Yazhen Zhu
VerifiedUniversity of California, Los Angeles · Pharmacology and Pharmaceutical Sciences
Active 1988–2025
About
Dr. Yazhen Zhu is an Assistant Professor and Research Pathologist at the David Geffen School of Medicine at UCLA. She received her MD degree from Wuhan University in 2005 and her PhD degree in Pathology from Fudan University in 2010. Her clinical training includes residency in the Pathology Department of Guangdong Provincial Hospital of TCM, where she served as an attending pathologist for five years, specializing in surgical pathology for gastrointestinal, thyroid, and breast cancers. She also founded and directed the Molecular Pathology Laboratory at Guangdong Provincial Hospital, developing and validating laboratory-developed tests for detecting oncogenic mutations to support targeted cancer therapy. In 2015, Dr. Zhu joined UCLA as a visiting assistant project scientist in the Pharmacology Department. She became a faculty member in 2020, co-directing the liquid biopsy laboratory at UCLA. Her research focuses on exploring the potentials of liquid biopsy markers, including circulating rare cells and extracellular vesicles, to facilitate the clinical translation and validation of cancer and prenatal diagnostic platforms. Her translational research programs aim to develop and validate liquid biopsy-based diagnostic platforms for noninvasive prenatal diagnosis and cancer diagnosis, involving collaborations with clinical faculties across UCLA, Cedars-Sinai Medical Center, and multiple national and international partners.
Research topics
- Cell biology
- Biology
- Medicine
- Biochemistry
- Cancer research
- Chemistry
- Chromatography
- Internal medicine
Selected publications
Advanced Science · 2025-04-17 · 3 citations
articleOpen accessCorrespondingAlzheimer's disease (AD), the most prevalent type of dementia, is characterized by a biological process that begins with the development of AD neuropathologic change (ADNPC) while individuals remain asymptomatic. A key molecular hallmark of ADNPC is the accumulation of amyloid-β plaques. β-secretase plays a critical role in the upstream pathological cleavage of amyloid precursor protein (APP), producing amyloid-β peptides that are prone to misfolding, ultimately contributing to plaque formation. Neuronal extracellular vesicles (NEVs) in the blood transport β-secretase and preserve its activity, allowing for noninvasive profiling of β-secretase activity for detecting early onset of ADNPC. In this study, a novel approach is approached for noninvasive assessment of β-secretase activity in AD patients using an NEV β-secretase activity assay. This assay identifies NEVs exhibiting colocalization of NEV markers with AD-associated β-secretase, generating a β-secretase activity profile for each patient. The NEV β-secretase activity assay represents a significant advancement in leveraging the diagnostic potential of NEVs, offering a noninvasive, quantitative method for reliably assessing β-secretase activity to detect the early onset of ADNPC.
Advanced Functional Materials · 2025-04-01 · 6 citations
articleOpen accessCorrespondingOsteosarcoma (OS), the most common bone cancer in children, is characterized by aggressive tumors and subclinical metastasis. Metastasis significantly impacts OS patient survival rate, highlighting the need for frequent assessment of disease progression and treatment response. The study introduces OS extracellular vesicles (EV) matrix metalloproteinase (MMP) Activity Assay, noninvasively analyzing six combinations of OS EV surface markers and OS-associated MMPs to generate a unique OS EV MMP profile for each patient. An OS EV MMP Activity Score is established from logistic regression of three top-performing combinations to specifically distinguish metastatic OS from localized OS, achieving an area under the receiver operating characteristic (AUROC) curve of 0.97. The Scores from longitudinal monitoring of six OS patients strongly correlate with disease progression and treatment response, as confirmed by radiographic imaging. The OS EV MMP Activity Assay enables noninvasive and timely monitoring of disease progression and treatment response during the critical disease window of progression to metastatic OS, enhancing clinical management for OS patients.
Translational Cancer Research · 2025-06-01
articleOpen accessBackground: An inadequate surgical margin is the major reason for disease recurrence; however, tumor recurrence sometimes even occurs in patients with pathologically negative surgical margins. The aim of this study is to determine the ideal surgical margin in radical colorectal cancer (CRC) surgery using panoramic pathology coupled with a molecular surgical margin (MSM) analysis. Methods: The surgical specimens and clinical data of 194 CRC patients at the Guangxi Medical University Cancer Hospital from January 2016 to December 2019 were collected. Specifically, whole pathological sections of intact primary lesions of CRC were collected. Carcinoembryonic antigen (CEA) and methylation detection were used to analyze the molecular changes and protein expression patterns of different regions. Results: A total of 194 patients with high-quality sections and complete clinical data were included in this study. Different tumor cells and different regions of the primary focus of CRC had different protein expression patterns, and some cells expressed multiple proteins. The submucosal interstitial space of the tumor margin (i.e., the extension area) and the submucosal space near the cancer area was obvious. The positive rate of CEA in the normal mucosal tissues of distant cancer was 19.15%. Conclusions: A tumor is a disease caused by molecular regulation failure and internal environment disorder at the high molecular level of the body. "Cell-cell" interactions may play an important role. In tumor surgery, the cutting edge may not always need to be as extensive as possible, especially when function preservation is important, which affects the quality of life of patients and ultimately affects the actual treatment outcomes. Further high-powered randomized trials need to be conducted to confirm the results of this study.
Cancer Research · 2025-04-21
articleAbstract Methylation alterations are pervasive in cancer, making cfDNA methylomes a promising tool for cancer detection and minimal residual disease (MRD) monitoring. However, cfDNA methylation-based MRD detection faces unique challenges. Unlike genomic mutations, methylation signals are prone to noise from non-tumor-related factors, such as heterogeneous cell types and environmental influences. This noise introduces confounding signals and dilutes tumor-specific methylation markers. While patient-specific tumor methylation profiles offer personalized insights, they often contain inherent noise that compromises their performance. Existing approaches failed to address this challenge, as they either (1) disregard personal tumor methylation profiles, yielding suboptimal performance, or (2) fail to account for noise, treating all personal markers as equally informative. These limitations underscore a crucial need for a novel mca and evaluates their prevalence across population cancer methylome data. Population prevalence quantifies the cancer relevance of noisy personal markers, filtering out non-tumor-related signals and prioritizing true tumor markers. With these purified personal markers, cfTrack-methyl achieves sensitive and specific MRD detection, even at ultra-low tumor fractions. In the titration series between fragmented liver tumor and WBC gDNA, the detection limit of cfTrack-methyl was calculated as < 0.01% with 100% sensitivity and 100% specificity. In simulated data with plasma spike-ins from various cancer types, cfTrack-methyl achieved a median AUC of 99.6% at a tumor fraction of 0.01% when tumor tissue was informed and maintained robust performance with a median AUC = 100.0% at a tumor fraction of 0.01% when tumor tissue was unavailable and pre-treatment plasma was used. Further, in liver cancer patients with complex comorbidities (cirrhosis or HBV), cfTrack-methyl achieved comparable detection limits, with a median AUC = 99.9% at a tumor fraction of 0.01%. In all scenarios, cfTrack-methyl outperformed the MRD detection using general cancer markers. In conclusion, cfTrack-methyl delivers unprecedented sensitivity for MRD detection in cfDNA at low tumor fractions across diverse clinical scenarios. By integrating personal and population data, this approach maximizes the potential of cfDNA methylomes for ultra-sensitive MRD detection. Ongoing studies using serial samples from patients with surgically resected tumors aim to further validate this personalized strategy. Citation Format: Li Shuo, Weihua Zeng, Xiaohui Ni, Chun-Chi Liu, Yonggang Zhou, Mary L. Stackpole, Wenyuan Li, Arjan Gower, Kostyantyn Krysan, Andrew Melehy, Angela H. Yeh, Megumi Yokomizo, Preeti Ahuja, Lopa Mishra, Kirti Shetty, Yazhen Zhu, Hsian-Rong Tseng, Clara Lajonchere, Daniel H. Geschwind, David S. Lu, Steven S. Raman, William Hsu, Clara E. Magyar, Samuel W. French, Denise R. Aberle, Steven-Huy B. Han, Edward B. Garon, Vatche G. Agopian, Wing H. Wong, Steven M. Dubinett, Xianghong Jasmine Zhou. cfTrack-methyl: A personalized approach using cfDNA methylomes for ultra-sensitive MRD detection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3244.
Journal of Clinical Oncology · 2025-05-28
articleSenior authore16321 Background: There are no validated biomarkers for assessing hepatocellular carcinoma (HCC) treatment response (TR). Extracellular vesicles (EVs) are promising circulating biomarkers that may detect minimal residual disease in patients with treated HCC. Methods: We developed the HCC EV TR Score using HCC EV Digital Scoring Assay involving click chemistry-mediated enrichment of HCC EVs, followed by absolute quantification of HCC EV-specific genes by RT-Digital PCR. Six HCC EV-specific genes were selected and validated through i) a comprehensive data analysis pipeline with an unprecedentedly large collection of liver transcriptome datasets (n = 9,160), ii) RNAscope validation on HCC tissues (n = 6), and iii) a pilot study on early- or intermediate-stage HCC and liver cirrhosis patients (n = 70). The performance of HCC EV TR Score was assessed in a phase-2 retrospective case-control study (n = 100). Results: HCC EV TR Scores, calculated from pre- and post-treatment plasma samples in the phase-2 case-control study, accurately differentiated post-treatment viable from nonviable HCC in the training (area under the ROC curve [AUROC] of 0.90, n = 49) and validation set (AUROC of 0.88, n = 51). At an optimal cutoff of 0.76 identified in the training set, HCC EV TR Score had high accuracy in detecting viable tumors (sensitivity: 76.5%, specificity: 88.2%) and found residual disease not initially observed on MRI in six patients with a median lead time of 63 days. Conclusions: This EV-based digital scoring approach shows great promise to augment cross-sectional imaging for the assessment of HCC treatment response.
2025-07-14
book-chapter1st authorCorrespondingExtracellular vesicles (EVs), released by various cell types, including tumors, offer a promising avenue for noninvasive cancer detection. Their mRNA cargo, protected within EVs, reflects the cell of origin, making them attractive for liquid biopsy strategies. This chapter summarizes tumor EV mRNA digital scoring assays, utilizing click chemistry-mediated EV enrichment platforms (EV Click Chips and EV Click Beads) coupled with RT-digital PCR for mRNA profiling. These assays enable the early detection of hepatocellular carcinoma, staging of prostate cancers, and the assessment of treatment response in Ewing sarcoma and pancreatic ductal carcinoma. EV Click Chips/Beads hold promise for enrichment of tumor EVs from other solid tumors, thus expanding the application of EV mRNA digital scoring assays for broader cancer diagnostics.
Advanced Science · 2025-07-01
articleOpen accessAlzheimer's DiseAseIn article number 2415289, Junseok Lee, Yazhen Zhu, Hsian-Rong Tseng, and co-workers present the NEV -secretase activity assay, a groundbreaking method for noninvasive evaluation of -secretase activity in Alzheimer's disease (AD) patients, enabling the generation of individualized -secretase activity profiles.The cover illustrates a brain AD, with neuronderived NEV selectively enriched via click chemistry, followed by quantifying activity of NEVderived -secretase using FRET probes for AD diagnosis.
Advanced Science · 2025-06-01
articleOpen accessSenior authorEarly PancrEatic cancEr DEtEctionNa Sun, Hsian-Rong Tseng, Yazhen Zhu, and colleagues have developed a simple, noninvasive test for early pancreatic cancer detection.Their approach uses click chemistry to capture extracellular vesicles (EVs) released by cancer cells.Once isolated, these cancer EVs are analyzed with a standard PCR machine to measure their mRNA content, providing a promising biomarker for pancreatic cancer.More details can be found in article number 2414982.
Advanced Functional Materials · 2025-10-01
articleOpen accessLiquid Biopsy In their Research Article (10.1002/adfm.202422469), Junseok Lee, Steven John Jonas, Shaohua Lu, Yazhen Zhu, Hsian-Rong Tseng, and co-workers present a novel osteosarcoma extracellular vesicle matrix metalloproteinase activity assay (OS EV MMP activity assay) for noninvasive and real-time monitoring of disease progression and treatment response in pediatric osteosarcoma. By combining click chemistry-enabled tumor EV enrichment with FRET-based protease activity profiling, the assay quantitatively evaluates six specific EV surface marker/MMP combinations. This minimally invasive platform enables early detection of metastasis and longitudinal disease monitoring, offering a powerful tool to improve clinical management of osteosarcoma.
Advanced Science · 2025-03-25 · 14 citations
articleOpen accessSenior authorCorrespondingPancreatic ductal adenocarcinoma (PDAC) is a leading cause of cancer-related mortality, largely due to late-stage diagnosis. Reliable early detection methods are critically needed. PDAC-derived extracellular vesicles (EVs) carry molecules that reflect their parental tumor cells and are detectable in early disease stages, offering a promising noninvasive diagnostic approach. Here, a streamlined PDAC EV Surface Protein Assay for quantifying PDAC EV subpopulations in 300-µL plasma through a two-step workflow is presented: i) click chemistry-mediated EV enrichment using EV Click Beads and trans-cyclooctene-grafted antibodies targeting three PDAC EV-specific surface proteins (MUC1, EGFR, and TROP2), and ii) quantification of enriched PDAC EVs through reverse transcription-quantitative polymerase chain reaction. The three PDAC EV-specific surface proteins are identified using a bioinformatics framework and validated on PDAC cell lines and tissue microarrays. The resultant PDAC EV Score, derived from signals of the three PDAC EV subpopulations, demonstrates robust differentiation of PDAC patients from noncancer controls, with area under the receiver operating characteristic curves of 0.94 in the training (n = 124) and 0.93 in the validation (n = 136) cohorts. This EV-based diagnostic approach successfully exploits PDAC EV subpopulations as novel biomarkers for PDAC early detection, translating PDAC surface proteins into an EV-based liquid biopsy platform.
Recent grants
NIH · $2.0M · 2023–2028
NIH · $3.3M · 2020–2026
Biomimetic NanoVilli Chips for Extracellular Vesicles in Hepatocellular Carcinoma
NIH · $419k · 2020–2023
Extracellular Vesicle-Based Digital Scoring Assay for Detecting Early-stage Hepatocellular Carcinoma
NIH · $3.2M · 2021–2026
Click Chemistry-Mediated Microfluidic Sorting for HCC CTCs
NIH · $543k · 2019–2023
Frequent coauthors
- 258 shared
Hsian‐Rong Tseng
University of California, Los Angeles
- 115 shared
Na Sun
Suzhou Institute of Nano-tech and Nano-bionics
- 85 shared
Yi‐Te Lee
Cedars-Sinai Medical Center
- 77 shared
Qing Xu
Northwest Institute of Nuclear Technology
- 74 shared
Dan Lü
Uniformed Services University of the Health Sciences
- 73 shared
Jianghong Xiong
University of Minnesota Rochester
- 73 shared
Mao Mao
Yonsei University
- 73 shared
Maruja E. Lira
Education
- 2010
PhD, Pathology
Fudan University
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