Pub Date : 2021-07-01DOI: 10.1158/1538-7445.AM2021-153
Claire J. Guo, Mary Saltarelli, S. Lambert, H. Fang, Chun Zhang
{"title":"Abstract 153: Development of a workflow to handle the quality control and analysis of Olink protein biomarker data in early phase oncology clinical trials","authors":"Claire J. Guo, Mary Saltarelli, S. Lambert, H. Fang, Chun Zhang","doi":"10.1158/1538-7445.AM2021-153","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-153","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73607351","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.AM2021-183
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:Early computer-aided detection systems for mammography have failed to improve the performance of radiologists. With the remarkable success of deep learning, some recent studies have described computer systems with similar or even superior performance to that of human experts. Among them, Shen et al. (Nature Sci. Rep., 2019) present a promising “end-to-end” training approach. Instead of training a convolutional net with whole mammograms, they first train a “patch classifier” that recognizes lesions in small subimages. Then, they generalize the patch classifier to “whole image classifier” using the property of fully convolutional networks and the end-to-end approach. Using this strategy, the authors have obtained a per-image AUC of 0.87 [0.84, 0.90] in the CBIS-DDSM dataset. Standard mammography consists of two views for each breast: bilateral craniocaudal (CC) and mediolateral oblique (MLO). The algorithm proposed by Shen et al. processes only single-view mammography. We extend their work, presenting the end-to-end training of convolutional net for two-view mammography. Methods:First, we reproduced Shen et al.9s work, using the CBIS-DDSM dataset. We trained a ResNet50-based net for classifying patches with 224x224 pixels using segmented lesions. Then, the weights of the patch classifier were transferred to the whole image single-view classifier, obtained by removing the dense layers from the patch classifier and stacking one ResNet block at the top. This single-view classifier was trained using full images from the same dataset. Trying to replicate Shen et al.9s work, we obtained an AUC of 0.8524±0.0560, less than 0.87 reported in the original paper. We attribute this worsening to the fact that we are using only 2260 images with two views, instead of 2478 images from the original work. Finally, we built the two-view classifier that receives CC and MLO views as input. This classifier has inside two copies of the patch classifier, loaded with the weights from the single-view classifier. The features extracted by the two patch classifiers are concatenated and submitted to the ResNet block. The two-view classifier is end-to-end trained using full images, refining all its weights, including those inside the two patch classifiers. Results:The two-view classifier yielded an AUC of 0.9199±0.0623 in 5-fold cross-validation to classify mammographies into malignant/non-malignant, using single-model and without test-time data augmentation. This is better than the Shen et al.9s AUC (0.87), our single-view AUC (0.85). Zhang et al. (Plos One, 2020) present another two-view algorithm (without end-to-end training) with AUC of 0.95. However, this work cannot directly be compared with ours, as it was tested on a different set of images. Conclusions:We presented end-to-end training of convolutional net for two-view mammography. Our system9s AUC was 0.92, better than the 0.87 obtained by the previous single-view system. Citation Format: Daniel G. Petrini, C
背景:早期乳腺x线摄影的计算机辅助检测系统未能提高放射科医生的工作水平。随着深度学习的显著成功，最近的一些研究已经描述了与人类专家相似甚至优于人类专家的计算机系统。其中，Shen等(Nature Sci.;Rep.， 2019)提出了一种有前途的“端到端”培训方法。他们不是用整个乳房x光照片训练卷积网络，而是首先训练一个“补丁分类器”，在小的子图像中识别病变。然后，他们利用全卷积网络的特性和端到端方法将patch分类器推广到“整幅图像分类器”。使用这种策略，作者在CBIS-DDSM数据集中获得了0.87[0.84,0.90]的单幅图像AUC。标准乳房x线照相术包括每个乳房的两个视图:双侧颅侧(CC)和中外侧斜位(MLO)。Shen等人提出的算法只处理单视图乳房x线检查。我们扩展了他们的工作，提出了卷积网络对双视图乳房x线检查的端到端训练。方法:首先，我们使用CBIS-DDSM数据集复制了Shen等人的工作。我们训练了一个基于resnet50的网络，用于使用分割的病灶对224x224像素的斑块进行分类。然后，将patch分类器的权重转移到整个图像的单视图分类器中，该分类器通过去除patch分类器中的密集层并在顶部堆叠一个ResNet块来获得。这个单视图分类器使用来自同一数据集的完整图像进行训练。我们试图复制Shen等人的工作，得到的AUC为0.8524±0.0560，小于原论文报道的0.87。我们将这种恶化归因于我们只使用了2260张带有两个视图的图像，而不是原始作品中的2478张图像。最后，我们构建了接收CC和MLO视图作为输入的双视图分类器。这个分类器有两个补丁分类器的副本，加载了来自单视图分类器的权重。两个补丁分类器提取的特征被连接并提交给ResNet块。双视图分类器使用完整图像进行端到端训练，精炼其所有权重，包括两个补丁分类器内的权重。结果:双视图分类器在单模型、无测试时间数据增强的情况下，5次交叉验证的AUC为0.9199±0.0623。这优于Shen等人的AUC(0.87)和我们的单视图AUC(0.85)。Zhang等人(Plos One, 2020)提出了另一种双视图算法(没有端到端训练)，AUC为0.95。但是，这项工作不能直接与我们的工作进行比较，因为它是在不同的图像集上进行测试的。结论:我们提出了用于双视图乳房x线摄影的卷积网络的端到端训练。该系统的AUC为0.92，优于以往单视图系统的0.87。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。基于卷积网络的乳腺癌双视图乳房x光检查端到端训练[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183期。
{"title":"Abstract 183: End-to-end training of convolutional network for breast cancer detection in two-view mammography","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.AM2021-183","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-183","url":null,"abstract":"Background:Early computer-aided detection systems for mammography have failed to improve the performance of radiologists. With the remarkable success of deep learning, some recent studies have described computer systems with similar or even superior performance to that of human experts. Among them, Shen et al. (Nature Sci. Rep., 2019) present a promising “end-to-end” training approach. Instead of training a convolutional net with whole mammograms, they first train a “patch classifier” that recognizes lesions in small subimages. Then, they generalize the patch classifier to “whole image classifier” using the property of fully convolutional networks and the end-to-end approach. Using this strategy, the authors have obtained a per-image AUC of 0.87 [0.84, 0.90] in the CBIS-DDSM dataset. Standard mammography consists of two views for each breast: bilateral craniocaudal (CC) and mediolateral oblique (MLO). The algorithm proposed by Shen et al. processes only single-view mammography. We extend their work, presenting the end-to-end training of convolutional net for two-view mammography. Methods:First, we reproduced Shen et al.9s work, using the CBIS-DDSM dataset. We trained a ResNet50-based net for classifying patches with 224x224 pixels using segmented lesions. Then, the weights of the patch classifier were transferred to the whole image single-view classifier, obtained by removing the dense layers from the patch classifier and stacking one ResNet block at the top. This single-view classifier was trained using full images from the same dataset. Trying to replicate Shen et al.9s work, we obtained an AUC of 0.8524±0.0560, less than 0.87 reported in the original paper. We attribute this worsening to the fact that we are using only 2260 images with two views, instead of 2478 images from the original work. Finally, we built the two-view classifier that receives CC and MLO views as input. This classifier has inside two copies of the patch classifier, loaded with the weights from the single-view classifier. The features extracted by the two patch classifiers are concatenated and submitted to the ResNet block. The two-view classifier is end-to-end trained using full images, refining all its weights, including those inside the two patch classifiers. Results:The two-view classifier yielded an AUC of 0.9199±0.0623 in 5-fold cross-validation to classify mammographies into malignant/non-malignant, using single-model and without test-time data augmentation. This is better than the Shen et al.9s AUC (0.87), our single-view AUC (0.85). Zhang et al. (Plos One, 2020) present another two-view algorithm (without end-to-end training) with AUC of 0.95. However, this work cannot directly be compared with ours, as it was tested on a different set of images. Conclusions:We presented end-to-end training of convolutional net for two-view mammography. Our system9s AUC was 0.92, better than the 0.87 obtained by the previous single-view system. Citation Format: Daniel G. Petrini, C","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"57 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76684620","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.AM2021-240
Kenneth B. Thomas, Y. Mou, C. Magnan, T. Gyuris, E. Shinbrot, Fernando Díaz, Steven Lau-Rivera, Segun Jung, V. Funari, L. Weiss
Introduction: Our goal is to improve gene fusion detection via RNA sequencing by combining multiple fusion callers through machine learning techniques. Background: Gene Fusion events are important drivers of malignancy. RNA sequencing (RNAseq) methods for detection of fusions have the advantage that multiple markers can be targeted at one time. Unlike DNA methods, in which it is challenging to capture fusion breakpoints, in RNA methods fusions are readily identified through chimeric transcripts. While many fusion calling algorithms exist for use on RNAseq data, sensitive fusion callers, needed for samples of low tumor content, often present high false positive rates - a result of aligning chimeric transcripts. Further, there currently is no single feature in NGS data that can be used to filter out false positive fusion calls. In order to achieve higher accuracy in fusion calls than can be achieved using individual fusion callers, we have weighted and combined the results of multiple fusion callers by systematic and objective means: an ensemble learning approach based on random forest models. Our method selects from data generated by three independent fusion callers supplemented by metrics obtained from in-house methods. It presents a metric that can be immediately interpreted as the probability that a candidate fusion call is a true fusion call. Methods: Random forest models were generated by use of the randomForest package in R, with tuning by the R caret package. Training data sets consisted of a balanced set of 394 fusion calls from clinical samples of solid tumors. For training, fusion calls with at least 10 supporting reads were deemed true or false based on manual review via IGV, and orthogonal methods including PCR with Sanger sequencing and the commercial Archer™ fusion CTL and Sarcoma panels. We present the results of training on data from the three well-known fusion callers Arriba, STAR-Fusion, and FusionCatcher, together with additional data from an in-house developed junction counting method, and fusion membership in a list of known fusions (a “white list”). Models were validated by 10-fold cross-validation. Results: In performance evaluations, false positive and false negative calls were presumed false based on orthogonal determinations. On that basis, our current best model has an accuracy of 94.9% (sensitivity 93.4%, specificity 96.7%). Currently, High Confidence fusion calls (calls with probability score greater than 70%) are the most common positive calls. These have been confirmed with 100% success. Conclusion: We have successfully integrated multiple fusion callers by means of random forest models. Our current model is validated for use on our solid tumor fusion calling pipeline. Citation Format: Kenneth B. Thomas, Yanglong Mou, Christophe Magnan, Tibor Gyuris, Eve Shinbrot, Fernando Lopez Diaz, Steven Lau-Rivera, Segun Jung, Vincent Funari, Lawrence M. Weiss. Gene fusion calling from RNA panel sequencing data: An ensemble lear
我们的目标是通过机器学习技术结合多个融合调用者，通过RNA测序改进基因融合检测。背景:基因融合事件是恶性肿瘤的重要驱动因素。RNA测序(RNAseq)检测融合物的方法具有一次检测多个标记物的优点。与DNA方法不同，在DNA方法中很难捕获融合断点，而RNA方法通过嵌合转录物很容易识别融合。虽然存在许多用于RNAseq数据的融合调用算法，但对于低肿瘤含量的样本来说，敏感的融合调用器通常会出现高假阳性率——这是嵌合转录物排列的结果。此外，目前在NGS数据中没有单一的特征可以用来过滤掉误报融合呼叫。为了获得比使用单个融合调用器更高的融合调用精度，我们通过系统和客观的方法对多个融合调用器的结果进行加权和组合:基于随机森林模型的集成学习方法。我们的方法从三个独立的融合调用程序生成的数据中进行选择，并辅以从内部方法获得的指标。它提出了一个度量，可以立即解释为候选融合调用是真正融合调用的概率。方法:使用R中的randomForest包生成随机森林模型，并使用R插入符号包进行调优。训练数据集包括来自实体瘤临床样本的394个融合呼叫的平衡集。对于训练，基于IGV和正交方法(包括PCR与Sanger测序和商业Archer™融合CTL和Sarcoma面板)的人工审查，具有至少10个支持读数的融合呼叫被认为是正确或错误的。我们介绍了三个著名的融合调用器Arriba、STAR-Fusion和FusionCatcher的数据训练结果，以及来自内部开发的结计数方法的额外数据，以及已知融合列表(“白名单”)中的融合成员。模型采用10倍交叉验证。结果:在绩效评估中，假阳性和假阴性呼叫被假定为基于正交确定的假。在此基础上，我们目前的最佳模型准确率为94.9%(灵敏度93.4%，特异性96.7%)。目前，高置信度融合呼叫(概率得分大于70%)是最常见的正面呼叫。这些已被证实100%成功。结论:我们利用随机森林模型成功地集成了多个融合调用者。我们目前的模型已被验证用于我们的实体肿瘤融合呼叫管道。引用格式:Kenneth B. Thomas, Yanglong Mou, Christophe Magnan, Tibor Gyuris, Eve Shinbrot, Fernando Lopez Diaz, Steven Lau-Rivera, Segun Jung, Vincent Funari, Lawrence M. Weiss来自RNA面板测序数据的基因融合调用:一种集成学习方法[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):摘要第240期。
{"title":"Abstract 240: Gene fusion calling from RNA panel sequencing data: An ensemble learning approach","authors":"Kenneth B. Thomas, Y. Mou, C. Magnan, T. Gyuris, E. Shinbrot, Fernando Díaz, Steven Lau-Rivera, Segun Jung, V. Funari, L. Weiss","doi":"10.1158/1538-7445.AM2021-240","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-240","url":null,"abstract":"Introduction: Our goal is to improve gene fusion detection via RNA sequencing by combining multiple fusion callers through machine learning techniques. Background: Gene Fusion events are important drivers of malignancy. RNA sequencing (RNAseq) methods for detection of fusions have the advantage that multiple markers can be targeted at one time. Unlike DNA methods, in which it is challenging to capture fusion breakpoints, in RNA methods fusions are readily identified through chimeric transcripts. While many fusion calling algorithms exist for use on RNAseq data, sensitive fusion callers, needed for samples of low tumor content, often present high false positive rates - a result of aligning chimeric transcripts. Further, there currently is no single feature in NGS data that can be used to filter out false positive fusion calls. In order to achieve higher accuracy in fusion calls than can be achieved using individual fusion callers, we have weighted and combined the results of multiple fusion callers by systematic and objective means: an ensemble learning approach based on random forest models. Our method selects from data generated by three independent fusion callers supplemented by metrics obtained from in-house methods. It presents a metric that can be immediately interpreted as the probability that a candidate fusion call is a true fusion call. Methods: Random forest models were generated by use of the randomForest package in R, with tuning by the R caret package. Training data sets consisted of a balanced set of 394 fusion calls from clinical samples of solid tumors. For training, fusion calls with at least 10 supporting reads were deemed true or false based on manual review via IGV, and orthogonal methods including PCR with Sanger sequencing and the commercial Archer™ fusion CTL and Sarcoma panels. We present the results of training on data from the three well-known fusion callers Arriba, STAR-Fusion, and FusionCatcher, together with additional data from an in-house developed junction counting method, and fusion membership in a list of known fusions (a “white list”). Models were validated by 10-fold cross-validation. Results: In performance evaluations, false positive and false negative calls were presumed false based on orthogonal determinations. On that basis, our current best model has an accuracy of 94.9% (sensitivity 93.4%, specificity 96.7%). Currently, High Confidence fusion calls (calls with probability score greater than 70%) are the most common positive calls. These have been confirmed with 100% success. Conclusion: We have successfully integrated multiple fusion callers by means of random forest models. Our current model is validated for use on our solid tumor fusion calling pipeline. Citation Format: Kenneth B. Thomas, Yanglong Mou, Christophe Magnan, Tibor Gyuris, Eve Shinbrot, Fernando Lopez Diaz, Steven Lau-Rivera, Segun Jung, Vincent Funari, Lawrence M. Weiss. Gene fusion calling from RNA panel sequencing data: An ensemble lear","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":"78213570","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.AM2021-243
L. Piccotti, L. Mirandola, M. Chiriva-Internati
Adoptive cell therapy has been proven a powerful approach for the cure of cancer and other diseases. In particular, the selection of appropriate immunogenic targets has been key to positive outcomes in clinical settings. The availability of RNA-Seq analysis, the accessibility to large data repositories such as TCGA and GTEx, and the creation of new bioinformatic tools have accelerated the process of neoantigen discovery. However, most of the current algorithms are encumbered by the intrinsic complexity of predicting antigen immunogenicity. Diamond™ is a novel artificial intelligence and cognitive machine and deep learning platform to predict peptide processing, HLA binding, and T cell activation. To validate the predictive value of DIAMOND algorithms, the meta-analyses of expression data of cancer-testis antigen New York Esophageal Squamous Cell Carcinoma 1 (NY-ESO-1) and predictions for the immunogenic peptides were compared to experimental data in the literature. In agreement with published clinical observations, DIAMOND metanalysis showed NY-ESO-1 genic overexpression in skin cutaneous melanoma, lung adenocarcinoma, and sarcoma. Moreover, DIAMOND predicted an MHC binding affinity of 0.289 with Supertype A2 for a new NY-ESO-1 peptide, which has been successfully targeted in clinical trials for patients with HLA-A*02:01, as well as it mirrored published data in its prediction of peptide affinity binding in NY-ESO-1–specific MHC II–restricted T cell receptors. Taken together these data support DIAMOND as a reliable platform for the discovery of new immunogenic targets for cancer therapy. Citation Format: Lucia Piccotti, Leonardo Mirandola, Maurizio Chiriva-Internati. Identification of novel epitopes of NY-ESO-1 for solid malignancies by Kiromic proprietary search engine Diamond [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 243.
{"title":"Abstract 243: Identification of novel epitopes of NY-ESO-1 for solid malignancies by Kiromic proprietary search engine Diamond","authors":"L. Piccotti, L. Mirandola, M. Chiriva-Internati","doi":"10.1158/1538-7445.AM2021-243","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-243","url":null,"abstract":"Adoptive cell therapy has been proven a powerful approach for the cure of cancer and other diseases. In particular, the selection of appropriate immunogenic targets has been key to positive outcomes in clinical settings. The availability of RNA-Seq analysis, the accessibility to large data repositories such as TCGA and GTEx, and the creation of new bioinformatic tools have accelerated the process of neoantigen discovery. However, most of the current algorithms are encumbered by the intrinsic complexity of predicting antigen immunogenicity. Diamond™ is a novel artificial intelligence and cognitive machine and deep learning platform to predict peptide processing, HLA binding, and T cell activation. To validate the predictive value of DIAMOND algorithms, the meta-analyses of expression data of cancer-testis antigen New York Esophageal Squamous Cell Carcinoma 1 (NY-ESO-1) and predictions for the immunogenic peptides were compared to experimental data in the literature. In agreement with published clinical observations, DIAMOND metanalysis showed NY-ESO-1 genic overexpression in skin cutaneous melanoma, lung adenocarcinoma, and sarcoma. Moreover, DIAMOND predicted an MHC binding affinity of 0.289 with Supertype A2 for a new NY-ESO-1 peptide, which has been successfully targeted in clinical trials for patients with HLA-A*02:01, as well as it mirrored published data in its prediction of peptide affinity binding in NY-ESO-1–specific MHC II–restricted T cell receptors. Taken together these data support DIAMOND as a reliable platform for the discovery of new immunogenic targets for cancer therapy. Citation Format: Lucia Piccotti, Leonardo Mirandola, Maurizio Chiriva-Internati. Identification of novel epitopes of NY-ESO-1 for solid malignancies by Kiromic proprietary search engine Diamond [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 243.","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"174 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88031642","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.AM2021-252
Kim Wager, Dheepa Chari, S. Ho, Tomas J Rees, R. J. Schijvenaars
{"title":"Abstract 252: Navigating networks of oncology biomarkers mined from the scientific literature: A new open research tool","authors":"Kim Wager, Dheepa Chari, S. Ho, Tomas J Rees, R. J. Schijvenaars","doi":"10.1158/1538-7445.AM2021-252","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-252","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84238585","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.AM2021-165
E. Crowgey, Pankaj Vats, Karl R. Franke, G. Burnett, Ankit Sethia, T. Harkins, T. Druley
{"title":"Abstract 165: Enhanced processing of genomic sequencing data for pediatric cancers: GPUs and machine learning techniques for variant detection","authors":"E. Crowgey, Pankaj Vats, Karl R. Franke, G. Burnett, Ankit Sethia, T. Harkins, T. Druley","doi":"10.1158/1538-7445.AM2021-165","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-165","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90481225","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.AM2021-210
Arpad M. Danos, Wan-Hsin Lin, J. Saliba, Angshumoy Roy, A. Church, Shruti Rao, D. Ritter, Kilannin Krysiak, A. Wagner, Erica K. Barnell, Lana M. Sheta, Adam C. Coffman, S. Kiwala, Joshua F. McMichael, L. Corson, Kevin E. Fisher, H. Williams, Matthew C. Hiemenz, K. Janeway, J. Ji, Kesserwan A. Chimene, L. Fuqua, L. Dyer, Huiling Xu, Jeffrey Jean, L. Satgunaseelan, Liying Zhang, T. Laetsch, D. Parsons, Ryan J. Schmidt, L. Schriml, K. Sund, S. Kulkarni, Subha Madhavan, Xinjie Xu, R. Kanagal-Shamana, M. Harris, Y. Akkari, Nurit Paz Yacov, P. Terraf, M. Griffith, O. Griffith, G. Raca
Childhood cancers are driven by unique profiles of somatic genetic alterations, with a significant contribution from predisposing germline variants. Understanding the genomic landscape of pediatric cancers is complicated by their rarity, the heterogeneity of variation within a given disease, and the complex forms of structural variation they contain. Variants in childhood disease may differ from those in adult versions of the same cancer type, or may have different clinical significance. Currently, pediatric variants are underrepresented in cancer variant databases, and an urgent need exists for their publicly available expert curation. To address this, the Pediatric Cancer Taskforce (PCT) was formed within the Clinical Genome Resource (ClinGen) Somatic Cancer Clinical Domain Working Group (CDWG) (https://www.clinicalgenome.org/working-groups/somatic/). The PCT is a multi-institutional group of 39 members with broad experience in childhood cancer and variant curation, whose work consists of standardization and classification of genetic variants in pediatric cancers. The CIViC knowledgebase (www.civicdb.org) is a freely available resource for Clinical Interpretation of Variants in Cancer, which leverages public curation and expert moderation to address the problem of annotating the large volume of clinically actionable cancer variants. PCT curators work together with PCT expert members and the CIViC team on variant curation, and have submitted over 230 Evidence Items and over 10 Assertions to CIViC. To further address issues specific to pediatric curation, the PCT is working with CIViC to develop new pediatric-specific CIViC features and modifications of the data model that will aid in pediatric curation. A pediatric user interface, as well as representation of large scale structural and copy number variation are being developed for version two of CIViC, expected to be released in 1-2 years, which will enable curation of a new class of structural variants often encountered in pediatric cancer. A novel standard operating procedure for childhood cancer curation in CIViC is being developed by PCT experts, curators and the CIViC team. This SOP will cover topics including curation of structural variants, as well as pediatric-specific variant tiering guidelines which take into account the sparse nature of evidence in pediatric cases. A companion resource, CIViCmine (http://bionlp.bcgsc.ca/civicmine/), will be further developed to incorporate pediatric data. These and other joint efforts of the PCT and CIViC will significantly enhance pediatric variant representation for public use, to support the care of children with cancer. Citation Format: Arpad Danos, Wan-Hsin Lin, Jason Saliba, Angshumoy Roy, Alanna J. Church, Shruti Rao, Deborah Ritter, Kilannin Krysiak, Alex Wagner, Erica Barnell, Lana Sheta, Adam Coffman, Susanna Kiwala, Joshua F. McMichael, Laura Corson, Kevin Fisher, Heather E. Williams, Matthew Hiemenz, Katherine A. Janeway, Jianling Ji, Kess
儿童癌症是由体细胞遗传改变的独特特征驱动的，其中重要的贡献来自易感的种系变异。了解儿童癌症的基因组景观是复杂的，因为它们的罕见性，特定疾病内变异的异质性，以及它们所包含的复杂形式的结构变异。儿童疾病的变异可能与相同癌症类型的成人版本不同，或者可能具有不同的临床意义。目前，儿科癌症变体在癌症变体数据库中的代表性不足，迫切需要对其进行公开的专家管理。为了解决这个问题，临床基因组资源(ClinGen)体细胞癌临床领域工作组(CDWG) (https://www.clinicalgenome.org/working-groups/somatic/)成立了儿科癌症工作组(PCT)。PCT是一个由39名成员组成的多机构小组，在儿童癌症和变异管理方面具有广泛的经验，其工作包括儿童癌症遗传变异的标准化和分类。CIViC知识库(www.civicdb.org)是癌症变异临床解释的免费资源，它利用公共管理和专家审核来解决注释大量临床可操作的癌症变异的问题。PCT策展人与PCT专家成员和思域团队一起进行变体策展，并向思域提交了230多条证据项和10多条断言。为了进一步解决儿科护理的具体问题，PCT正在与CIViC合作开发新的儿科专用CIViC功能，并修改数据模型，以帮助儿科护理。CIViC的第二版正在开发儿科用户界面，以及大规模结构和拷贝数变异的表示，预计将在1-2年内发布，这将使儿科癌症中经常遇到的一类新的结构变异得以管理。PCT专家、策展人和CIViC团队正在为CIViC的儿童癌症策展制定一个新的标准操作程序。本SOP将涵盖的主题包括结构变异的管理，以及考虑到儿科病例证据稀疏性的儿科特异性变异分级指南。将进一步开发配套资源CIViCmine (http://bionlp.bcgsc.ca/civicmine/)，以纳入儿科数据。PCT和CIViC的这些和其他共同努力将显著提高儿科变体的公共使用代表性，以支持癌症儿童的护理。引文格式:Arpad Danos, Lin Wan-Hsin, Jason Saliba, Angshumoy Roy, Alanna J. Church, Shruti Rao, Deborah Ritter, Kilannin Krysiak, Alex Wagner, Erica Barnell, Lana Sheta, Adam Coffman, Susanna Kiwala, Joshua F. McMichael, Laura Corson, Kevin Fisher, Heather E. Williams, Matthew Hiemenz, Katherine A. Janeway, Jianling Ji, Kesserwan A. Chimene, Laura Fuqua, Lisa Dyer，许惠玲，Jeffrey Jean, Laveniya Satgunaseelan, Liying Zhang, Ted W. Laetsch, Donald W. Parsons, Ryan Schmidt, Lynn M. Schriml，Kristen L. Sund, Shashikant Kulkarni, Subha Madhavan, Xinjie Xu, Rashmi Kanagal-Shamana, Marian Harris, Yasmine Akkari, Nurit Paz Yacov, Panieh Terraf, Malachi Griffith, Obi L. Griffith, Gordana Raca。通过ClinGen/CIViC合作推进儿科癌症变异的知识库表示[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210期。
{"title":"Abstract 210: Advancing knowledgebase representation of pediatric cancer variants through ClinGen/CIViC collaboration","authors":"Arpad M. Danos, Wan-Hsin Lin, J. Saliba, Angshumoy Roy, A. Church, Shruti Rao, D. Ritter, Kilannin Krysiak, A. Wagner, Erica K. Barnell, Lana M. Sheta, Adam C. Coffman, S. Kiwala, Joshua F. McMichael, L. Corson, Kevin E. Fisher, H. Williams, Matthew C. Hiemenz, K. Janeway, J. Ji, Kesserwan A. Chimene, L. Fuqua, L. Dyer, Huiling Xu, Jeffrey Jean, L. Satgunaseelan, Liying Zhang, T. Laetsch, D. Parsons, Ryan J. Schmidt, L. Schriml, K. Sund, S. Kulkarni, Subha Madhavan, Xinjie Xu, R. Kanagal-Shamana, M. Harris, Y. Akkari, Nurit Paz Yacov, P. Terraf, M. Griffith, O. Griffith, G. Raca","doi":"10.1158/1538-7445.AM2021-210","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-210","url":null,"abstract":"Childhood cancers are driven by unique profiles of somatic genetic alterations, with a significant contribution from predisposing germline variants. Understanding the genomic landscape of pediatric cancers is complicated by their rarity, the heterogeneity of variation within a given disease, and the complex forms of structural variation they contain. Variants in childhood disease may differ from those in adult versions of the same cancer type, or may have different clinical significance. Currently, pediatric variants are underrepresented in cancer variant databases, and an urgent need exists for their publicly available expert curation. To address this, the Pediatric Cancer Taskforce (PCT) was formed within the Clinical Genome Resource (ClinGen) Somatic Cancer Clinical Domain Working Group (CDWG) (https://www.clinicalgenome.org/working-groups/somatic/). The PCT is a multi-institutional group of 39 members with broad experience in childhood cancer and variant curation, whose work consists of standardization and classification of genetic variants in pediatric cancers. The CIViC knowledgebase (www.civicdb.org) is a freely available resource for Clinical Interpretation of Variants in Cancer, which leverages public curation and expert moderation to address the problem of annotating the large volume of clinically actionable cancer variants. PCT curators work together with PCT expert members and the CIViC team on variant curation, and have submitted over 230 Evidence Items and over 10 Assertions to CIViC. To further address issues specific to pediatric curation, the PCT is working with CIViC to develop new pediatric-specific CIViC features and modifications of the data model that will aid in pediatric curation. A pediatric user interface, as well as representation of large scale structural and copy number variation are being developed for version two of CIViC, expected to be released in 1-2 years, which will enable curation of a new class of structural variants often encountered in pediatric cancer. A novel standard operating procedure for childhood cancer curation in CIViC is being developed by PCT experts, curators and the CIViC team. This SOP will cover topics including curation of structural variants, as well as pediatric-specific variant tiering guidelines which take into account the sparse nature of evidence in pediatric cases. A companion resource, CIViCmine (http://bionlp.bcgsc.ca/civicmine/), will be further developed to incorporate pediatric data. These and other joint efforts of the PCT and CIViC will significantly enhance pediatric variant representation for public use, to support the care of children with cancer. Citation Format: Arpad Danos, Wan-Hsin Lin, Jason Saliba, Angshumoy Roy, Alanna J. Church, Shruti Rao, Deborah Ritter, Kilannin Krysiak, Alex Wagner, Erica Barnell, Lana Sheta, Adam Coffman, Susanna Kiwala, Joshua F. McMichael, Laura Corson, Kevin Fisher, Heather E. Williams, Matthew Hiemenz, Katherine A. Janeway, Jianling Ji, Kess","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84487140","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.AM2021-262
Richard E. Grewelle, Kalin L. Wilson, D. Brantley-Sieders
{"title":"Abstract 262: Statistical Bliss: A novel framework for statistical assessment of drug synergy","authors":"Richard E. Grewelle, Kalin L. Wilson, D. Brantley-Sieders","doi":"10.1158/1538-7445.AM2021-262","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-262","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88457008","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.AM2021-225
Chenhui Ma, A. Almasan, Evren Gurkan-Cavusoglu
{"title":"Abstract 225: Computational analysis of 5-fluorouracil antitumor activity in colon cancer using a mechanistic pharmacokinetic/pharmacodynamic model","authors":"Chenhui Ma, A. Almasan, Evren Gurkan-Cavusoglu","doi":"10.1158/1538-7445.AM2021-225","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-225","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72859961","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.AM2021-220
M. Parikh, O. Elemento, Neel S. Madhukar, Coryandar Gilvary
{"title":"Abstract 220: Identifying novel oncology targets and positioning existing targets through the prediction of cancer dependencies","authors":"M. Parikh, O. Elemento, Neel S. Madhukar, Coryandar Gilvary","doi":"10.1158/1538-7445.AM2021-220","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-220","url":null,"abstract":"","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":"83278925","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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