Huihui Ye

Bio: Huihui Ye is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Prostate cancer & Prostate. The author has an hindex of 30, co-authored 102 publications receiving 5557 citations. Previous affiliations of Huihui Ye include University of California, Berkeley & Harvard University.

The Molecular Taxonomy of Primary Prostate Cancer

Abstract: There is substantial heterogeneity among primary prostate cancers, evident in the spectrum of molecular abnormalities and its variable clinical course. As part of The Cancer Genome Atlas (TCGA), we present a comprehensive molecular analysis of 333 primary prostate carcinomas. Our results revealed a molecular taxonomy in which 74% of these tumors fell into one of seven subtypes defined by specific gene fusions (ERG, ETV1/4, and FLI1) or mutations (SPOP, FOXA1, and IDH1). Epigenetic profiles showed substantial heterogeneity, including an IDH1 mutant subset with a methylator phenotype. Androgen receptor (AR) activity varied widely and in a subtype-specific manner, with SPOP and FOXA1 mutant tumors having the highest levels of AR-induced transcripts. 25% of the prostate cancers had a presumed actionable lesion in the PI3K or MAPK signaling pathways, and DNA repair genes were inactivated in 19%. Our analysis reveals molecular heterogeneity among primary prostate cancers, as well as potentially actionable molecular defects.

Intense Androgen-Deprivation Therapy With Abiraterone Acetate Plus Leuprolide Acetate in Patients With Localized High-Risk Prostate Cancer: Results of a Randomized Phase II Neoadjuvant Study

Abstract: Purpose Cure rates for localized high-risk prostate cancers (PCa) and some intermediate-risk PCa are frequently suboptimal with local therapy. Outcomes are improved by concomitant androgen-deprivation therapy (ADT) with radiation therapy, but not by concomitant ADT with surgery. Luteinizing hormone–releasing hormone agonist (LHRHa; leuprolide acetate) does not reduce serum androgens as effectively as abiraterone acetate (AA), a prodrug of abiraterone, a CYP17 inhibitor that lowers serum testosterone (< 1 ng/dL) and improves survival in metastatic PCa. The possibility that greater androgen suppression in patients with localized high-risk PCa will result in improved clinical outcomes makes paramount the reassessment of neoadjuvant ADT with more robust androgen suppression. Patients and Methods A neoadjuvant randomized phase II trial of LHRHa with AA was conducted in patients with localized high-risk PCa (N = 58). For the first 12 weeks, patients were randomly assigned to LHRHa versus LHRHa plus AA. After a ...

Abiraterone Treatment in Castration-Resistant Prostate Cancer Selects for Progesterone Responsive Mutant Androgen Receptors

Abstract: Purpose: The CYP17A1 inhibitor abiraterone markedly reduces androgen precursors and is thereby effective in castration-resistant prostate cancer (CRPC). However, abiraterone increases progesterone, which can activate certain mutant androgen receptors (AR) identified previously in flutamide-resistant tumors. Therefore, we sought to determine if CYP17A1 inhibitor treatment selects for progesterone-activated mutant ARs. Experimental Design: AR was examined by targeted sequencing in metastatic tumor biopsies from 18 patients with CRPC who were progressing on a CYP17A1 inhibitor (17 on abiraterone, 1 on ketoconazole), alone or in combination with dutasteride, and by whole-exome sequencing in residual tumor in one patient treated with neoadjuvant leuprolide plus abiraterone. Results: The progesterone-activated T878A-mutant AR was present at high allele frequency in 3 of the 18 CRPC cases. It was also present in one focus of resistant tumor in the neoadjuvant-treated patient, but not in a second clonally related resistant focus that instead had lost one copy of PTEN and both copies of CHD1 . The T878A mutation appeared to be less common in the subset of patients with CRPC treated with abiraterone plus dutasteride, and transfection studies showed that dutasteride was a more potent direct antagonist of the T878A versus the wild-type AR. Conclusions: These findings indicate that selection for tumor cells expressing progesterone-activated mutant ARs is a mechanism of resistance to CYP17A1 inhibition. Clin Cancer Res; 21(6); 1–8. ©2014 AACR . See related commentary by Sharifi, p. 1240

Improved magnetic resonance fingerprinting reconstruction with low-rank and subspace modeling.

Abstract: Purpose This article introduces a constrained imaging method based on low-rank and subspace modeling to improve the accuracy and speed of MR fingerprinting (MRF). Theory and methods A new model-based imaging method is developed for MRF to reconstruct high-quality time-series images and accurate tissue parameter maps (e.g., T1 , T2 , and spin density maps). Specifically, the proposed method exploits low-rank approximations of MRF time-series images, and further enforces temporal subspace constraints to capture magnetization dynamics. This allows the time-series image reconstruction problem to be formulated as a simple linear least-squares problem, which enables efficient computation. After image reconstruction, tissue parameter maps are estimated via dictionary-based pattern matching, as in the conventional approach. Results The effectiveness of the proposed method was evaluated with in vivo experiments. Compared with the conventional MRF reconstruction, the proposed method reconstructs time-series images with significantly reduced aliasing artifacts and noise contamination. Although the conventional approach exhibits some robustness to these corruptions, the improved time-series image reconstruction in turn provides more accurate tissue parameter maps. The improvement is pronounced especially when the acquisition time becomes short. Conclusions The proposed method significantly improves the accuracy of MRF, and also reduces data acquisition time. Magn Reson Med 79:933-942, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Integrative Genomics Viewer

Abstract: Rapid improvements in sequencing and array-based platforms are resulting in a flood of diverse genome-wide data, including data from exome and whole-genome sequencing, epigenetic surveys, expression profiling of coding and noncoding RNAs, single nucleotide polymorphism (SNP) and copy number profiling, and functional assays. Analysis of these large, diverse data sets holds the promise of a more comprehensive understanding of the genome and its relation to human disease. Experienced and knowledgeable human review is an essential component of this process, complementing computational approaches. This calls for efficient and intuitive visualization tools able to scale to very large data sets and to flexibly integrate multiple data types, including clinical data. However, the sheer volume and scope of data pose a significant challenge to the development of such tools.