Jiaoti Huang

Overview:

I am a physician-scientist with clinical expertise in the pathologic diagnosis of genitourinary tumors including tumors of the prostate, bladder, kidney and testis. Another area of interest is gynecologic tumors. In my research laboratory we study prostate cancer, focusing on molecular mechanisms of carcinogenesis and tumor progression, as well as biomarkers, imaging and novel therapeutic strategies. In addition to patient care and research, I am also passionate about education. I have trained numerous residents, fellows, graduate students and postdocs.

Positions:

Endowed Department Chair of Pathology

Pathology
School of Medicine

Professor of Pathology

Pathology
School of Medicine

Chair

Pathology
School of Medicine

Professor of Pharmacology and Cancer Biology

Pharmacology & Cancer Biology
School of Medicine

Member of the Duke Cancer Institute

Duke Cancer Institute
School of Medicine

Education:

M.D. 1983

Anhui Medical University (China)

Ph.D. 1991

New York University

Grants:

Histologic and Immunohistochemical Biomarkers for Heavily Treated Metastatic Prostate Cancer

Administered By
Pathology
Awarded By
National Institutes of Health
Role
Principal Investigator
Start Date
End Date

A novel strategy to identify prostate cancer biomarkers for patient management

Administered By
Pathology
Awarded By
National Institutes of Health
Role
Principal Investigator
Start Date
End Date

Stand Up 2 Cancer West Coast Dream Team Grant

Administered By
Pathology
Awarded By
University of San Francisco
Role
Principal Investigator
Start Date
End Date

Confirmation of histologic SCNC (NEPC)

Administered By
Pathology
Awarded By
BioXcel Therapeutics
Role
Principal Investigator
Start Date
End Date

Assessment of macrophage density in historical tNEPC tissue samples.

Administered By
Pathology
Awarded By
BioXcel Therapeutics
Role
Principal Investigator
Start Date
End Date

Publications:

HSP90-specific nIR probe identifies aggressive prostate cancers: translation from preclinical models to a human phase I study.

A noninvasive test to discriminate indolent prostate cancers from lethal ones would focus treatment where necessary while reducing over-treatment. We exploited the known activity of heat shock protein 90 (Hsp90) as a chaperone critical for the function of numerous oncogenic drivers, including the androgen receptor and its variants, to detect aggressive prostate cancer. We linked a near infrared fluorescing molecule to an HSP90 binding drug and demonstrated that this probe (designated HS196) was highly sensitive and specific for detecting implanted prostate cancer cell lines with greater uptake by more aggressive subtypes. In a phase I human study, systemically administered HS196 could be detected in malignant nodules within prostatectomy specimens. Single-cell RNA sequencing identified uptake of HS196 by malignant prostate epithelium from the peripheral zone (AMACR+ERG+EPCAM+ cells), including SYP+ neuroendocrine cells that are associated with therapeutic resistance and metastatic progression. A theranostic version of this molecule is under clinical testing.
Authors
Osada, T; Crosby, EJ; Kaneko, K; Snyder, JC; Ginzel, JD; Acharya, CR; Yang, X-Y; Polascik, TJ; Spasojevic, I; Nelson, RC; Hobeika, A; Hartman, ZC; Neckers, LM; Rogatko, A; Hughes, PF; Huang, J; Morse, MA; Haystead, T; Lyerly, HK
MLA Citation
Osada, Takuya, et al. “HSP90-specific nIR probe identifies aggressive prostate cancers: translation from preclinical models to a human phase I study.Mol Cancer Ther, Oct. 2021. Pubmed, doi:10.1158/1535-7163.MCT-21-0334.
URI
https://scholars.duke.edu/individual/pub1499234
PMID
34675120
Source
pubmed
Published In
Mol Cancer Ther
Published Date
DOI
10.1158/1535-7163.MCT-21-0334

A pleiotropic ATM variant (rs1800057 C>G) is associated with risk of multiple cancers.

ATM (ataxia-telangiectasia mutated) is an important cell-cycle checkpoint kinase required for cellular response to DNA damage. Activated by DNA double strand breaks, ATM regulates the activities of many downstream proteins involved in various carcinogenic events. Therefore, ATM or its genetic variants may have a pleiotropic effect in cancer development. We conducted a pleiotropic analysis to evaluate associations between genetic variants of ATM and risk of multiple cancers. With genotyping data extracted from previously published genome-wide association studies of various cancers, we performed multivariate logistic regression analysis, followed by a meta-analysis for each cancer site, to identify cancer risk-associated single-nucleotide polymorphisms (SNPs). In the ASSET two-sided analysis, we found that two ATM SNPs were significantly associated with risk of multiple cancers. One tagging SNP (rs1800057 C>G) was associated with risk of multiple cancers (two-sided P=5.27×10 -7). Because ATM rs1800057 is a missense variant, we also explored the intermediate phenotypes through which this variant may confer risk of multiple cancers and identified a possible immune-mediated effect of this variant. Our findings indicate that genetic variants of ATM may have a pleiotropic effect on cancer risk and thus provide an important insight into common mechanisms of carcinogenesis.
Authors
Qian, D; Liu, H; Zhao, L; Luo, S; Walsh, KM; Huang, J; Li, C-Y; Wei, Q
MLA Citation
Qian, Danwen, et al. “A pleiotropic ATM variant (rs1800057 C>G) is associated with risk of multiple cancers.Carcinogenesis, Oct. 2021. Pubmed, doi:10.1093/carcin/bgab092.
URI
https://scholars.duke.edu/individual/pub1499401
PMID
34643693
Source
pubmed
Published In
Carcinogenesis
Published Date
DOI
10.1093/carcin/bgab092

A Hybrid Human-Machine Learning Approach for Screening Prostate Biopsies Can Improve Clinical Efficiency Without Compromising Diagnostic Accuracy.

CONTEXT.—: Prostate cancer is a common malignancy, and accurate diagnosis typically requires histologic review of multiple prostate core biopsies per patient. As pathology volumes and complexity increase, new tools to improve the efficiency of everyday practice are keenly needed. Deep learning has shown promise in pathology diagnostics, but most studies silo the efforts of pathologists from the application of deep learning algorithms. Very few hybrid pathologist-deep learning approaches have been explored, and these typically require complete review of histologic slides by both the pathologist and the deep learning system. OBJECTIVE.—: To develop a novel and efficient hybrid human-machine learning approach to screen prostate biopsies. DESIGN.—: We developed an algorithm to determine the 20 regions of interest with the highest probability of malignancy for each prostate biopsy; presenting these regions to a pathologist for manual screening limited the initial review by a pathologist to approximately 2% of the tissue area of each sample. We evaluated this approach by using 100 biopsies (29 malignant, 60 benign, 11 other) that were reviewed by 4 pathologists (3 urologic pathologists, 1 general pathologist) using a custom-designed graphical user interface. RESULTS.—: Malignant biopsies were correctly identified as needing comprehensive review with high sensitivity (mean, 99.2% among all pathologists); conversely, most benign prostate biopsies (mean, 72.1%) were correctly identified as needing no further review. CONCLUSIONS.—: This novel hybrid system has the potential to efficiently triage out most benign prostate core biopsies, conserving time for the pathologist to dedicate to detailed evaluation of malignant biopsies.
Authors
Dov, D; Assaad, S; Syedibrahim, A; Bell, J; Huang, J; Madden, J; Bentley, R; McCall, S; Henao, R; Carin, L; Foo, W-C
MLA Citation
URI
https://scholars.duke.edu/individual/pub1498438
PMID
34591085
Source
pubmed
Published In
Arch Pathol Lab Med
Published Date
DOI
10.5858/arpa.2020-0850-OA

Prognosis Associated With Luminal and Basal Subtypes of Metastatic Prostate Cancer.

Importance: Luminal and basal subtypes of primary prostate cancer have been shown to be molecularly distinct and clinically important in predicting response to therapy. These subtypes have not been described in metastatic prostate cancer. Objectives: To identify clinical and molecular correlates of luminal and basal subtypes in metastatic castration-resistant prostate cancer (mCRPC) and investigate differences in survival, particularly after treatment with androgen-signaling inhibitors (ASIs). Design, Setting, and Participants: In this cohort study, a retrospective analysis was conducted of 4 cohorts with mCRPC (N = 634) across multiple academic centers. Treatment was at the physicians' discretion. Details of the study cohorts have been published elsewhere between 2016 and 2019. Data were analyzed from March 2018 to February 2021. Main Outcomes and Measures: The primary clinical end point was overall survival from the date of tissue biopsy/molecular profiling. Luminal and basal subtypes were also stratified by postbiopsy ASI treatment. The primary molecular analyses included associations with small cell/neuroendocrine prostate cancer (SCNC), molecular pathways, and DNA alterations. Results: In the 634 patients, 288 (45%) had tumors classified as luminal, and 346 (55%) had tumors classified as basal. However, 53 of 59 (90%) SCNC tumors were basal (P < .001). Similar to primary prostate cancer, luminal tumors exhibited overexpression of AR pathway genes. In basal tumors, a significantly higher rate of RB1 loss (23% basal vs 4% luminal; P < .001), FOXA1 alterations (36% basal vs 27% luminal; P = .03) and MYC alterations (73% basal vs 56% luminal; P < .001) were identified. Patients with basal tumors had worse overall survival compared with those with luminal tumors only in patients treated with an ASI postbiopsy (East Coast Dream Team: hazard ratio [HR], 0.39; 95% CI, 0.20-0.74; P = .004; West Coast Dream Team: HR, 0.57; 95% CI, 0.33-0.97; P = .04). Among patients with luminal tumors, those treated with an ASI had significantly better survival (HR, 0.27; 95% CI, 0.14-0.53; P < .001), whereas patients with basal tumors did not (HR, 0.62; 95% CI, 0.36-1.04, P = .07). The interaction term between subtype and ASI treatment was statistically significant (HR, 0.42; 95% CI, 0.20-0.89; P = .02). Conclusions and Relevance: These findings represent the largest integrated clinical, transcriptomic, and genomic analysis of mCRPC samples to date, and suggest that mCRPC can be classified as luminal and basal tumors. Analogous to primary prostate cancer, these data suggest that the benefit of ASI treatment is more pronounced in luminal tumors and support the use of ASIs in this population. In the basal tumors, a chemotherapeutic approach could be considered in some patients given the similarity to SCNC and the diminished benefit of ASI therapy. Further validation in prospective clinical trials is warranted.
Authors
Aggarwal, R; Rydzewski, NR; Zhang, L; Foye, A; Kim, W; Helzer, KT; Bakhtiar, H; Chang, SL; Perry, MD; Gleave, M; Reiter, RE; Huang, J; Evans, CP; Alumkal, JJ; Lang, JM; Yu, M; Quigley, DA; Sjöström, M; Small, EJ; Feng, FY; Zhao, SG
MLA Citation
Aggarwal, Rahul, et al. “Prognosis Associated With Luminal and Basal Subtypes of Metastatic Prostate Cancer.Jama Oncol, Sept. 2021. Pubmed, doi:10.1001/jamaoncol.2021.3987.
URI
https://scholars.duke.edu/individual/pub1497415
PMID
34554200
Source
pubmed
Published In
Jama Oncol
Published Date
DOI
10.1001/jamaoncol.2021.3987

Tissue clearing techniques for three-dimensional optical imaging of intact human prostate and correlations with multi-parametric MRI.

BACKGROUND: Tissue clearing technologies have enabled remarkable advancements for in situ characterization of tissues and exploration of the three-dimensional (3D) relationships between cells, however, these studies have predominantly been performed in non-human tissues and correlative assessment with clinical imaging has yet to be explored. We sought to evaluate the feasibility of tissue clearing technologies for 3D imaging of intact human prostate and the mapping of structurally and molecularly preserved pathology data with multi-parametric volumetric MR imaging (mpMRI). METHODS: Whole-mount prostates were processed with either hydrogel-based CLARITY or solvent-based iDISCO. The samples were stained with a nuclear dye or fluorescently labeled with antibodies against androgen receptor, alpha-methylacyl coenzyme-A racemase, or p63, and then imaged with 3D confocal microscopy. The apparent diffusion coefficient and Ktrans maps were computed from preoperative mpMRI. RESULTS: Quantitative analysis of cleared normal and tumor prostate tissue volumes displayed differences in 3D tissue architecture, marker-specific cell staining, and cell densities that were significantly correlated with mpMRI measurements in this initial, pilot cohort. CONCLUSIONS: 3D imaging of human prostate volumes following tissue clearing is a feasible technique for quantitative radiology-pathology correlation analysis with mpMRI and provides an opportunity to explore functional relationships between cellular structures and cross-sectional clinical imaging.
Authors
Cipollari, S; Jamshidi, N; Du, L; Sung, K; Huang, D; Margolis, DJ; Huang, J; Reiter, RE; Kuo, MD
MLA Citation
Cipollari, Stefano, et al. “Tissue clearing techniques for three-dimensional optical imaging of intact human prostate and correlations with multi-parametric MRI.Prostate, vol. 81, no. 9, June 2021, pp. 521–29. Pubmed, doi:10.1002/pros.24129.
URI
https://scholars.duke.edu/individual/pub1480285
PMID
33876838
Source
pubmed
Published In
Prostate
Volume
81
Published Date
Start Page
521
End Page
529
DOI
10.1002/pros.24129