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2 citation(s) found for "Zhao, Qin".

Implementation, Evolution, and Laboratory Performance of Methods-Based Proficiency Testing for Next-Generation Sequencing Detection of Germline Sequence Variants
Tsuchiya, Karen D. , Funke, Birgit , Hegde, Madhuri , Santani, Avni , Souers, Rhona J., Szelinger, Szabolcs , Halley, Jaimie , Zhao, Qin , Mot, Nicole, Roy, Angshumoy , Zhang, Bing , Voelkerding, Karl V., Moyer, Ann M. , Smith, Vanessa L.

Mayo Clinic

Archives of Pathology & Laboratory Medicine | October 18, 2023 | Volume: 148 | Issue: 7 | Pages: 775-783

10.5858/arpa.2023-0090-cp | PMID 37852169 Get It

Context.— Next-generation sequencing (NGS)–based assays are used for diagnosis of diverse inherited disorders. Limited data are available pertaining to interlaboratory analytical performance of these assays. Objective.— To report on the College of American Pathologists (CAP) NGS Germline Program, which is methods based, and explore the evolution in laboratory testing practices. Design.— Results from the NGS Germline Program from 2016–2020 were analyzed for interlaboratory analytical performance. Self-reported laboratory testing practices were also evaluated. Results.— From 2016–2020, a total of 297 laboratories participated in at least 1 program mailing. Of the 289 laboratories that provided information on tests offered, 138 (47.8%) offered only panel testing throughout their enrollment, while 35 (12.1%) offered panels and exome testing, 30 (10.4%) offered only exomes, 9 (3.1%) offered only genomes, and 15 (5.2%) offered panels, exomes, and genomes. The remainder (62 laboratories, 21.4%) changed their test offerings during the 2016–2020 timeframe. Considering each genomic position/interval, the median detection percentage at variant positions across the 2016–2020 mailings ranged from 94.3% to 100%, while at reference positions (no variant detected), the median correct response percentage was 100% across all mailings. When considering performance of individual laboratories, 89.5% (136 of 152) to 98.0% (149 of 152) of laboratories successfully met the detection threshold (≥90% of the variants present), while 94.6% (87 of 92) to 100% (163 of 163) of laboratories met the 95% specificity threshold across mailings. Conclusions.— Since the inception of this program, laboratories have consistently performed well. The median sensitivity and specificity of detection of sequence variants included in this program (eg, single nucleotide variants, insertions, and deletions) were 100.0%.
Abstract P3-07-51: Regulation of DNA methyltransferases via TRAF6 determines breast cancer response to decitabine
Yu, J., Qin, Bing-zhao, Boughey, JC , Moyer, AM , Visscher, DW, Sinnwell, JP, Yin, Pen-Hui, Thompson, KJ, Docter, TJ, Kalari, KR, Suman, VJ, Wieben, ED, Felten, SJ, Conners, AL, Jones, KN, McLaughlin, SA, JA, III Copland, Aspitia, Alvaro Moreno, Northfelt, DW, Gray, RJ, Ingle, JN, Lou, Zhongguan, Weinshilboum, Richard, Goetz, MP, Wang, L, Yu, J, Yin, Pen‐Hui, Wang, L

Mayo Clinic

Cancer Research | May 20, 2016 | Volume: 76 | Issue: 4_Supplement | Pages: P3-07-51-P3-07-51

10.1158/1538-7445.sabcs15-p3-07-51 Get It

Abstract Background: Tumorigenesis involves both genetic and epigenetic changes. Epigenetic alterations are reversible and are promising cancer therapeutic targets. Decitabine (5-aza-2'-deoxycytidine), a DNA methyltransferase inhibitor, is FDA approved for hematological malignancies. However, the effect of decitabine in breast cancer is not completely understood. Previous reports indicated that one decitabine mechanism involves regulation of protein levels for DNMT1, the major DNA methyltransferase that methylates hemimethylated CpG di-nucleotides in DNA. However, the E3 ligase involved in this process has not been identified. Whether decitabine also regulates DNMT3A and 3B in a similar fashion remains unclear. Therefore, our goals were to 1) understand mechanisms underlying decitabine action, 2) test the antitumor activity of decitabine in breast cancer models and 3) identify biomarkers associated with response to decitabine. Methods and Results: Western blots of breast cancer cell lines showed that DNMT1, DNMT3A, and DNMT3B protein levels decreased following decitabine treatment without a reduction in mRNA levels. Bioinformatic analysis of DNA methyltransferase sequences revealed a potential TRAF6 binding motif, and the interaction with TRAF6 (TNF receptor-associated factor 6) was confirmed by IP. TRAF6 functions as an E3 ligase. To determine whether TRAF6 might be the E3 ligase responsible for the degradation of DNMTs after decitabine treatment, we knocked down TRAF6 by RNA interference or knocked out the TRAF6 gene by CRISPR/Cas9. Down regulation of TRAF6 attenuated DNMT ubiquitination and increased DNMT protein levels, suggesting that TRAF6 might mediate proteasome-dependent degradation of all three DNMTs. This was further confirmed by reconstituting the knockout cells with WT and a TRAF6-C70A mutant, followed by assessing DNMT protein levels. Global DNA methylation was also increased after TRAF6 depletion and was confirmed in TRAF6 knock out cells in which DNMT levels were unaffected by decitabine. Cell cytotoxicity and colony forming assays showed that TRAF6 knockout cells were resistant to decitabine, suggesting that a major decitabine mechanism of action is through the regulation of TRAF6 which, in turn, degrades DNMTs, leading to decreased global methylation. Finally, decitabine significantly induced TRAF6 at both mRNA and protein levels, a process that might create positive feedback leading to increased degradation of DNMT proteins upon decitabine treatment. Based on these results, we further hypothesized that levels of the three DNMTs might influence decitabine response. Using 18 breast cancer patient derived xenograft (PDX) models, we found a wide range of DNMT protein levels regardless of ER/HER2 status. DNMT levels in the PDX models were directly associated with sensitivity to decitabine treatment, confirming our hypothesis. Conclusion: Our data showed that decitabine might be an effective agent for treating breast cancer and revealed a novel mechanism underlying decitabine treatment. Baseline DNMT protein levels may serve as a biomarker for predicting decitabine drug response. Citation Format: Yu J, Qin B, Boughey JC, Moyer AM, Visscher DW, Sinnwell JP, Yin P, Thompson KJ, Docter TJ, Kalari KR, Suman VJ, Wieben ED, Felten SJ, Conners AL, Jones KN, McLaughlin SA, Copland JA III, Moreno Aspitia A, Northfelt DW, Gray RJ, Ingle JN, Lou Z, Weinshilboum R, Goetz MP, Wang L. Regulation of DNA methyltransferases via TRAF6 determines breast cancer response to decitabine. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P3-07-51.