ASH 2017 | Novel oncogenes and tumor suppressors in NDMM

On Saturday 9^th December 2017, an oral abstract session was held entitled: Session: 651. Myeloma: Biology and Pathophysiology, excluding Therapy: Transcriptional Regulatory Circuitries of Multiple Myeloma. The first talk in this session was given by Brian A. Walker, from the Myeloma Institute, the University of Arkansas for Medical Sciences, Little Rock, AR, and was entitled: Abstract 60: Identification of Novel Oncogenes and Tumor Suppressor Genes in Newly Diagnosed Multiple Myeloma.  This article is based on data presented at the live session, which may supersede information in the pre-published ASH abstract.

Dr. Walker began his talk with an explanation of the concept of driver mutations, which confer a survival advantage through proliferation, differentiation or apoptosis. He explained that the challenge in genomic studies is to identify driver mutations against a background of bystander or passenger mutations. Estimations suggest that out of 30-60 non-synonymous mutations in a tumor sample, only 5-8 are driver mutations. However, not all mutations in a driver gene are driver mutations, and therefore distinguishing these is important, as well as clearly defining passenger versus driver mutations in general.

The aim of this study was to identify novel oncogenes (ONC) and tumor suppressor genes (TSG) in order to define new therapeutic targets, using the largest sample set of Newly Diagnosed Multiple Myeloma (NDMM) patients to date. A set of 1,273 patients with NDMM were taken from several datasets: Myeloma XI trial, Dana-Faber Cancer Institute, The Myeloma Institute and the Multiple Myeloma Research Foundation CoMMpass study (IA1 - IA9). The analysis used two types of methodology: frequency based and functional consequence. The frequency based analysis used MutSigCV method where mutation rates are based on background mutation rate and gene size, and the dNdScv method, a redefined analysis for the normalized local ratio of non-synonymous/synonymous mutations. The functional consequence was assessed using the ONC/TSG 20/20 rule in which recurrent codons mutation in >20% of gene mutations defined ONC, and inactivation mutations (frameshift, nonsense, etc.) in >20 of gene mutations defined TSG. The SomInaClust method was also used, which is similar to the 20/20 rule but determines the background mutation rate and gene mutation rate differently.

Key Data:

• Using the dNdScv method 41 significant genes were identified
• ONC were defined as recurrently mutated codons: eg. NRAS had 229/234 recurrent mutations with an ONC score of 0.987
• TSG were defined as recurrent frameshift or nonsense mutations: eg. FAM46C had 88-150 recurrent mutations with a TSG score = 0.55
• Genes could not be classified if there were no recurrently mutated codons or a lack of frameshift/nonsense mutations eg. HUWE1
• Comparison of genes identified using different methods (58 genes identified in total) – see table below:

 

The conclusions as stated in the presentation were:

• Large datasets are needed to identify drivers, especially when they occur at low frequency
• Multiple methods are required, frequency and function of mutations can be used to identify drivers
• ONC tend to have a higher CCF compared to TSG, which may be indicative of them occurring earlier or being selected fro during progression
• Key pathways are the Ras and NK-ĸB pathways, epigenetic modifiers and RNA splicing
• There are possibly many more drivers to be discovered

 Relevant Video Links:

To listen to Professor G. J. Morgan talking about the relevance of this work, click here.

To listen to Professor N. Munshi discussing the genomic landscape of MM, click here.