A high-risk, Double-Hit, group of newly diagnosed myeloma identified by genomic analysis

Abstract

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.

Fig. 1

Study outline. Data from 784 patients were used to identify univariate and multivariate features associated with PFS. Genetic features were used for multivariate modeling and were also used in a recursive partitioning model with significantly different outcomes

Fig. 2

The association of myeloma-acquired genetic variants with clinical risk groups. a The distribution of driver mutations, translocations, and copy number alterations by IMWG risk status. It can be seen that a limited number of variables explain a proportion of risk, as would be anticipated based on how the IMWG risk status is assessed, but it can be seen clearly that these variants do not explain a significant amount of variability in clinical outcome. b The distribution of driver mutations, translocations, and copy number alterations by ISS. The distribution shows the independence of ISS from the genetic data, suggesting that a patient’s ISS stage cannot be predicted by mutational diagnosis (and vice-versa); also, that using both could be important for modeling patient outcomes. c Bar plot shows the contribution of each driver variant to relapse, with a breakdown of PFS over <6 months/6–12 months/12–18 months/>18 months, or no progression. Patients with censored follow-up <18 months were excluded from the analysis. d The same data as in plot (c) was only expressed as a proportion, with features sorted by the proportion, of patients who relapsed within the first year of therapy. Differences in rates in early relapse across genetic features suggest a motivation for the inclusion of such features in predictive modeling for poor patient outcome

Fig. 3

Molecular and clinical features associated with outcome. Significant associations of genetic and clinical factors with PFS (a) and OS (b) in univariate analyses. Covariates investigated include, age, ISS, IGH translocations, MYC translocation, APOBEC signature, hyperdiploidy, LOH%, homologous recombination deficiency mutations, copy number cluster, mutational data, copy number data, and bi-allelic inactivation data. Covariates significantly associated with at least one of PFS or OS (Wald P ≤ 0.05) in univariate models are presented. c The final multivariate model for PFS containing clinical and genetic factors has a cumulative R-squared of 34.3% compared to a cumulative R-squared of 18.4% for the model developed containing only genetic factors. d The final model for OS contains clinical and genetic factors, and has a cumulative R-squared of 46.5% compared to a cumulative R-squared of 25.2% for the model developed containing only genetic factors

Fig. 4

A recursive partitioning model for PFS and OS identified clinical and genomic markers associated with risk. a A recursive partitioning model for PFS based on the inclusion of genetic and clinical predictors, showing the terminal nodes. b Kaplan–Meier curves were generated for PFS for all terminal nodes of the tree. c Nodes with similar outcome profiles were combined to generate three risk groups. Nodes 8 and 18 were combined to designate low-risk patients (green); nodes 11, 19, and 6 were combined to designate intermediate-risk patients (red); nodes 10 and 7 were combined to designate Double-Hit patients (blue). Double-Hit comprised 6.1% of the total patient population and included patients who were either of the following: bi-allelic inactivation of TP53 or ISS stage III with amplification of CKS1B. Significant differences in PFS between the risk groups are identified (P < 0.0001). d As in (c) with OS. e The risk groups identified in (c) were applied to a subset of Total Therapy patients (n = 85) with available genetic data; significantly different PFS outcomes are observed, with especially poor PFS in Double-Hit patients (P < 0.0001). f As in (e) with OS

Fig. 5

Comparison of IMWG and Double-Hit cases. a Patients were classified by IMWG status and recursive partitioning risk groups, as detailed in Fig. 4. Double-Hit patients have very poor PFS, whether classified as high risk by IMWG (median PFS 11-month, 18-month PFS of 35%) or low/intermediate risk by IMWG (median PFS 16-month, 18-month PFS of 44%). b Similar trends were observed for OS classified by both IMWG and recursive partitioning status

Fig. 6

The sites of TP53 mutation and their Impact on survival. a Schematic of mutations detected in TP53. b Kaplan–Meier survival curve for PFS for complete set (n = 863) of NDMM patients <75 years of age who had SNV and CNV results, and survival data by TP53 bi-allelic, mono-allelic, or wild-type status. Note that this dataset is larger than the n = 784 dataset, since for this analysis, presence of ISS was not required. c OS in the same set of patients (n = 863)

Fig. 7

The association of gain and amplification of 1q21 with survival using CKS1B as the marker. a Kaplan–Meier survival curves for PFS based on either gain or amplification (≥4 copies) of CKS1B (1q21). The data are shown for the complete dataset (n = 863) of NDMM patients who were <75 years of age who had SNV and CNV results and survival data. Note that this dataset is larger than the n = 784 dataset, since for this analysis, the presence of ISS was not required. b OS in the same set of patients (n = 863)