Pub Date : 2022-01-01Epub Date: 2022-02-03DOI: 10.26502/jbsb.5107030
Arisnel Soto-Acabá, Pablo A Ortiz-Pineda, Joshua G Medina-Feliciano, Joseph Salem-Hernández, José E García-Arrarás
In recent years, transcriptomic databases have become one of the main sources for protein discovery. In our studies of nervous system and digestive tract regeneration in echinoderms, we have identified several transcripts that have attracted our attention. One of these molecules corresponds to a previously unidentified transcript (Orpin) from the sea cucumber Holothuria glaberrima that appeared to be upregulated during intestinal regeneration. We have now identified a second highly similar sequence and analyzed the predicted proteins using bioinformatics tools. Both sequences have EF-hand motifs characteristic of calcium-binding proteins (CaBPs) and N-terminal signal peptides. Sequence comparison analyses such as multiple sequence alignments and phylogenetic analyses only showed significant similarity to sequences from other echinoderms or from hemichordates. Semi-quantitative RT-PCR analyses revealed that transcripts from these sequences are expressed in various tissues including muscle, haemal system, gonads, and mesentery. However, contrary to previous reports, there was no significant differential expression in regenerating tissues. Nonetheless, the identification of unique features in the predicted proteins and their presence in the holothurian draft genome suggest that these might comprise a novel subfamily of EF-hand containing proteins specific to the Ambulacraria clade.
近年来，转录组数据库已成为蛋白质发现的主要来源之一。在对棘皮动物神经系统和消化道再生的研究中，我们发现了几个引起我们注意的转录本。其中一个分子对应于海参Holothuria glaberrima中一个以前未发现的转录本（Orpin），该转录本似乎在肠道再生过程中上调。我们现在又发现了第二个高度相似的序列，并使用生物信息学工具分析了预测的蛋白质。这两个序列都具有钙结合蛋白（CaBPs）特有的 EF-手基序和 N 端信号肽。序列比较分析（如多序列比对和系统发生学分析）只显示了与其他棘皮动物或半脊索动物序列的显著相似性。半定量 RT-PCR 分析显示，这些序列的转录本在肌肉、血液系统、性腺和肠系膜等多种组织中表达。然而，与之前的报道相反，在再生组织中没有明显的差异表达。尽管如此，这些预测蛋白质的独特特征的鉴定以及它们在全鸟类基因组草案中的存在表明，这些蛋白质可能构成了一个新的含 EF-手的蛋白质亚家族，为 Ambulacraria 支系所特有。
{"title":"Characterization of Two Novel EF-Hand Proteins Identifies a Clade of Putative Ca<sup>2+</sup>-Binding Protein Specific to the Ambulacraria.","authors":"Arisnel Soto-Acabá, Pablo A Ortiz-Pineda, Joshua G Medina-Feliciano, Joseph Salem-Hernández, José E García-Arrarás","doi":"10.26502/jbsb.5107030","DOIUrl":"10.26502/jbsb.5107030","url":null,"abstract":"<p><p>In recent years, transcriptomic databases have become one of the main sources for protein discovery. In our studies of nervous system and digestive tract regeneration in echinoderms, we have identified several transcripts that have attracted our attention. One of these molecules corresponds to a previously unidentified transcript (<i>Orpin</i>) from the sea cucumber <i>Holothuria glaberrima</i> that appeared to be upregulated during intestinal regeneration. We have now identified a second highly similar sequence and analyzed the predicted proteins using bioinformatics tools. Both sequences have EF-hand motifs characteristic of calcium-binding proteins (CaBPs) and N-terminal signal peptides. Sequence comparison analyses such as multiple sequence alignments and phylogenetic analyses only showed significant similarity to sequences from other echinoderms or from hemichordates. Semi-quantitative RT-PCR analyses revealed that transcripts from these sequences are expressed in various tissues including muscle, haemal system, gonads, and mesentery. However, contrary to previous reports, there was no significant differential expression in regenerating tissues. Nonetheless, the identification of unique features in the predicted proteins and their presence in the holothurian draft genome suggest that these might comprise a novel subfamily of EF-hand containing proteins specific to the Ambulacraria clade.</p>","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"5 1","pages":"1-25"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9648499/pdf/nihms-1843289.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10312693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"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":"1 1","pages":""},"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}
Pub Date : 2021-10-01DOI: 10.22541/au.163308148.84107640/v1
L. Li, X. Huang, M. Ye, J. Chen, Z. Zeng, H. Guo, Q. Liao, W. Hu, D. Tang, Y. Dai
2q13 CNV was associated with various diseases, with a lack of consensus. By CMA analysis, we found that four fetuses had deletion in the proximal region of 2q13, one had duplication, and one had duplication in the distal region of 2q13; however, they had variable outcomes.
{"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":"1 1","pages":""},"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}
Pub Date : 2021-07-01DOI: 10.1158/1538-7445.AM2021-184
Justin Du, S. Umrao, E. Chang, M. Joel, Aidan Gilson, G. Janda, R. Choi, Yongfeng Hui, S. Aneja
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.
{"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":"21 1","pages":""},"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}
Pub Date : 2021-07-01DOI: 10.1158/1538-7445.AM2021-SY01-03
J. Elmore
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.
{"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":"125 1","pages":""},"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}
Pub Date : 2021-07-01DOI: 10.1158/1538-7445.AM2021-237
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
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
肿瘤微环境(tumor microenvironment, TME)中不同细胞组分的空间异质性在肿瘤发生、生长、侵袭、转移和治疗反应的重编程中起着至关重要的作用。系统了解TME的空间组织与免疫浸润和肿瘤资源分布的关系具有重要的临床意义。高通量单细胞RNA测序(scRNA-seq)已成为研究细胞组成和TME发展的革命性方法。然而，细胞的空间信息丢失，因为组织必须在测序之前解离。虽然各种空间技术不断涌现，但其适用性仍然相当有限。为了在计算上解决这一挑战，我们开发了一个新的从头框架，从scRNA-seq数据中重建TME空间组织。我们假设微环境中的细胞空间组织主要由细胞身份和不同细胞之间的相互作用决定。特别是结构细胞和免疫细胞的空间组织遵循不同的机制。邻近的结构细胞，具有相似的整个转录组谱，形成TME的支架;免疫细胞的活动受到结构细胞的影响，在支架内迁移，与结构细胞相互作用，发挥其功能。该算法在考虑细胞密度估计的基础上，采用自适应最近邻图对结构细胞的支架进行建模，并根据LR相似性在支架的适当位置插入免疫细胞，进一步增强最近邻图。为了在保留图所表示的细胞拓扑的同时重建三维空间组织，我们采用了图嵌入策略来最小化图拓扑与嵌入的三维空间之间的差异。我们利用scRNA-seq和空间转录组(ST, 10倍Visium平台)数据对来自小鼠模型的两个弥漫性内生性脑桥胶质瘤(DIPG)样本进行了框架评估。预测的空间组织成功地分离了主要的细胞类型。t细胞浸润的肿瘤，通过ST图像的t细胞空间斑点验证，被很好地再现。我们通过使用SPOTlight整合scRNA-seq数据对ST数据进行反卷积。通过Kullback-Leibler散度测量，预测空间组织的邻域富集分布与点反卷积的ST数据具有较高的一致性。我们发现CD8+ t细胞的异质邻域组成，表明它们在TME中的位置具有不同的克隆性和功能。引用格式:丁亮，史浩，严坤桥，Yogesh Dhungana, Sivaraman Natarajan, John Easton, Jason Chiang, Christopher L. Tinkle，朱晓燕，蔡黎明，Suzanne J. Baker，迟洪波，余纪阳。利用图嵌入从单细胞RNA测序数据推断肿瘤微环境的空间组织[摘要]。见:美国癌症研究协会2021年年会论文集;2021年4月10日至15日和5月17日至21日。费城(PA): AACR;癌症杂志，2021;81(13 -增刊):摘要第237期。
{"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":"26 1","pages":""},"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}
Pub Date : 2021-07-01DOI: 10.1158/1538-7445.AM2021-187
Derek Van Booven, Victor Sandoval, O. Kryvenko, M. Parmar, A. Briseño, H. Arora
{"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":"39 1","pages":""},"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}
Pub Date : 2021-07-01DOI: 10.1158/1538-7445.AM2021-204
Stephanie Figueroa, R. Tiwari, J. Geliebter
{"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":"28 1","pages":""},"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}
Pub Date : 2021-07-01DOI: 10.1158/1538-7445.AM2021-LB022
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
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
背景:游离DNA (Cell-free DNA, cfDNA)是从包括肿瘤细胞在内的垂死细胞中释放出来的，可以从外周血中分离出来用于研究癌症。在血液中，cfDNA受到核小体和其他DNA结合蛋白的保护，不被降解，从而形成一种反映起源细胞基因组组织的覆盖模式。最近的研究表明，利用这种模式来预测癌细胞中的基因和转录因子活性是可能的。这被称为核小体分析。乳腺癌是最常见的癌症原因之一，占全球女性癌症诊断的23%和癌症相关死亡的14%。靶向治疗以肿瘤亚型为指导，包括三种关键受体:ER、PR和HER2的表达。通常，分型包括肿瘤活检和免疫组织化学。然而，在晚期癌症中，很难获得用于疾病监测的手术活检。准确的亚型测定对于解决转移或治疗抵抗期间的激素亚型转换至关重要。cfDNA提供了一种替代的、非侵入性的方法，通过核小体分析来识别肿瘤亚型，据我们所知，cfDNA还没有被用于乳腺癌。方法:我们开发了一种称为Griffin的方法，通过定量可达位点周围的cfDNA片段来检测核小体保护和基因组可达性。与以前的方法不同，Griffin使用基于片段长度的GC校正来去除模糊信号的GC偏差。我们使用来自TCGA的ATAC-seq数据来确定ER阳性和阴性乳腺癌之间可访问的差异位点。我们开发了一种机器学习分类器，可以根据这些不同可访问位置的信号预测ER亚型。结果:我们随后通过对来自ER阳性或阴性乳腺癌患者的数百个cfDNA样本进行超低通过测序(ULP-WGS, 0.1X)检查差异可达位点来测试Griffin。我们发现，总的来说，在各自亚型的cfDNA中，差异位点更容易被访问。此外，我们发现患者cfDNA样本中的位点可及性与cfDNA肿瘤分数相关。我们建立并测试了一个交叉验证的预测模型，该模型显示，从ULP-WGS数据集中正确分类肿瘤状态为ER阳性或阴性的准确率>80%。结论:与之前的核小体分析方法相比，这项研究有几个新的方面。首先，我们使用基于片段的GC校正，这减少了样本可变性，使我们能够观察到以前模糊的信号。其次，我们证明了信号与肿瘤分数相关。最后，我们将该方法应用于具有成本效益和可扩展的乳腺癌ULP-WGS，并证明了在这些样本中预测乳腺癌ER亚型的能力。引文格式:Anna- lisa Doebley, Hanna Liao, Caroline Kikawa, Eden Cruikshank, Minjeong Ko, Anna Hoge, Joseph Hiatt, Navonil De Sarkar, Viktor A. Adalsteinsson, Paz Polak, David MacPherson, Peter S. Nelson, Heather A. Parsons, Daniel Stover, Gavin Ha。Griffin:一种利用无细胞DNA超低通全基因组测序进行核小体谱分析和乳腺癌亚型预测的方法[摘要]。见:美国癌症研究协会2021年年会论文集;2021年4月10日至15日和5月17日至21日。费城(PA): AACR;癌症杂志，2021;81(13 -增刊):摘要nr LB022。
{"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":"43 1","pages":""},"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}
Pub Date : 2021-07-01DOI: 10.1158/1538-7445.AM2021-235
A. N. Forbes, Duo Xu, Ekta Khurana
{"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":"10 1","pages":""},"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}
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