The landscape and therapeutic relevance of cancer-associated transcript fusions

Abstract

Transcript fusions as a result of chromosomal rearrangements have been a focus of attention in cancer as they provide attractive therapeutic targets. To identify novel fusion transcripts with the potential to be exploited therapeutically, we analyzed RNA sequencing, DNA copy number and gene mutation data from 4366 primary tumor samples. To avoid false positives, we implemented stringent quality criteria that included filtering of fusions detected in RNAseq data from 364 normal tissue samples. Our analysis identified 7887 high confidence fusion transcripts across 13 tumor types. Our fusion prediction was validated by evidence of a genomic rearrangement for 78 of 79 fusions in 48 glioma samples where whole-genome sequencing data were available. Cancers with higher levels of genomic instability showed a corresponding increase in fusion transcript frequency, whereas tumor samples harboring fusions contained statistically significantly fewer driver gene mutations, suggesting an important role for tumorigenesis. We identified at least one in-frame protein kinase fusion in 324 of 4366 samples (7.4%). Potentially druggable kinase fusions involving ALK, ROS, RET, NTRK and FGFR gene families were detected in bladder carcinoma (3.3%), glioblastoma (4.4%), head and neck cancer (1.0%), low-grade glioma (1.5%), lung adenocarcinoma (1.6%), lung squamous cell carcinoma (2.3%) and thyroid carcinoma (8.7%), suggesting a potential for application of kinase inhibitors across tumor types. In-frame fusion transcripts involving histone methyltransferase or histone demethylase genes were detected in 111 samples (2.5%) and may additionally be considered as therapeutic targets. In summary, we described the landscape of transcript fusions detected across a large number of tumor samples and revealed fusion events with clinical relevance that have not been previously recognized. Our results support the concept of basket clinical trials where patients are matched with experimental therapies based on their genomic profile rather than the tissue where the tumor originated.

Figure 1. The distribution of fusion transcripts across twelve tumor types

(A) Bar plots show the fraction of samples in which at least a single fusion transcript was detected per tumor type (green). The dot plots illustrates the number of detected fusion transcripts per megabase per sample normalized by the sequencing coverage. Tumor types were sorted according to the fraction of samples with fusions. (B) Box-Whisker plots showing the number of DNA segments per sample as a relative measure of genome instability across 13 tumor types. (C) Barplots representing the fraction of different types of fusions classified based on the distance between the genes constituting the fusion and the presence or absence of a DNA copy number alteration within 100Kb of the junction point.

Figure 2. The chromosomal location of recurrent fusion genes for each tumor types

Line plots representing the frequency of fusion gene A and B across the genome (green), the negative log (q-value) of DNA amplifications (red) and deletions (blue) per tumor type. DNA copy number alterations with q-value less than 0.05 as determined by GISTIC are shown.

Figure 2. The chromosomal location of recurrent fusion genes for each tumor types

Line plots representing the frequency of fusion gene A and B across the genome (green), the negative log (q-value) of DNA amplifications (red) and deletions (blue) per tumor type. DNA copy number alterations with q-value less than 0.05 as determined by GISTIC are shown.

Figure 3. An overview of protein kinase fusions across 13 tumor types

(A) Bar plots show the fraction of in-frame protein kinase fusions relative to the total number of in frame fusions per tumor type. (B) Recurrent in-frame protein kinase fusion across 13 tumor types (n≥2). Color represents tumor type. (C) The landscape of protein kinase fusions across cancer. The horizontal and vertical axes represent tumor samples and kinase genes, respectively. Genes were ordered based on kinase family annotation. Color bar depicts tumor type.

Figure 4. Significance of RAF family fusions in thyroid cancer

(A) The top panel indicates frequencies of somatic mutations (lightblue) and significant mutations (pink). To compare the frequency between samples with and without recurrent fusions (n≥2), a Welch’ s t-test was performed. The bottom panel shows a heatmap of fusions and significant gene mutations in 312 thyroid cancers. (B) Position of each domain in BRAF gene and junction points of BRAF fusions. (C) Exon expression plots demonstrated Z-normalized exon expression for each exon in thyroid cancers. Red and blue represent relatively high and low exon expression.

Figure 5. A survey of chromatin modifier fusions across 13 tumor types

(A) Bar plots show the fraction of in-frame chromatin modifier fusions relative to the total number of in frame fusions per tumor type. (B) Recurrent in-frame chromatin modifier fusions across 13 tumor types (n≥2). Color represents tumor type. (C) The landscape of chromatin modifier fusions across cancer. The horizontal and vertical axes represent tumor samples and chromatin modifier genes, respectively. Genes were ordered based on chromatin modifier class. Color bar depicts tumor type.