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Featured researches published by David Akin.


Gastroenterology | 2010

Salivary Transcriptomic Biomarkers for Detection of Resectable Pancreatic Cancer

Lei Zhang; James J. Farrell; Hui Zhou; David Elashoff; David Akin; No-Hee Park; David Chia; David T. Wong

BACKGROUND & AIMS Lack of detection technology for early pancreatic cancer invariably leads to a typical clinical presentation of incurable disease at initial diagnosis. New strategies and biomarkers for early detection are sorely needed. In this study, we have conducted a prospective sample collection and retrospective blinded validation to evaluate the performance and translational utilities of salivary transcriptomic biomarkers for the noninvasive detection of resectable pancreatic cancer. METHODS The Affymetrix HG U133 Plus 2.0 Array (Affymetrix, Santa Clara, CA) was used to profile transcriptomes and discover altered gene expression in saliva supernatant. Biomarkers discovered from the microarray study were subjected to clinical validation using an independent sample set of 30 pancreatic cancer patients, 30 chronic pancreatitis patients, and 30 healthy controls. RESULTS Twelve messenger RNA biomarkers were discovered and validated. The logistic regression model with the combination of 4 messenger RNA biomarkers (KRAS, MBD3L2, ACRV1, and DPM1) could differentiate pancreatic cancer patients from noncancer subjects (chronic pancreatitis and healthy control), yielding a receiver operating characteristic plot, area under the curve value of 0.971 with 90.0% sensitivity and 95.0% specificity. CONCLUSIONS The salivary biomarkers possess discriminatory power for the detection of resectable pancreatic cancer, with high specificity and sensitivity. This report provides the proof of concept of salivary biomarkers for the noninvasive detection of a systemic cancer and paves the way for prediction model validation study followed by pivotal clinical validation.


PLOS ONE | 2010

Discovery and Preclinical Validation of Salivary Transcriptomic and Proteomic Biomarkers for the Non-Invasive Detection of Breast Cancer

Lei Zhang; Hua Xiao; Scott Karlan; Hui Zhou; Jenny Gross; David Elashoff; David Akin; Xinmin Yan; David Chia; Beth Y. Karlan; David T. Wong

Background A sensitive assay to identify biomarkers using non-invasively collected clinical specimens is ideal for breast cancer detection. While there are other studies showing disease biomarkers in saliva for breast cancer, our study tests the hypothesis that there are breast cancer discriminatory biomarkers in saliva using de novo discovery and validation approaches. This is the first study of this kind and no other study has engaged a de novo biomarker discovery approach in saliva for breast cancer detection. In this study, a case-control discovery and independent preclinical validations were conducted to evaluate the performance and translational utilities of salivary transcriptomic and proteomic biomarkers for breast cancer detection. Methodology/Principal Findings Salivary transcriptomes and proteomes of 10 breast cancer patients and 10 matched controls were profiled using Affymetrix HG-U133-Plus-2.0 Array and two-dimensional difference gel electrophoresis (2D-DIGE), respectively. Preclinical validations were performed to evaluate the discovered biomarkers in an independent sample cohort of 30 breast cancer patients and 63 controls using RT-qPCR (transcriptomic biomarkers) and quantitative protein immunoblot (proteomic biomarkers). Transcriptomic and proteomic profiling revealed significant variations in salivary molecular biomarkers between breast cancer patients and matched controls. Eight mRNA biomarkers and one protein biomarker, which were not affected by the confounding factors, were pre-validated, yielding an accuracy of 92% (83% sensitive, 97% specific) on the preclinical validation sample set. Conclusions Our findings support that transcriptomic and proteomic signatures in saliva can serve as biomarkers for the non-invasive detection of breast cancer. The salivary biomarkers possess discriminatory power for the detection of breast cancer, with high specificity and sensitivity, which paves the way for prediction model validation study followed by pivotal clinical validation.


Cancer Epidemiology, Biomarkers & Prevention | 2012

Prevalidation of Salivary Biomarkers for Oral Cancer Detection

David Elashoff; Hui Zhou; Jean Reiss; Jianghua Wang; Hua Xiao; Bradley S. Henson; Shen Hu; Martha Arellano; Uttam K. Sinha; Anh Le; Diana Messadi; Marilene Wang; Vishad Nabili; Mark W. Lingen; Darly Morris; Timothy W. Randolph; Ziding Feng; David Akin; Dragana Kastratovic; David Chia; Elliot Abemayor; David T. Wong

Background: Oral cancer is the sixth most common cancer with a 5-year survival rate of approximately 60%. Presently, there are no scientifically credible early detection techniques beyond conventional clinical oral examination. The goal of this study is to validate whether the seven mRNAs and three proteins previously reported as biomarkers are capable of discriminating patients with oral squamous cell carcinomas (OSCC) from healthy subjects in independent cohorts and by a National Cancer Institute (NCI)-Early Detection Research Network (EDRN)-Biomarker Reference Laboratory (BRL). Methods: Three hundred and ninety-five subjects from five independent cohorts based on case controlled design were investigated by two independent laboratories, University of California, Los Angeles (Los Angeles, CA) discovery laboratory and NCI-EDRN-BRL. Results: Expression of all seven mRNA and three protein markers was increased in OSCC versus controls in all five cohorts. With respect to individual marker performance across the five cohorts, the increase in interleukin (IL)-8 and subcutaneous adipose tissue (SAT) was statistically significant and they remained top performers across different cohorts in terms of sensitivity and specificity. A previously identified multiple marker model showed an area under the receiver operating characteristic (ROC) curve for prediction of OSCC status ranging from 0.74 to 0.86 across the cohorts. Conclusions: The validation of these biomarkers showed their feasibility in the discrimination of OSCCs from healthy controls. Established assay technologies are robust enough to perform independently. Individual cutoff values for each of these markers and for the combined predictive model need to be further defined in large clinical studies. Impact: Salivary proteomic and transcriptomic biomarkers can discriminate oral cancer from control subjects. Cancer Epidemiol Biomarkers Prev; 21(4); 664–72. ©2012 AACR.


Oral Oncology | 2011

Oral squamous cell carcinoma detection by salivary biomarkers in a Serbian population

Ole Brinkmann; Dragana Kastratovic; Milovan Dimitrijevic; Vitomir S. Konstantinović; D.B. Jelovac; Jadranka Antic; Vladimir S. Nesic; Srdjan Markovic; Zeljko R. Martinovic; David Akin; Nadine Spielmann; Hui Zhou; David T. Wong

Early detection of oral squamous cell cancer (OSCC) is the key to improve the low 5-year survival rate. Using proteomic and genomic technologies we have previously discovered and validated salivary OSCC markers in American patients. The question arises whether these biomarkers are discriminatory in cohorts of different ethnic background. Six transcriptome (DUSP1, IL8, IL1B, OAZ1, SAT1, and S100P) and three proteome (IL1B, IL8, and M2BP) biomarkers were tested on 18 early and 17 late stage OSCC patients and 51 healthy controls with quantitative PCR and ELISA. Four transcriptome (IL8, IL1B, SAT1, and S100P) and all proteome biomarkers were significantly elevated (p<0.05) in OSCC patients. The combination of markers yielded an AUC of 0.86, 0.85 and 0.88 for OSCC total, T1-T2, and T3-T4, respectively. The sensitivity/specificity for OSCC total was 0.89/0.78, for T1-T2 0.67/0.96, and for T3-T4 0.82/0.84. In conclusion, seven of the nine salivary biomarkers (three proteins and four mRNAs) were validated and performed strongest in late stage cancer. Patient-based salivary diagnostics is a highly promising approach for OSCC detection. This study shows that previously discovered and validated salivary OSCC biomarkers are discriminatory and reproducible in a different ethnic cohort. These findings support the feasibility to implement multi-center, multi-ethnicity clinical trials towards the pivotal validation of salivary biomarkers for OSCC detection.


Thoracic Cancer | 2016

Evaluation of a novel saliva‐based epidermal growth factor receptor mutation detection for lung cancer: A pilot study

Dan Pu; Hao Liang; Fang Wei; David Akin; Ziding Feng; Qing Xiang Yan; Yin Li; Yan Zhen; Lin Xu; Gaochao Dong; Huajing Wan; Jingsi Dong; Xiao-Ming Qiu; Chang-Long Qin; Daxing Zhu; Xi Wang; Tong Sun; Wenbiao Zhang; Canjun Li; Xiaojun Tang; You-Lin Qiao; David T. Wong; Qinghua Zhou

This article describes a pilot study evaluating a novel liquid biopsy system for non‐small cell lung cancer (NSCLC) patients. The electric field‐induced release and measurement (EFIRM) method utilizes an electrochemical biosensor for detecting oncogenic mutations in biofluids.


Neurobiology of Aging | 2014

Cheek cell-derived α-synuclein and DJ-1 do not differentiate Parkinson's disease from control.

Tessandra Stewart; Yu Ting Sui; Luis F. Gonzalez-Cuyar; David T. Wong; David Akin; Vitor Tumas; Jan O. Aasly; Emily Ashmore; Patrick Aro; Carmen Ginghina; Ane Korff; Cyrus P. Zabetian; James B. Leverenz; Min Shi; Jing Zhang

Recently, α-synuclein (α-syn) and DJ-1, 2 proteins critically involved in Parkinsons disease (PD), have been shown to be present in saliva, suggesting their potential utility as biomarkers of PD. However, the origin and influence of demographic characteristics (e.g., age or sex) on these proteins are unknown. We identified cheek epithelium, which forms the majority of the cellular component of saliva and is readily accessible clinically, as 1 of several potential sources of salivary α-syn and DJ-1. However, no PD-related trend in the cellular component was present. In the supernatant collected from 198 healthy subjects, no correlation was seen between salivary DJ-1 or α-syn with age. When male and female subjects were analyzed separately, a weak age-dependent increase in DJ-1 level was present in male subjects, along with slightly increased α-syn in female subjects. These results, albeit largely negative, provide critical information for understanding the salivary gland pathology and saliva as a PD biomarker source, and must be considered in future investigations of salivary changes in PD.


Clinical Chemistry | 2018

Discovery and Validation of Salivary Extracellular RNA Biomarkers for Noninvasive Detection of Gastric Cancer

Feng Li; Janice M. Yoshizawa; Kyoung-Mee Kim; Julie Kanjanapangka; Tristan Grogan; Xiaoyan Wang; David E. Elashoff; Shigeo Ishikawa; David Chia; Wei Liao; David Akin; Xinmin Yan; Min-Sun Lee; Rayun Choi; Su-Mi Kim; So Young Kang; Jae-Moon Bae; Tae-Sung Sohn; Jun Ho Lee; Min-Gew Choi; Byung-Hoon Min; Lee Jh; Jae J. Kim; Yong Kim; Sung Kim; David T. Wong

BACKGROUND Biomarkers are needed for noninvasive early detection of gastric cancer (GC). We investigated salivary extracellular RNA (exRNA) biomarkers as potential clinical evaluation tools for GC. METHODS Unstimulated whole saliva samples were prospectively collected from 294 individuals (163 GC and 131 non-GC patients) who underwent endoscopic evaluation at the Samsung Medical Center in Korea. Salivary transcriptomes of 63 GC and 31 non-GC patients were profiled, and mRNA biomarker candidates were verified with reverse transcription quantitative real-time PCR (RT-qPCR). In parallel, microRNA (miRNA) biomarkers were profiled and verified with saliva samples from 10 GC and 10 non-GC patients. Candidate biomarkers were validated with RT-qPCR in an independent cohort of 100/100 saliva samples from GC and non-GC patients. Validated individual markers were configured into a best performance panel. RESULTS We identified 30 mRNA and 15 miRNA candidates whose expression pattern associated with the presence of GC. Among them, 12 mRNA and 6 miRNA candidates were verified with the discovery cohort by RT-qPCR and further validated with the independent cohort (n = 200). The configured biomarker panel consisted of 3 mRNAs (SPINK7, PPL, and SEMA4B) and 2 miRNAs (MIR140-5p and MIR301a), which were all significantly down-regulated in the GC group, and yielded an area under the ROC curve (AUC) of 0.81 (95% CI, 0.72-0.89). When combined with demographic factors, the AUC of the biomarker panel reached 0.87 (95% CI, 0.80-0.93). CONCLUSIONS We have discovered and validated a panel of salivary exRNA biomarkers with credible clinical performance for the detection of GC. Our study demonstrates the potential utility of salivary exRNA biomarkers in screening and risk assessment for GC.


Gut | 2012

Variations of oral microbiota are associated with pancreatic diseases including pancreatic cancer

James J. Farrell; Lei Zhang; Hui Zhou; David Chia; David Elashoff; David Akin; Bruce J. Paster; Kaumudi Joshipura; David T. Wong


Cellular and Molecular Life Sciences | 2012

Development of transcriptomic biomarker signature in human saliva to detect lung cancer

Lei Zhang; Hua Xiao; Hui Zhou; Silverio Santiago; Jay M. Lee; Edward B. Garon; Jieping Yang; Ole Brinkmann; Xinmin Yan; David Akin; David Chia; David Elashoff; No-Hee Park; David T. Wong


Journal of Clinical Periodontology | 2018

Salivary exRNA biomarkers to detect gingivitis and monitor disease regression

Karolina Elżbieta Kaczor-Urbanowicz; Harsh M. Trivedi; Patricia O. Lima; Paulo M. Camargo; William V. Giannobile; Tristan Grogan; Frederico O. Gleber-Netto; Yair Whiteman; Feng Li; Hyo Jung Lee; Karan Dharia; Katri Aro; Carmen Martín Carreras-Presas; Saarah Amuthan; Manjiri Vartak; David Akin; Hiba Al-adbullah; Kanika Bembey; Perry R. Klokkevold; David Elashoff; Virginia Monsul Barnes; Rose Richter; DeVizio W; James G. Masters; David T. Wong

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David T. Wong

University of California

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David Chia

University of California

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David Elashoff

University of California

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Hui Zhou

University of California

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Lei Zhang

University of California

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Xinmin Yan

University of California

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Hua Xiao

Shanghai Jiao Tong University

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Ziding Feng

University of Texas MD Anderson Cancer Center

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Beth Y. Karlan

Cedars-Sinai Medical Center

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