Pub Date : 2021-07-01DOI: 10.1158/1538-7445.am2021-207
Jianjiong Gao, T. Mazor, Ino de Bruijn, Adam Abeshouse, Diana Baiceanu, Ziya Erkoç, Benjamin E. Gross, David M Higgins, P. Jagannathan, Karthik Kalletla, P. Kumari, Ritika Kundra, Xiang Li, James Lindsay, Aaron Lisman, Pieter Lukasse, Divya Madala, Ramyasree Madupuri, Angelica Ochoa, Oleguer Plantalech, Joyce Quach, Sander Y. A. Rodenburg, Anusha Satravada, F. Schaeffer, R. Sheridan, Lucas Sikina, S. O. Sumer, Yichao Sun, P. van Dijk, P. van Nierop, Avery Wang, Manda Wilson, Hongxin Zhang, Gaofei Zhao, Sjoerd van Hagen, K. van Bochove, U. Dogrusoz, Allison P. Heath, A. Resnick, Trevor J Pugh, C. Sander, E. Cerami, N. Schultz
207: The cBioPortal for Cancer Genomics Author & Article Information Cancer Res (2021) 81 (13_Supplement): 207. https://doi.org/10.1158/1538-7445.AM2021-207
{"title":"Abstract 207: The cBioPortal for Cancer Genomics","authors":"Jianjiong Gao, T. Mazor, Ino de Bruijn, Adam Abeshouse, Diana Baiceanu, Ziya Erkoç, Benjamin E. Gross, David M Higgins, P. Jagannathan, Karthik Kalletla, P. Kumari, Ritika Kundra, Xiang Li, James Lindsay, Aaron Lisman, Pieter Lukasse, Divya Madala, Ramyasree Madupuri, Angelica Ochoa, Oleguer Plantalech, Joyce Quach, Sander Y. A. Rodenburg, Anusha Satravada, F. Schaeffer, R. Sheridan, Lucas Sikina, S. O. Sumer, Yichao Sun, P. van Dijk, P. van Nierop, Avery Wang, Manda Wilson, Hongxin Zhang, Gaofei Zhao, Sjoerd van Hagen, K. van Bochove, U. Dogrusoz, Allison P. Heath, A. Resnick, Trevor J Pugh, C. Sander, E. Cerami, N. Schultz","doi":"10.1158/1538-7445.am2021-207","DOIUrl":"https://doi.org/10.1158/1538-7445.am2021-207","url":null,"abstract":"207: The cBioPortal for Cancer Genomics Author & Article Information Cancer Res (2021) 81 (13_Supplement): 207. https://doi.org/10.1158/1538-7445.AM2021-207","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84333756","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-LB019
F. Mehrabadi, S. Malikić, Kerrie L. Marie, Eva Pérez-Guijarro, Erfan Sadeqi Azer, Howard H. Yang, Can Kızılkale, Charli Gruen, Huaitian Liu, C. Marcelus, A. Buluç, Funda Ergün, M. Lee, G. Merlino, Chi-Ping Day, S. C. Sahinalp
Emerging sets of single-cell sequencing data makes it appealing to apply existing tumor phylogeny reconstruction methods to analyze associated intratumor heterogeneity. Unfortunately, tumor phylogeny inference is an NP-hard problem and existing principled methods typically fail to scale up to handle thousands of cells and mutations observed in emerging single-cell data sets. Even though there are greedy heuristics to build hierarchical clustering of cells and mutations, they suffer from well-documented issues in accuracy. Additionally even when “optimal” solutions are feasible, existing approaches only provide a single “most likely” tree to depict the evolutionary processes that may result in an observed collection of cells and mutations. To make matters worse, the vast majority of single-cell sequencing data sets are transcriptomic and as a result, suffer from considerable variation in coverage across mutational loci. In this paper, we introduce Trisicell, a computational toolkit for scalable tumor phylogeny reconstruction and validation from single-cell genomic, exomic or transcriptomic sequencing data. Trisicell has three components: (i) Trisicell-DnC, a new tumor phylogeny reconstruction method from genotype matrices derived from single-cell data, (ii) Trisicell-ConT a new algorithm for constructing the consensus for two or more tumor phylogenies - which may be built through the use of different data types on the same set of cells, or built through the use of different methods on the same data, and (iii) Trisicell-PF, a new partition function method for assessing the likelihood of any user-defined subtree/set of cells to be seeded by a given set of mutations in the phylogeny. Collectively, these tools provide means of identifying and validating robust portions of a tumor phylogeny, offering the ability to focus on the most important (sub)clones and the genomic alterations that seed the associated clonal expansion. We applied Trisicell to a panel of clonal sublines derived from single-cells of a parental mouse melanoma model on which we performed both whole exome and whole transcriptome sequencing. The tumor phylogenies of the clonal sublines built on exomic and transcriptomic mutations by Trisicell-DnC, were shown by Trisicell-ConT to be highly similar and the subtrees comprised of phenotypically similar clonal sublines were shown to be strongly associated by Trisicell-PF to their seeding mutations. In addition, we applied Trisicell to single-cell whole transcriptome sequencing data from a tumor derived from the same parental melanoma cell line, which was subjected to anti-CTLA-4 immunotherapy. The phylogenies generated from both studies featured distinct subtrees, strongly associated with phenotypes including cell differentiation status, tumor growth and therapeutic response. These results suggest that Trisicell can be used for scalable tumor phylogeny reconstruction and validation through both single-cell and clonal-subline sequencing data,
新出现的单细胞测序数据集使得应用现有的肿瘤系统发育重建方法来分析相关的肿瘤内异质性具有吸引力。不幸的是，肿瘤系统发育推断是一个np难题，现有的原则方法通常无法扩展到处理新兴单细胞数据集中观察到的数千个细胞和突变。尽管存在贪婪的启发式方法来构建细胞和突变的分层聚类，但它们在准确性方面存在众所周知的问题。此外，即使“最优”解决方案是可行的，现有的方法也只能提供一个单一的“最可能”树来描述可能导致观察到的细胞集合和突变的进化过程。更糟糕的是，绝大多数单细胞测序数据集都是转录组的，因此，在突变位点的覆盖范围上存在相当大的差异。在本文中，我们介绍了Trisicell，这是一个计算工具包，用于从单细胞基因组，外显子组或转录组测序数据进行可扩展的肿瘤系统发育重建和验证。Trisicell有三个组成部分:(i) Trisicell-DnC，一种新的基于单细胞数据的基因型矩阵的肿瘤系统发育重建方法，(ii) Trisicell-ConT，一种用于构建两个或多个肿瘤系统发育共识的新算法，可以通过在同一组细胞上使用不同的数据类型来构建，或者通过在同一数据上使用不同的方法来构建，以及(iii) Trisicell-PF，一种新的配分函数方法，用于评估任何用户定义的子树/细胞集被系统发育中给定的一组突变所播种的可能性。总的来说，这些工具提供了识别和验证肿瘤系统发育稳健部分的方法，提供了关注最重要(亚)克隆和为相关克隆扩增提供种子的基因组改变的能力。我们将Trisicell应用于来自亲代小鼠黑色素瘤模型单细胞的克隆亚系，并对其进行了全外显子组和全转录组测序。由Trisicell-DnC构建的外显组和转录组突变的克隆亚系的肿瘤系统发育与Trisicell-ConT显示高度相似，由表型相似的克隆亚系组成的亚树与Trisicell-PF显示的种子突变密切相关。此外，我们将Trisicell应用于来自同一亲本黑色素瘤细胞系的肿瘤的单细胞全转录组测序数据，该肿瘤接受抗ctla -4免疫治疗。两项研究产生的系统发育特征不同的亚树，与表型密切相关，包括细胞分化状态、肿瘤生长和治疗反应。这些结果表明，通过单细胞和克隆亚系测序数据，Trisicell可以用于可扩展的肿瘤系统发育重建和验证，这可能揭示出强烈的表型关联。特别是，他们认为黑色素瘤的发育状态和表型瘤内异质性源于可观察到的亚克隆变异。引文格式:Farid Rashidi Mehrabadi, Salem Malikic, Kerrie L. Marie, Eva perez - gujarro, Erfan Sadeqi Azer, Howard H. Yang, Can Kizilkale, Charli Gruen，刘怀天，Christina Marcelus, Aydin Buluc, Funda Ergun, Maxwell P. Lee, Glenn Merlino, Chi-Ping Day, S. Cenk Sahinalp。三细胞:可扩展的肿瘤系统发育重建和验证揭示了肿瘤内异质性的发育起源和治疗影响[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 LB019。
{"title":"Abstract LB019: Trisicell: Scalable Tumor Phylogeny Reconstruction and Validation Reveals Developmental Origin and Therapeutic Impact of Intratumoral Heterogeneity","authors":"F. Mehrabadi, S. Malikić, Kerrie L. Marie, Eva Pérez-Guijarro, Erfan Sadeqi Azer, Howard H. Yang, Can Kızılkale, Charli Gruen, Huaitian Liu, C. Marcelus, A. Buluç, Funda Ergün, M. Lee, G. Merlino, Chi-Ping Day, S. C. Sahinalp","doi":"10.1158/1538-7445.AM2021-LB019","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-LB019","url":null,"abstract":"Emerging sets of single-cell sequencing data makes it appealing to apply existing tumor phylogeny reconstruction methods to analyze associated intratumor heterogeneity. Unfortunately, tumor phylogeny inference is an NP-hard problem and existing principled methods typically fail to scale up to handle thousands of cells and mutations observed in emerging single-cell data sets. Even though there are greedy heuristics to build hierarchical clustering of cells and mutations, they suffer from well-documented issues in accuracy. Additionally even when “optimal” solutions are feasible, existing approaches only provide a single “most likely” tree to depict the evolutionary processes that may result in an observed collection of cells and mutations. To make matters worse, the vast majority of single-cell sequencing data sets are transcriptomic and as a result, suffer from considerable variation in coverage across mutational loci. In this paper, we introduce Trisicell, a computational toolkit for scalable tumor phylogeny reconstruction and validation from single-cell genomic, exomic or transcriptomic sequencing data. Trisicell has three components: (i) Trisicell-DnC, a new tumor phylogeny reconstruction method from genotype matrices derived from single-cell data, (ii) Trisicell-ConT a new algorithm for constructing the consensus for two or more tumor phylogenies - which may be built through the use of different data types on the same set of cells, or built through the use of different methods on the same data, and (iii) Trisicell-PF, a new partition function method for assessing the likelihood of any user-defined subtree/set of cells to be seeded by a given set of mutations in the phylogeny. Collectively, these tools provide means of identifying and validating robust portions of a tumor phylogeny, offering the ability to focus on the most important (sub)clones and the genomic alterations that seed the associated clonal expansion. We applied Trisicell to a panel of clonal sublines derived from single-cells of a parental mouse melanoma model on which we performed both whole exome and whole transcriptome sequencing. The tumor phylogenies of the clonal sublines built on exomic and transcriptomic mutations by Trisicell-DnC, were shown by Trisicell-ConT to be highly similar and the subtrees comprised of phenotypically similar clonal sublines were shown to be strongly associated by Trisicell-PF to their seeding mutations. In addition, we applied Trisicell to single-cell whole transcriptome sequencing data from a tumor derived from the same parental melanoma cell line, which was subjected to anti-CTLA-4 immunotherapy. The phylogenies generated from both studies featured distinct subtrees, strongly associated with phenotypes including cell differentiation status, tumor growth and therapeutic response. These results suggest that Trisicell can be used for scalable tumor phylogeny reconstruction and validation through both single-cell and clonal-subline sequencing data,","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89145061","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.AM2021-181
D. Petrini, C. Shimizu, G. Valente, Guilherme Folgueira, Guilherme Apolinario Silva Novaes, M. H. Katayama, P. Serio, R. A. Roela, T. Tucunduva, M. A. K. Folgueira, Hae Yong Kim
Background:Breast cancer (BC) is the second most common cancer among women. BC screening is usually based on mammography interpreted by radiologists. Recently, some researchers have used deep learning to automatically diagnose BC in mammography and so assist radiologists. The progress of BC detection algorithms can be measured by their performance on public datasets. The CBIS-DDSM is a widely used public dataset composed of scanned mammographies, equally divided into malignant and non-malignant (benign) images. Each image is accompanied by the segmentation of the lesion. Shen et al. (Nature Sci. Rep., 2019) presented a BC detection algorithm using an “end-to-end” approach to train deep neural networks. In this algorithm, a patch classifier is first trained to classify local image patches. The patch classifier9s weights are then used to initialize the whole image classifier, that is refined using datasets with the cancer status of the whole image. They achieved an AUC of 0.87 [0.84, 0.90] in classifying CBIS-DDSM images, using their best single-model, single-view breast classifier. They used ResNet (He et al., CVPR 2016) as the basis of their algorithm. Our hypothesis was that replacing the old ResNet with the modern EfficientNet (Tan et al., arXiv 2019) and MobileNetV2 (Sandler et al.,CVPR 2018) would result in greater accuracy. Methods:We tested many different models, to conclude that the best model is obtained using EfficientNet-B4 as the base model, with a MobileNetV2 block at the top, followed by a dense layer with two output categories. We trained the patch classifier using 52,528 patches with 224x224 pixels extracted from CBIS-DDSM. From each image, we extracted 20 patches: 10 patches containing the lesion and 10 from the background (without lesion). The patch classifier weights were then used to initialize the whole image classifier, that was trained using the end-to-end approach with CBIS-DDSM images resized to 1152x896 pixels, with data augmentation. The training was performed using a step learning rate of 1e-4 for the first 20 epochs then 1e-5 for the remaining 10 and batch size of 4, using 10-fold cross-validation. We used 81% of the dataset for training, 9% for validation and 10% for testing. Results:We obtained an AUC of 0.8963±0.06, using a single-model, single-view classifier and without test-time data augmentation. Conclusions:Using EfficientNet and MobileNetV2 as the basis of the BC detection algorithm (instead of ResNet), we obtained an improvement in classifying CBIS-DDSM images into malignant/non-malignant: AUC has increased from 0.87 to 0.896. Our AUC is also larger than other recent papers in the literature, such as Shu et al. (IEEE Trans Med. Image, 2020) that achieved an AUC of 0.838 in the same CBIS-DDSM dataset. Citation Format: Daniel G. Petrini, Carlos Shimizu, Gabriel V. Valente, Guilherme Folgueira, Guilherme A. Novaes, Maria L. Katayama, Pedro Serio, Rosimeire A. Roela, Tatiana C. Tucunduva, Maria Aparecida A. Folgu
背景:乳腺癌(BC)是女性中第二常见的癌症。BC筛查通常是基于由放射科医生解读的乳房x光检查。最近，一些研究人员已经使用深度学习来自动诊断乳房x光检查中的BC，从而帮助放射科医生。BC检测算法的进步可以通过它们在公共数据集上的表现来衡量。CBIS-DDSM是一个广泛使用的公共数据集，由扫描乳房x线照片组成，平均分为恶性和非恶性(良性)图像。每张图像都伴随着病灶的分割。《自然科学》;Rep.， 2019)提出了一种使用“端到端”方法训练深度神经网络的BC检测算法。在该算法中，首先训练一个补丁分类器对局部图像补丁进行分类。然后使用patch分类器权重初始化整个图像分类器，并使用具有整个图像癌症状态的数据集对其进行细化。他们使用他们最好的单模型、单视图乳腺分类器对CBIS-DDSM图像进行分类，AUC为0.87[0.84,0.90]。他们使用ResNet (He et al.， CVPR 2016)作为算法的基础。我们的假设是，用现代的EfficientNet (Tan等人，arXiv 2019)和MobileNetV2 (Sandler等人，CVPR 2018)取代旧的ResNet将导致更高的准确性。方法:我们测试了许多不同的模型，得出的结论是，以EfficientNet-B4为基础模型，顶部为MobileNetV2块，然后是具有两个输出类别的密集层，获得了最佳模型。我们使用从CBIS-DDSM中提取的224x224像素的52528个补丁来训练补丁分类器。从每张图像中，我们提取了20块补丁:10块包含病变，10块来自背景(没有病变)。然后使用patch分类器权重初始化整个图像分类器，使用端到端方法对cis - ddsm图像进行训练，将图像大小调整为1152x896像素，并进行数据增强。前20个epoch的步学习率为1e-4，其余10个epoch的步学习率为1e-5，批大小为4，使用10倍交叉验证。我们使用81%的数据集用于训练，9%用于验证，10%用于测试。结果:采用单模型、单视图分类器，未经测试时间数据增强，AUC为0.8963±0.06。结论:使用EfficientNet和MobileNetV2作为BC检测算法的基础(而不是ResNet)，我们对CBIS-DDSM图像的恶性/非恶性分类得到了改进:AUC从0.87提高到0.896。我们的AUC也比文献中最近的其他论文要大，例如Shu等人(IEEE Trans Med. Image, 2020)在相同的CBIS-DDSM数据集中实现了0.838的AUC。引用格式:Daniel G. Petrini, Carlos Shimizu, Gabriel V. Valente, Guilherme Folgueira, Guilherme A. Novaes, Maria L. Katayama, Pedro Serio, Rosimeire A. Roela, Tatiana C. Tucunduva, Maria aprecida A. Folgueira, Hae Y. Kim。基于高效网络和端到端训练的乳房x线摄影高精度乳腺癌检测[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181期。
{"title":"Abstract 181: High-accuracy breast cancer detection in mammography using EfficientNet and end-to-end training","authors":"D. Petrini, C. Shimizu, G. Valente, Guilherme Folgueira, Guilherme Apolinario Silva Novaes, M. H. Katayama, P. Serio, R. A. Roela, T. Tucunduva, M. A. K. Folgueira, Hae Yong Kim","doi":"10.1158/1538-7445.AM2021-181","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-181","url":null,"abstract":"Background:Breast cancer (BC) is the second most common cancer among women. BC screening is usually based on mammography interpreted by radiologists. Recently, some researchers have used deep learning to automatically diagnose BC in mammography and so assist radiologists. The progress of BC detection algorithms can be measured by their performance on public datasets. The CBIS-DDSM is a widely used public dataset composed of scanned mammographies, equally divided into malignant and non-malignant (benign) images. Each image is accompanied by the segmentation of the lesion. Shen et al. (Nature Sci. Rep., 2019) presented a BC detection algorithm using an “end-to-end” approach to train deep neural networks. In this algorithm, a patch classifier is first trained to classify local image patches. The patch classifier9s weights are then used to initialize the whole image classifier, that is refined using datasets with the cancer status of the whole image. They achieved an AUC of 0.87 [0.84, 0.90] in classifying CBIS-DDSM images, using their best single-model, single-view breast classifier. They used ResNet (He et al., CVPR 2016) as the basis of their algorithm. Our hypothesis was that replacing the old ResNet with the modern EfficientNet (Tan et al., arXiv 2019) and MobileNetV2 (Sandler et al.,CVPR 2018) would result in greater accuracy. Methods:We tested many different models, to conclude that the best model is obtained using EfficientNet-B4 as the base model, with a MobileNetV2 block at the top, followed by a dense layer with two output categories. We trained the patch classifier using 52,528 patches with 224x224 pixels extracted from CBIS-DDSM. From each image, we extracted 20 patches: 10 patches containing the lesion and 10 from the background (without lesion). The patch classifier weights were then used to initialize the whole image classifier, that was trained using the end-to-end approach with CBIS-DDSM images resized to 1152x896 pixels, with data augmentation. The training was performed using a step learning rate of 1e-4 for the first 20 epochs then 1e-5 for the remaining 10 and batch size of 4, using 10-fold cross-validation. We used 81% of the dataset for training, 9% for validation and 10% for testing. Results:We obtained an AUC of 0.8963±0.06, using a single-model, single-view classifier and without test-time data augmentation. Conclusions:Using EfficientNet and MobileNetV2 as the basis of the BC detection algorithm (instead of ResNet), we obtained an improvement in classifying CBIS-DDSM images into malignant/non-malignant: AUC has increased from 0.87 to 0.896. Our AUC is also larger than other recent papers in the literature, such as Shu et al. (IEEE Trans Med. Image, 2020) that achieved an AUC of 0.838 in the same CBIS-DDSM dataset. Citation Format: Daniel G. Petrini, Carlos Shimizu, Gabriel V. Valente, Guilherme Folgueira, Guilherme A. Novaes, Maria L. Katayama, Pedro Serio, Rosimeire A. Roela, Tatiana C. Tucunduva, Maria Aparecida A. Folgu","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"85 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86986647","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.AM2021-171
Christie L. Husted, F. Aguet, C. Shea, A. Gower, William J. Mischler, Y. Koga, R. Hong, S. Dubinett, A. Spira, S. Mazzilli, E. Cerami, I. Leshchiner, M. Lenburg, G. Getz, J. Beane, Joshua D. Campbell
{"title":"Abstract 171: Cloud-based bulk and single-cell RNAseq pipelines in the Terra platform for the Lung PCA","authors":"Christie L. Husted, F. Aguet, C. Shea, A. Gower, William J. Mischler, Y. Koga, R. Hong, S. Dubinett, A. Spira, S. Mazzilli, E. Cerami, I. Leshchiner, M. Lenburg, G. Getz, J. Beane, Joshua D. Campbell","doi":"10.1158/1538-7445.AM2021-171","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-171","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86554885","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.AM2021-3
R. Verma, Wei Wu, Neeraj Kumar, Elizabeth A. Yu, Won-Tak Choi, S. Umetsu, T. Bivona
{"title":"Abstract 3: Deep learning-based integration of esophageal cancer morphology with genomics","authors":"R. Verma, Wei Wu, Neeraj Kumar, Elizabeth A. Yu, Won-Tak Choi, S. Umetsu, T. Bivona","doi":"10.1158/1538-7445.AM2021-3","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-3","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"153 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86134801","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.AM2021-222
Chengyue Wu, D. Hormuth, F. Pineda, G. Karczmar, T. Yankeelov
{"title":"Abstract 222: Towards patient-specific optimization of neoadjuvant treatment protocols for breast cancer based on image-based fluid dynamics","authors":"Chengyue Wu, D. Hormuth, F. Pineda, G. Karczmar, T. Yankeelov","doi":"10.1158/1538-7445.AM2021-222","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-222","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74885438","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.AM2021-168
Sairahul R Pentaparthi, Brandon Burgman, Song Yi
{"title":"Abstract 168: Computational model for prediction of actionable drug combinations in cancer","authors":"Sairahul R Pentaparthi, Brandon Burgman, Song Yi","doi":"10.1158/1538-7445.AM2021-168","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-168","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"97 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78093613","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.AM2021-196
A. Akalin, B. Uyar, J. Ronen, V. Franke
Cancer is a heterogeneous collection of diseases traditionally classified by the tissue of origin. The diversity of the molecular profiles of cancers has a big impact on the way patients are diagnosed and treated, how they respond to their prescribed treatments, the duration of survival after diagnosis, and factors such as remission, recurrence, or spread (metastasis) of the disease. While such diagnostic and prognostic outcomes are potentially predictable by taking a closer look into the changes of the genome, epigenome, transcriptome, proteome, and various other omics platforms, the contemporary cancer treatments still predominantly don9t make the best use of such multi-omics profiling of patient samples. Therefore, multi-omics profiling of cancers holds great potential to define a molecularly coherent subtype definition of cancers in order to achieve the eventual goal of matching the best possible treatment to the subgroup of patients. However, the current subtypes from consortiums such as TCGA have been defined by heterogeneous methods and molecular markers by different teams. A subset of these studies have not attempted to characterize molecular subtypes, but rather taken histopathologically defined subtypes as the gold standard and tried to characterize molecular features of these subtypes. Here we evaluate TCGA cancer subtypes based on the molecular profile coherence score. This novel metric combines survival statistics, pathways information, tumor purity estimates, and mutational signatures. We expect that subtypes that are patient subgroups should display molecular signature homogeneity. We evaluate TCGA subtypes from 21 cancers using these criteria and compare the subtypes with our own definition using multi-omics data in a deep learning framework. We have refined the several subtypes from multiple cancers towards more molecularly coherent patient subgroups. Citation Format: Altuna Akalin, Bora Uyar, Jonathan Ronen, Vedran Franke. Redefining cancer subtypes using multi-omics and deep learning [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 196.
癌症是一种异质性的疾病集合，传统上按起源组织分类。癌症分子谱的多样性对患者的诊断和治疗方式、他们对处方治疗的反应、诊断后的生存时间以及疾病的缓解、复发或扩散(转移)等因素有很大影响。虽然通过对基因组、表观基因组、转录组、蛋白质组和各种其他组学平台的变化进行更仔细的观察，可以预测这些诊断和预后结果，但当代癌症治疗仍然主要没有充分利用这些患者样本的多组学分析。因此，癌症的多组学分析具有巨大的潜力，可以定义癌症的分子一致亚型定义，从而实现将最佳治疗方法与患者亚组相匹配的最终目标。然而，目前来自TCGA等联盟的亚型是由不同的团队通过异质方法和分子标记来定义的。这些研究的一个子集没有试图表征分子亚型，而是将组织病理学定义的亚型作为金标准，并试图表征这些亚型的分子特征。在这里，我们基于分子谱一致性评分来评估TCGA癌症亚型。这种新的度量结合了生存统计、途径信息、肿瘤纯度估计和突变特征。我们期望作为患者亚组的亚型应该表现出分子特征的同质性。我们使用这些标准评估了21种癌症的TCGA亚型，并在深度学习框架中使用多组学数据将亚型与我们自己的定义进行了比较。我们已经从多种癌症中提炼了几种亚型，使其趋向于分子上更一致的患者亚群。引文格式:Altuna Akalin, Bora Uyar, Jonathan Ronen, Vedran Franke。利用多组学和深度学习重新定义癌症亚型[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196期。
{"title":"Abstract 196: Redefining cancer subtypes using multi-omics and deep learning","authors":"A. Akalin, B. Uyar, J. Ronen, V. Franke","doi":"10.1158/1538-7445.AM2021-196","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-196","url":null,"abstract":"Cancer is a heterogeneous collection of diseases traditionally classified by the tissue of origin. The diversity of the molecular profiles of cancers has a big impact on the way patients are diagnosed and treated, how they respond to their prescribed treatments, the duration of survival after diagnosis, and factors such as remission, recurrence, or spread (metastasis) of the disease. While such diagnostic and prognostic outcomes are potentially predictable by taking a closer look into the changes of the genome, epigenome, transcriptome, proteome, and various other omics platforms, the contemporary cancer treatments still predominantly don9t make the best use of such multi-omics profiling of patient samples. Therefore, multi-omics profiling of cancers holds great potential to define a molecularly coherent subtype definition of cancers in order to achieve the eventual goal of matching the best possible treatment to the subgroup of patients. However, the current subtypes from consortiums such as TCGA have been defined by heterogeneous methods and molecular markers by different teams. A subset of these studies have not attempted to characterize molecular subtypes, but rather taken histopathologically defined subtypes as the gold standard and tried to characterize molecular features of these subtypes. Here we evaluate TCGA cancer subtypes based on the molecular profile coherence score. This novel metric combines survival statistics, pathways information, tumor purity estimates, and mutational signatures. We expect that subtypes that are patient subgroups should display molecular signature homogeneity. We evaluate TCGA subtypes from 21 cancers using these criteria and compare the subtypes with our own definition using multi-omics data in a deep learning framework. We have refined the several subtypes from multiple cancers towards more molecularly coherent patient subgroups. Citation Format: Altuna Akalin, Bora Uyar, Jonathan Ronen, Vedran Franke. Redefining cancer subtypes using multi-omics and deep learning [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 196.","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"284 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76851005","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.AM2021-202
N. Reyes, R. Tiwari, J. Geliebter
Background: Prostate cancer is the most frequently diagnosed malignancy and the fourth leading cause of cancer-related death in the global male population. Although the disease has a relatively low mortality rate with some patients surviving for 10-20 years after treatment, others respond poorly to treatment and die of metastatic disease within 2-3 years. Therefore, there is an urgent need to develop strategies to identify patients with clinically significant prostate cancer requiring aggressive treatment to improve survival, while sparing others unnecessary side effects. The purpose of this study was to identify survival associated genes in prostate cancer patients from the TCGA database using bioinformatics tools. Methods: Data from prostate cancer patients in the TCGA database were divided into two study groups: a high and a low expression group, relative to the median expression. The Gene Expression Profiling Interactive Analysis (GEPIA2) tool was used for the identification of the most differential survival genes. Metascape bioinformatics tool was subsequently used for clustering of genes based on processes, pathway enrichment analysis, and construction of Protein-Protein Interaction (PPI) network. Metascape was also used for molecular Complex Detection (MCODE) to identify the genes with the highest degree of connection, known as hub genes, and to screen modules of the PPI network. Results: Bioinformatics analysis allowed the identification of 361 genes whose expression levels were significantly associated with overall survival in prostate cancer patients from the TCGA. Survival associated genes were primarily enriched in mRNA processing, DNA repair, ncRNA processing, DNA replication, macromolecule methylation, among others. The 12 most connected genes were selected as hub genes and Kaplan-Meier analysis was used to verify survival associated with this set of genes. Hub genes included several splicing factors and components of the processing machinery of cellular pre-mRNAs. Conclusions: These hub genes may reveal basic mechanisms underlying the development of clinically relevant prostate cancer and contribute to the identification of novel markers for prognosis of this cancer. Citation Format: Niradiz Reyes, Raj Tiwari, Jan Geliebter. Identification of survival associated hub genes in prostate cancer patients from the TCGA database [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 202.
背景:前列腺癌是全球男性人群中最常见的恶性肿瘤，也是癌症相关死亡的第四大原因。虽然这种疾病的死亡率相对较低，一些患者在治疗后存活10-20年，但其他患者对治疗反应不佳，在2-3年内死于转移性疾病。因此，迫切需要制定策略来识别需要积极治疗的临床显著前列腺癌患者，以提高生存率，同时避免其他不必要的副作用。本研究的目的是利用生物信息学工具从TCGA数据库中识别前列腺癌患者的生存相关基因。方法:将TCGA数据库中前列腺癌患者的数据，相对于中位表达分为高表达组和低表达组两组。基因表达谱交互分析(GEPIA2)工具用于鉴定大多数差异生存基因。随后使用Metascape生物信息学工具进行基于过程的基因聚类、途径富集分析和蛋白质-蛋白质相互作用(PPI)网络的构建。metscape还用于分子复合物检测(MCODE)，以鉴定连接程度最高的基因，称为枢纽基因，并筛选PPI网络的模块。结果:生物信息学分析鉴定出361个基因，这些基因的表达水平与TCGA中前列腺癌患者的总生存率显著相关。生存相关基因主要富集于mRNA加工、DNA修复、ncRNA加工、DNA复制、大分子甲基化等。选取关联性最大的12个基因作为枢纽基因，采用Kaplan-Meier分析验证该组基因的相关生存率。枢纽基因包括几个剪接因子和细胞前mrna加工机制的组成部分。结论:这些中心基因可能揭示了临床相关前列腺癌发生的基本机制，并有助于发现前列腺癌预后的新标志物。引文格式:Niradiz Reyes, Raj Tiwari, Jan Geliebter。TCGA数据库中前列腺癌患者生存相关枢纽基因的鉴定[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202期。
{"title":"Abstract 202: Identification of survival associated hub genes in prostate cancer patients from the TCGA database","authors":"N. Reyes, R. Tiwari, J. Geliebter","doi":"10.1158/1538-7445.AM2021-202","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-202","url":null,"abstract":"Background: Prostate cancer is the most frequently diagnosed malignancy and the fourth leading cause of cancer-related death in the global male population. Although the disease has a relatively low mortality rate with some patients surviving for 10-20 years after treatment, others respond poorly to treatment and die of metastatic disease within 2-3 years. Therefore, there is an urgent need to develop strategies to identify patients with clinically significant prostate cancer requiring aggressive treatment to improve survival, while sparing others unnecessary side effects. The purpose of this study was to identify survival associated genes in prostate cancer patients from the TCGA database using bioinformatics tools. Methods: Data from prostate cancer patients in the TCGA database were divided into two study groups: a high and a low expression group, relative to the median expression. The Gene Expression Profiling Interactive Analysis (GEPIA2) tool was used for the identification of the most differential survival genes. Metascape bioinformatics tool was subsequently used for clustering of genes based on processes, pathway enrichment analysis, and construction of Protein-Protein Interaction (PPI) network. Metascape was also used for molecular Complex Detection (MCODE) to identify the genes with the highest degree of connection, known as hub genes, and to screen modules of the PPI network. Results: Bioinformatics analysis allowed the identification of 361 genes whose expression levels were significantly associated with overall survival in prostate cancer patients from the TCGA. Survival associated genes were primarily enriched in mRNA processing, DNA repair, ncRNA processing, DNA replication, macromolecule methylation, among others. The 12 most connected genes were selected as hub genes and Kaplan-Meier analysis was used to verify survival associated with this set of genes. Hub genes included several splicing factors and components of the processing machinery of cellular pre-mRNAs. Conclusions: These hub genes may reveal basic mechanisms underlying the development of clinically relevant prostate cancer and contribute to the identification of novel markers for prognosis of this cancer. Citation Format: Niradiz Reyes, Raj Tiwari, Jan Geliebter. Identification of survival associated hub genes in prostate cancer patients from the TCGA database [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 202.","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"201 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76923369","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-LB017
Zinaida Perova, Csaba Halmagyi, Abayomi Mosaku, N. Conte, J. Mason, Alex Follette, Ross Thorne, Mauricio Martinez, S. Neuhauser, D. Begley, D. Krupke, H. Parkinson, T. Meehan, C. Bult
Patient-derived tumor xenograft (PDX) models are a critical oncology platform for cancer research, drug development and personalized medicine. Because of the heterogeneous nature of PDXs repositories, finding models of interest is a challenge. The Jackson Laboratory and EMBL-EBI are developing PDX Finder, the world9s largest open PDX database containing millions of phenomic information from over 4300 models (www.pdxfinder.org, PMID: 30535239). In support of this initiative, we developed the PDX Minimal Information standard (PDX-MI) which defines metadata necessary to describe models (PMID: 29092942). Within PDX Finder, critical attributes like diagnosis, drug names or genes are harmonized into a cohesive ontological data model based on PDX-MI. An intuitive search and faceted search interface allow users to select models based on clinical/PDX attributes, tumor markers, dataset availability and/or drug dosing results. We provide PDX, patient, drug and molecular data detail pages where all available information can be browsed and downloaded. To further facilitate user9s model selection, we are linking key external resources like publication platforms and cancer-specific annotation tools enabling exploration and prioritization of PDX variation data (COSMIC, CIViC, OncoMx, OpenCRAVAT). Links to originating resource protocols and contact information are provided, facilitating data understanding and further collaboration. Alongside database development activities, PDX Finder has undertaken activities to tackle areas of standards and tool development, data integration and outreach. PDX Finder provides key expertise and software components to support several worldwide consortia including PDXNet, PDMR and EurOPDX. We are driving the development of, and promoting the use of descriptive standards to facilitate data interoperability and promote global sharing of models. Our standard has become established in the community for data exchange, adopted by PDX providers, consortia, and informatics tools integrating PDX data. It has been re-used by different initiatives in the context of data collection and data modeling allowing adherence to the FAIR data principles - Findability, Accessibility, Interoperability and Reusability. PDX Finder is increasing awareness of PDX models, facilitating data integration, and enabling international collaboration, maximizing the investment in, and translational capabilities of these important models of human cancer. PDX Finder is freely available under an Apache 2 license (github.com/pdxfinder). Work supported by NCI U24 CA204781 01 (ended 31Aug2020), U24 CA253539, and R01 CA089713. Citation Format: Zinaida Perova, Csaba Halmagyi, Abayomi Mosaku, Nathalie Conte, Jeremy Mason, Alex Follette, Ross Thorne, Mauricio Martinez, Steven Neuhauser, Dale Begley, Debra Krupke, Helen Parkinson, Terrence Meehan, Carol Bult. PDX Finder: An open and global catalogue of patient-derived xenograft models [abstract]. In: Proceedings of the America
患者源性肿瘤异种移植(PDX)模型是癌症研究、药物开发和个性化医疗的重要肿瘤学平台。由于pdx存储库的异构特性，找到感兴趣的模型是一项挑战。Jackson实验室和EMBL-EBI正在开发PDX Finder，这是世界上最大的开放PDX数据库，包含来自4300多个模型的数百万个现象信息(www.pdxfinder.org, PMID: 30535239)。为了支持这个计划，我们开发了PDX最小信息标准(PDX- mi)，它定义了描述模型所需的元数据(PMID: 29092942)。在PDX Finder中，诊断、药物名称或基因等关键属性被协调到基于PDX- mi的内聚本体数据模型中。直观的搜索和分面搜索界面允许用户根据临床/PDX属性、肿瘤标志物、数据集可用性和/或药物剂量结果选择模型。我们提供PDX，患者，药物和分子数据详细页面，所有可用的信息都可以浏览和下载。为了进一步方便用户的模型选择，我们正在链接关键的外部资源，如出版平台和癌症特定的注释工具，以便对PDX变异数据(COSMIC, CIViC, OncoMx, OpenCRAVAT)进行探索和优先排序。提供了原始资源协议和联系信息的链接，以促进数据理解和进一步合作。除了数据库开发活动，PDX Finder还承担了处理标准和工具开发、数据集成和扩展等领域的活动。PDX Finder提供关键的专业知识和软件组件，以支持几个全球联盟，包括PDXNet, PDMR和EurOPDX。我们正在推动描述性标准的开发和推广使用，以促进数据互操作性和促进模型的全球共享。我们的标准已经在数据交换社区中建立起来，被PDX提供者、联盟和集成PDX数据的信息学工具所采用。它在数据收集和数据建模的背景下被不同的计划重用，从而遵守FAIR数据原则——可查找性、可访问性、互操作性和可重用性。PDX Finder正在提高人们对PDX模型的认识，促进数据集成，实现国际合作，最大限度地提高对这些重要人类癌症模型的投资和转化能力。PDX Finder在Apache 2许可下免费提供(github.com/pdxfinder)。NCI U24 CA204781 01(截止2020年8月31日)、U24 CA253539、R01 CA089713支持。引文格式:Zinaida Perova, Csaba Halmagyi, Abayomi Mosaku, Nathalie Conte, Jeremy Mason, Alex Follette, Ross Thorne, Mauricio Martinez, Steven Neuhauser, Dale Begley, Debra Krupke, Helen Parkinson, Terrence Meehan, Carol Bult。PDX Finder:一个开放和全球的病人来源的异种移植模型目录[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 LB017。
{"title":"Abstract LB017: PDX Finder: An open and global catalogue of patient-derived xenograft models","authors":"Zinaida Perova, Csaba Halmagyi, Abayomi Mosaku, N. Conte, J. Mason, Alex Follette, Ross Thorne, Mauricio Martinez, S. Neuhauser, D. Begley, D. Krupke, H. Parkinson, T. Meehan, C. Bult","doi":"10.1158/1538-7445.AM2021-LB017","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-LB017","url":null,"abstract":"Patient-derived tumor xenograft (PDX) models are a critical oncology platform for cancer research, drug development and personalized medicine. Because of the heterogeneous nature of PDXs repositories, finding models of interest is a challenge. The Jackson Laboratory and EMBL-EBI are developing PDX Finder, the world9s largest open PDX database containing millions of phenomic information from over 4300 models (www.pdxfinder.org, PMID: 30535239). In support of this initiative, we developed the PDX Minimal Information standard (PDX-MI) which defines metadata necessary to describe models (PMID: 29092942). Within PDX Finder, critical attributes like diagnosis, drug names or genes are harmonized into a cohesive ontological data model based on PDX-MI. An intuitive search and faceted search interface allow users to select models based on clinical/PDX attributes, tumor markers, dataset availability and/or drug dosing results. We provide PDX, patient, drug and molecular data detail pages where all available information can be browsed and downloaded. To further facilitate user9s model selection, we are linking key external resources like publication platforms and cancer-specific annotation tools enabling exploration and prioritization of PDX variation data (COSMIC, CIViC, OncoMx, OpenCRAVAT). Links to originating resource protocols and contact information are provided, facilitating data understanding and further collaboration. Alongside database development activities, PDX Finder has undertaken activities to tackle areas of standards and tool development, data integration and outreach. PDX Finder provides key expertise and software components to support several worldwide consortia including PDXNet, PDMR and EurOPDX. We are driving the development of, and promoting the use of descriptive standards to facilitate data interoperability and promote global sharing of models. Our standard has become established in the community for data exchange, adopted by PDX providers, consortia, and informatics tools integrating PDX data. It has been re-used by different initiatives in the context of data collection and data modeling allowing adherence to the FAIR data principles - Findability, Accessibility, Interoperability and Reusability. PDX Finder is increasing awareness of PDX models, facilitating data integration, and enabling international collaboration, maximizing the investment in, and translational capabilities of these important models of human cancer. PDX Finder is freely available under an Apache 2 license (github.com/pdxfinder). Work supported by NCI U24 CA204781 01 (ended 31Aug2020), U24 CA253539, and R01 CA089713. Citation Format: Zinaida Perova, Csaba Halmagyi, Abayomi Mosaku, Nathalie Conte, Jeremy Mason, Alex Follette, Ross Thorne, Mauricio Martinez, Steven Neuhauser, Dale Begley, Debra Krupke, Helen Parkinson, Terrence Meehan, Carol Bult. PDX Finder: An open and global catalogue of patient-derived xenograft models [abstract]. In: Proceedings of the America","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":"73803204","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：