Driver Fusions and Their Implications in the Development and Treatment of Human Cancers

Abstract

Gene fusions represent an important class of somatic alterations in cancer. We systematically investigated fusions in 9,624 tumors across 33 cancer types using multiple fusion calling tools. We identified a total of 25,664 fusions, with a 63% validation rate. Integration of gene expression, copy number, and fusion annotation data revealed that fusions involving oncogenes tend to exhibit increased expression, whereas fusions involving tumor suppressors have the opposite effect. For fusions involving kinases, we found 1,275 with an intact kinase domain, the proportion of which varied significantly across cancer types. Our study suggests that fusions drive the development of 16.5% of cancer cases and function as the sole driver in more than 1% of them. Finally, we identified druggable fusions involving genes such as TMPRSS2, RET, FGFR3, ALK, and ESR1 in 6.0% of cases, and we predicted immunogenic peptides, suggesting that fusions may provide leads for targeted drug and immune therapy.

Keywords: RNA; cancer; fusion; gene fusions; translocation.

Figure 1. Fusion Detection and Landscape in Cancer

(A) Fusion calling and filtering pipeline. (B) Cartoon overview of fusion gene partner breakpoints. Purple indicates the 5′ gene partner, and green indicates the 3′ gene partner. For both the 5′ and 3′ gene partners, fusion gene breakpoints can occur in the following genomic regions: 5′ UTR (triangle), coding sequence (CDS; rectangle), 3′ UTR (circle), and non-coding region (rounded rectangle). For each fusion event, a dotted line connects the breakpoints in the 5′ and 3′ gene partners to create the predicted fusion and the circle size, while number represents the total fusion events classified into the associated fusion category. (C) The dot plot shows the frequency of recurrent fusions found in each cancer type. The most recurrent fusion in each cancer type is labeled. Cancer types without recurrent fusions are not shown.

Figure 2. Fusion Expression Outliers

(A) The dot plot indicates the percentage of fusions called in which one of the partner genes is an expression outlier (overexpression or underexpression). The size of the dot corresponds to the number of fusions called in each cancer type. Color corresponds to genes of interest coming from lists of oncogenes, protein kinases, and tumor suppressor genes. (B) The dot plot shows the relative expression level of samples with fusions compared with those without fusions. Each sample has a particular expression percentile at a given gene, and color indicates the median percentile of samples with a fusion in that gene. Genes are the 15 most recurrent oncogenes and tumor suppressor genes. Size corresponds to the number of samples in each cancer type with a fusion at that gene. (C and D) Expression of samples at RET and CBFB in thyroid carcinoma (THCA) (C) and acute myeloid leukemia (LAML) (D), respectively. Color indicates a categorical copy number ranging from deep deletion to high amplification.

Figure 3. Protein Kinase Fusions

(A) The bar chart indicates the number of protein kinase fusions with the kinase at the 5′ or 3′ end, inframe or frameshift, and kinase domain intact or disrupted. (B) The left bar plot shows the percentage of samples with kinase fusions across different cancer types. The number of samples with a kinase fusion is also indicated at the end of each bar. Light green and blue denote 5′ kinase and 3′ kinase fusions, respectively. The right bar plot shows the normalized percentage of kinase fusions broken down by kinase groups. (C) The dot plot shows the numbers of samples for recurrent fusions across different cancer types. Light green and blue denote 5′ kinase and 3′ kinase fusions, respectively.

Figure 4. Kinase Gene Expression Regulated by Fusion

(A) The scatterplot shows the gene expression quantile (y axis) for the 5′-kinase without copy number variation (between one and three copies; x axis). All genes are classified among three categories: kinase expression higher, equal, and lower, compared with partner expression, marked in blue, gray, and red, respectively. The density plot for expression quantile is also shown on the right. (B) The scatterplot shows the gene expression quantile (y axis) for the 3′-kinase without copy number variation (between one and three copies; x axis). The colors represent the same three categories as (A). The density plot for expression quantile is also shown. (C) Boxplot comparing the distribution of kinase gene expression quantile between the three groups defined in (A) for 5′-kinase and 3′-kinase, respectively. (D) Schematic of TBABD–DDR2 fusion gene structure in an HNSC sample and scatterplot of DDR2 copy number versus mRNA expression in HNSC. The samples with and without this fusion are marked in red and blue, respectively.

Figure 5. Mutual Exclusivity between Driver Mutations and Driver Fusions

(A) The bar plot shows the percentages of samples with driver mutations only (green), mutations only (orange), driver mutation and fusion (blue), mutation and fusion (pink), or fusion only (light green) events in 299 cancer driver genes. (B) Distribution of mutation burden across each alteration group designated in all figures. (C) All samples with fusions or mutations in any of the genes indicated on the left are displayed on the x axis. For each gene, samples are clustered by the alteration group. Bottom bar indicates cancer type.

Figure 6. Druggable Fusion Targets

(A) The bar chart indicates the number of samples potentially treatable on the basis of their fusion status. (B) Percentages of LUAD samples with known smoking status. (C) ESR1 domains kept in ESR1 fusions across cancer types. (D) ALK expression across cancer types indicating ALK fusion status.