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  • American Association for Cancer Research (AACR)  (9)
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  • American Association for Cancer Research (AACR)  (9)
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  • 1
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 78, No. 13_Supplement ( 2018-07-01), p. 3000-3000
    Abstract: We have characterised intra-tumour heterogeneity (ITH) across 2,778 whole genome sequences of tumours in the International Cancer Genome Consortium Pan-Cancer Analysis of Whole Genomes project, representing 36 distinct cancer types. We applied 6 copy number (CNA) callers and 11 subclonal reconstruction algorithms and developed approaches to integrate the results in robust, high-confidence CNA calls and subclonal architectures. The analysis reveals widespread ITH. We find at least one subclone in nearly all (96.7%) tumours with sufficient sequencing depth. Analysis using dN/dS ratios yields clear signs of positive selection in clonal and subclonal mutations and we find subclonal driver mutations in known driver genes. However, only 24% of subclones contain a driver mutation in a known driver gene, suggesting that a multitude of undiscovered late drivers exist and that tumours continue to undergo selection after tumourigenesis, at least until diagnosis. Consistent with other studies, we find that in 9% of tumours all clinically actionable mutations are subclonal, while 20% of tumours contain at least one subclonal actionable driver. These findings emphasise the relevance of ITH in treatment decision making. Distinct patterns of ITH emerge; for example, prostate, uterus and esophageal adenocarcinomas show high proportions of both subclonal single nucleotide variants (SNVs) and CNAs. Kidney chromophobe and pancreatic endocrine tumours also contain high proportions of subclonal SNVs, but few subclonal CNAs. On the other hand, hepatocellular carcinomas and head-and-neck and lung SCCs contain low proportions of subclonal SNVs and high proportions of subclonal CNAs. Mutational signature analysis reveals changes in signature activity. Exposures to UV light in melanomas and acid reflux in stomach and oesophageal cancers contribute more clonal mutations. While APOBEC and DNA damage repair response related signatures show increased activity in subclones. These findings highlight distinct evolutionary narratives between and within histologically distinct tumour types. Citation Format: Stefan Dentro, Ignaty Leshchiner, Kerstin Haase, Jeff Wintersinger, Amit Deshwar, Maxime Tarabichi, Yulia Rubanova, Kaixian Yu, Ignacio Vázquez García, Geoff Macintyre, Kortine Kleinheinz, Dimitri Livitz, Salem Malikic, Nilgun Donmez, Subhajit Sengupta, Yuan Ji, Jonas Demeulemeester, Pavana Anur, Clemency Jolly, Marek Cmero, Daniel Rosebrock, Steve Schumacher, Yu Fan, Matthew Fittall, Xiaotong Yao, Juhee Lee, Matthias Schlesner, Hongtu Zhu, David Adams, Gad Getz, Paul Boutros, Marcin Imielinski, Rameen Beroukhim, Cenk Sahinalp, Martin Peifer, Inigo Martincorena, Florian Markowetz, Ville Mustonen, Ke Yuan, Moritz Gerstung, Wenyi Wang, Paul Spellman, Quaid Morris, David Wedge, Peter Van Loo. Pervasive intra-tumour heterogeneity and subclonal selection across cancer types [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3000.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2018
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  • 2
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 78, No. 13_Supplement ( 2018-07-01), p. 218-218
    Abstract: Cancer develops through a continuous process of somatic evolution. Whole genome sequencing provides a snapshot of the tumor genome at the point of sampling, however, the data can contain information that permits the reconstruction of a tumor's evolutionary past. Here, we apply such life history analyses on an unprecedented scale, to a set of 2,658 tumors spanning 39 cancer types. We estimated the timing of large chromosomal gains during tumor evolution, by comparing the rates of doubled to non-doubled point mutations within gained regions. Although we find that such events typically occur in the second half of clonal evolution, we also observe distinctive and early chromosomal gains in some cancer types, such as gains of chromosomes 7, 19 and 20 in glioblastoma, and isochromosome 17q in medulloblastoma. By integrating these results with the qualitative timing of individual driver mutations, we obtained an overall ranking, from early to late, of frequent somatic events per cancer type, which both identified novel patterns of tumor evolution, and incorporated additional detail into known models, such as the progression of APC-KRAS-TP53 in colorectal cancer proposed by Vogelstein and Fearon. To estimate how mutational processes acting on the tumor genome change over time, we classified mutations in each sample according to three broad time periods (early clonal, late clonal, and subclonal), and quantified the activity of mutational signatures in each period. Most mutational processes appear to remain remarkably constant, however, certain signatures show clear and consistent changes during clonal evolution. Particularly, mutational signatures associated with exposure to carcinogens, such as smoking and UV light, tend to decrease over time. In contrast, signatures associated with defective endogenous processes, such as APOBEC mutagenesis and defective double strand break repair, show an increase between early and late phases of tumor evolution. Making use of clock-like mutational signatures, we converted mutational time estimates for large events, such as whole genome duplication (WGD), and the emergence of the most recent common ancestor (MRCA), into real time estimates, which allowed us to combine our analyses into overall timelines of cancer evolution, per tumor type. For example, the typical timeline of ovarian adenocarcinoma development shows that early tumor evolution is characterized by mutations in TP53, and widespread genome instability, with WGD events taking place on average 8 years prior to diagnosis. In later stages of evolution, signatures of defective repair processes increase, and the MRCA emerges on average 1 year before diagnosis. Taken together, these data reveal the common and divergent evolutionary trajectories available to a cancer, which might be crucial in understanding specific tumor biology, and in providing new opportunities for early detection and cancer prevention. Citation Format: Clemency Jolly, Moritz Gerstung, Ignaty Leshchiner, Stefan C. Dentro, Santiago Gonzalez, Thomas J. Mitchell, Yulia Rubanova, Pavana Anur, Daniel Rosebrock, Kaixian Yu, Maxime Tarabichi, Amit Deshwar, Jeff Wintersinger, Kortine Kleinheinz, Ignacio Vásquez-García, Kerstin Haase, Subhajit Sengupta, Geoff Macintyre, Salem Malikic, Nilgun Donmez, Dimitri G. Livitz, Mark Cmero, Jonas Demeulemeester, Steve Schumacher, Yu Fan, Xiaotong Yao, Juhee Lee, Matthias Schlesner, Paul C. Boutros, David D. Bowtell, Hongtu Zhu, Gad Getz, Marcin Imielinski, Rameen Beroukhim, S Cenk Sahinalp, Yuan Ji, Martin Peifer, Florian Markowetz, Ville Mustonen, Ke Juan, Wenyi Wang, Quaid D. Morris, Paul T. Spellman, David C. Wedge, Peter Van Loo, PCAWG Evolution and Heterogeneity Working Group. The evolutionary history of 2,658 cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 218.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2018
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  • 3
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 83, No. 7_Supplement ( 2023-04-04), p. 4272-4272
    Abstract: Subpopulations of tumor cells characterized by mutation profiles may confer differential fitness and consequently influence the prognosis of cancers. Understanding subclonal architecture has the potential to provide biological insight in tumor evolution and advance cancer precision treatment. Recent subclonal reconstruction methods require heavy computational resources, prior knowledge of the number of subclones, and extensive postprocessing. These drawbacks can be addressed by using a regularized likelihood modeling approach, which is novel to the field. Therefore, we propose a model-based method, Clonal structure identification through pair-wise Penalization, or CliP, to address these drawbacks. To evaluate the performance of CliP against other methods, we generated a benchmark dataset of 4,050 simulated samples with varied tumor purity, read depth, copy number alteration rate, and true numbers of clusters. Our results suggest that CliP outperforms popular methods such as PhyloWGS in accuracy and shows similar robustness in most scenarios. We further compared CliP performance against 10 other subclonal reconstruction methods to the consensus subclonal reconstruction results on whole-genome sequencing (WGS) data from the Pan-Cancer Analysis of Whole Genomes (PCAWG, n = 1,993). Our result shows that of all 11 methods, CliP achieves the highest correlation with the consensus calls. In terms of speed, CliP can finish running a sample with 5,000 SNVs within one minute, which is ~1,000 times faster than MCMC-based algorithms. Next, we profiled the subclonal structures of 7,711 patient samples with well-annotated clinical outcomes from The Cancer Genome Atlas (TCGA) across 32 cancer types applying CliP to the whole-exome sequencing (WES) data. This is the largest and most complete pan-cancer characterization of intratumor heterogeneity (ITH) through the lens of subclonal reconstruction. We further used CliP outputs to address a commonly asked question on which sequencing platform to use for cancer evolution studies: the cost-effect WES data at higher read depth versus the more comprehensive WGS data at lower read depth. There were a total of 588 tumor samples from 21 cancer types, for which both PCAWG and TCGA have profiled using the WGS and WES platform, respectively. Using both datasets to compare results from these samples as benchmark, we observed that for most cancer types, subclonal reconstruction from WES is as informative as that from the matched WGS data. In summary, our study represents a significant methodological advancement in subclonal reconstruction and highlights the importance of measuring tumor subclone structure. Citation Format: Yujie Jiang, Kaixian Yu, Matthew D. Montierth, Shuangxi Ji, Seung Jun Shin, Shuai Guo, Shaolong Cao, Yuxin Tang, Scott Kopetz, Pavlos Msaouel, Jennifer R. Wang, Marek Kimmel, Peter Van Loo, Hongtu Zhu, Wenyi Wang. Pan-cancer analysis of intra-tumor heterogeneity in 9,116 cancers using a novel regularized likelihood model. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4272.
    Type of Medium: Online Resource
    ISSN: 1538-7445
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2023
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  • 4
    Online Resource
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    American Association for Cancer Research (AACR) ; 2016
    In:  Cancer Research Vol. 76, No. 14_Supplement ( 2016-07-15), p. 2034-2034
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 76, No. 14_Supplement ( 2016-07-15), p. 2034-2034
    Abstract: Despite the rapid progress in personalized cancer therapy (PCT) for breast cancer, no previous studies have used genomic predictors to choose among multiple chemotherapy regimens. It is unclear that given the current regimens how much PCT can improve the response rate for patients who will receive chemotherapy. In this study, we reanalyzed data from published studies of 1111 breast cancer patients who were treated with neoadjuvant chemotherapies. Those patients were divided into three regimen groups: an anthracycline alone, anthracycline plus paclitaxel, and anthracycline plus docetaxel. We developed a new strategy called PRES (Personalized REgimen Selection) to reassign the optimal regimen to each of the patients. First, a variable selection scheme was developed to identify significant genetic predictors for chemotherapy response. The selected genetic variables were then combined with clinical variables to build random forest models to predict the response of a patient to each regimen using pCR (pathological complete response) as the measure of response. The models were used to assign an optimal regimen to each patient to maximize the chance of pCR. We found that the expected rate of pCR was improved from 21.2% to 39.6% (95% CI: 34.6% - 43.0%). We also found that 31.1% of the patients may have been overtreated and 8.2% patients undertreated. A validation study on 21 cell lines showed that our prediction agrees with their paclitaxel-sensitivity profiles. We performed additional analysis on the Cancer Genome Atlas (TCGA) data and found that 18 of the 19 genes identified are significantly differentially expressed between normal and tumor tissues, and 2 of them, TAF6L and METRN (meteorin), are associated with overall survival. In conclusion, PRES could substantially increase response rates for breast cancer patients who will receive one of the widely-accepted chemotherapy regimens at present. Citation Format: Jinfeng Zhang, Kaixian Yu, Qingxiang Amy Sang, Winston Tan, Mayassa B. Dargham, Jun S. Liu, Ty Lively, Cedric Sheffield. Personalized chemotherapy regimen selection for breast cancer. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2034.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2016
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  • 5
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    Online Resource
    American Association for Cancer Research (AACR) ; 2016
    In:  Cancer Research Vol. 76, No. 14_Supplement ( 2016-07-15), p. 5275-5275
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 76, No. 14_Supplement ( 2016-07-15), p. 5275-5275
    Abstract: The accumulation of publicly available high-throughput experimental data has made analyzing the data a bottleneck in scientific discovery. In this study, we explore a computational, high-throughtput approach for identifying cancer related biological factors using the data available at the Cancer Genome Atlas (TCGA). We developed an integrative genomic analysis (IGA) tool, which performs multiple analysis tasks, including gene/protein level differential expression analysis, gene set level enrichment analysis and network/pathway level comparison. Several data types can be analyzed, including RNA-seq, DNA methylation, miRNA-seq, and proteomics. By varying cancer types, race groups, and data types, we are able to generate a large set of novel findings, which may serve as experimental targets for biologists and biochemists. Interactive reports are generated to facilitate further exploration of the biological significance of findings. With the high-throughput approach and intgrative tools, the bottlenect for us now shifts to publishing these results. We invite researchers in cancer research community to collaborate with us to publish the findings. Citation Format: Kaixian Yu, Yun Xu, Ke Tang, Albert Steppi, Jun Zhou, Zheng Ouyang, Jinfeng Zhang. An integrated pipeline for TCGA data analysis. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5275.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2016
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  • 6
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 80, No. 16_Supplement ( 2020-08-15), p. 829-829
    Abstract: Background: At present, conventional clinical and histopathological evaluations are not sufficient to distinguish biologically indolent cancers from those that will exhibit aggressive behavior. We hypothesize that global transcriptomic activity of tumor cells reflects the end cumulative result of somatic, germline, and epigenetic alterations, as well as additional transcriptional regulatory events. Therefore, it may be more directly associated with clinical outcomes. However, the total number of mRNA molecules is not directly measurable, either in bulk or single-cell RNA sequencing data. To this end, we develop a novel metric: the transcriptional activity score (TAS), to measure the relative global tumor-cell specific transcriptional activity in heterogeneous tumor samples. Materials and Methods: We propose TAS as the ratio of average total transcript proportion over the count proportion of tumor cells versus surrounding non-tumor cells. The transcript proportions are estimated using RNAseq deconvolution method DeMixT and the count proportions are estimated using DNAseq deconvolution methods such as ASCAT and ABSOLUTE. Using matching bulk RNA and DNA sequencing data from the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), we calculated TAS for a total of 5,031 patient samples across 15 cancer types. For validation, we obtained TAS for two genomic studies: 1) from patients with early-onset prostate cancer (n=99) as part of the ICGC, and 2) from patients with localized prostate cancer as part of the Canadian Prostate Cancer Genome Network (CPC-GENE, n=144). Results: We found that higher TAS corresponds to a more aggressive state of cancer, as characterized by MYC dysregulation, genome instability, known marker genes, and molecular subtypes. By examining the association between TAS and survival outcomes across cancer types, we also found that TAS refines the prognostic ability of pathologic stage, identifying aggressive early-stage tumors associated with poor survival as well as late-stage tumors with favorable outcomes. In prostate cancer, TAS is linearly associated with progression-free probabilities, useful to rank patients within the median risk group (Gleason score = 7). This added prediction power is consistent in TCGA and two independent validation data (ICGC and CPC-GENE). Conclusion: We have developed a new summary metric using matched DNA and RNA sequencing data from tumor samples, to compute, in vivo and using deconvolution, the relative global gene expression level of tumor cells. The TAS metric evaluates global transcriptional activity, an intrinsic behavior of cells that is well-known, but now for the first time is shown through TAS to be associated with prognosis. TAS may serve as a tractable phenotype to help elucidate the biology that underlies metastasis, prognosis and response to treatment in cancer patients. Citation Format: Shaolong Cao, Jennifer R. Wang, Jonas Demeulemeester, Jingxiao Chen, Kaixian Yu, Peng Yang, Bora Lim, Alfonso Urbanucci, Peter Campbell, Hongtu Zhu, Peter Van loo, Wenyi Wang. Global tumor transcriptional activity reveals aggressiveness across multiple cancers [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 829.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2020
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    detail.hit.zdb_id: 1432-1
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  • 7
    Online Resource
    Online Resource
    American Association for Cancer Research (AACR) ; 2016
    In:  Cancer Epidemiology, Biomarkers & Prevention Vol. 25, No. 3_Supplement ( 2016-03-01), p. A05-A05
    In: Cancer Epidemiology, Biomarkers & Prevention, American Association for Cancer Research (AACR), Vol. 25, No. 3_Supplement ( 2016-03-01), p. A05-A05
    Abstract: The accumulation of public available high-throughput experimental data has made analyzing the data a bottleneck in scientific discovery. In this study, we explore a computational, high-throughput approach for identifying biological factors associated with cancer health disparity using the data available at the Cancer Genome Atlas (TCGA). We developed an integrative genomic analysis (IGA) tool, which performs multiple analysis tasks, including gene/protein level comparison, gene set level comparison and network/pathway level comparison. Several data types can be analyzed, including RNAseq, DNA methylation, miRNAseq, and protein expressions. By varying cancer types, race groups, and data types, we are able to generate a large set of novel findings, which may shed light on the biological factors associated with cancer health disparity for various cancer types and race groups. Interactive reports are generated to facilitate further exploration of the biological significance of the findings. With this high-throughput approach, the bottleneck for us now shifts to publishing the results. We invite researchers in cancer health disparity community to collaborate with us to publish the findings. Citation Format: Kaixian Yu, Yun Xu, Ke Tang, Albert Steppi, Jinfeng Zhang. Biological factors associated with cancer health disparity - a high-throughput approach using big data. [abstract]. In: Proceedings of the Eighth AACR Conference on The Science of Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; Nov 13-16, 2015; Atlanta, GA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2016;25(3 Suppl):Abstract nr A05.
    Type of Medium: Online Resource
    ISSN: 1055-9965 , 1538-7755
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2016
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  • 8
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    Online Resource
    American Association for Cancer Research (AACR) ; 2020
    In:  Cancer Research Vol. 80, No. 21_Supplement ( 2020-11-01), p. PO-029-PO-029
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 80, No. 21_Supplement ( 2020-11-01), p. PO-029-PO-029
    Abstract: Background Subpopulations of tumor cells characterized by mutation profiles may confer differential fitness to treatment and prognosis across cancers. Understanding subclonal architecture has the potential to provide biological insight into tumor evolution and advance the precision treatment of cancers. Recent methods comprehensively integrate single nucleotide variants (SNVs) and copy number alterations (CNAs) to reconstruct subclonal architecture using whole-genome sequencing (WGS) data from bulk tumor samples. However, most methods follow a Bayesian framework and require extensive computational resources, a prior knowledge of the number of subclones, as well as ad hoc post-analysis data processing. Altogether this creates a bottleneck in processing time in large-scale studies. Objectives The primary objective of this study is to introduce a fast and accurate subclonal architecture reconstruction method, which utilizes a model-based clustering approach and addresses all the limitations above. Methods We introduce a novel model-based clustering method: Clonal structure identification through pairwise penalization (CliP). CliP assumes the number of reads observed with variant alleles follows a binomial model, which is a function of mutation cellular prevalence (CP), copy number aberrations, and tumor purity. We propose to minimize a penalized likelihood of this model with a SCAD penalty on CPs across pairs of mutations. The optimization problem is then efficiently solved via Alternating Direction Method of Multipliers (ADMM). As a subclonal reconstruction algorithm, CliP attempts to infer the population structure of heterogeneous tumors, and is the first method to utilize a regularized maximum likelihood framework in subclonal reconstruction, therefore benefiting from its computational efficiency in parameter estimation. Results Rigorous and extensive simulation results demonstrate that CliP is 100 times faster than MCMC-based algorithms without decreased performance. Unlike previous models, the CliP model is applicable to regions with or without CNAs. Furthermore, CliP generates subclonal structure without prior knowledge or post-processing. In an application to WGS data from Pan-Cancer Analysis of Whole Genomes (PCAWG), it only took 8 hours to process 2,500 tumor samples. Conclusion Since CliP executes quickly, it is ultimately suitable for 1) processing large datasets with thousands of samples and 2) participation in a group of methods to generate consensus calls. As the sizes of datasets continue to grow, CliP represents an important step towards fast and accurate subclonal reconstruction. Citation Format: Yujie Jiang, Kaixian Yu, Hongtu Zhu, Wenyi Wang. CliP: A model-based method for subclonal architecture reconstruction using regularized maximum likelihood estimation [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-029.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2020
    detail.hit.zdb_id: 2036785-5
    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
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  • 9
    Online Resource
    Online Resource
    American Association for Cancer Research (AACR) ; 2016
    In:  Cancer Epidemiology, Biomarkers & Prevention Vol. 25, No. 3_Supplement ( 2016-03-01), p. A04-A04
    In: Cancer Epidemiology, Biomarkers & Prevention, American Association for Cancer Research (AACR), Vol. 25, No. 3_Supplement ( 2016-03-01), p. A04-A04
    Abstract: African Americans (AAs) have more severe breast cancer, and higher death rate from breast cancer, than that of Caucasian Americans (CAs) even after socioeconomic status are accounted for. Various studies have been done to understand the biological mechanism of this health disparity. In this study, we performed a genome-wide differential DNA methylation analysis between AA and CA breast cancer patients. We analyzed differentially methylated positions (DMPs) and differentially methylated regions (DMRs) using data from 143 AA and 554 CA breast tumor tissues available at the Cancer Genome Atlas (TCGA). We found that there were 1232 DMPs and 661 DMRs between AA and CA breast cancer patients. Both DMP and DMR showed that PACS2 and ATP1A4 were highly differentially methylated among other genes. Network analysis showed that differential methylation occurred significantly in p53, EGFR, and ERS1 subnetwork, which is consistent from an early study on differentially expressed transcripts. We also conducted a correlation analysis between gene expression by RNA sequencing and DNA methylation, which showed that expressions of 129 genes with either DMP or DMR were highly negatively correlated with the corresponding DNA methylations, suggesting that these DNA methylation may play important roles in the breast cancer health disparity observed in AA women. Our discoveries may help researchers to better understand the biological factors for breast cancer health disparity and the biology of breast cancer in general. The genes or DMPs/DMRs identified may serve as the starting points for further experimental validations towards discovering biomarkers for cancer diagnosis and prognosis, and drug targets for personalized treatments. Citation Format: Kaixian Yu, Albert Steppi, Yun Xu, Ke Tang, Jinfeng Zhang. Differential DNA methylation and network analysis in African American breast cancer. [abstract]. In: Proceedings of the Eighth AACR Conference on The Science of Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; Nov 13-16, 2015; Atlanta, GA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2016;25(3 Suppl):Abstract nr A04.
    Type of Medium: Online Resource
    ISSN: 1055-9965 , 1538-7755
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2016
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