Faculty Bibliography

Large-scale analyses of genomic data from newly-diagnosed multiple myeloma patients (ndMM) have been undertaken, however, large-scale analysis of relapsed/refractory multiple myeloma (rrMM) has not been performed. We hypothesize that somatic variants chronicle the therapeutic exposures and clonal structure of myeloma from ndMM to rrMM stages. We generated whole genome sequencing (WGS) data from 418 tumors (386 patients) derived from six rrMM clinical trials and compared them with WGS from 198 unrelated ndMM patients in a population-based case-control fashion. We identified significantly enriched events at the rrMM stage, including drivers (DUOX2, EZH2, TP53), biallelic inactivation (TP53), non-coding mutations in bona fide drivers (TP53BP1, BLM), copy number aberrations (CNA; 1qGain, 17pLOH) and double-hit events (Amp1q-ISS3, 1qGain-17pLOH). Mutational signature analysis identified a subclonal defective mismatch repair signature enriched in rrMM and highly active in high mutation burden tumors, a likely feature of therapy-associated expanding subclones. Further analysis focused on the association of genomic aberrations enriched at different stages of resistance to immunomodulatory agent (IMiDsÃ’)-based therapy. This analysis revealed that TP53, DUOX2, 1qGain and 17pLOH increased in prevalence from ndMM to lenalidomide (LEN)- to pomalidomide (POM)-resistant stages while enrichment of MAML3 along with IGL and MYC translocations distinguished POM from the LEN subgroup. Genomic drivers associated with rrMM are those that confer clonal selective advantage under therapeutic pressure. Their role in therapy evasion should be evaluated further in longitudinal patient samples, to confirm these associations with the evolution of clinical resistance and to identify molecular subsets of rrMM for the development of targeted therapies.

Alternative splicing plays a pivotal role in tumorigenesis and proliferation. However, its pattern and pathogenic role has not been systematically analyzed in multiple myeloma or its subtypes. Alternative splicing profiles for 598 newly diagnosed myeloma patients with comprehensive genomic annotation identified primary translocations, 1q amplification, and DIS3 events to have more differentially spliced events than those without. Splicing levels were correlated with expression of splicing factors. Moreover, the non-homologous end joining pathway was an independent factor that was highly associated with splicing frequency as well as an increased number of structural variants. We therefore identify an axis of high-risk disease encompassing expression of the non-homologous end joining pathway, increase structural variants, and increased alternative splicing that are linked together. This indicates a joint pathogenic role for DNA damage response and alternative RNA processing in myeloma.

Some cytogenetic abnormalities (CAs) are associated with poorer prognosis in multiple myeloma (MM); proteasome inhibitors appear to benefit patients with high-risk CAs. We evaluated 2247 MM patients from the TOURMALINE-MM1/-MM2/-MM3/-MM4 trials to assess the PFS benefit of ixazomib plus lenalidomide-dexamethasone (Rd) vs placebo-Rd (TOURMALINE-MM1/-MM2) or ixazomib vs placebo (TOURMALINE-MM3/-MM4) in specific high-risk CAs. After a pooled median follow-up of 25.6 months, the hazard ratio (HR) for PFS with ixazomib- vs placebo-based therapy for high-risk patients was 0.74 (95% confidence interval [CI]: 0.59-0.93; median PFS [mPFS] 17.8 vs 13.2 months), and 0.70 (95% CI: 0.62-0.80; mPFS 26.3 vs 17.6 months) for complementary standard-risk patients. The HR for expanded high-risk patients was 0.75 (95% CI: 0.64-0.87; mPFS 18.1 vs 14.1 months), and 0.71 (95% CI: 0.59-0.85; mPFS 36.1 vs 21.4 months) for complementary standard-risk patients. The HR for PFS with ixazomib- vs placebo-based therapy was 0.68 in patients with t(4;14) (95% CI: 0.48-0.96; mPFS 22.4 vs 13.2 months), and 0.77 for patients with amp1q21 (95% CI: 0.63-0.93; mPFS 18.8 vs 14.5 months). A PFS benefit was demonstrated with ixazomib- vs placebo-based therapy regardless of cytogenetic status, with greatest benefit observed in patients with t(4;14) and amp1q21.

Early alterations within the bone marrow microenvironment that contribute to the progression of multiple myeloma (MM) from its precursor stages could hold the key to identifying novel therapeutic approaches, yet the intrinsic variability in cellular populations between patients together with differences in sample processing and analysis methods have made it difficult to identify consistent changes between datasets. Here, we used single-cell RNA sequencing of BM cells from precursor stages, monoclonal gammopathy of unknown significance (MGUS), smoldering MM (SMM) and newly diagnosed MM and analyzed our data in combination with a previously published dataset that used a similar patient population and sample processing. Despite vast interpatient heterogeneity, some alterations were seen consistently in both datasets. We identified changes in immune cell populations as disease progressed that were characterized by a substantial decrease in memory and naïve CD4 T cells and an increase in CD8+ effector T cells and T-regulatory cells. These alterations were further accompanied by an enrichment of non-clonal memory B cells and an increase of CD14 and CD16 monocytes in MM compared to its precursor stages. These results provide crucial information of the immune changes associated with the progression to clinical MM and can help to develop immune based strategies for patient stratification and early therapeutic intervention.

IgD myeloma is a subtype often considered to have adverse features and inferior survival but there is a paucity of data from large clinical studies. We compare the clinical characteristics and outcomes of IgD myeloma patients from UK Phase III myeloma trials analysed in two groups; old (1980-2002) and recent (2002-2016) clinical trials, based on the time of adoption of novel myeloma therapies. IgD myeloma patients comprised 44/2789 (1.6%) and 70/5773 (1.2%) of the old and recent trials respectively. Overall, IgD myeloma was associated with male predominance, low-level paraproteinemia (<10g/l) and lambda light chain preference. The frequency of ultra-high risk cytogenetics was similar in IgD myeloma compared with other subtypes (4.3% vs 5.3%, p>0.99). Despite the old trial series being a younger group (median age: 59 years vs 63 years, p=0.015), there was a higher frequency of bone lesions, advanced stage at diagnosis, worse performance status and severe renal impairment compared with the recent trials. Furthermore, the early mortality rate was significantly higher for the old trial series (20% vs 4%, p=0.01). The overall response rate following induction therapy was significantly higher in the recent trials (89% vs 43%, p<0.0001) and this was consistent with improved median overall survival (48 months; 95% CI 35-67 months vs 22 months, 95% CI 16-29 months). Survival outcomes for IgD myeloma have significantly improved and are now comparable to other myeloma types due to earlier diagnosis, novel therapies and improved supportive care. (Myeloma IX International Standard Randomised Controlled Trial Number: 68454111, Myeloma XI International Standard Randomised Controlled Trial Number: 49407852).

Deciphering Multiple Myeloma evolution in the whole bone marrow is key to inform curative strategies. Here, we perform spatial-longitudinal whole-exome sequencing, including 140 samples collected from 24 Multiple Myeloma patients during up to 14 years. Applying imaging-guided sampling we observe three evolutionary patterns, including relapse driven by a single-cell expansion, competing/co-existing sub-clones, and unique sub-clones at distinct locations. While we do not find the unique relapse sub-clone in the baseline focal lesion(s), we show a close phylogenetic relationship between baseline focal lesions and relapse disease, highlighting focal lesions as hotspots of tumor evolution. In patients with ≥3 focal lesions on positron-emission-tomography at diagnosis, relapse is driven by multiple distinct sub-clones, whereas in other patients, a single-cell expansion is typically seen (p < 0.01). Notably, we observe resistant sub-clones that can be hidden over years, suggesting that a prerequisite for curative therapies would be to overcome not only tumor heterogeneity but also dormancy.

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/OBJECTIVE:We designed a comprehensive multiple myeloma (MM) targeted sequencing panel to identify common genomic abnormalities in a single assay and validated it against known standards. EXPERIMENTAL DESIGN/METHODS:The panel comprised 228 genes/exons for mutations, 6 regions for translocations, and 56 regions for copy number abnormalities (CNAs). Toward panel validation, targeted sequencing was conducted on 233 patient samples and further validated using clinical fluorescence in situ hybridization (FISH) (translocations), multiplex ligation probe analysis (MLPA) (CNAs), whole genome sequencing (WGS) (CNAs, mutations, translocations) or droplet digital PCR (ddPCR) of known standards (mutations). RESULTS:Canonical IgH translocations were detected in 43.2% of patients by sequencing, and aligned with FISH except for one patient. CNAs determined by sequencing and MLPA for 22 regions were comparable in 103 samples and concordance between platforms was R2=0.969. VAFs for 74 mutations were compared between sequencing and ddPCR with concordance of R2=0.9849. CONCLUSIONS:In summary, we have developed a targeted sequencing panel that is as robust or superior to FISH and WGS. This molecular panel is cost effective, comprehensive, clinically actionable and can be routinely deployed to assist risk stratification at diagnosis or post-treatment to guide sequencing of therapies.

Chromosomal translocations are important drivers of hematological malignancies whereby proto-oncogenes are activated by juxtaposition with super-enhancers, often called enhancer hijacking. We analysed the epigenomic consequences of rearrangements between the super-enhancers of the immunoglobulin heavy locus (IGH) and proto-oncogene CCND1 that are common in B cell malignancies. By integrating BLUEPRINT epigenomic data with DNA breakpoint detection, we characterised the normal chromatin landscape of the human IGH locus and its dynamics after pathological genomic rearrangement. We detected an H3K4me3 broad domain (BD) within the IGH locus of healthy B cells that was absent in samples with IGH-CCND1 translocations. The appearance of H3K4me3-BD over CCND1 in the latter was associated with overexpression and extensive chromatin accessibility of its gene body. We observed similar cancer-specific H3K4me3-BDs associated with super-enhancer hijacking of other common oncogenes in B cell (MAF, MYC and FGFR3/NSD2) and in T-cell malignancies (LMO2, TLX3 and TAL1). Our analysis suggests that H3K4me3-BDs can be created by super-enhancers and supports the new concept of epigenomic translocation, where the relocation of H3K4me3-BDs from cell identity genes to oncogenes accompanies the translocation of super-enhancers.