Abstract 497: Understanding oncogenic fusions: Lessons learned from inflammatory myofibroblastic tumor
Bibliographic record
Abstract
Abstract Oncogenic kinase fusions are validated targets for cancer therapy. With the advent of next generation sequencing (NGS) based clinical diagnostic tools, the detection of these kinase fusions is rapidly increasing across multiple adult and pediatric tumor types. However, there remains a need to better understand the functional impact of these fusions in order to help direct clinical therapies. To study fusion biology, we have made use of a large collection of inflammatory myofibroblastic tumor (IMT) samples. IMT is a rare mesenchymal malignancy, which typically occurs in children. We have recently demonstrated that IMTs harbor multiple therapeutically actionable kinase fusions, including ALK, ROS1, and PDGFRB fusions, using a targeted capture-based NGS assay in a CLIA laboratory (Foundation Medicine). Our initial results demonstrated both previously described ALK fusions, including TPM3-, TPM4-, TFG-, and RANBP2-ALK as well as novel ALK fusions, including PRKAR1A-ALK and LMNA-ALK. Although the presence of ALK fusions within a tumor has been correlated with response to ALK inhibitor therapy, the role that the 5′ prime partner gene may play in the functional biology of the fusion has not been systematically investigated. To address this, we stably transfected cDNAs encoding LMNA-ALK, RANBP2-ALK, FN1-ALK, TFG-ALK, and PRKAR1A-ALK into BA/F3 cells. All X-ALK (X = the fusion partner) variants were tyrosine phosphorylated and their subcellular distribution was in agreement with that observed in the primary tumors harboring the identical fusion. Subcellular localization was altered as a function of the fusion partner. For example, LMNA-ALK was predominantly cytoplasmic while RANBP2-ALK was predominantly perinuclear. Additionally, we compared proliferation rates, downstream signaling, and sensitivity to various structurally different ALK inhibitors amongst all of the X-ALK fusions. Overall, our results suggest that the specific fusion partner may affect the properties of the ALK fusion protein, including sensitivity to ALK inhibitors currently in clinical use. To date, most ALK fusions are detected by immunohistochemistry for ALK overexpression or by “break-apart” fluorescence in situ hybridization (FISH), techniques which may be falsely negative in some settings and in others cannot discern specific fusion present. As the role of NGS increases in clinical diagnostics, our findings may provide further biological and clinical insights into these kinase fusions. Citation Format: Merrida A. Childress, Abha Gupta, Doron Lipson, Geoff Otto, Tina Brennan, Catherine T. Chung, Scott C. Borinstein, Jeffrey S. Ross, Phillip J. Stephens, Vincent A. Miller, Cheryl M. Coffin, Jason L. Hornick, Christine M. Lovly. Understanding oncogenic fusions: Lessons learned from inflammatory myofibroblastic tumor. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 497. doi:10.1158/1538-7445.AM2015-497
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".