Despite advances in genomic analysis, the molecular origin of neuroendocrine tumors (NETs) is complex and poorly explained by described oncogenes. The neurotrophic TRK family, including NTRK1, 2, and 3, encode the proteins TRKA, TRKB, TRKC, respectively, involved in normal nerve development. Because NETs develop from the diffuse neuroendocrine system, we sought to determine whether NTRK alterations occur in NETs and whether TRK-targeted therapy would be effective. A patient with metastatic well-differentiated NET, likely of the small intestine, was enrolled on the STARTRK2 trial (ClinicalTrials.gov identifier: NCT02568267) and tissue samples were analyzed using an RNA-Seq next-generation sequencing platform. An ETV6:NTRK3 fusion was identified and therapy was initiated with the investigational agent entrectinib, a potent oral tyrosine kinase inhibitor of TRKA, TRKB, and TRKC. Upon treatment with entrectinib, the patient experienced rapid clinical improvement; his tumor response was characterized by initial tumor growth and necrosis. This is the first report of an NTRK fusion in NETs. Our patient's response to entrectinib suggests that NTRK fusions can be important in the pathogenesis of NETs. Recent DNA-based genomic analyses of NETs may have missed NTRK fusions due its large gene rearrangement size and multiple fusion partners. The tumor's initial pseudoprogression may represent a unique response pattern for TRK-targeted therapies. An effort to characterize the prevalence of NTRK fusions in NETs using optimal sequencing technology is important.
Darren Sigal, Marie Tartar, Marin Xavier, Fei Bao, Patrick Foley, David Luo, Jason Christiansen, Zachary Hornby, Edna Chow Maneval and Pratik Multani
Ang Li, Qian Wu, Suhong Luo, Greg S. Warnick, Neil A. Zakai, Edward N. Libby, Brian F. Gage, David A. Garcia, Gary H. Lyman and Kristen M. Sanfilippo
Background: Although venous thromboembolism (VTE) is a significant complication for patients with multiple myeloma (MM) receiving immunomodulatory drugs (IMiDs), no validated clinical model predicts VTE in this population. This study aimed to derive and validate a new risk assessment model (RAM) for IMiD-associated VTE. Methods: Patients with newly diagnosed MM receiving IMiDs were selected from the SEER-Medicare database (n=2,397) to derive a RAM and then data from the Veterans Health Administration database (n=1,251) were used to externally validate the model. A multivariable cause-specific Cox regression model was used for model development. Results: The final RAM, named the “SAVED” score, included 5 clinical variables: prior surgery, Asian race, VTE history, age ≥80 years, and dexamethasone dose. The model stratified approximately 30% of patients in both the derivation and the validation cohorts as high-risk. Hazard ratios (HRs) were 1.85 (P<.01) and 1.98 (P<.01) for high- versus low-risk groups in the derivation and validation cohorts, respectively. In contrast, the method of stratification recommended in the current NCCN Guidelines for Cancer-Associated Venous Thromboembolic Disease had HRs of 1.21 (P=.17) and 1.41 (P=.07) for the corresponding risk groups in the 2 datasets. Conclusions: The SAVED score outperformed the current NCCN Guidelines in risk-stratification of patients with MM receiving IMiD therapy. This clinical model can help inform providers and patients of VTE risk before IMiD initiation and provides a simplified clinical backbone for further prognostic biomarker development in this population.