Journal of bioinformatics and systems biology : Open access最新文献

{"title":"Integrative In-Silico Evaluation of Features on BRCA1 Cis Regulatory Element","authors":"Apeksha Arun Bhandarkar, Smeeta Shrestha","doi":"10.26502/jbsb.5107037","DOIUrl":"https://doi.org/10.26502/jbsb.5107037","url":null,"abstract":"Genomic cis regulatory elements support the gene transcriptional landscape which fine tune spatiotemporal gene expression via interaction with different transcription factors and co modulators during development. These regulatory elements are poorly conserved, highly heterogenous with limited understanding of their role in gene expression. Here we use a well-known human tumor suppressor gene, Breast Cancer Type 1 ( BRCA1 ) and UCSC human genome browser database to report the in-silico putative cis regulatory enhancer element and its features. We report a 2kb double elite enhancer, GH17J043079 located within intron 12 of the BRCA1 gene. The enhancer interacts with NBR1 , NBR2 , TMEM106A and RPL27 and VAT1 gene promoters. GH17J043079 showed histone activity in human embryonic stem cells, cancerous cells, housed transcription factors specific to liver cells and was enriched with Alu elements, indicative of ability for potential gene rearrangements. Additionally, it contained eQTLs, rs4793197, rs8176190, rs8176192, rs8176193 and rs8176194 with disparity in allele frequency across populations. Our in-silico review on the features present within GH17J043079 element in BRCA1 helps to postulate an intricate transcription regulation. Such candidate based analysis of features within cis regulatory element on a gene can help elucidate intricate genomic architecture, gene regulation and its impact on complex disorders.","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69367859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prenatal findings of 2q13 Duplication and Deletion: Further Evidence for Lack of Phenotypic-Genotype Correlation","authors":"L. Li, X. Huang, M. Ye, J. Chen, Z. Zeng, H. Guo, Q. Liao, W. Hu, D. Tang, Y. Dai","doi":"10.22541/au.163308148.84107640/v1","DOIUrl":"https://doi.org/10.22541/au.163308148.84107640/v1","url":null,"abstract":"2q13 CNV was associated with various diseases, with a lack of consensus.\u0000By CMA analysis, we found that four fetuses had deletion in the proximal\u0000region of 2q13, one had duplication, and one had duplication in the\u0000distal region of 2q13; however, they had variable outcomes.","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47622345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract 184: The utility of deep metric learning for breast cancer identification on mammographic images","authors":"Justin Du, S. Umrao, E. Chang, M. Joel, Aidan Gilson, G. Janda, R. Choi, Yongfeng Hui, S. Aneja","doi":"10.1158/1538-7445.AM2021-184","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-184","url":null,"abstract":"Purpose: Although deep learning (DL) models have shown increasing ability to accurately classify diagnostic images in oncology, significantly large amounts of well-curated data are often needed to match human level performance. Given the relative paucity of imaging datasets for less prevalent cancer types, there is an increasing need for methods which can improve the performance of deep learning models trained using limited diagnostic images. Deep metric learning (DML) is a potential method which can improve accuracy in deep learning models trained on limited datasets. Leveraging a triplet-loss function, DML exponentially increases training data compared to a traditional DL model. In this study, we investigated the utility of DML to improve the accuracy of DL models trained to classify cancerous lesions found on screening mammograms. Methods: Using a dataset of 2620 lesions found on routine screening mammogram, we trained both a traditional DL and DML models to classify suspicious lesions as cancerous or benign. The VGG16 architecture was used as the basis for the DL and DML models. Model performance was compared by calculating model accuracy, sensitivity, and specificity on a blinded test set of 378 lesions. In addition to individual model performance, we also measured agreement accuracy when both the DL and DML models were combined. Sub-analyses were conducted to identify phenotypes which were best suited for each model type. Both models underwent hyperparameters optimization to identify ideal batch size, learning rate, and regularization to prevent overfitting. Results: We found that the combination of the traditional DL model with DML model resulted in the highest overall accuracy (78.7%) representing a 7.1% improvement compared to the traditional DL model (p Conclusion: Our study suggests that addition of DML models with traditional DL models can improve diagnostic image classification performance in cancer. Our results suggest DML models may provide increased specificity and help with classification of unique populations often misclassified by traditional DL models. Further studied investigating the utility of DML on other cancer imaging tasks are necessary to successfully build more robust DL models in cancer imaging. Citation Format: Justin Du, Sachin Umrao, Enoch Chang, Marina Joel, Aidan Gilson, Guneet Janda, Rachel Choi, Yongfeng Hui, Sanjay Aneja. The utility of deep metric learning for breast cancer identification on mammographic images [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 184.","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73633562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract SY01-03: The gold standard cancer diagnosis: Studies of physician variability, interpretive behavior, and the impact of AI","authors":"J. Elmore","doi":"10.1158/1538-7445.AM2021-SY01-03","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-SY01-03","url":null,"abstract":"The current gold standard for cancer diagnoses is based on pathologists9 visual inspection of tissue sections. However, our research has found concerning levels of inter-observer and intra-observer variability among pathologists. Our prior work in melanoma shows that current diagnoses within the disease spectrum from benign nevi to melanoma in situ to invasive melanoma are neither reproducible nor accurate, yielding estimates that ~17% of all diagnoses for melanocytic lesions in the US are incorrect (Elmore et al. BMJ 2017). A study conducted by our team in breast pathology quantified the magnitude of diagnostic agreement among pathologists compared with a gold standard consensus reference: among DCIS cases, 16% of interpretations were discordant, while among atypia cases 52% of interpretations were discordant (Elmore et al. JAMA 2015). While computer systems, such as computer aided detection (CAD) tools, have been widely integrated into clinical practice to aid the interpretative and diagnostic process, our work has also found that the use of CAD can be associated with increases in potential harms, including higher recall and biopsy rates for screening mammography (Fenton et al. NEJM 2007). Given the critical need to improve the quality of our current diagnostic and prognostic capabilities, our multidisciplinary research team is conducting several studies that involve the development and integration of AI/machine learning and eye-tracking across clinical contexts. The challenges and implications associated with “gold standard” definitions for diagnoses, with data sharing infrastructure and with the eventual impact of AI on the human interface will be discussed. Citation Format: Joann Elmore. The gold standard cancer diagnosis: Studies of physician variability, interpretive behavior, and the impact of AI [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr SY01-03.","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74479392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract 237: Inferring spatial organization of tumor microenvironment from single-cell RNA sequencing data using graph embedding","authors":"Liang Ding, Hao Shi, Koon-Kiu Yan, Yogesh Dhungana, Sivaraman Natarajan, J. Easton, J. Chiang, C. Tinkle, Xiaoyan Zhu, Liming Cai, S. Baker, H. Chi, Jiyang Yu","doi":"10.1158/1538-7445.AM2021-237","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-237","url":null,"abstract":"Spatial heterogeneity of diverse cellular components in the tumor microenvironment (TME) plays a critical role in the reprogramming of tumor initiation, growth, invasion, metastasis, and response to therapies. Systematic knowledge of TME spatial organization with regards to immune infiltration and tumor resource distribution is of high clinical significance. High throughput single-cell RNA sequencing (scRNA-seq) has become a revolutionary approach for studying cell composition and the development of TME. However, the spatial information of cells is lost as the tissue must be dissociated before the sequencing is performed. While various spatial techniques are emerging, their applicability is still rather limited. To address this challenge computationally, we develop a novel de novo framework to reconstruct TME spatial organization from scRNA-seq data. We hypothesized that cell spatial organization in a microenvironment is mainly determined by cell identity and interactions between different cells. In particular, the spatial organization of structural cells and immune cells follow different mechanisms. Neighboring structural cells, which share similar whole transcriptome profiles, form a scaffold of the TME; immune cells, whose activities are influenced by the structural cells, migrate in the scaffold to interact with structural cells and exert their functions. The algorithm models the scaffold of structural cells using adaptive nearest neighbor graph by taking the cell density estimation into the consideration, where the nearest neighbor graph was further augmented by inserting immune cells into the appropriate locations of the scaffold according to the LR similarities. To reconstruct 3D spatial organization while preserving the cell topology represented by the graph, we employed a graph embedding strategy to minimize the discrepancy between the graph topology and the embedded 3D space. We evaluated the framework on two diffuse intrinsic pontine gliomas (DIPG) samples from a mouse model with coupled scRNA-seq and spatial transcriptome (ST, 10x Visium platform) data. The predicted spatial organization successfully separates the major cell types. The T-cell infiltrated tumor, verified by the T-cell spatial spots of the ST image, is well recapitulated. We deconvoluted the ST data by integrating the scRNA-seq data using SPOTlight. The neighborhood enrichment distributions of predicted spatial organization and the spot deconvoluted ST data show high consistency as measured by Kullback-Leibler divergence. We found heterogeneous neighborhood composition of CD8+ T-cells, indicating diverse clonality and functions with respect to their locations in the TME. Citation Format: Liang Ding, Hao Shi, Koon-Kiu Yan, Yogesh Dhungana, Sivaraman Natarajan, John Easton, Jason Chiang, Christopher L. Tinkle, Xiaoyan Zhu, Liming Cai, Suzanne J. Baker, Hongbo Chi, Jiyang Yu. Inferring spatial organization of tumor microenvironment from single-cell RNA sequencing data usin","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78201923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract 187: Automated deep-learning system for Gleason grading of prostate cancer using digital pathology and genomic signatures","authors":"Derek Van Booven, Victor Sandoval, O. Kryvenko, M. Parmar, A. Briseño, H. Arora","doi":"10.1158/1538-7445.AM2021-187","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-187","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75085466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract 204: Identification of gene expression signatures as potential novel biomarkers in malignant melanoma","authors":"Stephanie Figueroa, R. Tiwari, J. Geliebter","doi":"10.1158/1538-7445.AM2021-204","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-204","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77552198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract LB022: Griffin: A method for nucleosome profiling and breast cancer subtype prediction from ultra-low pass whole genome sequencing of cell-free DNA","authors":"Anna-Lisa Doebley, Hanna Liao, C. Kikawa, Eden Cruikshank, Minjeong Ko, A. C. Hoge, Joseph B Hiatt, N. Sarkar, V. Adalsteinsson, P. Polak, D. MacPherson, P. Nelson, H. Parsons, D. Stover, G. Ha","doi":"10.1158/1538-7445.AM2021-LB022","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-LB022","url":null,"abstract":"Background: Cell-free DNA (cfDNA) is released from dying cells, including tumor cells, and can be isolated from peripheral blood for studying cancer. In the bloodstream, cfDNA is protected from degradation by nucleosomes and other DNA binding proteins, leading to a coverage pattern that reflects the genomic organization in the cells-of-origin. Recent work has shown that it is possible to use this pattern to predict gene and transcription factor activity in cancer cells. This is known as nucleosome profiling. Breast cancer is among the most common causes of cancer, accounting for 23% of cancer diagnoses and 14% of cancer-related deaths among women worldwide. Targeted therapy is guided by tumor subtype, including the expression of three key receptors: ER, PR and HER2. Typically, subtyping involves a tumor biopsy and immunohistochemistry. However, in late-stage cancer, surgical biopsies for disease monitoring are difficult to obtain. Accurate subtype determination is critical to address hormone subtype switches during metastasis or treatment resistance. cfDNA offers an alternative, non-invasive method for identifying tumor subtypes through nucleosome profiling and, to the best of our knowledge, has not been shown for breast cancer. Methods: We developed a method, called Griffin, to examine nucleosome protection and genome accessibility by quantifying cfDNA fragments around accessible sites. Unlike previous methods, Griffin uses fragment length-based GC correction to remove GC biases that obscure signals. We used ATAC-seq data from TCGA to identify differentially accessible sites between ER positive and negative breast cancers. We developed a machine learning classifier that predicts ER subtype based upon the signals at these differentially accessible sites. Results: We then tested Griffin by examining differentially accessible sites in ultra-low pass sequencing (ULP-WGS, 0.1X) of several hundred cfDNA samples from patients with ER positive or negative breast cancer. We found that overall, differential sites were more accessible in the cfDNA of their respective subtypes. Additionally, we found that site accessibility within patient cfDNA samples was correlated to the cfDNA tumor fraction. We built and tested a prediction model with cross-validation, which revealed an accuracy of >80% for correctly classifying tumor status as ER positive or negative from this ULP-WGS dataset. Conclusion: This study has several novel aspects compared to prior nucleosome profiling approaches. First, we use fragment-based GC correction which reduces sample variability and allows us to observe previously obscured signals. Second, we demonstrated that signals are correlated to tumor fraction. And finally, we applied this method to cost-effective and scalable ULP-WGS of breast cancer and demonstrated the ability to predict breast cancer ER subtype in these samples. Citation Format: Anna-Lisa Doebley, Hanna Liao, Caroline Kikawa, Eden Cruikshank, Minjeong Ko, Anna Hoge, Jose","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79320321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract 235: Identifying potential drug targets using patient-derived, tissue specific, gene regulatory networks","authors":"A. N. Forbes, Duo Xu, Ekta Khurana","doi":"10.1158/1538-7445.AM2021-235","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-235","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81526747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract 259: Comparison of Illumina NovaSeq 6000 and MGISEQ-2000 in profiling xenograft models","authors":"W. Qian, Chen Xiaobo, H. Li, Sheng Guo","doi":"10.1158/1538-7445.AM2021-259","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-259","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85004100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}