Mutational analysis of selected high-grade malignancies in a premenopausal gynecologic cancer population: a potential for targeted therapies?

摘要

In 2017, there will be 107,000 cases of gynecologic cancer diagnosed in the US with an overall survival of around 70%-most occurring in post-menopausal individuals. In this study, we have examined a younger (≤?40?years of age) subpopulation of these women with high grade/ high stage gynecologic malignancies, attempting to identify unique genetic abnormalities or combinations thereof through tissue block specimens. This information was then analyzed in light of known target therapies to see if genetic analysis in this setting would yield significant therapeutic advantage. We retrospectively evaluated patients with high grade/high stage gynecologic cancers (≤?40?years of age), examined the presence and status of 400 oncogenes and tumors suppressor genes from Formalin-fixed, Paraffin-embedded (FFPE) tissue and functionally classified mutations by SIFT and Polyphen. Twenty women were identified and stratified into positive and negative outcomes. No demographic, clinicopathologic or treatment factors were significant between these groups. Of the 400 genes evaluated, twelve mutations were significant between the groups, six with targeted therapies. Mutations associated with negative outcomes within histologies/locations were evaluated: ERBB3 in epithelial (ovarian), ALK/GPR124/KMT2D in neuroendocrine (ovarian/endometrial), ROS1/EGFR, ROS1/ERBB3/KMT2D/NIRK1 and GPR124 in sarcoma. All negative outcomes were void of mutations in APC/ABL2. A predictive model for negative outcomes in our cohort was developed from these data: AKAP9-/MBD1-/APC-/ABL2- with a mutation load of ?20.5. Unique multi-gene and mutational outcome correlations were identified in our cohort. Resulting complex mutational profiles in distinctly aggressive gynecologic cancers suggested potential for novel therapeutic treatment. Future larger scale studies will be needed to correlate the genotypic and phenotypic features identified here.