Faculty Bibliography

Multiple myeloma (MM) is consistently preceded by monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). While these precursor conditions are asymptomatic, they are not entirely benign and carry a lifelong risk of progression to MM. Unlike other cancers defined by pathology, malignant transformation from MGUS or SMM to MM has so far relied on demonstration of clinical end-organ damage as morphology and cytogenetics cannot reliably distinguish them. In this study, using genomic data from 374 patients with MGUS or SMM (277 training, 97 validation), to our knowledge, we demonstrate for the first time the ability to identify malignant transformation in MGUS and SMM. We introduce the concept of genomic MM and genomic MGUS to differentiate the subsets of MGUS and SMM that are biologically malignant with genomic features indistinguishable from MM from the subset that is premalignant and unlikely to progress to malignancy. Importantly, we find that most SMM has biological features of malignant transformation indistinguishable from MM. As expected, this subset that we consider having genomic MM is associated with a high risk of progression to MM although some patients remained progression-free beyond 5 years. Conversely, 60% of MGUS and 10% of SMM have no evidence of malignant transformation (genomic MGUS), with no progression during follow-up. Integration of genomic features with the 2/20/20 International Myeloma Working Group model significantly improved the prediction of progression among genomic MM. These findings support the use of genomic criteria to refine the classification and the risk stratification in myeloma precursor conditions.

Multiple myeloma evolution is characterized by the accumulation of genomic drivers over time. To unravel this timeline and its impact on clinical outcomes, we analyzed 421 whole-genome sequences from 382 patients. Using clock-like mutational signatures, we estimated a time lag of two to four decades between the initiation of events and diagnosis. We demonstrate that odd-numbered chromosome trisomies in patients with hyperdiploidy can be acquired simultaneously with other chromosomal gains (for example, 1q gain). We show that hyperdiploidy is acquired after immunoglobulin heavy chain translocation when both events co-occur. Finally, patients with early 1q gain had adverse outcomes similar to those with 1q amplification (>1 extra copy), but fared worse than those with late 1q gain. This finding underscores that the 1q gain prognostic impact depends more on the timing of acquisition than on the number of copies gained. Overall, this study contributes to a better understanding of the life history of myeloma and may have prognostic implications.

Functional high-risk (FHR) multiple myeloma (MM) is defined as an unexpected, early relapse (ER) of disease in the absence of baseline molecular or clinical risk factors (RF), making FHR MM inherently dependent on which RFs were assessed at diagnosis, but also on what treatment patients received. To establish the true incidence of FHR, we analysed uniformly treated, transplant-eligible (TE) patients from the UK NCRI Myeloma-XI trial that had been profiled for IMS/IMWG defined high-risk cytogenetic aberrations (HRCA) and the SKY92 gene expression HR signature (GEP-HR). 135 TE MyXI patients meeting these criteria were studied, with a median follow-up of 88 months. 25 patients (18.5%) experienced ER, defined as relapse <18 months from maintenance randomization post-autologous stem-cell transplantation. Hereof, 15 (60%) were classified as IMS/IMWG-HR at diagnosis, of whom 8 were also GEP-HR. Another 6 patients were GEP-HR only and would have been missed by IMS/IMWG-HR. Among 4 patients with both IMS/IMWG- & GEP-standard risk (SR), 2 had isolated HR markers at diagnosis, leaving only 2 patients (8% of ER; 1.5% of all) truly meeting all FHR criteria. The combination of IMS/IMWG-HR and GEP-HR profiling identified 84% of ER, and differentiated long-term outcome across all 135 patients: co-occurring IMS/IMWG-HR and GEP-HR was associated with very short overall survival compared to the absence of both (HR=13.1, 95%-CI: 6.5-26.1, P<0.0001), followed by GEP-HR only (HR=5.1, 95%-CI: 2.4-11.1, P<0.0001) and IMS/IMWG-HR only (HR=3.2, 95%-CI: 1.6-6.2, P=0.0007). Our results support more comprehensive baseline diagnostic profiling to identify those at risk of ER upfront. ISRCTN49407852, NCT01554852.

MOTIVATION/BACKGROUND:Efforts to address health disparities are often limited by the lack of robust computational tools for inferring genetic ancestry by calculating an individual's genetic similarity to continental groups. We have already shown that a preferred alternative to self-described race is using ancestry informative markers (AIMs) that can be classified into ancestral components and used to estimate their similarity to those of known populations to identify continental groups. However, real-world genomic data can present challenges, including limited availability of germline DNA, a small number of AIMs for each sample, and the use of different variant calling software, limiting the application of existing solutions. RESULTS:Here, we describe a novel supervised machine-learning tool AncestryGeni, which infers genetic ancestry for samples with even a hundred markers and is applicable to any genomic data, including exome sequencing (WES) and RNA sequencing (RNA-Seq) data. Applying AncestryGeni to a real-world genomic dataset obtained from the Multiple Myeloma Research Foundation (MMRF) CoMMpass study, we show that it is more accurate than the commonly used FastNGSadmix when using non-standard genomic material. We also demonstrate that when using AncestryGeni, the tumor-derived sequence obtained from WES and RNA-Seq can be a robust data source to accurately estimate an individual's genetic similarity to a continental group. AVAILABILITY AND IMPLEMENTATION/METHODS:AncestryGeni pipeline is available at https://github.com/eelhaik/AncestryGeni/tree/main. SUPPLEMENTARY INFORMATION/BACKGROUND:Supplementary data are available at Bioinformatics online.

A key treatment for patients with multiple myeloma is high-dose melphalan followed by autologous stem cell transplant (ASCT). It can provide a deep response with long-term remission. However, some patients progress quickly, and it is not clear why that is. Here, we performed single-cell RNA and T-cell receptor (TCR) sequencing of the immune microenvironment of 40 patients before and after ASCT to determine if differences in the immune composition could define those who would progress. Clear differences in cell populations were identified in progressors, including increased T-cell infiltration, decreased TCR diversity, and decreased frequency of monocytes and CD56bright NK cells. We identified cell interactions that predicted progression including increased frequency of CD8+ exhausted T cells and stromal cells and decreased frequency of CD56bright NK cells and plasmacytoid dendritic cells. We propose and validate a model of progression that can also be determined by flow cytometry. Together these data highlight the importance of the immune microenvironment in understanding responses to ASCT.

Bispecific antibodies (BsAbs) and chimeric antigen receptor T-cells (CAR-T) are T-cell engagers (TCEs) becoming increasingly important for treatment of multiple myeloma. The purpose of this paper is to review TCE side effects and their management. In doing so, we will demonstrate that outpatient delivery of TCEs can be safe and advantageous for patients and healthcare systems. The initial introduction of TCE therapy has been limited to the inpatient setting due to risk of cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS). These complications, which typically occur in the first few weeks of initiating therapy, are mediated by an exaggerated inflammatory response triggered by TCE binding to tumor cells. BsAb trials have demonstrated a high overall incidence of CRS, though severe cases are rare as is development of ICANS. The incidence and severity of CRS and ICANS seem to be higher with CAR-T therapy. Predictors of development and severity of CRS and ICANS include disease bulk, lymphodepletion strategy, CAR-T construct used, and pattern of expression of the tumor antigen. Prevention strategies include step-up dosing, disease bulk reduction, prophylactic steroid use and premedication. Treatment strategies include the use of steroids and cytokine binders/blockers, such as anti-IL6 and anti-IL1 agents. Effective prophylaxis and management of CRS and ICANS has reduced the impact of these complications and opened the potential for outpatient delivery. Outpatient delivery of TCEs is possible with appropriate preventative strategies, education, and established pathways for prompt transition to inpatient management if needed.

Despite significant improvements in survival of patients with multiple myeloma (MM), outcomes remain heterogeneous, and a significant proportion of patients experience suboptimal outcomes. Importantly, traditional prognostic factors based on data from patients treated with older therapies no longer capture prognosis accurately in the contemporary era of novel triplet or quadruplet therapies. Therefore, risk stratification requires refinement in the context of available and investigational treatment options in routine practice and clinical trials, respectively. The current identification of high-risk MM (HRMM) in routine practice is based on the Revised International Staging System, which stratifies patients using a combination of widely available serum biomarkers and chromosomal abnormalities assessed via fluorescence in situ hybridization. In recent years, a substantial body of evidence concerning additional clinical, biological, and molecular/genomic prognostic factors has accumulated, along with new MM risk stratification tools and consensus reports. The International Myeloma Society, along with the International Myeloma Working Group, convened an Expert Panel with the primary aim of revisiting the definition of HRMM and formulating a practical and data-driven consensus definition, based on new evidence from molecular/genomic assays, updated clinical data, and contemporary risk stratification concepts. The Panel proposes the following Consensus Genomic Staging (CGS) of HRMM which relies upon the presence of at least one of these abnormalities: (1) del(17p), with a cutoff of >20% clonal fraction, and/or TP53 mutation; (2) an IgH translocation including t(4;14), t(14;16), or t(14;20) along with 1q+ and/or del(1p32); (3) monoallelic del(1p32) along with 1q+ or biallelic del(1p32); or (4) β2 microglobulin ≥5.5 mg/L with normal creatinine (<1.2 mg/dL).

Reliable strategies to capture patients at risk of progression from precursor stages of multiple myeloma (MM) to overt disease are still missing. We assembled a comprehensive collection of MM genomic data comprising 1,030 patients (218 with precursor conditions) that we used to identify recurrent coding and non-coding candidate drivers as well as significant hotspots of structural variation. We used those drivers to define and validate a simple 'MM-like' score, which we could use to place patients' tumors on a gradual axis of progression toward active disease. Our MM precursor genomic map provides insights into the time of initiation and cell-of-origin of the disease, order of acquisition of genomic alterations and mutational processes found across the stages of transformation. Taken together, we highlight here the potential of genome sequencing to better inform risk assessment and monitoring of MM precursor conditions.

Ionizing radiotherapy (RT) is a widely used treatment strategy for malignancies. In solid tumors, RT-induced double-strand breaks lead to the accumulation of indels, and their repair by non-homologous end-joining has been linked to the ID8 mutational signature in surviving cells. However, the extent of RT-induced mutagenesis in hematologic malignancies and its impact on their mutational profiles and interplay with commonly used chemotherapies has not yet been explored. Here, we interrogated 580 whole-genome sequence samples (WGS) from patients with large B-cell lymphoma, multiple myeloma, and myeloid neoplasms and identified ID8 only in relapsed disease. Yet, ID8 was detected after exposure to both RT and mutagenic chemotherapy (i.e., platinum and melphalan). Using WGS of single-cell colonies derived from treated lymphoma cells, we revealed a dose-response relationship between RT and platinum and ID8. Finally, using ID8 as a genomic barcode we demonstrate that a single RT-surviving cell may seed distant relapse.

We carried out a single-cell (sc) multiomic analysis on a series of MYD88 mutated Waldenström macroglobulinemia (WM) cases and identified two distinct subtypes of disease, memory B-cell-like (MBC-like) and plasma cell-like (PC-like), based on their expression of key lineage defining genes. Biologically, the subtypes are characterized by their variable capacity to differentiate fully towards a plasma cell (PC) and exhibit unique transcriptomic, chromatin accessibility, and genomic profiles. The MBC-like subtype is unable to differentiate beyond the memory B-cell (MBC) stage, upregulates key MBC genes, and is characterized by upregulated BCR and AKT/mTOR signaling. In contrast, the PC-like subtype can partially differentiate towards a PC, upregulates key PC genes, has enhanced NF-kB signaling, and has an upregulated unfolded protein response. Pseudotime trajectory analysis of combined scRNA-sequencing and scATAC-sequencing supports the variable differentiation capacity of each subtype and implicate key transcription factors SPI1, SPIB, BCL11A, and XBP1 in these features. The existence and generalizability of the two disease subtypes were validated further using hierarchical clustering of bulk RNA-seq data from a secondary set of cases. The biological significance of the subtypes was further established using whole genome sequencing, where it was shown that CXCR4, NIK, and ARID1A mutations occur predominantly in the MBC-like subtype and 6q deletions in the PC-like subtype. We conclude that the variable differentiation blockade seen in WM manifests itself clinically as two disease subtypes with distinct epigenetic, mutational, transcriptional, and clinical features with potential implications for WM treatment strategies.