Widespread genetic heterogeneity in multiple myeloma: implications for targeted therapy

Abstract

We performed massively parallel sequencing of paired tumor/normal samples from 203 multiple myeloma (MM) patients and identified significantly mutated genes and copy number alterations and discovered putative tumor suppressor genes by determining homozygous deletions and loss of heterozygosity. We observed frequent mutations in KRAS (particularly in previously treated patients), NRAS, BRAF, FAM46C, TP53, and DIS3 (particularly in nonhyperdiploid MM). Mutations were often present in subclonal populations, and multiple mutations within the same pathway (e.g., KRAS, NRAS, and BRAF) were observed in the same patient. In vitro modeling predicts only partial treatment efficacy of targeting subclonal mutations, and even growth promotion of nonmutated subclones in some cases. These results emphasize the importance of heterogeneity analysis for treatment decisions.

Figure 1. Determining significantly mutated genes in 203 patients with MM

(A) The rate of synonymous and nonsynonymous mutations is displayed as mutations per megabase (of exome), with individual MM samples ranked by total number of mutations. (B) The heat map represents individual mutations in 203 patient samples, color-coded by type of mutation. Only one mutation per gene is shown if multiple mutations were found in a sample. Left: Histogram shows the number of mutations in each gene. Percentages represent the fraction of tumors with at least one mutation in the specified gene. Right: The 11 genes with the lowest q value (q-combined in Table S1), ranked by level of significance. (C) Base substitution and allelic fraction distribution of individual samples, ranked in the same order as in A. See also Figure S1 and Tables S1–S3.

Figure 2. Mutational profile, LOH, and copy number profile in subtypes of MM

Data for all 203 patient samples for which whole genome or whole exome sequencing was performed are displayed in columns. The first panel from the top displays patient characteristics (“NA”, if information on a characteristic was unavailable). Classification into hyperdiploid versus non-hyperdiploid samples was performed as described in Experimental Procedures. The second panel displays the 11 significantly mutated genes and IRF4 (which harbors K123R mutations in 3 patients), color-coded by the cancer cell fraction in which these mutations occur, and circles within symbols representing LOH. p value HD and p value Tx represent differences in the prevalence of mutations in the indicated gene between hyperdiploid and non-hyperdiploid, or between previously treated and untreated samples, respectively. The third panel highlights samples harboring homozygous deletions at the most significant loci. Only selected genes within those loci are displayed. Grey symbols denote samples with unavailable high density copy number array or ABSOLUTE data. The lower two panels display focal deletions and amplifications across 153 patients with high density copy number arrays, as determined by GISTIC analysis. Grey symbols denote samples without high density copy number array. See also Figure S2 and Tables S4–S9.

Figure 3. Clonal heterogeneity of significantly mutated genes in MM

(A) The numbers of predicted subclones by clustering of cancer cell fractions are shown, as described in Supplemental Experimental Procedures. As a comparison the predicted distribution of the number of subclones is also shown for a cohort of patients with ovarian cancer. Error bars represent standard deviation. (B) The CCF, i.e. the expected fraction of MM cells that harbor a coding mutation in the indicated gene, is shown. Each symbol represents a somatic mutation in an individual patient. The most significantly mutated genes are shown. Based on the probability distribution, mutations were determined to be either clonal (red circles, upper bound of CCF confidence interval ≥ 0.95) or subclonal (blue circles, upper bound of CCF confidence interval < 0.95). Error bars represent the 95% confidence interval. (C) Co-occurrence of significant mutations in the same patient is depicted. Results of the Bayesian clustering procedure applied to SSNV CCF distributions for KRAS, NRAS, and BRAF in samples which harbor mutations in at least two of these 3 oncogenes. Probability distributions over CCF for the co-occurring SSNV in the indicated oncogenes before clustering (black curves), and after clustering (filled red bars). (D) The fraction of somatic mutations that are present at the indicated CCF are shown for the 11 most significantly mutated genes. Mutations in significantly mutated genes occur at significantly higher CCFs in previously treated patients, compared to untreated patients (p = 0.007, Wilcoxon rank sum test). See also Figure S3.

Figure 4. Heterogeneity composition determines the response to targeted therapy

(A) The BRAF WT MM cell lines OPM2 (NRAS and KRAS WT, FGFR3 K650E), MM1S (KRAS G12A), SKMM1 (NRAS G12D) and the BRAF-mutant MM cell line U266 (BRAF K601N) were treated with the BRAF-inhibitor PLX4720 at the indicated concentrations. Phosphorylated and total MEK and ERK were detected by western blot at the indicated timepoints. (B) The indicated cell lines were cultured for 5 days in the absence or presence of increasing concentrations of the BRAF-inhibitor dabrafenib. Cell numbers were determined by flow cytometry on day 5 of culture and normalized to the cell number at a dabrafenib concentration of 0 µM (=100%). Error bars represent standard deviation. (C) The indicated MM cell lines were cultured in the presence of the MEK-inhibitor trametinib with or without dabrafenib at varying doses. The cell number on day 5 of culture was determined by cell titer glo. Curves with darker shades of grey represent higher concentrations of dabrafenib.