ALBERT

Description: While the majority of acute myeloid leukemia (AML) patients respond to induction chemotherapy, disease recurrence and drug resistance is common. Recently, mutations underlying AML pathogenesis have been extensively characterized by sequencing large numbers of samples obtained at diagnosis. However, mutations driving disease progression and drug resistance in relapsed AML are not well characterized. In addition, understanding the clonal composition of relapsed AML is compounded by interference of donor cell variants present in those patients who have received an allogeneic hematopoietic stem cell transplant (alloHSCT). In this study we sought to identify mutations and copy number aberrations associated with development of drug resistant AML, and at the same time develop methods to identify and filter out donor variants. For the study we analyzed samples from patients who had relapsed after therapy (N=18) by exome sequencing. This included a set of patients where diagnosis and relapse samples were available (n=10), and one patient with diagnosis, remission and relapse samples. All patients had received prior chemotherapy and a subset had relapsed after receiving an allogeneic hematopoietic stem cell transplant (alloHSCT, n=6). Four patients had secondary AML that had developed after treatment for earlier hematologic malignancy. Tumor DNA was from bone marrow mononuclear cells and germline DNA from matched skin biopsies. Exome libraries were prepared then sequenced with the Illumina HiSeq instrument. Sequence data was processed and somatic variants identified as described previously (Koskela et al., NEJM, 2012). We identified relapse specific and relapse enriched somatic mutations by comparing mutation profiles of diagnosis and relapse samples. Donor derived germline variants in chimeric samples from patients relapsing after alloHSCT were identified with a bioinformatic methodology utilizing the dbSNP population variant database. Somatic mutations called from chimeric samples were filtered for common population variants present in the donor’s genome. Rare donor derived population variants that have not been previously described were identified as variants not present in the patient’s germline genome and which had similar tumor variant allele frequencies as the common donor derived variants. We estimated the level of chimerism based on the variant allele frequencies of all donor derived variants. In chimeric samples, the number of donor derived variants vastly exceeded the number of somatic mutations in AMLs (Fig 1). Donor cell content varied widely ranging from close to 100% in a post transplant remission sample to 10-40% in relapse samples. In post-transplant samples, we identified on average 6800 donor germline variants within the exome-capture regions, many of which occurred within cancer genes which could potentially be misinterpreted as driver mutations. Many recurrent driver mutations in cancer genes were identified in the relapse samples: FLT3 (n=6, 33%), DNMT3A (n=4, 22%), NPM1 (n=2, 11%), WT1 (n=2, 11%), TP53 (n=2, 11%), CBL (n=2, 11%), NRAS (n=1, 6%), KRAS (n=1, 6%), IDH1 (n=1, 6%), PHF6 (n=1, 6%) and PTPN11 (n=1, 6%). In several cases, we observed that relapse-specific driver mutations occurred in the same genes or pathways that already had initial mutations at diagnosis. For example, one patient’s AML had a FLT3-ITD at diagnosis; at relapse an activating mutation in CBL and a loss of function mutation in PTPN11 were acquired. Both CBL and PTPN11 act downstream of FLT3 (Fig 2). In two patients with a heterozygous WT1 mutation at diagnosis, we found additional WT1 mutations or deletion of the remaining wild type allele in the relapse sample, suggesting full loss of normal WT1 function contributes to disease progression. Our results suggest that AML progression and drug resistance may be caused by strengthening aberrant signaling through pathways already affected by a mutation present at diagnosis. Hence, the pattern of mutual exclusivity of mutations to genes affecting the same pathway, which has been observed in diagnostic samples, does not occur at relapse. On the contrary, in several cases the relapse specific mutations affected genes in pathways already affected at diagnosis. In addition, we show that donor derived germline variants can be identified and filtered from exome sequence data. Figure 1 Figure 1. Disclosures Porkka: BMS: Honoraria; BMS: Research Funding; Novartis: Honoraria; Novartis: Research Funding; Pfizer: Research Funding. Kallioniemi:Medisapiens: Consultancy, Membership on an entity's Board of Directors or advisory committees.

Description: Key Points Germline activating STAT3 mutations were detected in 3 patients with autoimmunity, hypogammaglobulinemia, and mycobacterial disease. T-cell lymphoproliferation, deficiency of regulatory and helper 17 T cells, natural killer cells, dendritic cells, and eosinophils were common.

Description: Background T-cell acute lymphoblastic leukemia (T-ALL) is caused by the cooperation of multiple oncogenic lesions. Recent evidence supports that IL-7 and its receptor IL-7R contribute to T-ALL development (Zenatti et al, 2011). The two main pathways induced by IL-7R are JAK/STAT5 and PI3K/Akt/mTOR. Activating mutations to IL7R, JAK1, JAK2 or JAK3 are estimated to occur in 20-30% of all T-ALL patients (Cools 2013). STAT5 plays an important role in many hematologic malignancies but constitutive STAT5 activation often is a secondary event. Mutations in STAT5B (N642H) were recently described in LGL-leukemia in patients with an unusually aggressive and fatal form of the disease (Rajala et al, 2013). In other cancers, including ALL, patients with mutations in STAT5B have not been described. Here we report novel activating STAT5B mutations as drivers of T-ALL. Methods We performed exome sequencing of bone marrow (BM) samples from an 18-year-old female with relapsed T-ALL. Targeted next-generation amplicon sequencing and Sanger sequencing was used to analyze the region encoding the STAT5B SRC homology 2 (SH2) domain including the N642, T648 and I704 codons in a cohort of 17 adult and pediatric T-ALL patients treated at HUCH 2008-2013. For functional studies STAT5B expression vectors with the N642H, T648S or I704L mutation and an expression vector with both N642H and T648S mutations were used to transiently transfect HEK293 cells. To investigate the effect on transcriptional activity we co-transfected the mutant constructs with a STAT5 luciferase reporter plasmid and used Western blot analysis to study the phosphorylation status of the generated constructs. For drug sensitivity of STAT5B mutated cells we performed ex vivo drug testing on primary blasts from the index patient using a comprehensive set of 202 oncology drugs (approved and in clinical development). Each drug was tested over a 10,000-fold concentration range. Results Sequencing of the index patient revealed 3 different somatic missense mutations in STAT5B (T648S, N642H, I704L) and mutations in KRAS, WT1 and SUZ12. No mutations affecting the JAK genes or IL7R were detected. All STAT5B mutations were located in the SH2 domain, which mediates dimerization and activation by trans-phosphotyrosine binding. The same three STAT5B mutations were also found in the diagnostic sample and most likely represent founding events in leukemogenesis. The N642H and T648S mutations occurred on the same allele with tumor mutation frequencies of approximately 40% while the I704L mutation occurred on a different allele with a similar tumor mutation frequency. To investigate the prevalence of STAT5B mutations in T-ALL we sequenced 17 BM samples from T-ALL patients. In this cohort we could not detect any other patients carrying mutations in the STAT5B SH2 domain. Western blot analysis made with mutant constructs showed that the N642H and I704L mutations induced constitutive phosphorylation of STAT5B. Compared to wild type STAT5B the N642H and I704L mutants induced 47- and 6-fold increases in transcriptional activity, respectively, while T648S mutation had no effect in the assays. The construct with both the N642H and T648S mutations showed the highest amount of constitutive phosphorylation and induced a 56-fold increase in transcriptional activity compared to wild type STAT5B. Using ex vivo drug testing the STAT5B mutated blasts were resistant (EC50≥1 uM) to inhibitors of PI3K (e.g. idelalisib, XL147), dual inhibitors of PI3K/mTOR (PF-04691502, dactolisib) and mTOR inhibitors (temsirolimus, everolimus). Furthermore the blasts showed no response to AKT1 inhibitors (MK-2220) or JAK inhibitors (ruxolitinb, tofacitinib). In contrast, the cells were most sensitive to the BCL-2/BCL-XL inhibitor navitoclax (EC50 83 nM). Summary STAT5B mutations are uncommon in T-ALL but their occurrence underlines the significance of the IL7R-JAK-STAT5 pathway in the pathogenesis of T-ALL. While STAT5B mutant blasts were not sensitive to inhibitors targeting JAK kinases, the cells were unusually sensitive to inhibitors of target molecules of STAT5B, including anti-apoptotic BCL-2 proteins. These results suggest that BCL-2/BCL-XL inhibitors such as navitoclax are novel candidate therapies for T-ALL patients. Disclosures: Mustjoki: Novartis: Consultancy, Speakers Bureau; BMS: Consultancy, Speakers Bureau. Porkka:BMS: Consultancy, Research Funding, Speakers Bureau; Novartis: Consultancy, Research Funding, Speakers Bureau.

Description: Introduction Response to treatment for multiple myeloma (MM) patients is variable and often unpredictable, which may be attributed to the heterogeneous genomic landscape of the disease. However, the effect of recurrent molecular alterations on drug response is unclear. To address this, we systematically profiled 50 samples from 43 patients to assess ex vivo sensitivity to 308 anti-cancer drugs including standard of care and investigational drugs, with results correlated to genomic alterations. Our results reveal novel insights about patient stratification, therapies for high-risk (HR) patients, signaling pathway aberrations and ex-vivo-in-vivo correlation. Methods Bone marrow (BM) aspirates (n=50) were collected from MM patients (newly diagnosed n=17; relapsed/refractory n=33) and healthy individuals (n=8). CD138+ plasma cells were enriched by Ficoll separation followed by immunomagnetic bead selection. Cells were screened against 308 oncology drugs tested in a 10,000-fold concentration range. Drug sensitivity scores were calculated based on the normalized area under the dose response curve (Yadav et al, Sci Reports, 2014). MM selective responses were determined by comparing data from MM patients with those of healthy BM cells. Clustering of drug sensitivity profiles was performed using unsupervised hierarchical ward-linkage clustering with Spearman and Manhattan distance measures of drug and sample profiles. Somatic alterations were identified by exome sequencing of DNA from CD138+ cells and skin biopsies from each patient, while cytogenetics were determined by fluorescence in situ hybridization. Results Comparison of the ex vivo chemosensitive profiles of plasma cells resulted in stratification of patients into four distinct subgroups that were highly sensitive (Group I), sensitive (Group II), resistant (Group III) or highly resistant (Group IV) to the panel of drugs tested. Many of the drug responses were specific for CD138+ cells with little effect on CD138- cells from the same patient or healthy BM controls. We generated a drug activity profile for the individual drugs correlating sensitivity to recurrent alterations including mutations to KRAS, DIS3, NRAS, TP53, FAM46C, and cytogenetic alterations del(17p), t(4;14), t(14;16), t(11;14), t(14;20), +1q and -13. Cells from HR patients with del(17p) exhibited the most resistant profiles (enriched in Groups III and IV), but were sensitive to some drugs including HDAC and BCL2 inhibitors. Samples from patients with t(4;14) were primarily in Group II and very sensitive to IMiDs, proteasome inhibitors and several targeted drugs. Along with known recurrently mutated genes in myeloma, somatic mutations were identified in genes involved in several critical signaling pathways including DNA damage response, IGF1R-PI3K-AKT, MAPK, glucocorticoid receptor signaling and NF-κB signaling pathways. The predicted impact of these mutations on the activity of the pathways often corresponded to the drug response. For example, all samples bearing NF1 (DSS=21±7.9) and 67% with NRAS (DSS=15±4.35) mutations showed higher sensitivity to MEK inhibitors compared to healthy controls (DSS=5±.21). However, sensitivity was less predictable for KRAS mutants with modest response only in 47% samples (DSS=7±2.14) . One sample bearing the activating V600E mutation to BRAF showed no sensitivity to vemurafenib, which otherwise has good activity towards V600E mutated melanoma and hairy-cell leukemia. Comparison of the chemosensitive subgroups with survival showed patients in Groups I and IV had high relapse rate and poor overall survival. The ex vivo drug sensitivity results were used to decide treatment for three HR patients with results showing good ex vivo -in vivo correlation. Summary Our initial results suggest that ex vivo drug testing and molecular profiling of MM patients aids stratification. Grouping of patients based on their ex vivo chemosensitive profile proved extremely informative to predict clinical phenotype and identify responders from non-responders. While some molecular markers could be used to predict drug response, others were less predictive. Nevertheless, ex vivo drug testing identified active drugs, particularly for HR and relapsed/refractory patients, and is a powerful method to determine treatment for this group of patients. Disclosures Silvennoinen: Genzyme: Honoraria; Sanofi: Honoraria; Janssen: Research Funding; Celgene: Research Funding; Research Committee of the Kuopio University Hospital Catchment Area for State Research Funding, project 5101424, Kuopio, Finland: Research Funding; Amgen: Consultancy, Honoraria. Porkka:Bristol-Myers Squibb: Honoraria; Celgene: Honoraria; Novartis: Honoraria; Pfizer: Honoraria. Heckman:Celgene: Honoraria, Research Funding.

Description: Introduction T-PLL is a rare mature post-thymic T-cell neoplasm with an aggressive clinical course and median overall survival of less than one year. Almost 75% of T-PLL cases harbor chromosome 14 translocations involving the T-cell receptor A/D locus resulting in aberrant activation of the proto-oncogenes TCL1A or MTCP1. T-PLL patients are difficult to treat as the leukemic cells are often resistant to most available chemotherapeutic drugs. Due to the rareness and aggressive nature of the disease, large clinical trials are difficult to execute. We therefore aimed to discover novel potential therapeutic targets using a high-throughput ex vivo drug sensitivity and resistance testing (DSRT) platform covering 306 approved and investigational oncology drugs. Methods Primary T-PLL cells were available from two patients. The first patient had a double positive CD4+CD8+CD3+ Vβ.14+ T-cell phenotype (patient 1) and cells underwent DSRT twice during a 5-month time-period (no treatment during that time). The second patient had a CD4+CD3+ phenotype (patient 2) and the cells were assayed once by DSRT. Fresh blood mononuclear cells (MNCs) were separated by Ficoll centrifugation from the patient samples (over 85 % leukemic cells in the MNC fraction) and healthy controls. Cells were seeded in 384-well plates and 306 active substances were tested using a 10,000-fold concentration range resulting in a dose-response curve for each compound. Cell viability was measured after 72 h incubation and differential drug sensitivity scores (DSS), representing leukemia-specific responses, were calculated by comparing patient samples with those obtained from healthy donors. In addition, both exome and RNA sequencing was performed from T-PLL cells (patient 1). Results Both patient samples showed high sensitivity to small molecule BCL2-inhibitors navitoclax (EC50 values 44nM and 10nM) and ABT-199 (EC50 23nM and 20nM) (Fig. 1 and 2). HDAC-inhibitors (quisinostat, belinostat and panobinostat) also showed high sensitivity in both patients in low nM concentrations (EC50 values 1-80nM). As AKT1/mTOR pathway is activated in most T-PLL patients due to the TCL1 oncoprotein, it was interesting to observe that neither of the patient samples showed any response to an AKT1 inhibitor (MK-2206 EC50 values 〉1000 nM) nor to mTOR inhibitors (temsirolimus and everolimus)(Fig. 1). Furthermore, T-PLL cells were resistant to corticosteroids such as prednisolone and methylprednisolone. To further elucidate the molecular mechanism behind the drug responses, exome and RNA sequencing was performed from T-PLL cells (patient 1). No deletion was found in the ATM gene, but instead a homozygous missense mutation K2413Q was detected. This particular mutation is in the region coding for the FAT domain and while it has not been described earlier in T-PLL, it is in a cancer mutation hotspot region of ATM, suggesting that it is inactivating. No mutations directly linked to the BCL2-family genes were observed. In the RNA sequencing analysis, TCL1A was overexpressed when compared to the healthy CD4+ cells as expected. Similarly, AKT1 was overexpressed. The expression of BCL-2 and BCL-XL did not differ from those observed in healthy CD4+ cells while pro-apoptotic BCL-2 family members BID and BAD were elevated compared to the healthy control. Conclusions Primary T-PLL cells showed sensitivity to BCL-2 and HDAC inhibitors in a systematic high-throughput ex vivo drug sensitivity testing across a range of clinical and investigational drugs. The BCL-2 inhibitor sensitivity was not related to increased BCL-2 expression or activating mutations in the BCL-2 family genes, and further studies are needed to clarify the mechanism of action. However, the results suggest that BCL-2 inhibitors could be a novel promising candidate drug for T-PLL-patients and warrant further clinical development in this group of patients. In contrast, inhibitors of AKT and mTOR, kinases known to be activated by TCL1, showed no efficacy ex vivo in this assay. Disclosures: Porkka: BMS: Consultancy, Research Funding, Speakers Bureau; Novartis: Consultancy, Research Funding, Speakers Bureau. Mustjoki:Novartis: Consultancy, Speakers Bureau; BMS: Consultancy, Speakers Bureau.

Description: Introduction The oncogenic TCF3-PBX1 (TP; also known as E2A-PBX1) fusion gene results from a translocation between chromosomes 1 and 19 in pre-B-cell acute leukemia (pre-B-ALL). Both TCF3 and PBX1 function as transcription factors (TF), and their fusion generates a unique transcriptional landscape in t(1;19) leukemias which differs from other pre-B-ALL subtypes. Here we explored the transcriptional regulatory landscape of t(1;19)-leukemia genome-wide and sought novel targeted therapy options. Materials & Methods We modeled the oncogenicity of the fusion protein in leukemic cells by either expressing (Nalm6-TP, induced for 16 hours) or silencing (697-shTP, down to appr. 40 %) the fusion. Proliferation, apoptosis and cell cycle were studied using fluorometric reactions and flow cytometry. Patient samples (TP, n=4; other subtypes, n=18) and various cell line models were subjected to global nuclear run-on sequencing (GRO-seq), which provides a genome-wide map of nascent (primary) transcription. All transcripts that were altered after overexpression or silencing of the fusion-TF in cell models were inspected from the patient GRO-seq samples. TCF3-PBX1-regulated genomic regions were studied for enrichment of TF binding motifs and altered signaling pathways. Mature RNA levels and potential novel long non-coding RNAs were further validated by qPCR. Gene expression differences between t(1;19) and other subtypes were compared using a curated microarray data set containing 1304 pre-B-ALL samples retrieved from 15 different data sets from the Gene Expression Omnibus. ARACNE, a network inference algorithm, and GRO-seq were used to identify TFs correlating strongly with the t(1;19) subtype and to find novel drug targets. Drugs were tested in cell culture using t(1;19)-positive cell line models and patient samples either alone or in combination with known leukemia therapies. Results GRO-seq analysis allowed elucidation of the regulatory landscape downstream of the TCF3-PBX1 fusion protein. Directly regulated enhancer RNAs were matched to the cis regulated genes, and vice versa, to clarify enhancer-gene relations. As an example, correspondingly regulated enhancer regions were located for WNT16 and ANKS1B, two genes that are known targets of TCF3-PBX1, and that were found consistently upregulated in the studied sample sets. EBF3, a tumor suppressor gene, was one of the top hits in the network inference analysis and was also found consistently regulated by TCF3-PBX1. One of the identified druggable target was RORB which was directly upregulated by TCF3-PBX1. An inhibitor targeting RORB decreased viability of TCF3-PBX1-positive cell lines and cells from a t(1;19) patient. The effect was especially prominent when the inhibitor was combined with a low dose (1 nM) of vincristine, yielding a marked synergistic effect. Conclusions Our results provide the first genome-wide transcriptional regulatory landscape of TCF3-PBX1 leukemia. We also identified novel putative druggable targets and a potential inhibitor for this leukemia subtype. Disclosures Heckman: Celgene: Research Funding; Pfizer: Research Funding.

Description: Introduction Multiple myeloma (MM) is an incurable malignant plasma cell disease with the highest incidence occurring at 65-70 years of age while 10% of patients are diagnosed below 55 years of age. The International Myeloma Working Group recently proposed new risk stratification standards for MM patients: high-risk (HR), standard (SR) and low-risk (LR) groups (Leukemia 2014, 28, 269−77). Although a median overall survival of LR patients is 〉 10 years from the diagnosis, new drugs and therapeutic innovations are urgently needed for HR patients (20%) who have a median overall survival of only two years. To identify new treatment options for MM patients, we compared ex vivo drug sensitivity data from primary CD138+ cells to standard risk stratification markers. Ex vivo responses indicated a number of investigational drugs as potential novel options for HR MM patients with links to risk markers. Methods Bone marrow aspirates were collected from newly diagnosed (n=14) and relapsed/refractory (n=21) MM patients. Cytogenetics were determined by fluorescence in situ hybridization (FISH) and the patients stratified based on the presence or absence of adverse FISH markers (t(4;14) and 17p del). Plasma cells (CD138+) were enriched from freshly isolated bone marrow samples and exome sequencing performed using DNA extracted from the CD138+ cells and matched skin biopsies. Ex vivo drug sensitivity was assessed by measuring the viability of the cells after 3-day incubation with 306 different oncology drugs in a 10,000-fold concentration range. Drug sensitivity scores were calculated based on the normalized area under the dose response curve (Scientific Reports 2014, 4, 5193) and select sensitivities determined by comparing results to healthy bone marrow cells. Based on drug sensitivities, the patients were classified in four different groups (sensitive, moderately sensitive, resistant and highly resistant). Results Of the 35 patients included in this study, 11 were classified as HR (31%) and 24 as SR/LR (69%). In the HR group 6/11 (55%) had t(4;14) and 5/11 patients (45%) had 17p13 del. In the SR/LR group common abnormalities included 13 monosomy/13q del (10/24), 1q gain (10/24) and K/NRAS mutation (11/24). Within the HR group, other co-occurring abnormalities included 1q gain (9/11), 13 monosomy/13q del (6/11), K/NRAS mutation (5/11), and TP53 mutation (2/11). Based on overall ex vivo drug sensitivity profiles of all patients, the majority of HR patients were classified as moderately sensitive (8/11; 73%) while SR/LR patients had diverse responses from sensitive to highly resistant. In the HR group, the highest select sensitivities were to BH3 mimetics and PI3K/mTOR inhibitors. While the t(4;14) is predicted to lead to upregulation and increased activity of the FGFR3, which could be targeted by FGFR inhibitors, none of the t(4;14) samples showed sensitivity to these drugs. However, with the exception of one t(4;14) sample, the rest all showed good sensitivity to dual PI3K/mTOR inhibitors, but not to rapalogs, suggesting that inhibition of PI3K and the mTORC1/2 complexes is required to inhibit t(4;14) cell growth rather than mTORC1 alone. Of the 17p del patients, 3/5 were classified as moderately sensitive, 1/5 sensitive and 1/5 highly resistant based on ex vivo drug response of CD138+ cells. All showed select sensitivity to BH3 mimetics/BCL2 inhibitors (navitoclax/ABT-263 and venetoclax/ABT-199/GDC-0199), while response to other drugs varied. Therefore, blocking cell survival signaling is likely essential for this group of HR MM patients. Conclusion By assessing the ex vivo sensitivity of primary plasma cells to a large collection of oncology drugs and comparing these data to standard risk stratification markers for MM, we have been able to identify potential new treatment options for high risk MM patients including dual PI3K/mTOR and BCL2- inhibitors. Although a larger cohort of patients is required to support the correlation between specific drug sensitivities and risk markers, these preliminary data indicate that currently used risk markers may be useful to predict the use of novel treatments. Disclosures Silvennoinen: Janssen-Cilag: Research Funding; Celgene: Research Funding; Janssen-Cilag: Honoraria; Sanofi: Honoraria; Celgene: Honoraria. Porkka:BMS: Honoraria; BMS: Research Funding; Novartis: Honoraria; Novartis: Research Funding; Pfizer: Research Funding. Heckman:Celgene: Research Funding.

Description: Introduction New drugs have improved survival for multiple myeloma (MM) patients, however, patient outcome remains highly variable, unpredictable and often very poor. To identify novel treatments and potential biomarkers, we applied high throughput ex vivo drug sensitivity testing combined with exome and transcriptome sequencing to samples collected from newly diagnosed and relapsed MM patients. Integration of results from the different platforms indicated several oncogenic signaling pathways driving drug response and highlighted the importance of a multi-targeted approach for treatment. Methods Bone marrow (BM) aspirates (n=48) were collected from MM patients (newly diagnosed n=14; relapsed/refractory n=26) and healthy individuals (n=8). CD138+ plasma cells were enriched by Ficoll separation followed by immunomagnetic bead selection. Cells were screened against 306 oncology drugs with the drugs tested in a 10,000-fold concentration range. Drug sensitivity scores were calculated based on the normalized area under the dose response curve (Yadav et al, Sci Reports, 2014). Importantly, MM selective responses were determined by comparing data from MM patients with those of healthy BM cells. Clustering of drug sensitivity profiles was performed using unsupervised hierarchical ward-linkage clustering with Spearman and Manhattan distance measures of drug and sample profiles. Somatic mutations were identified by exome sequencing of DNA from CD138+ cells and skin biopies from each patient, while gene expression profiles were derived from RNA sequencing of CD138+ cells. Results Cluster analysis of drug response profiles segregated the samples into four MM specific groups (Figure). Group I patients (n=12) were highly sensitive to many drugs, including several signal transduction inhibitors such as those targeting PI3K-AKT, MAPK and IGF pathways, as well as HSP90 and BCL2 inhibitors plus epigenetic/chromatin modifiers such as BET and HDAC inhibitors. Group II (n=15) showed a more modest response profile and were moderately sensitive to signal transduction inhibitors and epigenetic modifiers. Group III (n=9) were largely insensitive to most drugs in the panel except for BCL2 and proteasome inhibitors, while group IV (n=3) were resistant to all drugs except BCL2 inhibitors. Many samples were selectively sensitive to navitoclax (55%), dual PI3K/mTOR inhibitors (45%) and aminopeptidase inhibitors (20%), which had little effect on healthy control or MM CD138- cells. Only 33% of the samples responded to glucocorticoids. The majority of samples including healthy BM controls were sensitive to proteasome and CDK inhibitors, suggesting low selective cytotoxicity. However, drug sensitivity profiles of healthy control and CD138- cell populations were distinct from MM CD138+ samples indicating that observed CD138+ drug responses were specific for malignant plasma cells. In addition, we observed that drugs with overlapping target profiles tended to cluster together, indicating sample responses were similar to related drugs. Diagnostic and relapse samples were spread across the different response groups. Samples with mutations to genes involved in PI3K and NF-κB signaling tended to cluster in group I, while most samples with t(4;14) fell in Group II. Samples with RAS mutations were present in all response groups and no correlation with MEK inhibitor sensitivity was observed. 17p deletion samples were also found in all response groups, however, those with additional TP53 mutation tended to have increased drug sensitivity. Summary Our results indicate that PI3K/mTOR, MAPK, IGF1R, NF-κB and cell survival (e.g. BCL2, BCLXL) signaling are important pathways mediating MM ex vivo drug response. This matched with genomic and transcriptomic data, which identified alterations of genes involved in these pathways. Although additional work is needed to correlate ex vivo drug sensitivity with in vivo treatment response, our initial results suggest the possibility that MM patients could be subjected to stratified treatment based on combined ex vivo drug testing and molecular profiling. In addition, these results highlight the multiple signaling pathways active in MM and emphasize the need for improved combination strategies for treatment. Figure: Subgrouping of MM patient samples (I-IV) based on selective drug response profiles. H/D/R denotes healthy, diagnostic and relapse, respectively. Figure:. Subgrouping of MM patient samples (I-IV) based on selective drug response profiles. H/D/R denotes healthy, diagnostic and relapse, respectively. Disclosures Silvennoinen: Research Funding of Finland Government, Research Funding from Janssen-cilag, research funding from Celgene: Research Funding; Janssen-Cilag, Sanofi, Celgene: Honoraria. Wennerberg:Pfizer: Research Funding. Kallioniemi:Medisapiens: Consultancy, Membership on an entity's Board of Directors or advisory committees. Porkka:Bristol-Myers Squibb: Honoraria, Research Funding; Novartis: Honoraria, Research Funding. Heckman:Celgene: Research Funding.

Description: Abstract 871 Background: Large granular lymphocytic (LGL) leukemia is a rare lymphoproliferative disease, characterized by the clonal expansion of cytotoxic CD3+CD8+ T-cells or CD3-CD16/56+ natural killer (NK)-cells. It is often associated with autoimmune phenomena (e.g. cytopenias, rheumatoid arthritis). We recently identified somatic mutations in the STAT3 gene in 40% of monoclonal T-LGL cases (Koskela et al, NEJM, 2012). Here, we report the discovery and functional analysis of novel STAT5b mutations as well as small subclones of STAT3 mutations in other LGL patients, expanding the evidence implicating STAT activation in LGL. METHODS: In order to find novel LGL-leukemia associated mutations, exome sequencing was done from untreated STAT3 mutation negative T-LGL leukemia patients using CD8+ LGL-leukemic cells and matched CD4+ control cells. Samples from 158 T-LGL and 40 NK-LGL leukemia patients were further analyzed using both targeted Sanger sequencing and ultra-deep targeted next-gen amplicon sequencing with up to 10,000x coverage (MiSeq, Illumina). Functional analysis of mutated proteins was carried out in Hela cells by Western analysis and luciferase reporter assays. RESULTS: Exome sequencing revealed a novel somatic missense mutation Y665F in the STAT5b gene in two T-LGL patients diagnosed with WHO2009 criteria with a large CD8+ T-LGL clone (〉90%). Only wild-type STAT5b was seen in the matched CD4+ control cells of these patients. Amplicon sequencing of exon 16 of STAT5b (corresponding to the Y665F site) in 158 T-LGL and 40 NK-LGL patients revealed an additional mutation (N642H) in one T-LGL and one NK-LGL patient, resulting in the 2% total frequency (4/198) of STAT5b mutations across all patients. The N642H and Y665F mutations were both located in the Src homology 2 (SH2) domain of STAT5b, which mediates dimerization and activation by trans-phosphotyrosine binding. STAT3 mutations previously reported in T-LGL patients were located in the corresponding domain. The transcriptional activity of wild-type and mutant STAT5b proteins (N642H and Y665F) was assayed in cells carrying a luciferase reporter with STAT5 binding elements. Luciferase activity of Hela cells transfected with the mutated STAT5b constructs was significantly increased compared to wild-type STAT5b. Furthermore, both the N642H and Y665F variants of STAT5b exhibited higher levels of tyrosine (Y694) phosphorylation than the wild type protein. The exon 21 in the SH2 domain of the STAT3 gene was also screened by ultra-deep next-gen amplicon sequencing, both from the original T-LGL patient cohort (n=77, patients with monoclonal disease) and 142 additional monoclonal/oligoclonal LGL patients. In the monoclonal cohort, a total of nine new STAT3 mutation positive patients were detected by amplicon sequencing, raising the total number of positive cases to 41 (53%) from the 32 identified by Sanger sequencing. Concomitant to the previously described Y640F, N647I, K658N, D661H, D661V and D661Y STAT3 mutations, several novel mutations in this gene were found: I659L, Q643H, G656C, K658H, K658R and D661I. In the oligoclonal LGL cohort, the mutation frequency was lower (31/142, 22%), suggesting that it may also include patients with reactive polyclonal LGL proliferation. CONCLUSIONS: Our mutational and functional data affirm that STAT family transcription factors play a critical role in the pathogenesis of LGL leukemia. In addition to the previously identified mutations in the STAT3 gene, we found recurrent somatic STAT5b mutations in LGL leukemia. Furthermore, our ultra-deep (10,000x) next-gen sequencing revealed small subclones of STAT3 mutations in patients with oligoclonal LGL. Both STAT3 and STAT5b mutations increased the phosphorylation and transcriptional activity of corresponding proteins. The detection of STAT mutations should be included in the diagnostic assessment of LGL leukemia. Disclosures: Koskela: Novartis: Honoraria; BMS: Honoraria; Janssen-Cilag: Honoraria. Kallioniemi:TEKES-FiDiPro: Research Funding. Porkka:Bristol-Myers Squibb: Honoraria, Research Funding; Novartis: Honoraria, Research Funding. Maciejewski:NIH: Research Funding; Aplastic Anemia&MDS International Foundation: Research Funding. Mustjoki:Bristol-Myers Squibb: Honoraria, Research Funding; Novartis: Honoraria.