Caleb K. Stein

Clonal selection and double-hit events involving tumor suppressor genes underlie relapse in myeloma.

To elucidate the mechanisms underlying relapse from chemotherapy in multiple myeloma, we performed a longitudinal study of 33 patients entered into Total Therapy protocols investigating them using gene expression profiling, high-resolution copy number arrays, and whole-exome sequencing. The study illustrates the mechanistic importance of acquired mutations in known myeloma driver genes and the critical nature of biallelic inactivation events affecting tumor suppressor genes, especially TP53, the end result being resistance to apoptosis and increased proliferation rates, which drive relapse by Darwinian-type clonal evolution. The number of copy number aberration changes and biallelic inactivation of tumor suppressor genes was increased in GEP70 high risk, consistent with genomic instability being a key feature of high risk. In conclusion, the study highlights the impact of acquired genetic events, which enhance the evolutionary fitness level of myeloma-propagating cells to survive multiagent chemotherapy and to result in relapse.

Spatial genomic heterogeneity in multiple myeloma revealed by multi-region sequencing

In multiple myeloma malignant plasma cells expand within the bone marrow. Since this site is well-perfused, a rapid dissemination of “fitter” clones may be anticipated. However, an imbalanced distribution of multiple myeloma is frequently observed in medical imaging. Here, we perform multi-region sequencing, including iliac crest and radiology-guided focal lesion specimens from 51 patients to gain insight into the spatial clonal architecture. We demonstrate spatial genomic heterogeneity in more than 75% of patients, including inactivation of CDKN2C and TP53, and mutations affecting mitogen-activated protein kinase genes. We show that the extent of spatial heterogeneity is positively associated with the size of biopsied focal lesions consistent with regional outgrowth of advanced clones. The results support a model for multiple myeloma progression with clonal sweeps in the early phase and regional evolution in advanced disease. We suggest that multi-region investigations are critical to understanding intra-patient heterogeneity and the evolutionary processes in multiple myeloma.In multiple myeloma, malignant cells expand within bone marrow. Here, the authors use multi-region sequencing in patient samples to analyse spatial clonal architecture and heterogeneity, providing novel insight into multiple myeloma progression and evolution.

Clinical value of molecular subtyping multiple myeloma using gene expression profiling

Using a data set of 1217 patients with multiple myeloma enrolled in Total Therapies, we have examined the impact of novel therapies on molecular and risk subgroups and the clinical value of molecular classification. Bortezomib significantly improved the progression-free survival (PFS) and overall survival (OS) of the MMSET (MS) subgroup. Thalidomide and bortezomib positively impacted the PFS of low-risk (LoR) cases defined by the GEP70 signature, whereas high-risk (HiR) cases showed no significant changes in outcome. We show that molecular classification is important if response rates are to be used to predict outcomes. The t(11;14)-containing CD-1 and CD-2 subgroups showed clear differences in time to response and cumulative response rates but similar PFS and OS. Furthermore, complete remission was not significantly associated with the outcome of the MAF/MAFB (MF) subgroup or HiR cases. HiR cases were enriched in the MF, MS and proliferation subgroups, but the poor outcome of these groups was not linked to subgroup-specific characteristics such as MAF overexpression per se. It is especially important to define risk status if HiR cases are to be managed appropriately because of their aggressive clinical course, high rates of early relapse and the need to maintain therapeutic pressure on the clone.

Removing batch effects from purified plasma cell gene expression microarrays with modified ComBat

BackgroundGene expression profiling (GEP) via microarray analysis is a widely used tool for assessing risk and other patient diagnostics in clinical settings. However, non-biological factors such as systematic changes in sample preparation, differences in scanners, and other potential batch effects are often unavoidable in long-term studies and meta-analysis. In order to reduce the impact of batch effects on microarray data, Johnson, Rabinovic, and Li developed ComBat for use when combining batches of gene expression microarray data.We propose a modification to ComBat that centers data to the location and scale of a pre-determined, ‘gold-standard’ batch. This modified ComBat (M-Combat) is designed specifically in the context of meta-analysis and batch effect adjustment for use with predictive models that are validated and fixed on historical data from a ‘gold-standard’ batch.ResultsWe combined data from MIRT across two batches (‘Old’ and ‘New’ Kit sample preparation) as well as external data sets from the HOVON-65/GMMG-HD4 and MRC-IX trials into a combined set, first without transformation and then with both ComBat and M-ComBat transformations. Fixed and validated gene risk signatures developed at MIRT on the Old Kit standard (GEP5, GEP70, and GEP80 risk scores) were compared across these combined data sets.Both ComBat and M-ComBat eliminated all of the differences among probes caused by systematic batch effects (over 98% of all untransformed probes were significantly different by ANOVA with 0.01 q-value threshold reduced to zero significant probes with ComBat and M-ComBat). The agreement in mean and distribution of risk scores, as well as the proportion of high-risk subjects identified, coincided with the ‘gold-standard’ batch more with M-ComBat than with ComBat. The performance of risk scores improved overall using either ComBat or M-Combat; however, using M-ComBat and the original, optimal risk cutoffs allowed for greater ability in our study to identify smaller cohorts of high-risk subjects.ConclusionM-ComBat is a practical modification to an accepted method that offers greater power to control the location and scale of batch-effect adjusted data. M-ComBat allows for historical models to function as intended on future samples despite known, often unavoidable systematic changes to gene expression data.

Overexpression of EZH2 in multiple myeloma is associated with poor prognosis and dysregulation of cell cycle control

Myeloma is heterogeneous at the molecular level with subgroups of patients characterised by features of epigenetic dysregulation. Outcomes for myeloma patients have improved over the past few decades except for molecularly defined high-risk patients who continue to do badly. Novel therapeutic approaches are, therefore, required. A growing number of epigenetic inhibitors are now available including EZH2 inhibitors that are in early-stage clinical trials for treatment of haematological and other cancers with EZH2 mutations or in which overexpression has been correlated with poor outcomes. For the first time, we have identified and validated a robust and independent deleterious effect of high EZH2 expression on outcomes in myeloma patients. Using two chemically distinct small-molecule inhibitors, we demonstrate a reduction in myeloma cell proliferation with EZH2 inhibition, which leads to cell cycle arrest followed by apoptosis. This is mediated via upregulation of cyclin-dependent kinase inhibitors associated with removal of the inhibitory H3K27me3 mark at their gene loci. Our results suggest that EZH2 inhibition may be a potential therapeutic strategy for the treatment of myeloma and should be investigated in clinical studies.

Bi-allelic inactivation is more prevalent at relapse in multiple myeloma, identifying RB1 as an independent prognostic marker

The purpose of this study is to identify prognostic markers and treatment targets using a clinically certified sequencing panel in multiple myeloma. We performed targeted sequencing of 578 individuals with plasma cell neoplasms using the FoundationOne Heme panel and identified clinically relevant abnormalities and novel prognostic markers. Mutational burden was associated with maf and proliferation gene expression groups, and a high-mutational burden was associated with a poor prognosis. We identified homozygous deletions that were present in multiple myeloma within key genes, including CDKN2C, RB1, TRAF3, BIRC3 and TP53, and that bi-allelic inactivation was significantly enriched at relapse. Alterations in CDKN2C, TP53, RB1 and the t(4;14) were associated with poor prognosis. Alterations in RB1 were predominantly homozygous deletions and were associated with relapse and a poor prognosis which was independent of other genetic markers, including t(4;14), after multivariate analysis. Bi-allelic inactivation of key tumor suppressor genes in myeloma was enriched at relapse, especially in RB1, CDKN2C and TP53 where they have prognostic significance.

Dose-dense and less dose-intense Total Therapy 5 for gene expression profiling-defined high-risk multiple myeloma

Multiple myeloma (MM) is a heterogeneous disease with high-risk patients progressing rapidly despite treatment. Various definitions of high-risk MM are used and we reported that gene expression profile (GEP)-defined high risk was a major predictor of relapse. In spite of our best efforts, the majority of GEP70 high-risk patients relapse and we have noted higher relapse rates during drug-free intervals. This prompted us to explore the concept of less intense drug dosing with shorter intervals between courses with the aim of preventing inter-course relapse. Here we report the outcome of the Total Therapy 5 trial, where this concept was tested. This regimen effectively reduced early mortality and relapse but failed to improve progression-free survival and overall survival due to relapse early during maintenance.

The varied distribution and impact of RAS codon and other key DNA alterations across the translocation cyclin D subgroups in multiple myeloma

We examined a set of 805 cases that underwent DNA sequencing using the FoundationOne Heme (F1H) targeted sequencing panel and gene expression profiling. Known and likely variant calls from the mutational data were analyzed for significant associations with gene expression defined translocation cyclin D (TC) molecular subgroups. The spectrum of KRAS, NRAS, and BRAF codon mutations varied across subgroups with NRAS mutations at Q61 codon being common in hyperdiploid (HRD) and t(11;14) myeloma while being rare in MMSET and MAF. In addition, the presence of RAS-RAF mutations was inversely associated with NFκB pathway activation in all subgroups excluding MAF. In the MMSET subgroup, cases with low FGFR3 expression frequently had RAS-RAF mutations. Conditional inference tree analysis determined that mutation and homozygous deletion of TP53, CDKN2C, and RB1 were key prognostic factors associated with adverse outcome in a non-relapse clinical setting. In conclusion, this study highlights the heterogeneity in the distribution and clinical outcomes of RAS codon and other mutations in multiple myeloma dependent upon primary molecular subgroup.

Search for rare protein altering variants influencing susceptibility to multiple myeloma

The genetic basis underlying the inherited risk of developing multiple myeloma (MM) is largely unknown. To examine the impact of rare protein altering variants on the risk of developing MM we analyzed high-coverage exome sequencing data on 513 MM cases and 1,569 healthy controls, performing both single variant and gene burden tests. We did not identify any recurrent coding low-frequency alleles (1–5%) with moderate effect that were statistically associated with MM. In a gene burden analysis we did however identify a promising relationship between variation in the marrow kinetochore microtubule stromal gene KIF18A, which plays a role in control mitotic chromosome positioning dynamics, and risk of MM (P =3.6×10−6). Further analysis showed KIF18A displays a distinct pattern of expression across molecular subgroups of MM as well as being associated with patient survival. Our results inform future study design and provide a resource for contextualizing the impact of candidate MM susceptibility genes.

Hyperhaploidy is a novel high-risk cytogenetic subgroup in multiple myeloma

Hyperhaploid clones (24–34 chromosomes) were identified in 33 patients with multiple myeloma (MM), demonstrating a novel numerical cytogenetic subgroup. Strikingly, all hyperhaploid karyotypes were found to harbor monosomy 17p, the single most important risk stratification lesion in MM. A catastrophic loss of nearly a haploid set of chromosomes results in disomies of chromosomes 3, 5, 7, 9, 11, 15, 18, 19 and 21, the same basic set of odd-numbered chromosomes found in trisomy in hyperdiploid myeloma. All other autosomes are found in monosomy, resulting in additional clinically relevant monosomies of 1p, 6q, 13q and 16q. Hypotriploid subclones (58–68 chromosomes) were also identified in 11 of the 33 patients and represent a duplication of the hyperhaploid clone. Analysis of clones utilizing interphase fluorescence in situ hybridization (iFISH), metaphase FISH and spectral karyotyping identified either monosomy 17 or del17p in all patients. Amplification of 1q21 was identified in eight patients, demonstrating an additional high-risk marker. Importantly, our findings indicate that current iFISH strategies may be uninformative or ambiguous in the detection of these clones, suggesting this patient subgroup maybe underreported. Overall survival for patients with hyperhaploid clones was poor, with a 5-year survival rate of 23.1%. These findings identify a distinct numerical subgroup with cytogenetically defined high-risk disease.