Identification of novel mutational drivers reveals oncogene dependencies in multiple myeloma

Abstract

Understanding the profile of oncogene and tumor suppressor gene mutations with their interactions and impact on the prognosis of multiple myeloma (MM) can improve the definition of disease subsets and identify pathways important in disease pathobiology. Using integrated genomics of 1273 newly diagnosed patients with MM, we identified 63 driver genes, some of which are novel, including IDH1, IDH2, HUWE1, KLHL6, and PTPN11 Oncogene mutations are significantly more clonal than tumor suppressor mutations, indicating they may exert a bigger selective pressure. Patients with more driver gene abnormalities are associated with worse outcomes, as are identified mechanisms of genomic instability. Oncogenic dependencies were identified between mutations in driver genes, common regions of copy number change, and primary translocation and hyperdiploidy events. These dependencies included associations with t(4;14) and mutations in FGFR3, DIS3, and PRKD2; t(11;14) with mutations in CCND1 and IRF4; t(14;16) with mutations in MAF, BRAF, DIS3, and ATM; and hyperdiploidy with gain 11q, mutations in FAM46C, and MYC rearrangements. These associations indicate that the genomic landscape of myeloma is predetermined by the primary events upon which further dependencies are built, giving rise to a nonrandom accumulation of genetic hits. Understanding these dependencies may elucidate potential evolutionary patterns and lead to better treatment regimens.

Graphical abstract

Figure 1.

The mutational driver landscape of newly diagnosed multiple myeloma. (A) Driver gene frequencies in the total data set (N = 1273) identified by either frequency-based (yellow) or functional-based (blue) methods or both (red). (B) Cancer clonal fraction (CCF) of driver genes colored by oncogene (ONC; red) or tumor suppressor gene (TSG; blue) score. Genes in gray did not score for either ONC or TSG. Genes are ordered by mean CCF (thick line). (C) ONC (red) and TSG (blue) scores determined by the 20/20 rule (dark) or SomInaclust (light). (D) Mean CCFs of ONCs and TSGs.

Figure 2.

An increasing number of driver abnormalities is associated with poor prognosis. (A) Bar plot of number of mutated driver genes per sample; 203 samples (15.9%) did not contain any nonsynonymous single-nucleotide variants or indels in any of the 63 driver genes; 1070 samples (84.1%) contained ≥1 mutation, 700 samples (55.0%) contained ≥2 mutations, 351 samples (27.6%) contained ≥3 mutations, and 151 samples (11.9%) contained ≥4 mutations (N = 1273). (B) The distribution of all driver abnormalities identified per sample. The number of drivers per sample was calculated using the drivers listed in (supplemental Table 7). Each marker counts for a score of 1; score summed for each patient; maximum score = 91, because some drivers were mutually exclusive (eg, IG translocations), and some copy number features were summarized as a chromosomal arm alteration. The median number of drivers per sample was 5, with a range of 0 to 24. (C) Progression-free survival of patients was significantly negatively affected as the number of drivers increased (P < .001; N = 1273). (D) Overall survival of patients was significantly negatively affected as the number of drivers increased (P < .001; N = 1273).

Figure 3.

The definition of copy number (CN) clusters using hierarchical clustering and their association with cytogenetic subgroups and significantly mutated genes. (A) CN data derived from 1074 whole-exome sequencing samples identify recurrent regions of gain and loss across the genome. (B) Hierarchical K-means clustering analysis of recurrent CN abnormalities identifies 9 CN clusters, of which 7 were predominantly translocation groups and 2 were hyperdiploid; additional details are listed in the supplemental methods. CN cluster 1 (13.7%) was hyperdiploid and associated with gains of 1q and 6p and expression of CCND2. CN cluster 2 (32.9%) was hyperdiploid with gain of 11q and expression of CCND1, but inversely associated with gain of 1q. CN cluster 3 (5.6%) was associated with t(14;16), deletions of 1p, 8p, 13q, 14q, and 16q, and gain of 1q. CN cluster 4 (5.2%) was associated with t(4;14), del13q, and del14q. CN cluster 5 (6.7%) was associated with t(4;14), del4p, del13q, and del14q as well mutations of NFKBIA, MAX, and TRAF3. CN cluster 6 (5.9%) was associated with deletions of 8p, 14q, and 16q, gain of 1q, and mutation of CYLD. CN cluster 7 (7.9%) was associated with t(4;14) and t(14;16), the APOBEC signature, deletions of 11q and 13q, gain of 1q, and mutation of DIS3. CN cluster 8 (17.3%) was associated with t(11;14) and mutations of CCND1 and PRKD2, but not with any deletions or gains. CN cluster 9 (4.8%) was associated with t(11;14) and gain of 11q as well as mutation of BRAF. The data plotted in this figure are listed in supplemental Table 11. (C) The associations of genetic markers with CN clusters illustrating significant associations and their directionality by the size of the circle; red, positive association; blue, negative association. (D) Progression-free survival Kaplan-Meier plots indicating differences in outcome between CN cluster 7 compared with clusters 1, 2, and 5.

Figure 4.

Oncogenic dependencies and their impact on survival. (A) The associations between acquired mutations, cytogenetic subgroups, CNAs, and copy number (CN) clusters. Positive (red) and negative (blue) associations and their odds ratios are shown, where the size of the circle represents the significance of the P value defined by the 2-sided Fisher’s exact test. Both gain- and loss-of-function mutations were associated with translocations. The t(4;14) group was associated with mutations of FGFR3, DIS3, and PRKD2, gain of 1q, and deletion of 13q, 14q, 4p16.3 (FGFR3), 1p22.1 (RPL5), 11q22.1 (BIRC3), and 12p13.1 (CDKN1B). The t(11;14) group was associated with mutations in IRF4 and CCND1, but not with gain of 1q or 6p or deletion of 13q, 14q, 8p, or 1p22.1 (RPL5). The t(14;16) group was associated with mutations in BRAF, DIS3, and TRAF2, del13q, gain of 1q, and the APOBEC signature. The t(14;20) group was associated with the APOBEC signature. Hyperdiploidy was associated with gain of 6p and translocations involving MYC, but not with mutations in MAX, DIS3, IRF4, or CCND1 or del13q or del14q. The most significant associations were between CN changes on the same chromosomes (del CDKN2C, RPL5, and FAM46C; del TRAF3 and ABCD4; del WWOX and CYLD), as well as CN cluster 2 with HRD, CN cluster 7 with gain of 1q, and CN cluster 6 with del16q. (B) Significant enrichment/depletion of mutated genes within translocation and hyperdiploid CN clusters. The percentage of samples with the gene mutated within the subgroup is shown. Significance is indicated by hatched lines. MAX mutations are significantly underrepresented in the CN-2 group. (C) The distribution of codon usage within KRAS, NRAS, and BRAF by molecular subgroup. Proportion of codon usage is indicated by the distance from the center. KRAS mutations are split between codons G12, G13, and Q61, whereas NRAS is predominantly mutated at codon Q61 (P < 2.2 × 10⁻¹⁶). BRAF mutations mostly affect codon V600, except in the t(14;16) group, where codon D594 is mutated (P = .003).

Figure 5.

Mutations in tumor suppressor genes and oncogenes are expressed. (A) Scatterplot with oncogene and tumor suppressor gene exome and RNA-seq VAFs (r² = 0.585). Translocation partner oncogene mutations are colored and are generally outliers with increased expression. (B) TP53 mutations, including missense, nonsense, and frameshift, are detectable according to VAF. High VAF on either axis is due to deletion of TP53 and is indicative of biallelic inactivation. (C) KRAS, NRAS, and BRAF mutations are expressed according to VAF. NRAS has more high VAF-expressed mutations (>0.5), because it is on 1p, and the nonmutated allele is frequently deleted. (D) NF-κB genes CYLD, NFKBIA, TRAF2, and TRAF3.