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Clinical progress in multiple myeloma (MM), an incurable plasma cell (PC) neoplasia, has been driven by therapies that have limited applications beyond MM/PC neoplasias and do not target specific oncogenic mutations in MM. Instead, these agents target pathways critical for PC biology yet largely dispensable for malignant or normal cells of most other lineages. Here we systematically characterized the lineage-preferential molecular dependencies of MM through genome-scale clustered regularly interspaced short palindromic repeats (CRISPR) studies in 19 MM versus hundreds of non-MM lines and identified 116 genes whose disruption more significantly affects MM cell fitness compared with other malignancies. These genes, some known, others not previously linked to MM, encode transcription factors, chromatin modifiers, endoplasmic reticulum components, metabolic regulators or signaling molecules. Most of these genes are not among the top amplified, overexpressed or mutated in MM. Functional genomics approaches thus define new therapeutic targets in MM not readily identifiable by standard genomic, transcriptional or epigenetic profiling analyses.

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

Supplementary Table from Perspectives on the Risk-Stratified Treatment of Multiple Myeloma

Purpose:Multiple myeloma is a malignancy of plasma cells. Extensive genetic and transcriptional characterization of myeloma has identified subtypes with prognostic and therapeutic implications. In contrast, relatively little is known about the myeloma epigenome.Experimental Design:CD138+CD38+ myeloma cells were isolated from fresh bone marrow aspirate or the same aspirate after freezing for 1–6 months. Gene expression and chromatin accessibility were compared between fresh and frozen samples by RNA sequencing (RNA-seq) and assay for transpose accessible chromatin sequencing (ATAC-seq). Chromatin accessible regions were used to identify regulatory RNA expression in more than 700 samples from newly diagnosed patients in the Multiple Myeloma Research Foundation CoMMpass trial (NCT01454297).Results:Gene expression and chromatin accessibility of cryopreserved myeloma recapitulated that of freshly isolated samples. ATAC-seq performed on a series of biobanked specimens identified thousands of chromatin accessible regions with hundreds being highly coordinated with gene expression. More than 4,700 of these chromatin accessible regions were transcribed in newly diagnosed myelomas from the CoMMpass trial. Regulatory element activity alone recapitulated myeloma gene expression subtypes, and in particular myeloma subtypes with immunoglobulin heavy chain translocations were defined by transcription of distal regulatory elements. Moreover, enhancer activity predicted oncogene expression implicating gene regulatory mechanisms in aggressive myeloma.Conclusions:These data demonstrate the feasibility of using biobanked specimens for retrospective studies of the myeloma epigenome and illustrate the unique enhancer landscapes of myeloma subtypes that are coupled to gene expression and disease progression.

Enrichment of transcription factor consensus binding motifs in regulatory elements predictive of gene expression associated with poor overall survival.

Additional Longitudinal Analysis of Relapsed and Refractory Childhood Solid Tumors.

Correlation of regulatory element transcription and gene expression in CoMMpass specimens.

Table S1. Extended Cohort Annotations. Table S2. Mutational Signatures. Table S3. GISTIC Copy Number Analysis. Table S4. Somatic Variants in Relapsed/Refractory Childhood Solid Tumors (n=184 Patients with T/N WES). Table S5. Clinvar Pathogenic and Likely Pathogenic Germline Variants in Relapsed/Refractory Childhood Solid Tumors(n=184 Patients with T/N WES). Table S6. Gene Lists. Table S7. Log2(TPM+1) Values for the 2,199 Most Variable Genes Expressed Across the Cohort of Relapsed/Refractory Childhood Solid Tumors with RNA-seq (n=245). Table S8. Differential Expression Analysis of 2,199 Most Variable Genes Among Clusters. Table S9. Genes Filtered for COSMIC, Transcription Factors and Potential Drug Targets from Table S8 with log2FC greater than or equal to 2. Table S10. Differential Expression Analysis Among C3a, C3b, and C3c Subclusters. Table S11. Samples Falling in Clusters C3a-C3c and Genes that are Uniformly Highly Expressed in the Clusters. Table S12. Longitudinal Samples.

ATAC-seq data for Emory patients.

Purpose:Duration of first remission is important for the survival of patients with multiple myeloma.Experimental Design:From the CoMMpass study (NCT01454297), 926 patients with newly diagnosed multiple myeloma, characterized by next-generation sequencing, were analyzed to evaluate those who experienced early progressive disease (PD; time to progression, TTP ≤18 months).Results:After a median follow-up of 39 months, early PD was detected in 191/926 (20.6%) patients, 228/926 (24.6%) patients had late PD (TTP >18 months), while 507/926 (54.8%) did not have PD at the current follow-up. Compared with patients with late PD, patients with early PD had a lower at least very good partial response rate (47% vs. 82%, P < 0.001) and more frequently acquired double refractoriness to immunomodulatory drugs (IMiD) and proteasome inhibitors (PI; 21% vs. 8%, P < 0.001). Patients with early PD were at higher risk of death compared with patients with late PD and no PD (HR, 3.65; 95% CI, 2.7–4.93; P < 0.001), showing a dismal median overall survival (32.8 months). In a multivariate logistic regression model, independent factors increasing the early PD risk were TP53 mutation (OR, 3.78, P < 0.001), high lactate dehydrogenase levels (OR, 3.15, P = 0.006), λ-chain translocation (OR, 2.25, P = 0.033), and IGLL5 mutation (OR, 2.15, P = 0.007). Carfilzomib-based induction (OR, 0.15, P = 0.014), autologous stem-cell transplantation (OR, 0.27, P < 0.001), and continuous therapy with PIs and IMiDs (OR, 0.34, P = 0.024) mitigated the risk of early PD.Conclusions:Early PD identifies a high-risk multiple myeloma population. Further research is needed to better identify baseline features predicting early PD and the optimal treatment approaches for patients at risk.

Correlation of chromatin accessibility and gene expression for the 5% most variably expressed genes.

Regulatory elements predictive of gene expression for genes prognostic of overall survival in CoMMpass.

Supplementary Appendix: Additional methods. Carfilzomib-treated patients - Trial design; Table S1. List and classification method of the analyzed variables; Table S2. Induction treatment classification; Table S3. List of the 21 genes analyzed and mutation frequency; Table S4. Distribution of upfront treatment and ASCT in CT subgroups; Table S5. Distribution of variables in Early PD vs reference population; Figure S1. Multivariate logistic regression model evaluating risk factors associated with death within 24 months; Figure S2. Sub-analysis on patients with or without baseline del(17p) and/or TP53 mutation; Figure S3. TP53 and IGLL5 mutations at diagnosis and at first relapse in available longitudinal samples; Figure S4. Epanechnikov kernel-smoothed estimated hazard rates of progressive disease (PD) over time; Figure S5. Comparison of Seq-FISH and conventional FISH in a subgroup of patients enrolled in clinical trials and analyzed in the same centralized laboratory.

Regulatory element transcription in newly diagnosed myelomas from CoMMpass.

Gene set enrichment analysis of transcribed regulatory elements in CoMMpass.

Children with treatment-refractory or relapsed (R/R) tumors face poor prognoses. As the genomic underpinnings driving R/R disease are not well defined, we describe here the genomic and transcriptomic landscapes of R/R solid tumors from 202 patients enrolled in Beat Childhood Cancer Consortium clinical trials. Tumor mutational burden (TMB) was elevated relative to untreated tumors at diagnosis, with one-third of tumors classified as having a pediatric high TMB. Prior chemotherapy exposure influenced the mutational landscape of these R/R tumors, with more than 40% of tumors demonstrating mutational signatures associated with platinum or temozolomide chemotherapy and two tumors showing treatment-associated hypermutation. Immunogenomic profiling found a heterogenous pattern of neoantigen and MHC class I expression and a general absence of immune infiltration. Transcriptional analysis and functional gene set enrichment analysis identified cross-pathology clusters associated with development, immune signaling, and cellular signaling pathways. While the landscapes of these R/R tumors reflected those of their corresponding untreated tumors at diagnosis, important exceptions were observed, suggestive of tumor evolution, treatment resistance mechanisms, and mutagenic etiologies of treatment.Significance:Tumor heterogeneity, chemotherapy exposure, and tumor evolution contribute to the molecular profiles and increased mutational burden that occur in treatment-refractory and relapsed childhood solid tumors.

Supplementary Table 2 from Identification of Copy Number Abnormalities and Inactivating Mutations in Two Negative Regulators of Nuclear Factor-κB Signaling Pathways in Waldenström's Macroglobulinemia

Supplementary Table 3 from Identification of Copy Number Abnormalities and Inactivating Mutations in Two Negative Regulators of Nuclear Factor-κB Signaling Pathways in Waldenström's Macroglobulinemia

Supplementary Table 7 from Identification of Copy Number Abnormalities and Inactivating Mutations in Two Negative Regulators of Nuclear Factor-κB Signaling Pathways in Waldenström's Macroglobulinemia

Waldenström's macroglobulinemia (WM) is a distinct clinicobiological entity defined as a B-cell neoplasm characterized by a lymphoplasmacytic infiltrate in bone marrow (BM) and IgM paraprotein production. Cytogenetic analyses were historically limited by difficulty in obtaining tumor metaphases, and the genetic basis of the disease remains poorly defined. Here, we performed a comprehensive analysis in 42 WM patients by using a high-resolution, array-based comparative genomic hybridization approach to unravel the genetic mechanisms associated with WM pathogenesis. Overall, 83% of cases have chromosomal abnormalities, with a median of three abnormalities per patient. Gain of 6p was the second most common abnormality (17%), and its presence was always concomitant with 6q loss. A minimal deleted region, including MIRN15A and MIRN16-1, was delineated on 13q14 in 10% of patients. Of interest, we reported biallelic deletions and/or inactivating mutations with uniparental disomy in tumor necrosis factor (TNF) receptor–associated factor 3 and TNFα-induced protein 3, two negative regulators of the nuclear factor-κB (NF-κB) signaling pathway. Furthermore, we confirmed the association between TRAF3 inactivation and increased transcriptional activity of NF-κB target genes. Mutational activation of the NF-κB pathway, which is normally activated by ligand receptor interactions within the BM microenvironment, highlights its biological importance, and suggests a therapeutic role for inhibitors of NF-κB pathway activation in the treatment of WM. [Cancer Res 2009;69(8):3579–88]

Supplementary Table 6 from Identification of Copy Number Abnormalities and Inactivating Mutations in Two Negative Regulators of Nuclear Factor-κB Signaling Pathways in Waldenström's Macroglobulinemia

Supplementary Figure Legends 1-2 from Identification of Copy Number Abnormalities and Inactivating Mutations in Two Negative Regulators of Nuclear Factor-κB Signaling Pathways in Waldenström's Macroglobulinemia

Supplementary Table 1 from Identification of Copy Number Abnormalities and Inactivating Mutations in Two Negative Regulators of Nuclear Factor-κB Signaling Pathways in Waldenström's Macroglobulinemia

Supplementary Figure 1 from Identification of Copy Number Abnormalities and Inactivating Mutations in Two Negative Regulators of Nuclear Factor-κB Signaling Pathways in Waldenström's Macroglobulinemia

Supplementary Figure 2 from Identification of Copy Number Abnormalities and Inactivating Mutations in Two Negative Regulators of Nuclear Factor-κB Signaling Pathways in Waldenström's Macroglobulinemia

Supplementary Table 5 from Identification of Copy Number Abnormalities and Inactivating Mutations in Two Negative Regulators of Nuclear Factor-κB Signaling Pathways in Waldenström's Macroglobulinemia

Supplementary Table 4 from Identification of Copy Number Abnormalities and Inactivating Mutations in Two Negative Regulators of Nuclear Factor-κB Signaling Pathways in Waldenström's Macroglobulinemia

The lack of samples for generating standardized DNA datasets for setting up a sequencing pipeline or benchmarking the performance of different algorithms limits the implementation and uptake of cancer genomics. Here, we describe reference call sets obtained from paired tumor–normal genomic DNA (gDNA) samples derived from a breast cancer cell line—which is highly heterogeneous, with an aneuploid genome, and enriched in somatic alterations—and a matched lymphoblastoid cell line. We partially validated both somatic mutations and germline variants in these call sets via whole-exome sequencing (WES) with different sequencing platforms and targeted sequencing with >2,000-fold coverage, spanning 82% of genomic regions with high confidence. Although the gDNA reference samples are not representative of primary cancer cells from a clinical sample, when setting up a sequencing pipeline, they not only minimize potential biases from technologies, assays and informatics but also provide a unique resource for benchmarking ‘tumor-only’ or ‘matched tumor–normal’ analyses.

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

Purpose: Multiple myeloma is a malignancy of plasma cells. Extensive genetic and transcriptional characterization of myeloma has identified subtypes with prognostic and therapeutic implications. In contrast, relatively little is known about the myeloma epigenome. Experimental Design: CD138+CD38+ myeloma cells were isolated from fresh bone marrow aspirate or the same aspirate after freezing for 1–6 months. Gene expression and chromatin accessibility were compared between fresh and frozen samples by RNA sequencing (RNA-seq) and assay for transpose accessible chromatin sequencing (ATAC-seq). Chromatin accessible regions were used to identify regulatory RNA expression in more than 700 samples from newly diagnosed patients in the Multiple Myeloma Research Foundation CoMMpass trial (NCT01454297). Results: Gene expression and chromatin accessibility of cryopreserved myeloma recapitulated that of freshly isolated samples. ATAC-seq performed on a series of biobanked specimens identified thousands of chromatin accessible regions with hundreds being highly coordinated with gene expression. More than 4,700 of these chromatin accessible regions were transcribed in newly diagnosed myelomas from the CoMMpass trial. Regulatory element activity alone recapitulated myeloma gene expression subtypes, and in particular myeloma subtypes with immunoglobulin heavy chain translocations were defined by transcription of distal regulatory elements. Moreover, enhancer activity predicted oncogene expression implicating gene regulatory mechanisms in aggressive myeloma. Conclusions: These data demonstrate the feasibility of using biobanked specimens for retrospective studies of the myeloma epigenome and illustrate the unique enhancer landscapes of myeloma subtypes that are coupled to gene expression and disease progression.

Venetoclax is a highly potent, selective BCL2 inhibitor capable of inducing apoptosis in cells dependent on BCL2 for survival. Most myeloma is MCL1-dependent; however, a subset of myeloma enriched for translocation t(11;14) is codependent on BCL2 and thus sensitive to venetoclax. The biology underlying this heterogeneity remains poorly understood. We show that knockdown of cyclin D1 does not induce resistance to venetoclax, arguing against a direct role for cyclin D1 in venetoclax sensitivity. To identify other factors contributing to venetoclax response, we studied a panel of 31 myeloma cell lines and 25 patient samples tested for venetoclax sensitivity. In cell lines, we corroborated our previous observation that BIM binding to BCL2 correlates with venetoclax response and further showed that knockout of BIM results in decreased venetoclax sensitivity. RNA-sequencing analysis identified expression of B-cell genes as enriched in venetoclax-sensitive myeloma, although no single gene consistently delineated sensitive and resistant cells. However, a panel of cell surface makers correlated well with ex vivo prediction of venetoclax response in 21 patient samples and may serve as a biomarker independent of t(11;14). Assay for transposase-accessible chromatin sequencing of myeloma cell lines also identified an epigenetic program in venetoclax-sensitive cells that was more similar to B cells than that of venetoclax-resistant cells, as well as enrichment for basic leucine zipper domain–binding motifs such as BATF. Together, these data indicate that remnants of B-cell biology are associated with BCL2 dependency and point to novel biomarkers of venetoclax-sensitive myeloma independent of t(11;14).

Multiple myeloma (MM) prognosis incorporates a variety of metrics including treatment received, clinical factors, and genomic characteristics, with several specific genomic features predicting for shorter progression free survival (PFS). No study to date has integrated genomic data from a systems view, incorporating interactions between biomarkers in a network. We use a geometric network analysis that integrates complex interactions to characterize patterns of biological behavior not captured by individual genomic events. The methodology is mathematically well-defined and has no fitting parameters. We hypothesized that such a systems mathematical approach applied to gene interaction networks may delineate biologically relevant MM subtypes and potential new therapeutic targets. We overlaid RNA-Seq and copy number alteration data from the MMRF CoMMpass study (IA19) on a gene interactome derived from the Human Protein Reference Database using a novel graph metric of network robustness — Ollivier-Ricci curvature (ORC). Results were clustered, with the optimal number determined via silhouette score. Survival analysis for PFS was performed employing Kaplan-Meier and log-rank tests. A differential gene expression analysis between high and low risk groups was conducted. Differences in scalar ORC between the low-risk and high-risk groups were examined and contextualized using a pathway analysis. Pathway analysis was performed using the Broad Institute’s Gene Set Enrichment Analysis tool and the pathways used are from the hallmark gene set from the Human MSigDB collection. The dataset included 659 patients and the incorporated protein-protein interactions resulted in a network with 8,468 nodes and 33,695 edges. The ORC analysis discovered 6 clusters, with specific genomic features being associated with clusters predicting for long [hyperdiploidy, t(11:14)], and short [t(4;14), MAF/MAFB translocations] PFS. A differential gene expression analysis comparing the high risk and low risk groups identified 118 key genes. These genes were associated with various pathways both known and unknown to be associated with multiple myeloma, including mitotic spindle, DNA repair, inflammatory response, and the P53 pathways. Further scalar curvature analysis showed differences in the apoptosis, TGF beta signaling, and other signaling pathways. In summary, we applied the geometric network analysis tool ORC to multi-omics data in MM represented as biological networks to identify individuals at high risk of short PFS and relevant biological correlates. Decreased robustness of signaling near immune-related genes was associated with shorter survival, highlighting the plausible utility of using these methods to uncover new biological insights. Citation Format: Anish K. Simhal, Kylee H. Maclachlan, Rena Elkin, Jiening Zhu, Saad Z. Usmani, Jonathan J. Keats, Larry Norton, Joseph O. Deasy, Jung Hun Oh, Allen Tannenbaum. Protein network analysis uncovers a poor-survival subtype in multiple myeloma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2061.

Multiple myeloma (MM) is a plasma cell malignancy that is often driven by chromosomal translocations. In particular, patients with t(4;14)-positive disease have worse prognosis compared to other MM subtypes. Herein, we demonstrated that t(4;14)-positive cells are highly dependent on the mevalonate (MVA) pathway for survival. Moreover, we showed that this metabolic vulnerability is immediately actionable, as inhibiting the MVA pathway with a statin preferentially induced apoptosis in t(4;14)-positive cells. In response to statin treatment, t(4;14)-positive cells activated the integrated stress response (ISR), which was augmented by co-treatment with bortezomib, a proteasome inhibitor. We identified that t(4;14)-positive cells depend on the MVA pathway for the synthesis of geranylgeranyl pyrophosphate (GGPP), as exogenous GGPP fully rescued statin-induced ISR activation and apoptosis. Inhibiting protein geranylgeranylation similarly induced the ISR in t(4;14)-positive cells, suggesting that this subtype of MM depends on GGPP, at least in part, for protein geranylgeranylation. Notably, fluvastatin treatment synergized with bortezomib to induce apoptosis in t(4;14)-positive cells and potentiated the anti-tumor activity of bortezomib in vivo. Our data implicate the t(4;14) translocation as a biomarker of statin sensitivity and warrant further clinical evaluation of a statin in combination with bortezomib for the treatment of t(4;14)-positive disease.

Multiple myeloma (MM) is a plasma-cell neoplasm that is treated with high-dose chemotherapy, autologous stem cell transplant (ASCT) and long-term immunomodulatory drug (IMiD) maintenance. The presence of somatic mutations in the peripheral blood is termed clonal hematopoiesis of indeterminate potential (CHIP) and is associated with adverse outcomes. Targeted sequencing of the stem cell product from 629 MM patients treated by ASCT at the Dana-Farber Cancer Institute (2003–2011) detects CHIP in 136/629 patients (21.6%). The most commonly mutated genes are DNMT3A, TET2, TP53, ASXL1 and PPM1D. Twenty-one from fifty-six patients (3.3%) receiving first-line IMiD maintenance develop a therapy-related myeloid neoplasm (TMN). However, regardless of CHIP status, the use of IMiD maintenance associates with improved PFS and OS. In those not receiving IMiD maintenance, CHIP is associated with decreased overall survival (OS) (HR:1.34, p = 0.02) and progression free survival (PFS) (HR:1.45, p, Multiple myeloma (MM) is treated with induction chemotherapy, autologous stem cell transplant (ASCT) and long-term immunomodulatory drug (IMiD) maintenance. Here, the authors show that the presence of clonal haematopoiesis of indeterminate potential (CHIP) at time of ASCT is associated with adverse outcomes in MM patients.

The type I Melanoma Antigen Gene (MAGE) A3 is a functional target associated with survival and proliferation in multiple myeloma (MM). To investigate the mechanisms of these oncogenic functions, we performed gene expression profiling (GEP) of p53 wild-type human myeloma cell lines (HMCL) after MAGE-A knockdown, which identified a set of 201 differentially expressed genes (DEG) associated with apoptosis, DNA repair, and cell cycle regulation. MAGE knockdown increased protein levels of pro-apoptotic BIM and of the endogenous cyclin-dependent kinase (CDK) inhibitor p21Cip1. Depletion of MAGE-A in HMCL increased sensitivity to the alkylating agent melphalan but not to proteasome inhibition. High MAGEA3 was associated with the MYC and Cell Cycling clusters defined by a network model of GEP data from the CoMMpass database of newly diagnosed, untreated MM patients. Comparative analysis of CoMMpass subjects based on high or low MAGEA3 expression revealed a set of 6748 DEG that also had significant functional associations with cell cycle and DNA replication pathways, similar to that observed in HMCL. High MAGEA3 expression correlated with shorter overall survival after melphalan chemotherapy and autologous stem cell transplantation (ASCT). These results demonstrate that MAGE-A3 regulates Bim and p21Cip1 transcription and protein expression, inhibits apoptosis, and promotes proliferation.

Homologous recombination repair deficiency (HRD) is a common driver of tumorigenesis that carries therapeutic implications, particularly in breast, ovarian, prostate, and pancreatic cancers. The influence of genetic ancestry on the incidence of HRD in these tumors remains to be defined. Previous studies have reported a higher incidence of variants of unknown significance (VUS) in individuals of non-European ancestry. Here, we evaluated the frequency of HR-deficiency within a diverse cohort of 566 patients with breast, prostate, ovarian, or pancreatic cancer. Genetic ancestry was estimated from constitutional whole exome sequencing data using a single nucleotide polymorphism weight model containing five ancestral populations: European, Native American, African, East Asian, and South Asian. Frequencies of clinical genetic testing, germline variants in HR-genes, and tumor HRD were compared between individuals of admixed Native American and European ancestry (n=82) and those of European ancestry (n=378). Admixed Native American/European individuals had a younger age at diagnosis compared to individuals of European ancestry (median 52.4 years (range, 20.8-83.6) vs 62.9 years (range, 20.9-87.9), p Citation Format: Sara A. Byron, Guangfa Zhang, Kimber Slater, Jiaming Zhang, Tyler Izatt, Bryce Turner, Xiao-Yu Xia, Rick A. Kittles, Jonathan J. Keats, Jeffrey M. Trent, Nicholas J. Schork, Lorna Rodriguez-Rodriguez. Influence of patient ancestry on homologous recombination repair deficiency in cancer [abstract]. In: Proceedings of the 15th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2022 Sep 16-19; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr C039.

The accessibility of cell surface proteins makes them tractable for targeting by cancer immunotherapy, but identifying suitable targets remains challenging. Here we describe plasma membrane profiling of primary human myeloma cells to identify an unprecedented number of cell surface proteins of a primary cancer. We used a novel approach to prioritize immunotherapy targets and identified a cell surface protein not previously implicated in myeloma, semaphorin-4A (SEMA4A). Using knock-down by short-hairpin RNA and CRISPR/nuclease-dead Cas9 (dCas9), we show that expression of SEMA4A is essential for normal myeloma cell growth in vitro, indicating that myeloma cells cannot downregulate the protein to avoid detection. We further show that SEMA4A would not be identified as a myeloma therapeutic target by standard CRISPR/Cas9 knockout screens because of exon skipping. Finally, we potently and selectively targeted SEMA4A with a novel antibody–drug conjugate in vitro and in vivo.

Description, Integrative multiomics analysis of myeloma identifies 12 disease subtypes defined by specific patterns of genetic alterations., The remarkable genetic heterogeneity of multiple myeloma poses a substantial challenge for proper prognostication and clinical management of patients. Here, we introduce MM-PSN, the first multiomics patient similarity network of myeloma. MM-PSN enabled accurate dissection of the genetic and molecular landscape of the disease and determined 12 distinct subgroups defined by five data types generated from genomic and transcriptomic profiling of 655 patients. MM-PSN identified patient subgroups not previously described defined by specific patterns of alterations, enriched for specific gene vulnerabilities, and associated with potential therapeutic options. Our analysis revealed that co-occurrence of t(4;14) and 1q gain identified patients at significantly higher risk of relapse and shorter survival as compared to t(4;14) as a single lesion. Furthermore, our results show that 1q gain is the most important single lesion conferring high risk of relapse and that it can improve on the current International Staging Systems (ISS and R-ISS).

Multiple myeloma is a treatable, but currently incurable, hematological malignancy of plasma cells characterized by diverse and complex tumor genetics for which precision medicine approaches to treatment are lacking. The MMRF CoMMpass study is a longitudinal, observational clinical study of newly diagnosed multiple myeloma patients where tumor samples are characterized using whole genome, exome, and RNA sequencing at diagnosis and progression, and clinical data is collected every three months. Analyses of the baseline cohort identified genes that are the target of recurrent gain- and loss-of-function events. Consensus clustering identified 8 and 12 unique copy number and expression subtypes of myeloma, respectively, identifying high- risk genetic subtypes and elucidating many of the molecular underpinnings of these unique biological groups. Analysis of serial samples showed 25.5% of patients transition to a high-risk expression subtype at progression. We observed robust expression of immunotherapy targets in this subtype, suggesting a potential therapeutic option.

Minimal residual disease (MRD) tracking, by next generation sequencing of immunoglobulin sequences, is moving towards clinical implementation in multiple myeloma. However, there is only sparse information available to address whether clonal sequences remain stable for tracking over time, and to what extent light chain sequences are sufficiently unique for tracking. Here, we analyzed immunoglobulin repertoires from 905 plasma cell myeloma and healthy control samples, focusing on the third complementarity determining region (CDR3). Clonal heavy and/or light chain expression was identified in all patients at baseline, with one or more subclones related to the main clone in 3.2%. In 45 patients with 101 sequential samples, the dominant clonal CDR3 sequences remained identical over time, despite differential clonal evolution by whole exome sequencing in 49% of patients. The low frequency of subclonal CDR3 variants, and absence of evolution over time in active multiple myeloma, indicates that tumor cells at this stage are not under selective pressure to undergo antibody affinity maturation. Next, we establish somatic hypermutation and non-templated insertions as the most important determinants of light chain clonal uniqueness, identifying a potentially trackable sequence in the majority of patients. Taken together, we show that dominant clonal sequences identified at baseline are reliable biomarkers for long-term tracking of the malignant clone, including both IGH and the majority of light chain clones.

Key Points Teriflunomide, the active metabolite of leflunomide, downregulates c-Myc expression through inhibition of PIM kinases. Leflunomide together with lenalidomide significantly extended survival in an in vivo MM model.

Multiple myeloma is a malignancy of antibody-secreting plasma cells. Most patients benefit from current therapies, however, 20% of patients relapse or die within two years and are deemed high risk. Here we analyze structural variants from 795 newly-diagnosed patients as part of the CoMMpass study. We report translocations involving the immunoglobulin lambda (IgL) locus are present in 10% of patients, and indicative of poor prognosis. This is particularly true for IgL-MYC translocations, which coincide with focal amplifications of enhancers at both loci. Importantly, 78% of IgL-MYC translocations co-occur with hyperdiploid disease, a marker of standard risk, suggesting that IgL-MYC-translocated myeloma is being misclassified. Patients with IgL-translocations fail to benefit from IMiDs, which target IKZF1, a transcription factor that binds the IgL enhancer at some of the highest levels in the myeloma epigenome. These data implicate IgL translocation as a driver of poor prognosis which may be due to IMiD resistance., Multiple myeloma is frequently characterised by translocation of genes next to the immunoglobulin heavy chain locus. In this study, the authors sequence a large cohort of high risk myeloma samples and find translocations of cMyc to the immunoglobulin heavy chain locus and this is associated with poor prognosis.

Patients with newly diagnosed multiple myeloma (NDMM) with high-risk disease are in need of new treatment strategies to improve the outcomes. Multiple clinical, cytogenetic, or gene expression features have been used to identify high-risk patients, each of which has significant weaknesses. Inclusion of molecular features into risk stratification could resolve the current challenges. In a genome-wide analysis of the largest set of molecular and clinical data established to date from NDMM, as part of the Myeloma Genome Project, we have defined DNA drivers of aggressive clinical behavior. Whole-genome and exome data from 1273 NDMM patients identified genetic factors that contribute significantly to progression free survival (PFS) and overall survival (OS) (cumulative R2 = 18.4% and 25.2%, respectively). Integrating DNA drivers and clinical data into a Cox model using 784 patients with ISS, age, PFS, OS, and genomic data, the model has a cumlative R2 of 34.3% for PFS and 46.5% for OS. A high-risk subgroup was defined by recursive partitioning using either a) bi-allelic TP53 inactivation or b) amplification (≥4 copies) of CKS1B (1q21) on the background of International Staging System III, comprising 6.1% of the population (median PFS = 15.4 months; OS = 20.7 months) that was validated in an independent dataset. Double-Hit patients have a dire prognosis despite modern therapies and should be considered for novel therapeutic approaches.