A Survey of Fusion Genes in Myeloma Identifies Kinase Domain Activation Which Could be Targeted with Available Treatments
Bibliographic record
Abstract
Abstract Introduction Although fusion genes other than the immunoglobulin (Ig) translocation, t(4;14), which results in IGH-WHSC1fusions, are not frequently detected in multiple myeloma (MM), recent evidence suggests that kinase fusion gene fusions do occur relatively frequently and may inform on treatment algorithms. Here we use a hybrid-capture based, next-generation sequencing assay to survey fusion genes in patients with MM. Methods We report on 1421 samples from 958 individuals diagnosed with either monoclonal gammopathy of unknown significance (MGUS), smoldering multiple myeloma (SMM) or MM who underwent targeted sequencing with the FoundationOne Heme® (F1H) assay. Tumor samples were obtained from bone marrow aspirates, enriched by CD138+selection using magnetic beads (AutoMACs, Miltenyi Biotech, Cologne, Germany or RoboSep, StemCell Technologies, Vancouver, Canada). RNA and DNA were extracted using the AllPrep DNA/RNA mini kit (Qiagen, Hilden, Germany), RNeasy RNA extraction kit (Qiagen) or Puregene DNA extraction kit (Qiagen). ≥ 50 ng of extracted DNA or RNA was processed on the F1H assay. The current assay analyzes the complete coding DNA sequence of 405 genes, as well as selected introns of 31 genes involved in chromosomal rearrangements as well as the RNA sequence of 265 commonly rearranged genes resulting in gene fusions. Genes included in this assay encode known or likely targets of therapy, either approved or in clinical trials, or are otherwise known drivers of oncogenesis. Sequencing was to an average depth of 510x and was performed using the Illumina HiSeq 2500. Sequences were analyzed for selected gene rearrangements including fusion genes which were detected by a combination of DNA and RNA sequencing. Results Rearrangements into the Ig loci were detected and included the 5 main translocations: t(4;14), t(6;14), t(11;14), t(14;16), and t(14;20), as well as translocations involving MYC at 8q24. From a combination of DNA capture and RNA-seq expression values we used 107 samples in a training set with matching gene expression profiling data to determine cut-offs for FGFR3, WHSC1, CCND3, MAF, MAFB, CCND2 and CCND1to stratify patients into the 5 main translocation groups. We used these values to classify a further 391 samples with corresponding gene expression profiling (GEP) data, resulting in sensitivities and specificities of t(4;14), 98% and 100%; t(6;14), 100% and 99%; t(11;14), 99% and 95%; t(14;16), 77% and 100%; t(14;20), 100% and 100%, respectively. 40 non-Ig rearrangements were detected in 38 patients (4.2%), of which 21 in-frame fusion genes were predicted. Recurrent fusion-genes, identified in more than one patient, included EIF4E3-FOXP1, TXNDC5-MYC and SUB1-WHSC1. As well as TXNDC5, MYC was also partnered with FOXO3, both of which are known partners of the MYC translocation. 12 of the 21 in-frame fusion genes involved kinase domains, including fusions with BRAF (n=4), NTRK3 (n=2), ALK (n=1), ROS1 (n=1), MAPK14 (n=1), MAP3K14 (n=1), FGFR1 (n=1), and DLG2 (n=1). Fusions involving each of these genes have been documented in other cancers. BRAF fusions are thought to partner with genes encoding homodimerization domains, resulting in downstream activation of Ras signaling. Other kinase fusions result in receptor signaling and downstream activation of the Ras signaling pathway. Of the patients with kinase fusions, 2 had an activating KRAS, NRAS or BRAF mutation but only one was clonal (84% cancer clonal fraction). One patient with samples taken at different timepoints had a GTF2I-BRAF fusion and concomitant KRAS G13C mutation (16% allele frequency), both of which were not detectable 8 months later but an AGK-BRAF fusion was detected at that time suggesting clonal selection. Conclusion Non-Ig fusion genes are present in myeloma patients, but at a low frequency. Most of the fusions detected contained a kinase domain indicating activation of the Ras signaling pathway, which is also activated through KRAS, NRAS and BRAF mutations in 50% of patients. Although rare (1%), these kinase fusions are potential clinical targets in myeloma where kinase inhibitors, such as crizotinib, can be used which has shown to be effective against ALK and ROS1 fusions. Disclosures He: Foundation Medicine, Inc: Employment, Equity Ownership. Zhong:foundation medicine: Employment. Bailey:Foundation Medicine, Inc: Employment, Equity Ownership. Vergillo:Foundation Medicine, Inc: Employment. Ross:Foundation Medicine, Inc: Employment. Miller:Foundation Medicine: Employment, Equity Ownership. Stephens:Foundation Medicine: Employment, Equity Ownership. Mughal:Foundation Medicine: Employment, Equity Ownership. Davies:Celgene: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Janssen: Consultancy, Honoraria. Morgan:Janssen: Research Funding; Univ of AR for Medical Sciences: Employment; Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Bristol Meyers: Consultancy, Honoraria.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".