Further, regarding the hr CA group, 8/28 pts (29%) also revealed a hr SKY92 signature. These patients may need further attention. Further, regarding the sr CA group, 7/65 (11%) revealed a hr SKY92 signature. Focusing on NGS, we found 115 mut (with mut load ≥10%) in 67/93 pts (72%; range 0-5) affecting 17 genes. Most commonly mut genes were NRAS (26%), KRAS (21%), TLR4 (11%), BRAF (8%), FAM46C (8%) and TP53 (7%). No difference in mut frequency between hr and sr CA pts was observed. However, association of FAM46Cmut with hyperdiploidy as well as CCND1mut with t(11;14) could be corroborated (12% vs 0%, p=0.095; 9% vs 0%, p=0.056, respectively). Besides, FAM46Cmut was associated with del(17p) (23% vs 5%, p=0.058) and a strong association of KRASmut to 1q gain was found (32% vs 11%, p=0.029), while KRASmut and NRASmut were mutually exclusive of del(12p) and t(4;14). Of note, 3/6 TP53mut pts concomitantly harbored del(17p) detected by FISH. According to CA 2/3 of these pts without del(17p) would have been classified sr. Thus, molecular data might improve risk classification. Considering biological pathways (pw) connected to currently used therapeutics (e.g. vemurafenib, bortezomib), we found the following mut frequencies in genes associated with the respective pw: 46% in MAPK/ERK-pw, 16% in NFkB-pw, 2% in HOXA9-pw and 15% associated with RNA processing. Interestingly, we found mut in IKZF1 (6%), IKZF2 (6%), IKZF3 (1%) and IRF4 (4%), which represent critical targets of the immunomodulatory drug/CRBN mediated anti-tumor activity. Conclusion: 1) A comprehensive analysis of MM based on cytogenetic, gene expression and mutation data may lead to the identification of new biologic subgroups. 2) Molecular mutations should be further evaluated to allow precision medicine approaches including respective pathway components. Disclosures Weber: MLL Munich Leukemia Laboratory: Employment. Truger:MLL Munich Leukemia Laboratory: Employment. Kern:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. van Vliet:SkylineDx: Employment. van Beers:SkylineDx: Employment. Nadarajah:MLL Munich Leukemia Laboratory: Employment. Meggendorfer:MLL Munich Leukemia Laboratory: Employment. Haupt:MLL Munich Leukemia Laboratory: Employment. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership.

Description: Introduction Multiple Myeloma (MM) is a heterogeneous disease with diverse gene expression patterns (GEP) across patients. This has led to the development of various signatures allowing virtual karyotyping, defining different clusters of patients, and prognostication by high risk signatures (e.g. EMC92/SKY92). Several GEP datasets exist, but may have scaling/offset differences (batch effects) in the data, e.g. due to differences in reagents used, location, etc. Batch wise normalization approaches can reduce batch effects, and have allowed successful validation of those signatures across independent datasets. Batch wise normalization requires groups of patients that have a similar distribution of clinical characteristics, and hence cannot be applied on single patients. Here we demonstrate the validity of applying GEP algorithms on single patients using the MMprofiler, enabling the application of GEP in a routine clinical setting. Materials and Methods The MMprofiler GEP assay is a standardized assay from bone marrow to data analysis and result reporting. It was used for 77 MM patients that were enrolled in the HOVON87/NMSG18 trial (73 patients) or HOVON95/EMN02 trial (4 patients). A representative reference set of 30 HOVON patients was selected from which normalization parameters were derived, to be used for normalization of a single sample against this HOVON reference dataset. The remaining 47 samples served as an independent set of samples. In addition, we have also used the publicly available GEP data from 247 patients (MRC-IX trial) as independent samples. This MRC-IX dataset has been produced using different reagents and sample work-up procedures. Therefore, it is likely that a batch effect will exist relative to the HOVON reference dataset, which may influence correctness of single sample analyses. The GEP data from the 47 and 247 independent samples were normalized using two approaches. Firstly, by batch wise mean variance normalization (i.e. across the 47 and 247 patient batches separately). And secondly, by single sample normalization using the normalization parameters from the initial 30 HOVON samples. Subsequently, several classifiers (EMC92/SKY92 etc.) were applied to the data, and their results were compared between the two normalization approaches. Results Figure 1 shows the EMC92/SKY92 scores that were obtained after batch normalization (x-axis) and single sample normalization (y-axis). For the 47 HOVON samples there is a high degree of concordance with data points close to the identity line (y=x). Only 2 out of the 47 samples would switch assignment, which is not unexpected since those 2 samples are really close to the threshold (e.g. might also switch due to technical variation). For the MRC-IX dataset, based on single sample normalization more patients would be predicted as high risk (87 (35.2%) instead of 52 (21.0%), see Figure 1), which is caused by a positive offset (i.e. intersect with the y-axis) due to the batch effect. For the Virtual t(4;14) classifier, both datasets have a very high concordance with 0 out 47 HOVON samples, and 5 out of 247 MRC-IX samples (but really close to the threshold) switching assignment (see Figure 1). Hence, even in the presence of a potential batch effect in the MRC-IX dataset, the single sample predictions are accurate. These data suggest that single sample normalization of microarray GEP is possible but requires the strict standardization of the MMprofiler assay and algorithms. Conclusions Scores for the EMC92/SKY92 signature were nearly equivalent when derived from the data following single sample normalization and batch normalization in the Skyline generated data. In the external dataset, a much higher discrepancy was found, highlighting the need to use highly standardized methods to generate Affymetrix GeneChip results. Further validation of this method is planned, and will include replicate runs systematically controlled for various conditions. Acknowledgments This research was performed within the framework of CTMM, the Center for Translational Molecular Medicine, project BioCHIP grant 03O-102. Figure 1. Scatterplots and confusion matrices of the batch (x-axis, columns) and single sample scores (y-axis, rows) of the EMC92/SKY92 signature (left), and Virtual t(4;14) classifier (right). Scores above/below the threshold correspond to high risk/standard risk (EMC92/SKY92) and positive/negative (Virtual t(4;14)). Figure 1. Scatterplots and confusion matrices of the batch (x-axis, columns) and single sample scores (y-axis, rows) of the EMC92/SKY92 signature (left), and Virtual t(4;14) classifier (right). Scores above/below the threshold correspond to high risk/standard risk (EMC92/SKY92) and positive/negative (Virtual t(4;14)). Disclosures Van Vliet: SkylineDX: Employment. Dumee:SkylineDx: Employment. de Best:SkylineDx: Employment. Sonneveld:SkylineDx: Membership on an entity's Board of Directors or advisory committees. van Beers:SkylineDX: Employment.

Description: Introduction High rates of complete response (CR) have previously been demonstrated in KRd-treated NDMM pts in a phase 1/2 trial (trial 1; NCT01029054) and a phase 2 trial that combined KRd with autologous stem cell transplant (ASCT) (trial 2; NCT01816971). Here, we report results of extended follow-up from the 2 trials and correlate response and PFS with GEP performed using the MMprofiler GEP assay, which provides results for the SKY92 signature, 7 virtual karyotyping markers (t[4;14], t[11;14], t[14;16]/t[14;20], gain[1q], del[13q], del[17p], H-MM [virtual gain(9q)]), and 3 clusters (MF, MS, CD2). Previously, a combination of 5 markers (SKY92, virtual gain[1q], virtual t[14;16]/t[14;20], MF and CD2 clusters) was identified that predicts improved outcomes when treated with the proteasome inhibitor (PI) bortezomib (van Vliet et al, EHA 2014). We evaluated the SKY92 prognostic signature, virtual karyotyping markers, and this 5-marker PI predictor signature in order to confirm these markers as predictive signatures in the KRd setting. Materials and Methods In the consecutive trials 1 and 2, pts received 4 cycles of KRd induction, followed by extended KRd treatment with deferred ASCT (trial 1; NCT01029054) or ASCT followed by extended KRd treatment (trial 2; NCT01816971). In both trials, pts received single-agent lenalidomide as maintenance after completion of KRd. The MMprofiler GEP assay was performed on RNA from CD138+ purified plasma cells. As depth of response with KRd is associated with improved time-to-event outcomes (Jasielec et al, ASH 2013), data were analyzed for associations between any of the MMprofiler markers and the groups that achieved ≥near CR (nCR) vs

Description: Abstract 1420 Background High expression of several genes have been found to be prognostically relevant in AML, such as EVI1, BAALC, MN1 and ERG [1–4]. These studies have mostly reported cutpoints at expression level quartiles with the highest prognostic value. Translation to the clinic remains difficult because a quartile definition cannot be applied to an individual patient. Therefore, from a clinical practice point, standardization has remained an important objective. Aim Using a standardized microarray for gene expression profiling we set out to develop and validate expression cutpoints for the EVI1, BAALC, MN1 and ERG genes that best predict overall survival (OS) for intermediate cytogenetic risk cases, rather than normal karyotype. Methods For training purposes, we employed a cohort of 147 intermediate cytogenetic risk cases [5, 6], and an external dataset of 242 cases [7]. For validation purposes, a cohort of 215 intermediate cytogenetic risk cases was employed [6]. Expression of the EVI1, BAALC, MN1 and ERG genes was measured using a custom microarray (AMLprofiler) with IVD-grade reagents and Affymetrix DX2 equipment. For the BAALC, MN1 and ERG genes, cutpoints were considered from the 10th to 90th percentile in steps of 5%. Optimal cutpoints were selected by performing 100-fold cross validations of the expression data of the training cohort and external cohort. EVI1 has a discontinuous distribution of expression [1]. Because of the low prevalence of high-EVI1 in AML we did not perform cross validation but used the entire training cohort to derive the best cutpoint. Results For BAALC the 30th percentile cutpoint was verified to be clinically relevant in the training and external cohorts (Figure 1). ERG had no significant splits in the training data, and around the 65th percentile in the external cohort (Figure 1). MN1 had a broad range of significant splits around its 30th expression percentile in the training cohort but none in the external cohort (Figure 1). Due to the lack of consistent results across datasets, we have not attempted a validation of ERG and MN1. The best EVI1 cutpoint in the training set was found to be the 6th percentile. This cutpoint turned out to be concordant with a previously developed Q-PCR cutpoint [8, 9]. Validation of the BAALC and EVI1 cutpoints on the independent validation cohort of intermediate cytogenetic risk cases resulted in significantly more favorable OS for cases with low-BAALC expression (HR 0.686, p=0.0205, Figure 2) and worse OS for cases with high-EVI1 expression (HR 2.27, p=0.004, Figure 2). In a multivariable analysis, including age, gender, white blood cell count and, blast percentage in BM, high-EVI1 retained independent prognostic value [HR 2.39, p=.008] while low-BAALC retained independent prognostic value only within the group of low-EVI1 cases [HR 0.602, p=.015]. Conclusion For BAALC and EVI1, but not for MN1 and ERG, clinically relevant cutpoints have been developed that remain independent prognostic factors in intermediate cytogenetic risk AML and which through their incorporation in the AMLprofiler assay can be used for standardized measurements across different laboratories. Disclosures: van Beers: skyline diagnostics: Employment, Equity Ownership, Patents & Royalties. Brand:skyline diagnostics: Employment, Equity Ownership. van Vliet:skyline diagnostics: Employment, Equity Ownership. Vietor:skyline diagnostics: CEO skyline diagnostics Other, Equity Ownership. Valk:skyline diagnostics: Equity Ownership. Lowenberg:skyline diagnostics: CSO skyline diagnostics Other, Equity Ownership.