General MM

ASH 2017 | Mechanisms of resistance and prognosis: the role of crowdsourcing, genomic characterization and a seven-gene signature approach

On 9–12 December 2017, the 59th American Society of Hematology (ASH) Annual Meeting took place in Atlanta, GA. On Saturday 9th December, an oral abstract session was held entitled: Session 651. Myeloma: Biology and Pathophysiology, excluding Therapy: Mechanisms of Resistance and Prognosis. This session was moderated by Helena Jernberg-Wiklund, of the Rudbeck Laboratory, Uppsala, Sweden, and Dirk Hose, of the Heidelberg University Hospital, Heidelberg, Germany. The first three talks of this session are summarized in the article below, which is based on data presented at the live session and therefore may supersede information in the pre-published ASH Abstracts.

Abstract 265: Crowdsourcing a high-risk classifier for multiple myeloma patients

The session was initiated by Fadi Towfic from Celgene Corporation, Summit, NJ. The data presented in this abstract was collected from multiple collaborative groups and recruited a community of computational biologists, statisticians and experts in Multiple Myeloma (MM) to develop and test models in order to create a highly accurate risk stratification model to identify high-risk MM patients. A prize was nominated to the best performing research teams, comparing their performance against state of the art classifiers based on somatic mutations, gene expression and patient characteristics. Three community challenges were set up to optimize potential for future diagnostics; 190 teams participated in the challenge. The aim was to create the best predictor for this subset of high-risk MM patients, using:

  1. Somatic mutations from tumors and clinical data
  2. Gene expression (RNA) and clinical data
  3. A combination of DNA, RNA and clinical data

The initial step of this study was the acquisition and integration of rich model of datasets; this included at least 500 samples for training and 300 samples for validation. Different platforms and assays were used, which included RNA seq and microarrays for the gene expression profiling. The next step was to ensure that the collection of data was cohesive and involved bringing top MM experts together with Dialogue for Reverse Engineering Assessment and Methods (DREAM) to enable modelers to develop high-risk classifiers. Following this, the models were assessed and the validation data were secured. This study incentivized and engaged top modelers by granting consortium authorship and monetary rewards.  All participants were assessed on their capability to identify high-risk MM patients using different metrics (integrated AUC (iAUC) and BAC).

Key findings:
  • Gene expression data seems to be more informative for predicting risk than genomic mutation data
  • Top expression based models help expand high-risk defined by previously published models
  • Multiple models refine the high-risk definition
  • Some participants noted that validation cohorts vary due to inclusion/exclusion criteria

The speaker also mentioned that they are currently uncovering the biology of the clinical population identified by each of the models. 

Abstract 266: Comprehensive genomic characterization of refractory multiple myeloma reveals a complex mutational and structural landscape associated with drug resistance

The next talk was given by Nicola Lehners from Heidelberg University Hospital, Heidelberg, Germany, who stated that, until now, the mutational landscape of Refractory Relapsed Multiple Myeloma (RRMM) has remained undefined and that patients refractory to proteasome inhibitors (PIs) and immunomodulatory agents (IMiDs) still have inferior outcomes. With the aim of determining the genetic landscape of RRMM, Lehners and colleagues initiated a program including Whole Genome sequencing (WGS) and transcriptome sequencing of RMM patients. The speaker reported data on 38 RRMM tumor/germline pairs with a median coverage of 70.5x for WGS, including nine patients with consecutive tumor samples. All patients (pts) had received a median of five prior lines of therapy (range 2–13), all had relapsed after PIs and IMiDs, and 90% had received an autologous transplant. They were refractory to carfilzomib (72%), pomalidomide (79%), or were quadruple refractory (48%). FISH cytogenetics revealed high-risk features in 62% of patients with del(17p) present in 48%, gain(1q21) (> 3 copies) in 28%, and t(4;14) in 14%.

Key findings:
  • Analysis of significantly mutated oncogenic drivers revealed several genes affected in RRMM: NRAS (38% of pts), KRAS (28%), mutated TP53 (31%) and BRAF (17%)
  • KMT2C was one of the genes identified in the RRMM patients and not commonly reported in Newly Diagnosed Multiple Myeloma (NDMM) patients
  • Clustering of mutations observed in several genes affecting pathways:
    • 76% of pts showed mutations that affect the RAS-RAF pathway
    • 69% of pts showed mutations in genes that affect the sensitivity of Poly ADP Ribose Polymerase Inhibitors (PARPi) and Homologous Recombination (HR)
  • Analysis showed recurrent mutations in epigenetic regulators:
    • Histone methyltransferases (28% of pts)
    • Histone DNA demethylases (21% of pts)
  • Analysis of recurrent mutational signatures identified nine different signatures in their cohort, which included BRCAness, deficient DNA mismatch repair and apolipoprotein B editing catalytic polypeptide (APOBEC)
  • Analysis of recurrent structural variants (SVs) showed:
    • Median of 110 SVs per patient
    • Similar hallmarks to NDMM pts, which are translocations (t(11;14), t(4;14)) and c-Myc rearrangements (mainly FAM46C)
    • A third of pts showed simultaneous combinations of immunoglobulin (IG) rearrangements (e.g. translocations: t(2,14 [IgH;SOS1] and t(6,14) [IgH;IRF4])
  • Current fusion genes reported and related to activation of NFĸB pathway and activation of MYC

To conclude, the presentation was summarized in the statements below:

  • There is a different mutational and structural landscape in the RRMM cohort in comparison to the data reported in NDMM pts
  • There is an enrichment of recurrently mutated genes in pathways that regulate protein homeostasis, gene transcription, DNA integrity and cellular signaling
  • BRCAness mutational signature has a major impact in RRMM and there is an enrichment of mutations in genes associated with PARP inhibitor sensitivity, which could potentially be targeted therapeutically
  • The concept of a ‘multi-hit’ in RRMM can be derived from the complex structural aberrations observed in this study
  • The mechanisms of drug resistance in RRMM patients are most likely to be multifactorial
Abstract 267: A seven-gene signature to distinguish bortezomib- and lenalidomide- responsive myeloma: RNAseq from the PADIMAC Study

The third talk was given by Michael Chapman from the University of Cambridge, Cambridge, UK. He presented the results from the phase II PADIMAC trial. One of the current dilemmas in MM is the lack of therapy selection rationale in treating transplant ineligible elderly MM patients (>65 yrs). Additionally, bortezomib-lenalidomide-dexamethasone (VRD) is an expensive drug combination that is not suitable for all, therefore there is a need for better drug selection strategies. This talk summarizes the findings from the PADIMAC trial, in which a seven-gene signature was identified, and used to rationally select bortezomib- or lenalidomide- based therapy.

Key findings:
  • Nine of 41 patients with RNAseq data achieved ≥ Very Good Partial Response (VGPR), sustained for over 12 months with no further treatment
  • Identification of a seven-gene signature best performing in ten-fold cross-validation
  • The seven genes identified are: EMC9, FAM171B, PLEK, MYO9B, RCN3, FLNB, KIF1C
  • Seven-gene signature was trained on PADIMAC trial and showed improved PFS and OS benefit in patients receiving correct predicted therapy (median PFS 20.1 vs. not reached; median OS 31.2 vs. not reached)
  • Based on the seven-gene signature the following was observed:
    • Improved PFS in CoMMpass patients that were predicted to do well with bortezomib without iMiD: median PFS 21.9 vs. 36.2 months
    • Improved PFS and OS for those predicted to need bortezomib in patients treated with bortezomib from the Millenium data: median PFS 3.7 vs 6.2 months; median OS 13.5 vs 21.9 months
  • Seven-gene signature had no predictive value in VRD
  • Improved PFS and OS for those predicted to need lenalidomide, in lenalidomide-treated patients with PCL from Neri group (median PFS 1 month vs not reached; median OS 12.5 months vs not reached)

 Dr. Chapman concluded that the seven-gene signature had the ability to distinguish between bortezomib- and lenalidomide-sensitive patients, and therefore displays considerable therapeutic potential in transplant ineligible, elderly patients. He also commented that the signature is adaptable for qPCR and manageable as it only consists of seven genes. Finally, Dr. Chapman mentioned that this seven-gene signature approach will be extended to other drugs.

References
  1. A. P. Dervan et al. Crowdsourcing a High-Risk Classifier for Multiple Myeloma Patients. #Abstract 265. 59th ASH Annual Meeting and Exposition 2017, Atlanta, GA.
  2. N. Lehners et al. Comprehensive Genomic Characterization of Refractory Multiple Myeloma Reveals a Complex Mutational and Structural Landscape Associated with Drug Resistance. #Abstract 266. 59th ASH Annual Meeting and Exposition 2017, Atlanta, GA.
  3. M. Chapman et al. A Seven Gene Signature to Distinguish Bortezomib- and Lenalidomide-Responsive Myeloma: Rnaseq from the Padimac Study. #Abstract 267. 59th ASH Annual Meeting and Exposition 2017, Atlanta, GA.
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