Feifan Blood Cloud Classroom November Myeloma Literature Intensive Reading Workshop

• Research highlights

• Using the largest whole genome sequencing (WGS) dataset, increased biallele and double-whammy events in RRMM were discovered, as well as new rare driver genes
  .
  

• EZH2, PIGO, and DUOX2 are new driver candidates
  in RRMM that are almost mutually exclusive.
  

• Research background

• Large-scale analysis of genomic data from NDMM patients has been performed in the past, however, genomic data from RRMM patients has not been analyzed
  on a large scale.
  

• Tumor genomic variants associated with drug-specific resistance, as well as immune cell abnormalities
  associated with immunotherapy resistance, have previously been described.
  However, how drug-specific genomic evolution enhances "high-risk" genetic signatures
  in successive therapeutic exposures has not been systematically explored.
  

• Research methods

• The study hypothesized that somatic variants recorded the therapeutic exposure and clonal structures
  of patients from NDMM to RRMM stage.
  

• WGS data
  were obtained from 418 tumor samples (386 patients) from 6 RRMM clinical trials.
  Patients with RRMM were classified according to their resistance status of lenalidomide (LENR) or pomalidomide (POMR): LENR cohort (n=269, 70%), POMR cohort (n=55, 14%); Patients have been previously exposed to proteasome inhibitors and/or corticosteroids
  .
  

• Based on a population-based case-control study, WGS data from patients with RRMM were compared with WGS data from 198 patients with unrelated NDMM, and an in-depth analysis
  of how the genome of MM patients after exposure to lenalidomide and pomalidomide-based therapy regimens was analyzed.
  

• Study results

• 44 genes were identified in RRMM tumors, including 34 NDMM genome-wide specific driver genes and 10 newly discovered candidate driver
  genes.
  Clonality analysis based on the distribution of driver gene cancer cell fraction (CCF) showed that the new driver gene was generally subclonal.
  
  Driver genes (DUOX2, EZH2, TP53) increase significantly during the RRMM stage (Figure 2).
  
  

Figure 2 Clonal analysis based on CCF distribution

• A total of 15 driver genes were observed to have biallele inactivation, of which the bialleles CDKN2A and CREBBP were unique
  to RRMM.
  TP53 was the most common biallele event in RRMM (9.
  3%), with a significant increase (1.
  8-fold)
  compared with NDMM.
  

• RRMM noncoding region mutation hot spot analysis identified 10 genome-wide signals (FDR<0.
  05), such as TP53BP1, BLM, etc
  .
  

• IgH translocation events were found in 42% of RRMM tumors, similar to NDMM datasets (44%) and previously reported (41%), with the most common IgH translocation event being t(11; 14）、t（4; 14) and t(8; 14), while t(6; 14) and t(14; 16) Less common
  .
  

• The study found that copy number distortion (CNA) 1qGain, 6qLOH and 17pLOH increased significantly (FDR<0.
  01).
  and found significant increases
  from NDMM to LENER to POMR, 1qGain and 17pLOH.
  

• Somatic cell interaction analysis found that the most significant interactions were the coexistence of TP53 and LOH17p, and the mutual exclusion of HRD and WGD.
  In addition, a significant increase in double-hit events was found in patients with RRMM, with 1qGain-17pLOH being the most common double-whammy event in RRMM (11%)
  .
  

• The analysis of mutation characteristics showed that the incidence and activity of SBS2, SBS13, SBS12 and SBS-MM1 increased in RRMM, and the mismatch repair defect (DMR)-related gene SBS12 showed strong activity growth
  at the subclonal level.
  In addition, a significant increase
  in mutational burden was observed in tumors with increased DMR.
  

• Further analysis focused on the enhancement of genomic aberrations in IMiDs-based treatments at different stages of drug resistance
  .
  The analysis results showed that the incidence of TP53, DUOX2, 1qGain, and 17pLOH increased from NDMM to LENER to POMR, while MAML3 increase and IGL and MYC translocations distinguished POMR from the LENR subgroup
  .
  

• discuss

• This study is the largest RRMM WGS data analysis to date (616 total patients) and provides the most comprehensive RRMM genome-wide analysis to date, identifying new RRMM driver genes
  through coding and noncoding region variants of the genome.
  

• Interestingly, the 10 coding region driver genes in RRMM showed significant clonal amplification or increase
  compared to NDMM.
  This observation suggests that these driver genes are associated with therapeutic resistance, are present before treatment exposure, and provide clonal advantages after treatment exposure; or emerging and increasing
  when resistance develops at the RRMM stage.
  Further analysis is needed to understand the role of
  these new driver genes in treating drug resistance.
  

• When comparing CNA increase patterns, 1qGain and 17pLOH were not only enriched in RRMM whole gene datasets, but also increased as a double-whammy, and there was also a significant increase in the trend
  from LENER to POMR.
  

• The increase in the RRMM subclonal feature SBS12 may be due to DMR
  .
  DMR tumors accumulate high levels of mutations, a feature that may be associated with
  therapeutic resistance to myeloma.
  

• The driver genes associated with RRMM are those that confer a selective advantage for cloning under treatment
  .
  Their role in therapeutic resistance should be further evaluated in longitudinal patient samples to clarify associations with clinical resistance evolution and to refine the molecular subsets
  of targeted therapies for RRMM.