Understanding the profile of oncogene and tumor suppressor gene mutations with their interactions and impact on the prognosis of multiple myeloma (MM) can improve the definition of disease subsets and identify pathways important in disease pathobiology. Using integrated genomics of 1273 newly diagnosed patients with MM, we identified 63 driver genes, some of which are novel, including ,, ,, and Oncogene mutations are significantly more clonal than tumor suppressor mutations, indicating they may exert a bigger selective pressure. Patients with more driver gene abnormalities are associated with worse outcomes, as are identified mechanisms of genomic instability. Oncogenic dependencies were identified between mutations in driver genes, common regions of copy number change, and primary translocation and hyperdiploidy events. These dependencies included associations with t(4;14) and mutations in, , and; t(11;14) with mutations in and; t(14;16) with mutations in ,, , and; and hyperdiploidy with gain 11q, mutations in , and rearrangements. These associations indicate that the genomic landscape of myeloma is predetermined by the primary events upon which further dependencies are built, giving rise to a nonrandom accumulation of genetic hits. Understanding these dependencies may elucidate potential evolutionary patterns and lead to better treatment regimens.

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 R = 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 R 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.

Emergence of drug resistance to all available therapies is the major challenge to improving survival in myeloma. Cereblon (CRBN) is the essential binding protein of the widely-used IMiD and novel CelMOD drugs in myeloma, as well as certain PROTACs in development for a range of diseases. Using whole genome sequencing data from 455 patients and RNASeq data from 655 patients, including a newly-diagnosed cohort (n=198 WGS, n=437 RNASeq), a lenalidomide (LEN)-refractory cohort (n=203 WGS, n=176 RNASeq) and a pomalidomide (POM)-refractory cohort (n=54 WGS, n=42 RNASeq), we find incremental increase in the frequency of three CRBN aberrations, namely point mutations, copy loss/structural variation and a specific variant transcript (exon 10-spliced), with progressive IMiD exposure, until almost one third of patients have CBRN alterations by the time they are POM-refractory. We find all 3 CRBN aberrations are associated with an inferior outcome to POM in those already refractory to LEN, including those with gene copy loss and structural variation, which has not previously been described. This is the first comprehensive analysis of CBRN alterations in myeloma patients as they progress through therapy, and the largest dataset. It will help inform patient selection for sequential therapies with CRBN-targeting drugs.

K E Y P O I N T S l Association of del17p CCF threshold and poor prognosis in NDMM is established via fluorescence in situ hybridization and sequencing methods. l TP53 mutations are enriched in a high-risk patient segment characterized by high del17p CCF.Deletions of chromosome 17p (del17p) that span the TP53 gene are associated with poor outcome in multiple myeloma (MM), but the prognostic value of del17p cancer clonal fraction (CCF) remains unclear. We applied uniform cytogenetic assessments in a large cohort of newly diagnosed MM (NDMM) patients carrying varying levels of del17p. Incremental CCF change was associated with shorter survival, and a robust CCF threshold of 0.55 was established in discovery and replication data sets. After stratification on the 0.55-CCF threshold, high-risk patients had statistically significantly poorer outcomes compared with low-risk patients (median progression-free survival [PFS] and overall survival [OS], 14 and 32 vs 23.1 and 76.2 months, respectively). Analyses of a third data set comprising whole-exome sequencing data from NDMM patients identified presence of TP53 deletions/mutations as a necessary requirement for high-risk stratification in addition to exceeding the del17p CCF threshold. Meta-analysis conducted across 3 data sets confirmed the robustness of the CCF threshold for PFS and OS. Our analyses demonstrate the feasibility of fluorescence in situ hybridization-and sequencingbased methods to identify TP53 deletions, estimate CCF, and establish that both CCF threshold of 0.55 and presence of TP53 deletion are necessary to identify del17p-carrying NDMM patients with poor prognosis. (Blood. 2019; 133(11):1217-1221

While the past decade has seen meaningful improvements in clinical outcomes for multiple myeloma patients, a subset of patients does not benefit from current therapeutics for unclear reasons. Many gene expression-based models of risk have been developed, but each model uses a different combination of genes and often involves assaying many genes making them difficult to implement. We organized the Multiple Myeloma DREAM Challenge, a crowdsourced effort to develop models of rapid progression in newly diagnosed myeloma patients and to benchmark these against previously published models. This effort lead to more robust predictors and found that incorporating specific demographic and clinical features improved gene expression-based models of high risk. Furthermore, post-challenge analysis identified a novel expression-based risk marker, PHF19, which has recently been found to have an important biological role in multiple myeloma. Lastly, we show that a simple four feature predictor composed of age, ISS, and expression of PHF19 and MMSET performs similarly to more complex models with many more gene expression features included.