Heterogeneity Of Multiple Myeloma Research Articles

Single Nuclei Multiomics Analysis of Transcriptional and Chromatin Accessibility of Tumor Cells Uncovers Molecular Signatures and Regulatory Elements of Malignant Clonal Evolution in Multiple Myeloma

Category Multiple Myeloma and Plasma Cell Dyscrasias: Basic and Translational Introduction Multiple myeloma (MM) is a hematological cancer resulting from abnormal proliferation of plasma cells (PC) in bone marrow. The progression of the disease from Monoclonal Gammopathy of Undetermined Significance (MGUS), Smoldering Multiple Myeloma (SMM), to MM involves multiple drivers and dynamic interactions of premalignant/malignant cells. In the advancement of single-cell technologies, the same-cell readouts of nucleus RNA sequencing (snRNA-seq) and Assay for Transposase-Accessible Chromatin sequencing (snATAC-seq) provide a less biased measurement of gene expressions and chromatin accessible regions. Profiling and analyzing both omics data at different stages of MM may reveal essential molecular signatures and regulatory elements for microenvironmental modification and evolution. These insights could benefit MM patients as novel targets for early therapeutic intervention at different stages of progression. Methods Bone marrow aspirates were collected from 33 individuals: Healthy donors (n=3), patients with MGUS (n=2), SMM (n=5), MM (n=21), and Plasma Cell Leukemia (PCL; n=2). Viable mononuclear cells underwent CD138+ sorting and nuclei isolation prior to library preparation. Libraries were prepared with the Chromium Multiome kit (10X Genomics) and sequenced on NovaSeq S4 flow cells at UAMS. The clonal V(D)J arrangements of each sample and expression level from HD samples were used to distinguish malignant (MM) from normal plasma cells (PC). The MM data from snATAC-seq and snRNA-seq were integrated using Signac with the weighted nearest neighbor methods from Seurat. All inter- and intra-tumor clusters were evaluated for markers of high-risk disease, differential gene expression, differentially accessible regions, and transcription factor activities. Results We performed snRNA-seq and snATAC-seq sequencing to generate multi-stage, molecular classified-specific chromatin accessibility and transcriptional profiles of plasma cell neoplasms comprising 439,134 cells across 33 samples. The malignant plasma cells for each sample were extracted by the unique clonotypic signature of the immunoglobulin gene rearrangement. Of these, we detected the highest clonality in MGUS samples, suggesting clonal competition at the premalignant stage. Based on our primary microarray data, we detected the overexpression of molecular signatures for most cells in each sample, confirming the intratumor cell heterogeneity. Furthermore, we used the ratio of 70-gene signature of aggressive disease and proliferation signatures to investigate the clonal composition of MM cells corresponding to the risk of relapse. Surprisingly, the proportion of high-risk cells increased respectively from low to high-risk samples. The expansion of high-risk clonality might exhibit competition among malignant cell subpopulations regarding microenvironmental adaptation. The multiomics integration generated less distinct clusters between MGUS and SMM, indicating the gradual complex alterations of subclonal gene expression and chromatin accessibility in malignant transformation. Interestingly, most accessible regions were found in intronic/distal intergenic regions implying the long-range gene regulation from cis-regulatory elements. Based on the positive/negative correlation of binding motifs within differentially accessible regions, we identified unique enriched transcription factors for each disease stage, suggesting the mechanism patterns in the evolutionary process of malignant cells. Conclusion In the largest multiomics study to date of CD138+ cells, we demonstrate the malignant clonal evolution using gene expression and chromatin accessibility profiling from patients with early-stage disease to highly aggressive form. Throughout the time of disease progression, we found an increase in the proportion of malignant cells with high-risk signatures. Also, we uncover unique signatures of chromatin accessibility for each molecular classification that can further refine our understanding of functional heterogeneity in multiple myeloma. Collectively, our joint single nuclei profiling reveals additional insights into the malignant clonal evolution and investigates novel regulatory elements involved in disease progression.

Resolving the spatial architecture of myeloma and its microenvironment at the single-cell level

In multiple myeloma spatial differences in the subclonal architecture, molecular signatures and composition of the microenvironment remain poorly characterized. To address this shortcoming, we perform multi-region sequencing on paired random bone marrow and focal lesion samples from 17 newly diagnosed patients. Using single-cell RNA- and ATAC-seq we find a median of 6 tumor subclones per patient and unique subclones in focal lesions. Genetically identical subclones display different levels of spatial transcriptional plasticity, including nearly identical profiles and pronounced heterogeneity at different sites, which can include differential expression of immunotherapy targets, such as CD20 and CD38. Macrophages are significantly depleted in the microenvironment of focal lesions. We observe proportional changes in the T-cell repertoire but no site-specific expansion of T-cell clones in intramedullary lesions. In conclusion, our results demonstrate the relevance of considering spatial heterogeneity in multiple myeloma with potential implications for models of cell-cell interactions and disease progression.

Spatiotemporal assessment of immunogenomic heterogeneity inmultiple myeloma.

Spatial heterogeneity is a common phenomenon in metastatic solid tumors and an evolving concept in multiple myeloma (MM). The interplay between malignant plasma cells (PCs) and the microenvironment has not yet been analyzed in MM. For this purpose, we performed bone marrow aspirates and imaging-guided biopsies of corresponding lesions in newly diagnosed MM (NDMM) and relapsed/refractory MM (RRMM) patients. PCs were isolated and subjected to whole-exome sequencing (WES). Non-PCs were studied with next-generation flow (NGF) and T-cell receptor sequencing (TCRseq) to analyze the connection between malignant and nonmalignant cells in the bone marrow and in lesions. Although we observed a strong overlap from WES, NGF, and TCRseq in patients with intramedullary disease, WES revealed significant spatial heterogeneity in patients with extramedullary disease. NGF showed significant immunosuppression in RRMM compared with NDMM as indicated by fewer myeloid dendritic cells, unswitched memory B cells, Th9 cells, and CD8 effector memory T cells but more natural killer and regulatory T cells. Additionally, fewer T-cell receptor (TCR) sequences were detected in RRMM compared with NDMM and healthy individuals. After induction therapy, TCR repertoire richness increased to levels of healthy individuals, and NGF showed more regulatory T cells and myeloid-derived suppressor cells, regardless of depth of response. Clinical significance of imaging-guided biopsies of lesions was demonstrated by detection of monoclonal PCs in patients without measurable residual disease (MRD) in aspirates from the iliac crest as well as identification of secondary primary malignancies in MRD- patients. Furthermore, site-specific clones with different drug susceptibilities and genetically defined high-risk features were detected by our workflow.

MSC Senescence-Related Genes Are Associated with Myeloma Prognosis and Lipid Metabolism-Mediated Resistance to Proteasome Inhibitors.

Complex carcinogenic mechanisms and the existence of tumour heterogeneity in multiple myeloma (MM) prevent the most commonly used staging system from effectively interpreting the prognosis of patients. Since the microenvironment plays an important role in driving tumour development and MM occurs most often in middle-aged and elderly patients, we hypothesize that ageing of bone marrow mesenchymal stem cells (BM-MSCs) may be associated with the progression of MM. In this study, we collected the transcriptome data on MM from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Differentially expressed genes in both senescent MSCs and MM tumour cells were considered relevant damaged genes. GO and KEGG analyses were applied for functional evaluation. A PPI network was constructed to identify hub genes. Subsequently, we studied the damaged genes that affected the prognosis of MM. Least absolute shrinkage and selection operator (lasso) regression was used to identify the most important features, and a risk model was created. The reliability of the risk model was evaluated with the other 3 GEO validation cohorts. In addition, ROC analysis was used to evaluate the novel risk model. An analysis of immune checkpoint-related genes, tumour immune dysfunction and exclusion (TIDE), and immunophenotypic scoring (IPS) were performed to assess the immune status of risk groups. pRRophetic was utilized to predict the sensitivity to administration of chemotherapeutic agents. We identified that MAPK, PI3K, and p53 signalling pathways were activated in both senescent MSCs and tumour cells, and we also located hub genes. In addition, we constructed a 14-gene prognostic risk model, which was analysed with the ROC and validated in different datasets. Further analysis revealed significant differences in predicted risk values across the International Staging System (ISS) stage, sex, and 1q21 copy number. A high-risk group with higher immunogenicity was predicted to have low proteasome inhibitor sensitivity and respond poorly to immunotherapy. Lipid metabolism pathways were found to be significantly different between high-risk and low-risk groups. A nomogram was created by combining clinical data, and the optimization model was further improved. Finally, real-time qPCR was used to validate two bortezomib-resistant myeloma cell lines, and the test confirmed that 10 genes were detected to be expressed in resistant cell lines with the same trend as in the high-risk cohort compared to nonresistant cells. Fourteen genes related to ageing in BM-MSCs were associated with the prognosis of MM, and by combining this genotypic information with clinical factors, a promising clinical prognostic model was established.

Association of Progression Patterns in Monoclonal Gammopathy of Undetermined Significance (MGUS) with Outcome in Multiple Myeloma: A Cohort Study of over 35 Years

Introduction: Guidelines suggest following monoclonal gammopathy of undetermined significance (MGUS) according to risk of multiple myeloma progression. Follow-up of low-risk MGUS is debated as progression risk is low. Worse myeloma-related outcome was observed in patients followed for low-risk MGUS, potentially due to less optimal follow-up (Bianchi et al. Blood 2010, Sigurdardottir et al. Jama Oncology 2015). However, it is not clear if progressing low-risk MGUS by its nature displays more aggressive biological behavior. To gain better understanding of progression patterns in MGUS, we investigated, independent of follow-up, whether progression from low-risk MGUS is associated with worse outcome in multiple myeloma. The main outcome was overall survival from the time of myeloma diagnosis.Methods: The Northern Sweden Health and Disease Study (NSHDS) is a longitudinal prospective cohort with more than 100,000 participants. Typically, NSHDS participants donate repeated blood samples at intervals of several years. Samples are frozen within one hour and stored at -80° C at Umea University Hospital. Linkage to the Swedish Cancer Registry facilitated identification of myeloma patients with two pre-diagnostic samples in NSHDS (N = 61). Of these, we screened repeated pre-diagnostic samples using protein- and immunofixation electrophoresis and free light chain assays. We identified 42 individuals who had detectable monoclonal gammopathy in both pre-diagnostic samples without MGUS follow-up before myeloma diagnosis, allowing to investigate natural progression patterns in relation to outcome. Overall survival was determined using Kaplan-Meier plots. Hazard ratios and 95% confidence intervals were calculated using multivariable Cox regression including known prognostic factors. Fisher's exact test was used to compare categorical variables.Results: The first pre-diagnostic sample was collected in November 1986 and the last follow-up since myeloma diagnosis was in February 2021, resulting in a 35-year study duration. Median age at myeloma diagnosis was 62 (range 48-84) with a median follow-up of 7 years (range 0.2-18). At first pre-diagnostic blood draw, 12 had low-risk and 30 had MGUS of other risk (Mayo Clinic criteria). Characteristics at myeloma diagnosis except sex were similarly distributed between the two groups. Comorbidities, myeloma treatment and access to novel drugs were balanced between groups. Bone disease (osteolytic lesions and/or vertebral compression fractures attributable to myeloma) at myeloma diagnosis was more common in patients who had low-risk MGUS at first pre-diagnostic blood draw (P = 0.04). This association was pronounced excluding light chain myeloma (P = 0.01). Access to other radiographic imaging than conventional skeletal survey such as CT or MRI was similar for both groups.In low-risk vs. other MGUS, median overall survival since myeloma diagnosis was 2.3 (95% CI, 1.8-2.9) years and 7.5 (95% CI, 4.8-10.2) years (Figure A). Results were similar investigating overall survival since frontline therapy start (excluding 5 patients not requiring therapy) (Figure B). Sex and bone disease were not associated with survival.At repeated pre-diagnostic blood draw (in median 3.7 years prior myeloma diagnosis), 67% vs. 19% had low- or low-intermediate risk MGUS in patients with low-risk vs. other MGUS at first pre-diagnostic blood draw (P = 0.01), suggesting more rapid progression close to myeloma diagnosis in patients with low-risk MGUS at first blood draw. Investigating this further, we plotted M spikes in low-risk vs. other MGUS of IgG isotype (Figures C-D). M spike trajectories were largely similar between groups, although the annual median M spike increase from repeated pre-diagnostic blood draw to myeloma diagnosis was 6.0 g/L in low-risk vs. 2.3 g/L in other MGUS (P = 0.14).Conclusions: Progression from low-risk MGUS is, independent of MGUS follow-up, associated with a higher proportion of bone disease and worse survival. Based on the known phenotypic heterogeneity in multiple myeloma, we speculate that low-risk MGUS in case of malignant progression belongs to a group of more aggressive tumors. Our results need to be interpreted carefully because of the small sample size. Replication and further investigation are needed. If replicated, these findings could help to improve current MGUS follow-up strategies, which are solely based on progression risk. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Spatial Genomic Heterogeneity in Multiple Myeloma: Surrogate Markers and Its Significance for Understanding the Evolutionary Processes Leading to Relapse

IntroductionMultiple Myeloma (MM) frequently presents with genomic variation between individual cancer foci, which is also termed spatial heterogeneity (SH). However, the extent of SH differs considerably between patients with some having extensive SH while others show homogeneous mutational profiles within the bone marrow (BM). Since SH poses a significant challenge to personalized therapy, surrogate markers for its extent would be useful clinically. In this respect an understanding of the spatial evolutionary patterns taking place during MM treatment and the clonal architecture they give rise to at relapse would also be of fundamental importance. To address these important points we have analyzed paired samples from multiple BM sites collected at baseline (n=52 patients) or in a longitudinal fashion during treatment (n=30).Patients and MethodsA genomic analysis was performed on iliac crest BM and CT-guided fine needle aspirates from distinct anatomical sites from 52 newly diagnosed patients. The impact of treatment on SH was analyzed in 30 patients with a median number of 6 samples per patient (range 4-14). Plasma cells were CD138-enriched and underwent whole exome sequencing using an Illumina HiSeq 2500. Sequencing data were aligned using BWA. Mutations were identified using MuTect. Copy number aberrations were derived from Illumina HumanOmni 2.5 bead chip data using ASCAT. Subclonal reconstruction was performed using SciClone. Statistical analyses were carried out using R packages.ResultsWe investigated whether the extent of SH was associated with clinical features and found that age, an adverse prognostic factor, was positively correlated with SH (P=0.01). Patients over the age of 60 had a 3-fold higher proportion of private mutations compared to younger patients (median 15 vs. 5%). Hypothetically, this could reflect the longer evolutionary time period to MM arising at older ages. The size of the biopsied FLs was also significantly associated with the proportion of private mutations (P <0.001), with large FLs (>2.5 cm) having the highest values. Of note, a linear model including age and FL size explained 49% of the variation in SH. In contrast, neither the ISS, adverse molecular markers identified at the iliac crest, the number of FLs, nor the anatomical distance between investigated sites correlated with SH. In summary, we show that SH is increased in elderly patients and/or patients with large FLs.Next, we investigated the impact of treatment on the spatial clonal architecture. For patients who had achieved a CR, we expected relapse to be dominated by a limited number of selected clones and as a result less SH. Indeed, analyzing samples derived from multiple sites at baseline and at subsequent relapses, the predominant pattern was characterized by sweeps of advanced clones resulting in BM-wide replacement of previous clones. Of note, evidence for clonal sweeps was also seen in patients with stable disease according to IMWG criteria, which is a perplexing observation since stable serological parameters do not seem to be consistent with major sub-clonal changes.In contrast to cases with clonal sweeps, there were also patients with multiple site-specific clones at relapse. On the one hand, “mixed responses” of FLs, which are frequently seen in medical imaging after multiple relapses, could be related to this pattern of pronounced SH. On the other hand, we found private mutations affecting the same genes or pathways in patients with site-specific subclones, indicating parallel evolution. In this context, one patient showed distinct KRAS mutations which affected the exact same amino acid (Gly12Val vs. Gly12Asp), emphasizing that treatment could select for multiple clones with similar or even identical features. This observation may explain, why individual patients still respond to MAPK pathway inhibitors despite pronounced SH.ConclusionsWe provide new insights into the biology of MM progression, which has implications for diagnostic and treatment strategies. The strong association of private mutations with age and FL size may allow for the identification of patients with increased levels of SH for whom treatment approaches can be modified. The existence of clonal sweeps of aggressive clones, which replace previously dominant clones, even in poorly responding patients, highlights the need for regular molecular testing, especially if personalized treatment is intended. DisclosuresBarlogie:Millenium Pharmaceuticals: Consultancy, Research Funding; Celgene Corporation: Consultancy, Research Funding. Davies:Takeda: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Janssen: Consultancy, Honoraria. Morgan:Takeda: Consultancy, Honoraria; Bristol Myers: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding.

Spatiotemporal Assessment of Immunogenomic Heterogeneity in Multiple Myeloma

Introduction: Therapy and immune mediated processes are associated with clonal evolution in multiple myeloma (MM). In this study, we performed whole-exome sequencing (WES) and single cell RNA sequencing (scRNA-seq) on plasma cells (PC) from bone marrow aspirates of the iliac crest (BM) and corresponding osteolytic lesions (OL) to investigate spatial heterogeneity in patients with newly diagnosed (NDMM) and relapsed/refractory MM (RRMM). Next generation flow (NGF) and T-cell receptor sequencing (TCRseq) were performed to investigate the immunogenomic landscape surrounding malignant PC. Methods: In a prospective trial, 18 patients (NDMM: n=10; RRMM: n=8) consented to an imaging-guided biopsy of an OL in addition to the regular BM sampling. At inclusion, 37 different locations were biopsied. Follow-up samples were obtained from 5 patients in remission after therapy. After CD138+ selection, PC were subjected to WES and scRNA-seq (Chromium, 10x genomics). TCRseq was performed using multiplex PCR (ImmunoSEQ, Adaptive biotechnologies) on the CD138- fraction. For scRNA-seq data analyses, Cell Ranger (v3.1.0) and the Seurat R toolkit (v3.1) were used. TCRseq data were analyzed with immunoSEQ ANALYZER (v3.0) and the immunarch R toolkit (v0.6.6.). NGF was performed to study subsets of T-, B-, NK- and dendritic cells (DC). Results: Median PC infiltration was higher in OL compared to random BM (50.0% vs 12.5%, p=0.041). WES revealed more mutations in RRMM compared to NDMM (median; range: 189;120-523 vs 71;23-136, p&amp;lt;0.001). Based on mutational profiles from WES, 4 of 18 patients showed a branching evolution in PC isolated from OL. Three of the 4 patients had RRMM and one patient with NDMM had a prior history of solitary plasmacytoma. PC were obtained from OL with adjacent extramedullary disease (EMD) in 3 of 4 patients with branching evolution. Among site-specific mutations, we found in one patient two distinct BRAF mutations: V600E in the BM and G469R in the OL. An additional NRAS mutation (G12D) was found in the OL. BRAF G469R and NRAS G12D cause resistance to BRAF inhibitors, although this patient was naïve to BRAF-inhibitors. Clonal evolution was also reflected by chromosomal aberrations, including site-specific chromothripsis of chromosome 1 in a patient with RRMM. Even in patients without spatially divergent clones as detected by WES, scRNA-seq of more than 150,000 PC from 10 patients and 21 different locations revealed multiple clones. Distinct PC clones were identified by differential expression of genes associated with homing to the BM (CXCR4), malignant transformation (Jun/Fos, CD27, CD79a), apoptosis (BCL-2) bone disease (DKK1) and LAMP-5. In a patient with NDMM in remission after induction therapy, scRNA-seq demonstrated the emergence of a PC clone characterized by the overexpression of Interferon-induced genes (ISG15, IFI27, IFI44L) compared to the initially predominant PC clones. Next, we investigated spatiotemporal differences of immune cells. Estimation of median TCR richness using an abundance-based estimator (Chao1) revealed significantly lower values in patients with RRMM (120444; 57706-212744) compared to NDMM (389341; 50318-525082, p&amp;lt;0.001) and nine healthy individuals (460278; 138326-696419, p&amp;lt;0.001). No significant differences were found for TCR clonality as indicated by Simpson's D. While longitudinal tracking of TCR clones at primary diagnosis showed no clonal expansion after treatment, induction therapy restored sample richness in patients with NDMM to levels of healthy individuals (p=0.61). Overlap analysis showed a high concordance of TCR repertoires from OL and random BM with Morisita indices ranging in 90% of patients from 0.80 to 0.95. Nevertheless, significant site-specific expansion of TCR clones was detected. In accordance with TCRseq, NGF showed in the BM of patients with RRMM more regulatory T-cells (p=0.048) and less myeloid DC (p=0.024), Th9 cells and CD8 effector memory T-cells compared to NDMM. Conclusion: We report the first prospective clinical trial to investigate spatiotemporal immunogenomic heterogeneity in multiple myeloma as assessed by WES and scRNA-seq of PC and NGF and TCRseq of the non-PC compartment. We demonstrate spatial evolution and reduced TCR diversity especially in patients with RRMM and/or EMD. ScRNA-seq adds another layer of complexity compared to WES and helps identifying how PC create an immune suppressive BM niche. Disclosures Merz: Amgen, BMS, Celgene, Takeda: Honoraria. Block:GlaxoSmithKline LLC: Current Employment. McCarthy:Karyopharm: Consultancy, Honoraria; Magenta: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Advisory Board; Juno Therapeutics, a Bristol-Myers Squibb Company: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Advisory Board , Research Funding is to Roswell Park, Research Funding; AbbVie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Advisory Board; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Advisory Board; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Advisory Board; Starton: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Advisory Board; Genentech: Consultancy, Honoraria. Hillengass:Adaptive, Amgen, BMS, Celgene, GSK, Janssen, Oncotracker, Takeda: Honoraria.

Advanced Imaging and Targeted Myeloma Lesion Biopsies to Enhance Global Response Assessment and Evaluate Spatial Heterogeneity in Multiple Myeloma

Advanced Imaging and Targeted Myeloma Lesion Biopsies to Enhance Global Response Assessment and Evaluate Spatial Heterogeneity in Multiple Myeloma

MM-167: Phenotypic and Prognostic Evaluation of Circulating Plasma Cells in Newly Diagnosed Multiple Myeloma Detected with Next-Generation Flow Cytometry (NGF)

Context: The apparent heterogeneity of multiple myeloma (MM) necessitates the identification of novel biomarkers with a strong prognostic value. The presence of circulating plasma cells (CPCs) in newly diagnosed patients (NDMM) has been proposed as a valuable prognostic biomarker, though current methodologies reported yielded heterogeneous results due to variations in their detection efficacy. Objective: To evaluate the characteristics and prognostic value of CPCs, utilizing highly sensitive next-generation flow (NGF) cytometry, able to detect aberrant plasma cells (APCs) at levels reaching 10−6. Design, Patients, and Methods: Peripheral blood (PB) and matched bone marrow (BM) samples from 325 NDMM patients were prospectively evaluated for the presence of APCs according to Euroflow-NGF protocol for a median LOD reached of 3.6 × 10−6. CPC numbers correlated with various clinical parameters and the median follow-up period since CPC assessment was 24 months. Main outcome measures: Clinical outcome endpoints considered were the type of response to induction therapy and PFS. Our hypothesis was that higher CPC numbers would correlate with adverse prognostic features and worse clinical outcomes. Results: CPCs were detected in 288/325 (88.6%) samples (range, 0.0002% - 63.8% of PBNCs). CPCs and matched BM APCs had similar phenotypic characteristics; notably, when two or more phenotypically distinct APC-subsets were detectable in BM, the same subsets were also present in PB with similar relative frequency. Higher CPC numbers (>0.1% of PBNCs) correlated with increased BM infiltration, ISS-3 stage, and high-risk cytogenetics (p Conclusions: NGF enables the detection of rare CPCs in NDMM, many of which would have been falsely missed with a lower sensitivity approach. The similarity of CPCs with BM APCs and the strong correlation of higher CPC numbers with adverse disease characteristics highlight a valuable surrogate prognostic tool, which could representatively replace standard BM invasive methods.

VarianThinker: a classification method to confidently approach the mutation heterogeneity in Multiple Myeloma

Multiple Myeloma (MM) is a genomically heterogeneous malignancy, characterized by a relevant mutational burden, as compared to other types of cancer. By employing a frequentist approach to define MM driver mutation, a landscape of few variants in a limited number of genes, mostly associated to survival and/or proliferative pathways (eg. KRAS, NRAS, TP53, BRAF), have been reported so far. Nevertheless, given the high genomic heterogeneity of MM plasma cells, new possible driver mutations, being rare and/or scattered across the genome, might be diluted within the genomic complexity and therefore missed.

PS1366 TISSUE ENGINEERING OF AN ORTHOTOPIC HUMANIZED BONE-ORGAN AS A PLATFORM FOR PRECLINICAL MULTIPLE MYELOMA RESEARCH

Background: Genetic heterogeneity in multiple myeloma (MM) affects not only the disease development but also therapeutic response and prognosis. Nowadays, karyotype and FISH are standard diagnostic tools for the detection of genetic aberrations in MM. However, comprehensive analysis of genome architecture, particularly structural variations (SVs), still remain a challenge. Aims: To study the genomic architecture of MM cells using a novel nanochannel technology based on optical mapping of high-molecular weight DNA in order to refine genomic variability in human MM. Methods: We analysed five samples of bone marrow aspirates from patients with newly diagnosed MM. High-molecular weight DNA was isolated from sorted MM cells, labelled by DLS chemistry and visualised by Saphyr optical system (BioNano Genomics). Created single molecules were de novo assembled into physical maps spanning whole genome based on known labelled specific motifs. After filtering of structural variants against the database of healthy controls, only rare de novo variants were analysed. Karyotype, FISH, and arrayCGH results were available for the enrolled patients and were compared to the results of optical mapping. Results: For all MM samples, the optical genome maps (480 Gbp) were assembled with an average effective coverage of 124X and aligned to a human reference genome (GRCh38). In our cohort, numerous copy number variations and large somatic SVs, including deletions, insertions, inversions, duplications and translocations, were identified (Table 1) with majority of SVs being deletions (∼60%). All genetic variants detected by arrayCGH were also confirmed by mapping. Additional SVs with novel intra- and inter-chromosomal translocations and copy number variants were found in all patients, not detected by FISH/arrayCGH. Regarding novel translocations, majority were intra-chromosomal (64%) within chromosomes 3, 4, 6, 14, 16, 17, and 18. Additional translocations (36%), detected only by mapping, involved unusual chromosomes in MM: t(2;8), t(3;6), t(3;12), t(6;8), t(6;17), t(8;9), and t(8;22). All samples carried novel SVs on chromosome 17, including two intra- (P1) and one inter-chromosomal translocations (P2), insertions in four (P2-P5) and deletions in two (P4-P5) patients. Although no IGH locus rearrangements were detected by standard methods, monosomy on chromosome 14 within IGH locus was observed in four samples (P2-P5) by mapping. The analyses of associations of SVs and affected genes with clinical characteristics are ongoing.Summary/Conclusion: A large spectrum of novel genomic rearrangements was detected in MM using next-generation mapping technology, showing a high potential of optical maps for refinement of genomic variability in human MM. Grant support: research grant Celgene, MZ ČR VES16–32339A, NV18–03–00500, MZ ČR – RVO (FNOl, 00098892)

PS1365 NEXT‐GENERATION OPTICAL MAPPING REVEALS LARGE GENETIC HETEROGENEITY IN MULTIPLE MYELOMA

Background:Genetic heterogeneity in multiple myeloma (MM) affects not only the disease development but also therapeutic response and prognosis. Nowadays, karyotype and FISH are standard diagnostic tools for the detection of genetic aberrations in MM. However, comprehensive analysis of genome architecture, particularly structural variations (SVs), still remain a challenge.Aims:To study the genomic architecture of MM cells using a novel nanochannel technology based on optical mapping of high‐molecular weight DNA in order to refine genomic variability in human MM.Methods:We analysed five samples of bone marrow aspirates from patients with newly diagnosed MM. High‐molecular weight DNA was isolated from sorted MM cells, labelled by DLS chemistry and visualised by Saphyr optical system (BioNano Genomics). Created single molecules were de novo assembled into physical maps spanning whole genome based on known labelled specific motifs. After filtering of structural variants against the database of healthy controls, only rare de novo variants were analysed. Karyotype, FISH, and arrayCGH results were available for the enrolled patients and were compared to the results of optical mapping.Results:For all MM samples, the optical genome maps (480 Gbp) were assembled with an average effective coverage of 124X and aligned to a human reference genome (GRCh38). In our cohort, numerous copy number variations and large somatic SVs, including deletions, insertions, inversions, duplications and translocations, were identified (Table 1) with majority of SVs being deletions (∼60%). All genetic variants detected by arrayCGH were also confirmed by mapping. Additional SVs with novel intra‐ and inter‐chromosomal translocations and copy number variants were found in all patients, not detected by FISH/arrayCGH. Regarding novel translocations, majority were intra‐chromosomal (64%) within chromosomes 3, 4, 6, 14, 16, 17, and 18. Additional translocations (36%), detected only by mapping, involved unusual chromosomes in MM: t(2;8), t(3;6), t(3;12), t(6;8), t(6;17), t(8;9), and t(8;22). All samples carried novel SVs on chromosome 17, including two intra‐ (P1) and one inter‐chromosomal translocations (P2), insertions in four (P2‐P5) and deletions in two (P4‐P5) patients. Although no IGH locus rearrangements were detected by standard methods, monosomy on chromosome 14 within IGH locus was observed in four samples (P2‐P5) by mapping. The analyses of associations of SVs and affected genes with clinical characteristics are ongoing.Summary/Conclusion:A large spectrum of novel genomic rearrangements was detected in MM using next‐generation mapping technology, showing a high potential of optical maps for refinement of genomic variability in human MM.imageGrant support: research grant Celgene, MZ ČR VES16–32339A, NV18–03–00500, MZ ČR – RVO (FNOl, 00098892)

Molecular signatures of multiple myeloma progression through single cell RNA-Seq

We used single cell RNA-Seq to examine molecular heterogeneity in multiple myeloma (MM) in 597 CD138 positive cells from bone marrow aspirates of 15 patients at different stages of disease progression. 790 genes were selected by coefficient of variation (CV) method and organized cells into four groups (L1–L4) using unsupervised clustering. Plasma cells from each patient clustered into at least two groups based on gene expression signature. The L1 group contained cells from all MGUS patients having the lowest expression of genes involved in the oxidative phosphorylation, Myc targets, and mTORC1 signaling pathways (p < 1.2 × 10−14). In contrast, the expression level of these pathway genes increased progressively and were the highest in L4 group containing only cells from MM patients with t(4;14) translocations. A 44 genes signature of consistently overexpressed genes among the four groups was associated with poorer overall survival in MM patients (APEX trial, p < 0.0001; HR, 1.83; 95% CI, 1.33–2.52), particularly those treated with bortezomib (p < 0.0001; HR, 2.00; 95% CI, 1.39–2.89). Our study, using single cell RNA-Seq, identified the most significantly affected molecular pathways during MM progression and provided a novel signature predictive of patient prognosis and treatment stratification.

Abstract A015: Intrapatient heterogeneity of multiple myeloma is evenly distributed in multiple myeloma lesions

Abstract Background: We previously reported that circulating tumor cells (CTCs) from myeloma patients and clonal plasma cells from the BM biopsy of the same patients have quite similar clonal structures (Cell Rep. 2017;19(1):218). Specifically, overall, 90% of CTC mutations were present in BM tumors and 93% of BM mutations were present in CTC samples, and upon analyzing the mutational variants by nucleotide change, we found that the percentages of each change in BM myeloma PCs and CTCs were concordant. This finding suggests the possibility that multiple lesions of a multiple myeloma patient basically share similar clonal structure. Methods: To examine this hypothesis, we performed xenograft dissemination model of multiple myeloma using DNA-barcoded cell library. Briefly, two million human multiple myeloma cell lines barcoded with 12mer-DNA tags were inoculated into the bone chips harvested from syngeneic donor mice and the tumor-bearing bone chips were implanted to recipient mice. After the implanted myeloma cells were disseminated to murine host bone marrow via hematogenous dissemination, tumor cells were harvested from multiple BM sites and peripheral blood then analyzed the clonal structure by next-generation sequencing. Results: The clonal structures of tumor cells were highly concordant between left and right femur bone marrow from same mice. In addition, the clonal structures of tumor in host murine bone marrow were also highly concordant with those of CTCs harvested from same mice as we observed in clinical samples previously. Discussion: Recent studies of massive parallel sequencing of tumor cells obtained from the BM of patients with MM have demonstrated significant intrapatient clonal heterogeneity. Most patients have both clonal and subclonal variants even in single biopsy site. In a previous study, we have shown that the variant landscapes of tumors were highly concordant between CTCs and single BM biopsy specimen in same patients. Together with these findings, the result of our xenograft model may indicate that the tumor lesions of myeloma patients basically consist of heterogeneous but highly shared tumor cell population across the multiple lesions. The concordance of clonal structures among the multiple BM lesions is thought to be due to the fact that the clonal structure of BM myeloma tumors reflects that of CTC. Together, this study supports validity of the CTC for genetic profiling. Citation Format: Yuji Mishima, Yasuhito Terui, Kiyohiko Hatake. Intrapatient heterogeneity of multiple myeloma is evenly distributed in multiple myeloma lesions [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2017 Oct 26-30; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Ther 2018;17(1 Suppl):Abstract nr A015.

Spatial genomic heterogeneity in multiple myeloma revealed by multi-region sequencing

In multiple myeloma malignant plasma cells expand within the bone marrow. Since this site is well-perfused, a rapid dissemination of “fitter” clones may be anticipated. However, an imbalanced distribution of multiple myeloma is frequently observed in medical imaging. Here, we perform multi-region sequencing, including iliac crest and radiology-guided focal lesion specimens from 51 patients to gain insight into the spatial clonal architecture. We demonstrate spatial genomic heterogeneity in more than 75% of patients, including inactivation of CDKN2C and TP53, and mutations affecting mitogen-activated protein kinase genes. We show that the extent of spatial heterogeneity is positively associated with the size of biopsied focal lesions consistent with regional outgrowth of advanced clones. The results support a model for multiple myeloma progression with clonal sweeps in the early phase and regional evolution in advanced disease. We suggest that multi-region investigations are critical to understanding intra-patient heterogeneity and the evolutionary processes in multiple myeloma.

Extensive Regional Intra-Clonal Heterogeneity in Multiple Myeloma - Implications for Diagnostics, Risk Stratification and Targeted Treatment

INTRODUCTIONIn multiple myeloma (MM) samples for diagnostics, prognostication and response evaluation are most commonly obtained from the patients' posterior iliac crest due to its accessibility and safety, assuming a homogenous spread throughout the bone marrow. However, imaging studies revealed a highly imbalanced distribution of the disease in the majority of the patients, presenting with accumulations of malignant plasma cells (PC) in restricted areas in the bone marrow (BM), so called focal lesions (FL). In line with this pattern, our recently reported preliminary results of paired FL and random BM (RBM) samples strongly indicate an unequal distribution of sub-clones in the BM.Spatial genomic heterogeneity has not been systematically analyzed in MM thus far, although its existence would have a high impact on interpretation of drug resistance studies, risk stratification and personalized treatment based on genomic markers. Here we report on an extended genomic analysis of regional heterogeneity in paired FL and RBM samples including 42 newly diagnosed and 11 extensively treated MM patients with 10 of these patients also being studied longitudinally.MATERIAL & METHODSMM PCs were CD138-enriched. Leukapheresis products were used as controls. For whole exome sequencing (WES) we applied the qXT kit and the SureSelect Clinical Research Exome bait design (Agilent). Paired-End sequencing was performed on an Illumina HiSeq 2500. Sequencing data were aligned to the GRCh37/hg19 reference using BWA. Somatic single nucleotide variants (SNV) were identified using MuTect. Copy number aberrations (CNA) were derived from Illumina HumanOmni 2.5 bead chip data using ASCAT. Subclonal reconstruction was performed using SciClone. Gene expression profiles (GEP) were generated using Affymetrix U133plus2 microarrays. Statistical analyses were carried out using the R software package 3.1.1.RESULTSIn 42 newly diagnosed patients we detected a median number of 86 (34 to 807) mutations per patient with up to 42% (median 5%) of them being unique to a specific site (non-ubiquitous). Among known MM driver genes, BRAF (n=2) and KRAS (n=4) were the genes that most often showed non-ubiquitous mutations at baseline. In treated patients mutations in KRAS, NRAS and RB1 contributed to regional heterogeneity in one patient each. Furthermore, we found temporal heterogeneity in mutations affecting ATRin two patients, aberrations recently associated with poor outcome.Analyzing chromosomal aberrations with known prognostic value we observed three newly diagnosed patients with a site-specific del(1p) affecting CDKN2C and/or FAM46Cwith two of these patients also showing regional heterogeneity in del(17p13). Non-ubiquitous gain(1q) or amp(1q) was seen in two patients at baseline. Of note, in all of these cases the unique event was detected in a FL and one case with a unique gain(1q) at baseline presented with this aberration in subsequent samples. These observations strongly support the concept of FLs being sites of resistant clones able to cause relapse.In four patients a MYC translocation was seen at only one site. In the longitudinal analysis we found one patient in whom a MYC translocation clone was replaced by a clone with a different MYC translocation, indicating that events at the MYC locus are secondary and can be sub-clonal. In contrast, primary IgH translocations were always shared, confirming that they are initiating events.Paired samples from RBM and FLs derived from three newly diagnosed patients showed discordant GEP risk profiles, further supporting the existence of site-specific high risk (HR) clones. To investigate the clinical relevance of this finding we analyzed outcome data of 263 newly diagnosed patients with paired GEP data. The 34 cases with discordant GEP based risk scores showed no significant differences in outcome compared to cases with HR at both sites, suggesting that HR sub-clones drive prognosis even if they are not ubiquitously present.CONCLUSIONSWe show that spatial genomic heterogeneity is common in MM. The existence of site-specific sub-clones highlights the importance of heterogeneity analyses for accurate risk prediction, detection of suitable targets for precision medicine and identification of aberrations contributing to treatment resistance. As a result we strongly recommend to include FL examinations into routine diagnostics and follow-up analyses in MM. DisclosuresAshby:University of Arkansas for Medical Sciences: Employment. Barlogie:Signal Genetics: Patents & Royalties. Davies:Celgene: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Janssen: Consultancy, Honoraria. Morgan:Univ of AR for Medical Sciences: Employment; Janssen: Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Bristol Meyers: Consultancy, Honoraria.

Spatiotemporal Analysis of Intraclonal Heterogeneity in Multiple Myeloma: Unravelling the Impact of Treatment and the Propagating Capacity of Subclones Using Whole Exome Sequencing

IntroductionMultiple myeloma (MM) is characterised by the malignant expansion of clonal plasma cells in the bone marrow (BM). We and others have used massive parallel sequencing to describe the somatic aberrations acquired in different subclones in newly diagnosed MM (NDMM). These studies have showed that chemotherapy has an impact on intra-clonal heterogeneity, but more analyses are required in paired presentation/relapse samples and samples from multiple sites at the same and different time points.Materials and methodsWe have studied 49 paired presentation/relapse patients from a series of 463 NDMM patients entered into the Myeloma XI trial (NCT01554852). To understand the impact of spatial separation within the MM clone and the consideration that MM is a metastatic disease, we examined BM aspirates and compared them to targeted biopsies from extramedullary disease sites in 9 MM patients. These cases were 1 patient with samples bilaterally collected from the hip during the course of the disease, 4 MM cases with plasma cell leukemia (PCL), 3 MM cases with plasmacytomas, 1 MM patient with ascites, and 1 MM case with pleural effusion.DNA from both BM and peripheral blood samples were used for whole exome sequencing plus a pull down of the MYC, IGH, IGL and IGK loci following the SureSelect Target Enrichment System for Illumina Paired-End Sequencing Library v1.5. Exome reads were used to call single nucleotide variants, indels, translocations, and copy number aberrations. Mean sequencing depth was 59.3x. The proportion of mutant tumor cells carrying a mutation was inferred. The presence and proportion of subclones will be defined using bioinformatics tools.ResultsFor the 463 NDMM samples, the following 15 significantly mutated genes are seen KRAS (n=103 mutations), NRAS (n=88), LTB (n=53), DIS3 (n=49), BRAF (n=37), EGR1 (n=22), FAM46C (n=20), IRF4 (n=19), TRAF3 (n=17), HIST1H1E (n=16), TP53 and FGFR3 (n=14), CYLD (n=13), MAX (n=12), and RB1 (n=5). These mutations were seen within all clonal cells and at subclonal levels, consistent with the mutations being acquired at different time points and being associated with different subclonal fitness.We show that NDMM have a mean number of exonic mutations of 61.1±13.0, in contrast to samples taken at the time of relapse, which show an average of 80.6±25.4, Figure 1A. We report diverse patterns of subclonal evolution: no change, subclonal tiding, and subclonal tiding with new subclones arising. We are currently examining samples taken during clinical remission to track subclones at the time of response.For patient with multiple samples taken at different timepoints, 77 mutations were shared across all samples but, of note, specific mutations were seen at the same timepoint in different sites (13/1662 R2R vs 13/1662 R2L), which illustrates the impact of sampling differences in reporting mutation calls and differential response to therapy, Figure 1B. This is also observed in a plasmacytoma case with both a BM aspirate sample containing 11 mutations (including NRAS c.183A>T and BRAF c.1783T>C), and a femur plasmacytoma with 18 mutations, of which only 2 are shared with the BM sample, Figure 3. One of these shared lesions is BRAF c.1783T>C, the cancer clonal fraction of which increases ten-fold, suggesting that the sub-clone with this mutation disseminated from the BM and founded the plasmacytoma.ConclusionOur preliminary data demonstrate that MM subclones not only respond differently to clinical treatment, but also have different biological properties leading to cause extramedullary disease. To our knowledge, this is the first comprehensive genetic analysis of the spatio-temporal heterogeneity in myeloma and reveals genetic differences due to sampling bias. [Display omitted] DisclosuresJones:Celgene: Other: Travel support, Research Funding. Peterson:University of Arkansas for Medical Sciences: Employment. Brioli:Celgene: Honoraria; Janssen: Honoraria. Pawlyn:Celgene: Honoraria, Other: Travel support; The Institute of Cancer Research: Employment. Gregory:Janssen: Honoraria; Celgene: Honoraria. Davies:Onyx-Amgen: Membership on an entity's Board of Directors or advisory committees; Array-Biopharma: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Takeda-Millennium: Membership on an entity's Board of Directors or advisory committees; University of Arkansas for Medical Sciences: Employment. Morgan:CancerNet: Honoraria; University of Arkansas for Medical Sciences: Employment; MMRF: Honoraria; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Weisman Institute: Honoraria; Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda-Millennium: Honoraria, Membership on an entity's Board of Directors or advisory committees.

Abstract IA25: Targeting genetic heterogeneity in multiple myeloma through immune activation.

Abstract IA25: Targeting genetic heterogeneity in multiple myeloma through immune activation.

Abstract B16: The pro-apoptotic effect of dexamethasone mediated by GILZ and Bim up-regulation is related to genetic heterogeneity of multiple myeloma.

Abstract Multiple myeloma (MM) is heterogeneous with respect to its causative molecular abnormalities and the treatment response of patients. Briefly, MM that are hyperdiploid or have a t(11;14) translocation have a better prognosis than those with t(4;14) or t(14;16) translocations. Dexamethasone (Dex) is widely used in all phases of MM treatment as induction, consolidation or maintenance. However, the mechanism of Dex sensitivity still remains elusive. In the present study, we analyzed the ability of Dex to induce cell death by Apo-2.7 staining in 33 human myeloma cell lines (HMCLs) representative of the molecular subsets carrying t(11;14), t(4;14) or t(14;16) translocations, which deregulate CCND1, MMSET and c-MAF, respectively. The mechanisms controlling Dex-induced apoptosis were evaluated by analyzing glucocorticoid receptor (GR), glucocorticoid-induced leucin zipper (GILZ) and Bcl-2 protein family expression in the different MM molecular subtypes. Transient knock-down of glucocorticoid-responsive proteins were performed to define their role in the pro-apoptotic effect of Dex. We first demonstrated that direct pro-apoptotic effect of Dex was related to the genetic heterogeneity of MM, since sensitive cell lines were restricted to t(14;16) and t(4;14) subgroups. We next demonstrated by transcriptomic Affymetrix analysis that GR expression was heterogeneous among the different molecular subtypes of HMCLs. MAF subgroup significantly expressed higher levels of GR than all other subgroups (p=0.02). This result was also confirmed on 300 newly diagnosed MM patients (p&amp;lt;.0001). We also demonstrated that Dex sensitivity was associated with its ability to down-regulate MAF protein level and its well-known target CCND2. This result was in agreement with the fact that elevated levels of MAF were associated not only with t(14;16) translocation but also with MMSET over-expression, which is a characteristic of t(4;14) subgroup. We next demonstrated that Dex induced an up-regulation of GILZ, a glucocorticoid-responsive molecule, in dex-sensitive and insensitive HMCLs. Nevertheless, resistant HMCLs, which expressed low levels of GR, failed to up-regulate GILZ to the same extent than sensitive HMCLs, suggesting that GR levels could be an important limiting factor for GILZ up-regulation. Of note, GILZ silencing induced a strong reduction of Dex-induced cell death. These results suggest that transactivation has a pivotal role in the induction of cell death by Dex in myeloma cells. Dex also induced an up-regulation of Bim isoforms and a down-regulation of Bcl-xL in all sensitive cell lines. We further demonstrated that Bim up-regulation is required for the apoptotic program in response to Dex. Finally, the silencing of GILZ impaired the up-regulation of Bim and the down-regulation of Bcl-xL under Dex treatment. In conclusion, Dex exerted a direct anti-tumor effect on HMCLs of t(14;16) and t(4;14) molecular subgroups while t(11;14) subgroup was insensitive to it. This result suggested that conventional therapeutic approaches should be re-evaluated within molecular subgroups of MM patients to favor potential molecular subtype therapy based on rational preclinical data. Finally, while Dex interferes with multiple pathways, we begin to unravel its complex mechanism of action demonstrating that transactivation, inducing GILZ up-regulation, plays a pivotal role in Dex induced cell death through the regulation of the Bcl-2 protein network. Citation Format: Charlotte Kervoëlen, Emmanuelle Ménoret, Patricia Gomez-Bougie, Régis Bataille, Philippe Moreau, Catherine Pellat-Deceunynck, Martine Amiot. The pro-apoptotic effect of dexamethasone mediated by GILZ and Bim up-regulation is related to genetic heterogeneity of multiple myeloma. [abstract]. In: Proceedings of the AACR Special Conference on Hematologic Malignancies: Translating Discoveries to Novel Therapies; Sep 20-23, 2014; Philadelphia, PA. Philadelphia (PA): AACR; Clin Cancer Res 2015;21(17 Suppl):Abstract nr B16.

Abstract A45: Osteoblastic niche supports the growth of quiescent multiple myeloma cells.

Abstract The heterogeneity of multiple myeloma (MM) contributes to variable responses to therapy. In this study, we aim to correlate the heterogeneity of MM to the presence of quiescent cells using the PKH26 dye. We tracked the rare quiescent cells in different niches of the bone marrow by allowing the cells to cycle in vivo. Surprisingly, quiescent PKH+ MM cells prefer to reside within the osteoblastic niches of the bone marrow (PKH+/OS) rather than the vascular niches or the spleen. These cells (PKH+/OS) displayed enhanced stem-like properties by forming colonies in semi-solid medium. PKH+ cells were highly tumorigenic, as compared to PKH- cells, and were resistant to a variety of drugs. However, the levels of drug resistance were somewhat similar regardless of where the PKH+ cells were isolated. Our data indicate that osteoblastic niches support the growth of quiescent PKH+ cells and allow them to have stem-like functions. Citation Format: Zheng Chen, Robert Z. Orlowski, Michael Wang, Larry Kwak, Nami McCarty. Osteoblastic niche supports the growth of quiescent multiple myeloma cells. [abstract]. In: Proceedings of the AACR Special Conference on Hematologic Malignancies: Translating Discoveries to Novel Therapies; Sep 20-23, 2014; Philadelphia, PA. Philadelphia (PA): AACR; Clin Cancer Res 2015;21(17 Suppl):Abstract nr A45.