Network


Latest external collaboration on country level. Dive into details by clicking on the dots.

Hotspot


Dive into the research topics where Andrew V. Uzilov is active.

Publication


Featured researches published by Andrew V. Uzilov.


PLOS ONE | 2015

Personalized Circulating Tumor DNA Biomarkers Dynamically Predict Treatment Response and Survival In Gynecologic Cancers

Elena Pereira; Olga Camacho-Vanegas; Sanya Anand; Robert Sebra; Sandra Catalina Camacho; Leopold Garnar-Wortzel; N. Nair; Erin Moshier; Melissa Wooten; Andrew V. Uzilov; Rong Chen; Monica Prasad-Hayes; K. Zakashansky; Ann Marie Beddoe; Eric E. Schadt; Peter Dottino; John A. Martignetti

Background High-grade serous ovarian and endometrial cancers are the most lethal female reproductive tract malignancies worldwide. In part, failure to treat these two aggressive cancers successfully centers on the fact that while the majority of patients are diagnosed based on current surveillance strategies as having a complete clinical response to their primary therapy, nearly half will develop disease recurrence within 18 months and the majority will die from disease recurrence within 5 years. Moreover, no currently used biomarkers or imaging studies can predict outcome following initial treatment. Circulating tumor DNA (ctDNA) represents a theoretically powerful biomarker for detecting otherwise occult disease. We therefore explored the use of personalized ctDNA markers as both a surveillance and prognostic biomarker in gynecologic cancers and compared this to current FDA-approved surveillance tools. Methods and Findings Tumor and serum samples were collected at time of surgery and then throughout treatment course for 44 patients with gynecologic cancers, representing 22 ovarian cancer cases, 17 uterine cancer cases, one peritoneal, three fallopian tube, and one patient with synchronous fallopian tube and uterine cancer. Patient/tumor-specific mutations were identified using whole-exome and targeted gene sequencing and ctDNA levels quantified using droplet digital PCR. CtDNA was detected in 93.8% of patients for whom probes were designed and levels were highly correlated with CA-125 serum and computed tomography (CT) scanning results. In six patients, ctDNA detected the presence of cancer even when CT scanning was negative and, on average, had a predictive lead time of seven months over CT imaging. Most notably, undetectable levels of ctDNA at six months following initial treatment was associated with markedly improved progression free and overall survival. Conclusions Detection of residual disease in gynecologic, and indeed all cancers, represents a diagnostic dilemma and a potential critical inflection point in precision medicine. This study suggests that the use of personalized ctDNA biomarkers in gynecologic cancers can identify the presence of residual tumor while also more dynamically predicting response to treatment relative to currently used serum and imaging studies. Of particular interest, ctDNA was an independent predictor of survival in patients with ovarian and endometrial cancers. Earlier recognition of disease persistence and/or recurrence and the ability to stratify into better and worse outcome groups through ctDNA surveillance may open the window for improved survival and quality and life in these cancers.


BMC Genomics | 2015

Disease-associated variants in different categories of disease located in distinct regulatory elements

Meng Ma; Ying Ru; Ling-Shiang Chuang; Nai-Yun Hsu; Lisong Shi; Jörg Hakenberg; Wei-Yi Cheng; Andrew V. Uzilov; Wei Ding; Benjamin S. Glicksberg; Rong Chen

BackgroundThe invention of high throughput sequencing technologies has led to the discoveries of hundreds of thousands of genetic variants associated with thousands of human diseases. Many of these genetic variants are located outside the protein coding regions, and as such, it is challenging to interpret the function of these genetic variants by traditional genetic approaches. Recent genome-wide functional genomics studies, such as FANTOM5 and ENCODE have uncovered a large number of regulatory elements across hundreds of different tissues or cell lines in the human genome. These findings provide an opportunity to study the interaction between regulatory elements and disease-associated genetic variants. Identifying these diseased-related regulatory elements will shed light on understanding the mechanisms of how these variants regulate gene expression and ultimately result in disease formation and progression.ResultsIn this study, we curated and categorized 27,558 Mendelian disease variants, 20,964 complex disease variants, 5,809 cancer predisposing germline variants, and 43,364 recurrent cancer somatic mutations. Compared against nine different types of regulatory regions from FANTOM5 and ENCODE projects, we found that different types of disease variants show distinctive propensity for particular regulatory elements. Mendelian disease variants and recurrent cancer somatic mutations are 22-fold and 10- fold significantly enriched in promoter regions respectively (q<0.001), compared with allele-frequency-matched genomic background. Separate from these two categories, cancer predisposing germline variants are 27-fold enriched in histone modification regions (q<0.001), 10-fold enriched in chromatin physical interaction regions (q<0.001), and 6-fold enriched in transcription promoters (q<0.001). Furthermore, Mendelian disease variants and recurrent cancer somatic mutations share very similar distribution across types of functional effects.We further found that regulatory regions are located within over 50% coding exon regions. Transcription promoters, methylation regions, and transcription insulators have the highest density of disease variants, with 472, 239, and 72 disease variants per one million base pairs, respectively.ConclusionsDisease-associated variants in different disease categories are preferentially located in particular regulatory elements. These results will be useful for an overall understanding about the differences among the pathogenic mechanisms of various disease-associated variants.


BMC Bioinformatics | 2016

Integrating 400 million variants from 80,000 human samples with extensive annotations: towards a knowledge base to analyze disease cohorts

Jörg Hakenberg; Wei-Yi Cheng; Philippe Thomas; Ying-Chih Wang; Andrew V. Uzilov; Rong Chen

BackgroundData from a plethora of high-throughput sequencing studies is readily available to researchers, providing genetic variants detected in a variety of healthy and disease populations. While each individual cohort helps gain insights into polymorphic and disease-associated variants, a joint perspective can be more powerful in identifying polymorphisms, rare variants, disease-associations, genetic burden, somatic variants, and disease mechanisms.DescriptionWe have set up a Reference Variant Store (RVS) containing variants observed in a number of large-scale sequencing efforts, such as 1000 Genomes, ExAC, Scripps Wellderly, UK10K; various genotyping studies; and disease association databases. RVS holds extensive annotations pertaining to affected genes, functional impacts, disease associations, and population frequencies. RVS currently stores 400 million distinct variants observed in more than 80,000 human samples.ConclusionsRVS facilitates cross-study analysis to discover novel genetic risk factors, gene–disease associations, potential disease mechanisms, and actionable variants. Due to its large reference populations, RVS can also be employed for variant filtration and gene prioritization.AvailabilityA web interface to public datasets and annotations in RVS is available at https://rvs.u.hpc.mssm.edu/.


Clinical Cancer Research | 2017

Inhibition of the Nuclear Export Receptor XPO1 as a Therapeutic Target for Platinum-Resistant Ovarian Cancer

Ying Chen; Sandra Catalina Camacho; Thomas Silvers; Albiruni R. A. Razak; Nashat Y. Gabrail; John F. Gerecitano; Eva Kalir; Elena Pereira; Brad R. Evans; Susan J. Ramus; Fei Huang; Nolan Priedigkeit; Estefania Rodriguez; Michael J. Donovan; Faisal M. Khan; Tamara Kalir; Robert Sebra; Andrew V. Uzilov; Rong Chen; Rileen Sinha; Richard Halpert; Jean-Noel Billaud; Sharon Shacham; Dilara McCauley; Yosef Landesman; Tami Rashal; Michael Kauffman; Mansoor Raza Mirza; Morten Mau-Sorensen; Peter Dottino

Purpose: The high fatality-to-case ratio of ovarian cancer is directly related to platinum resistance. Exportin-1 (XPO1) is a nuclear exporter that mediates nuclear export of multiple tumor suppressors. We investigated possible clinicopathologic correlations of XPO1 expression levels and evaluated the efficacy of XPO1 inhibition as a therapeutic strategy in platinum-sensitive and -resistant ovarian cancer. Experimental Design: XPO1 expression levels were analyzed to define clinicopathologic correlates using both TCGA/GEO datasets and tissue microarrays (TMA). The effect of XPO1 inhibition, using the small-molecule inhibitors KPT-185 and KPT-330 (selinexor) alone or in combination with a platinum agent on cell viability, apoptosis, and the transcriptome was tested in immortalized and patient-derived ovarian cancer cell lines (PDCL) and platinum-resistant mice (PDX). Seven patients with late-stage, recurrent, and heavily pretreated ovarian cancer were treated with an oral XPO1 inhibitor. Results: XPO1 RNA overexpression and protein nuclear localization were correlated with decreased survival and platinum resistance in ovarian cancer. Targeted XPO1 inhibition decreased cell viability and synergistically restored platinum sensitivity in both immortalized ovarian cancer cells and PDCL. The XPO1 inhibitor–mediated apoptosis occurred through both p53-dependent and p53-independent signaling pathways. Selinexor treatment, alone and in combination with platinum, markedly decreased tumor growth and prolonged survival in platinum-resistant PDX and mice. In selinexor-treated patients, tumor growth was halted in 3 of 5 patients, including one with a partial response, and was safely tolerated by all. Conclusions: Taken together, these results provide evidence that XPO1 inhibition represents a new therapeutic strategy for overcoming platinum resistance in women with ovarian cancer. Clin Cancer Res; 23(6); 1552–63. ©2016 AACR.


JCI insight | 2017

Genomic profiling reveals mutational landscape in parathyroid carcinomas

Chetanya Pandya; Andrew V. Uzilov; Justin Bellizzi; Chun Yee Lau; Aye S. Moe; Maya Strahl; Wissam Hamou; Leah C. Newman; Marc Y. Fink; Yevgeniy Antipin; Willie Yu; Mark Stevenson; Branca Cavaco; Bin Tean Teh; Rajesh V. Thakker; Hans Morreau; Eric E. Schadt; Robert Sebra; Shuyu D. Li; Andrew Arnold; Rong Chen

Parathyroid carcinoma (PC) is an extremely rare malignancy lacking effective therapeutic intervention. We generated and analyzed whole-exome sequencing data from 17 patients to identify somatic and germline genetic alterations. A panel of selected genes was sequenced in a 7-tumor expansion cohort. We show that 47% (8 of 17) of the tumors harbor somatic mutations in the CDC73 tumor suppressor, with germline inactivating variants in 4 of the 8 patients. The PI3K/AKT/mTOR pathway was altered in 21% of the 24 cases, revealing a major oncogenic pathway in PC. We observed CCND1 amplification in 29% of the 17 patients, and a previously unreported recurrent mutation in putative kinase ADCK1. We identified the first sporadic PCs with somatic mutations in the Wnt canonical pathway, complementing previously described epigenetic mechanisms mediating Wnt activation. This is the largest genomic sequencing study of PC, and represents major progress toward a full molecular characterization of this rare malignancy to inform improved and individualized treatments.


Cold Spring Harb Mol Case Stud | 2017

Identification of a novel RASD1 somatic mutation in a USP8-mutated corticotroph adenoma

Andrew V. Uzilov; Khadeen C. Cheesman; Marc Y. Fink; Leah C. Newman; Chetanya Pandya; Yelena Lalazar; Marco M. Hefti; Mary Fowkes; Gintaras Deikus; Chun Yee Lau; Aye S. Moe; Yayoi Kinoshita; Yumi Kasai; Micol Zweig; Arpeta Gupta; Daniela Starcevic; Milind Mahajan; Eric E. Schadt; Kalmon D. Post; Michael J. Donovan; Robert Sebra; Rong Chen; Eliza B. Geer

Cushings disease (CD) is caused by pituitary corticotroph adenomas that secrete excess adrenocorticotropic hormone (ACTH). In these tumors, somatic mutations in the gene USP8 have been identified as recurrent and pathogenic and are the sole known molecular driver for CD. Although other somatic mutations were reported in these studies, their contribution to the pathogenesis of CD remains unexplored. No molecular drivers have been established for a large proportion of CD cases and tumor heterogeneity has not yet been investigated using genomics methods. Also, even in USP8-mutant tumors, a possibility may exist of additional contributing mutations, following a paradigm from other neoplasm types where multiple somatic alterations contribute to neoplastic transformation. The current study utilizes whole-exome discovery sequencing on the Illumina platform, followed by targeted amplicon-validation sequencing on the Pacific Biosciences platform, to interrogate the somatic mutation landscape in a corticotroph adenoma resected from a CD patient. In this USP8-mutated tumor, we identified an interesting somatic mutation in the gene RASD1, which is a component of the corticotropin-releasing hormone receptor signaling system. This finding may provide insight into a novel mechanism involving loss of feedback control to the corticotropin-releasing hormone receptor and subsequent deregulation of ACTH production in corticotroph tumors.


Scientific Reports | 2017

Novel Therapeutics Identification for Fibrosis in Renal Allograft Using Integrative Informatics Approach

Li Li; Ilana Greene; Benjamin Readhead; Madhav C. Menon; Brian A. Kidd; Andrew V. Uzilov; Chengguo Wei; Nimrod Philippe; Bernd Schröppel; John Cijiang He; Rong Chen; Joel T. Dudley; Barbara Murphy

Chronic allograft damage, defined by interstitial fibrosis and tubular atrophy (IF/TA), is a leading cause of allograft failure. Few effective therapeutic options are available to prevent the progression of IF/TA. We applied a meta-analysis approach on IF/TA molecular datasets in Gene Expression Omnibus to identify a robust 85-gene signature, which was used for computational drug repurposing analysis. Among the top ranked compounds predicted to be therapeutic for IF/TA were azathioprine, a drug to prevent acute rejection in renal transplantation, and kaempferol and esculetin, two drugs not previously described to have efficacy for IF/TA. We experimentally validated the anti-fibrosis effects of kaempferol and esculetin using renal tubular cells in vitro and in vivo in a mouse Unilateral Ureteric Obstruction (UUO) model. Kaempferol significantly attenuated TGF-β1-mediated profibrotic pathways in vitro and in vivo, while esculetin significantly inhibited Wnt/β-catenin pathway in vitro and in vivo. Histology confirmed significantly abrogated fibrosis by kaempferol and esculetin in vivo. We developed an integrative computational framework to identify kaempferol and esculetin as putatively novel therapies for IF/TA and provided experimental evidence for their therapeutic activities in vitro and in vivo using preclinical models. The findings suggest that both drugs might serve as therapeutic options for IF/TA.


Methods of Molecular Biology | 2016

High-Throughput Nuclease Probing of RNA Structures Using FragSeq

Andrew V. Uzilov; Jason G. Underwood

High-throughput sequencing of cDNA (RNA-Seq) can be used to generate nuclease accessibility data for many distinct transcripts in the same mixture simultaneously. Such assays accelerate RNA structure analysis and provide researchers with new technologies to tackle biological questions on a transcriptome-wide scale. FragSeq is an experimental assay for transcriptome-wide RNA structure probing using RNA-Seq, coupled with data analysis tools that allow quantitative determination of nuclease accessibility at single-base resolution. We provide a practical guide to designing and carrying out FragSeq experiments and data analysis.


Cancer Research | 2015

Abstract 2398: Precision molecular biomarkers for the surveillance of gynecologic malignancies: Rapid and efficient pipeline for the design and highly sensitive detection of circulating tumor DNA

Elena Pereira; Olga Camacho-Vanegas; Sanya Anand; Chanpreet Singh; Andrew V. Uzilov; Robert Sebra; David Chappell; Peter Dottino; John A. Martignetti

Proceedings: AACR 106th Annual Meeting 2015; April 18-22, 2015; Philadelphia, PA Gynecologic malignancies affect over 94,000 women in the U.S. and will result in close to 29,000 deaths this year alone. Currently available biomarkers are inadequate for the early detection of disease recurrence. The development of more sensitive and accurate biomarkers represents a critical and immediate necessity towards improving both patient outcome and quality of life. The measurement of circulating tumor DNA (ctDNA), the so-called “liquid biopsy,” represents a powerful emerging technology capable of providing accurate assessment of both tumor behavior and disease burden, and has great potential for revolutionizing our standard approach to tumor surveillance. The ability to translate the various ctDNA detection technologies from the laboratory to the clinic and begin defining clinical utility will in part be determined by the time and cost efficiencies of the pipelines and inherent detection sensitivities. We have developed a precision-medicine based approach that couples targeted tumor-specific mutation identification for each patient to droplet digital PCR-based (ddPCR) ctDNA detection. Using two rapid-turnaround sequencing panels (targeted full-gene PacBio single-molecule real time (SMRT) long-read sequencing coupled with the Ion AmpliSeq™Cancer Hotspot Panel v2) in lieu of more expensive and time-consuming approaches like whole-exome or whole-genome sequencing, we comprehensively identified tumor mutations for each of our ovarian and endometrial cancer patients by sequencing normal and tumor DNA from each patient. Analysis of an initial cohort of ovarian and endometrial cancer patients resulted in mutation identification frequencies comparable to previously reported data with mutations in TP53, PTEN, and PIK3CA being most frequently represented. Candidate mutations for ctDNA detection were identified for greater than 80% of subjects using this approach. By targeting specific genes, we raise the likelihood that identified tumor mutations are drivers essential to the disease, rather than passenger mutations that may be selected out during disease progression. ctDNA biomarkers specific to each patients tumor mutations were generated and tested using ddPCR, allowing for detection down to 0.05% mutant allele fraction. ctDNA levels were assessed longitudinally from prospectively collected serums during multiple time points throughout each patients clinical course and compared to clinical findings and current gold-standard serum and radiologic tests. ctDNA was successfully detected in all subjects; and to our knowledge for the first time in endometrial cancer. We demonstrate that our molecular approach allows for rapid, highly sensitive and accurate assessment of patient tumor status and, for some patients, is an improved and better predictor of outcome than current gold-standard surveillance strategies. Citation Format: Elena B. Pereira, Olga Camacho-Vanegas, Sanya Anand, Chanpreet Singh, Andrew Uzilov, Robert Sebra, David Chappell, Peter Dottino, John A. Martignetti. Precision molecular biomarkers for the surveillance of gynecologic malignancies: Rapid and efficient pipeline for the design and highly sensitive detection of circulating tumor DNA. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2398. doi:10.1158/1538-7445.AM2015-2398


Genome Medicine | 2016

Development and clinical application of an integrative genomic approach to personalized cancer therapy

Andrew V. Uzilov; Wei Ding; Marc Y. Fink; Yevgeniy Antipin; Andrew Scott Brohl; Claire R. Davis; Chun Yee Lau; Chetanya Pandya; Hardik Shah; Yumi Kasai; James Powell; Mark Micchelli; Rafael Castellanos; Zhongyang Zhang; Michael D. Linderman; Yayoi Kinoshita; Micol Zweig; Katie Raustad; Kakit Cheung; Diane Castillo; Melissa Wooten; Imane Bourzgui; Leah C. Newman; Gintaras Deikus; Bino Mathew; Jun Zhu; Benjamin S. Glicksberg; Aye S. Moe; Jun Liao; Lisa Edelmann

Collaboration


Dive into the Andrew V. Uzilov's collaboration.

Top Co-Authors

Avatar

Rong Chen

Icahn School of Medicine at Mount Sinai

View shared research outputs
Top Co-Authors

Avatar

Robert Sebra

Icahn School of Medicine at Mount Sinai

View shared research outputs
Top Co-Authors

Avatar

Eric E. Schadt

Icahn School of Medicine at Mount Sinai

View shared research outputs
Top Co-Authors

Avatar

Elena Pereira

Icahn School of Medicine at Mount Sinai

View shared research outputs
Top Co-Authors

Avatar

Michael J. Donovan

Icahn School of Medicine at Mount Sinai

View shared research outputs
Top Co-Authors

Avatar

Peter Dottino

Icahn School of Medicine at Mount Sinai

View shared research outputs
Top Co-Authors

Avatar

Yevgeniy Antipin

Icahn School of Medicine at Mount Sinai

View shared research outputs
Top Co-Authors

Avatar

Aye S. Moe

Icahn School of Medicine at Mount Sinai

View shared research outputs
Top Co-Authors

Avatar

Chetanya Pandya

Icahn School of Medicine at Mount Sinai

View shared research outputs
Top Co-Authors

Avatar

Chun Yee Lau

Icahn School of Medicine at Mount Sinai

View shared research outputs
Researchain Logo
Decentralizing Knowledge