Kim, Jayoung

Bioreducible nanoparticles were engineered for safe and effective systemic siRNA delivery, including crossing the blood–brain barrier to achieve in vivo gene silencing in an orthotopic glioblastoma mouse model.

Abstract In the era of precision medicine, genomic classifications of prostate cancer (PC) have not broadly impacted patient care. Here we present an integrative transcriptome analysis of 14 disease-related pathways in over 4,600 clinical specimens and 25 PC preclinical models. This approach provides a novel classification scheme, inclusive of 3 PC subtypes (PCS1-3), which predict disease progression and drug resistance. The PCS1 and PCS2 categories possess characteristics of luminal cells, while PCS3 exhibits basal features. Castration-resistant and metastatic cancers are over-represented in PCS1 and PCS3 tumors. All 3 subtypes are represented in commonly used PC cell lines; however, none of the mouse models we analyzed resembles PCS3. PCS classification using RNA expression data appears to be stable across primary and metastatic human tumors, cell lines, xenografts and mouse models. This new subtyping method provides novel opportunities for patient stratification that reflect specific features of tumor biology and may influence therapeutic decisions. Citation Format: Sungyong You, Beatrice Knudsen, Nicholas Erho, Mohammed Alshalalfa, Mandeep Takhar, Hussam Al-deen Ashab, Elai Davicioni, Robert Jeffrey Karnes, Eric A. Klein, Robert B. Den, Ashley E. Ross, Edward M. Schaeffer, Isla P. Garraway, Jayoung Kim, Michael R. Freeman. Three intrinsic subtypes of prostate cancer with distinct pathway activation profiles differ in prognosis and treatment response. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 1502.

Abstract Prostate cancer is a biologically heterogeneous disease with variable molecular alterations underlying cancer initiation and progression. Despite recent advances in understanding prostate cancer heterogeneity, better methods for classification of prostate cancer are still needed to improve prognostic accuracy and therapeutic outcomes. In this study, we computationally assembled a large virtual cohort (n = 1,321) of human prostate cancer transcriptome profiles from 38 distinct cohorts and, using pathway activation signatures of known relevance to prostate cancer, developed a novel classification system consisting of three distinct subtypes (named PCS1–3). We validated this subtyping scheme in 10 independent patient cohorts and 19 laboratory models of prostate cancer, including cell lines and genetically engineered mouse models. Analysis of subtype-specific gene expression patterns in independent datasets derived from luminal and basal cell models provides evidence that PCS1 and PCS2 tumors reflect luminal subtypes, while PCS3 represents a basal subtype. We show that PCS1 tumors progress more rapidly to metastatic disease in comparison with PCS2 or PCS3, including PSC1 tumors of low Gleason grade. To apply this finding clinically, we developed a 37-gene panel that accurately assigns individual tumors to one of the three PCS subtypes. This panel was also applied to circulating tumor cells (CTC) and provided evidence that PCS1 CTCs may reflect enzalutamide resistance. In summary, PCS subtyping may improve accuracy in predicting the likelihood of clinical progression and permit treatment stratification at early and late disease stages. Cancer Res; 76(17); 4948–58. ©2016 AACR.