bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 198: Molecular genetic characterization of primary breast cancer via clinical next-generation sequencing
Authors: Park Jung Ho , Ko Seung Yeon , Kim Sang Hwa , Kim Ho Young , Ko Sung Hoon , Kim Lee Su 
Published: September 2020

Abstract
Targeting appropriate driver genes is crucial in cancer precision medicine. To find oncogenic drivers, targeted next-generation sequencing is becoming widely used in the clinical field. Using targeted next-generation sequencing, we aimed to detect targetable genetic alterations and characterize mutational profiles of breast cancer. We analyzed breast cancer biopsy specimens using customized 50-gene panel. In total, 196 patients of primary operable breast cancer patients were selected from single institution in South Korea. Among the 196 patients, 22 kinds of genes were significantly mutated or amplified. They comprised 161 nonsynonymous missense mutations, 54 frameshift mutations, 29 missense mutations and 5 small indels; 64 gene amplifications and 2 deletions. The most frequently mutated gene was PIK3CA (n=81/196; 41.3%), followed by TP53 (n=62; 31.6%), AKT1 (n=21; 10.7%), GATA3 (n=17; 8.7%) and PTEN (n=14; 7.1%). The most frequently amplified gene was ERBB2 (n=25/196; 12.8%), followed by FGFR1 (n=11; 5.6%), MYC (n=9; 4.6%), FGFR2 (n=3; 1.5%) and FGFR3 (n=3; 1.5%). Targeted next-generation sequencing can be readily performed in daily clinical practice. The most commonly found targetable genetic alterations were mutations of PI3K/AKT/mTOR pathway genes and FGFR family gene amplifications. Despite the limited number of available targeted agents, our study shows the feasible application of precision medicine in breast cancer.
Citation Format: Jung Ho Park, Seung Yeon Ko, Sang Hwa Kim, Ho Young Kim, Sung Hoon Ko, Lee Su Kim. Molecular genetic characterization of primary breast cancer via clinical next-generation sequencing [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 198.
Abstract 1986: Mutation analysis in cell-free DNA upon development of resistance to afatinib
Authors: Terada Nanao , Kimura Hideharu , Miyakawa Mako , Koba Hayato , Yoneda Taro , Takeda Takayuki , Shirasaki Hiroki , Shibata Kazuhiko , Nishi Koichi , Takayama Koichi , Kasahara Kazuo 
Published: September 2020

Abstract
Introduction: EGFR-mutant lung cancers are genomically heterogeneous in terms of compound EGFR mutations and mutations in other genes. The purpose of this study was to explore gene mutations in circulating cell-free DNA that are related to resistance to afatinib.
Patients and methods: Patients with EGFR-mutant non-small cell lung cancer who received afatinib monotherapy were enrolled in this trial. Plasma samples were longitudinally collected before afatinib treatment, during treatment, and at the time of development of afatinib resistance. Written informed consent was obtained from all patients. Cell-free DNA was extracted from plasma using the QIAamp Circulating Nucleic Acid Kit (QIAGEN). Mutation profiles were determined by next-generation sequencing and analyzed using a human lung cancer panel (QIAGEN).
Results: A total of 71 patients were enrolled in this study. Plasma samples both before treatment and at the time of resistance to afatinib were collected from 21 patients. Although currently under analysis, the mutation profiles determined by next-generation sequencing revealed the following changes. 1) The number of gene mutations per patient that newly appeared at resistance development ranged from 0 to 7. 2) The allele frequency of the T790M EGFR mutation, a mutation responsible for resistance to EGFR tyrosine kinase inhibitors, was increased after development of afatinib resistance in approximately half of the patients.
Conclusions: The T790M allele frequency tended to increase after the development of resistance to afatinib. Some mutations were detected after resistance to afatinib. The role of this mutation needs to be investigated in a further study.
Citation Format: Nanao Terada, Hideharu Kimura, Mako Miyakawa, Hayato Koba, Taro Yoneda, Takayuki Takeda, Hiroki Shirasaki, Kazuhiko Shibata, Koichi Nishi, Koichi Takayama, Kazuo Kasahara. Mutation analysis in cell-free DNA upon development of resistance to afatinib [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1986.
Abstract 1988: Enabling personalized biomarker discovery in challenging oncology samples by coupling a novel library preparation chemistry with hybridization capture
Authors: Scott Karissa , Chakravarty Ushati Das , Huang Hsiao-Yun , Barnes Timothy , Lai Kevin , Sheu Jessica , Chen Tzu-Chun , Lopez Ramses , Lewis Lynette , Potts Anastasia , Henck Steven 
Published: September 2020

Abstract
Discovery and identification of new, targetable biomarkers is driven by comprehensive tumor profiling using next generation sequencing (NGS). However, converting tissue samples into NGS libraries is often challenging due to the low quantity and quality of DNA in such samples. Here we present sensitive and accurate detection of low-frequency variants by combining a novel library preparation optimized for low-input and degraded samples with IDT's xGen hybridization capture. The library preparation is enabled by an engineered mutant ligase and proprietary sequencing adapters that together prevent chimeras, suppress dimer-formation, enable double strand consensus calling, and maximize conversion. We demonstrate the performance of these reagents with archived tissue samples collected from five individuals with lung cancer. These matched samples consisted of DNA isolated from formalin-fixed paraffin-embedded (FFPE) tumor, DNA isolated from fresh-frozen normal tissue, and cell-free DNA (cfDNA) isolated from plasma. Sequencing libraries were prepared from 250 ng of tumor and healthy samples and 10 ng of cfDNA. Libraries were captured using a pan-cancer panel designed to simultaneously detect copy number variations, indels, rearrangements, and microsatellite instability across 532 oncogene targets. For each individual, tumor-associated mutations were identified using single-stranded consensus calling of variants found in tumor but not normal samples. As cfDNA has been correlated to disease progression, we wanted to determine if tumor-associated variants could be identified in the matched cfDNA samples. Custom capture panels were designed and delivered within 5 business days to target each individual's tumor-associated mutations. Relevant cfDNA samples were captured with these custom panels, sequenced, and variants were called after double stranded consensus calling. Mutations called from both FFPE tumor and cfDNA samples were verified by droplet digital PCR. By combining a high conversion library preparation with efficient hybridization capture and double stranded consensus calling, we have demonstrated an effective approach to extract information from difficult samples.
Citation Format: Karissa Scott, Ushati Das Chakravarty, Hsiao-Yun Huang, Timothy Barnes, Kevin Lai, Jessica Sheu, Tzu-Chun Chen, Ramses Lopez, Lynette Lewis, Anastasia Potts, Steven Henck. Enabling personalized biomarker discovery in challenging oncology samples by coupling a novel library preparation chemistry with hybridization capture [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1988.
Abstract 2075: GPCRs for pituitary adenylate cyclase activating polypeptide regulate transactivation of HER3 in non-small cell lung cancer cells
Authors: Moody Terry W. , Lee Lingaku , Jensen Robert T. 
Published: September 2020

Abstract
HER3, which lacks intrinsic kinase activity, plays an important role in cancer proliferation by formation of heterodimers with the EGFR or HER2 (Kiavue et al., Oncogene, 2019). Peptide GPCRs for pituitary adenylate cyclase activating polypeptide (PACAP) stimulate growth by transactivation of the EGFR and HER2 (Moody et al., JPET, 2012 and Peptides, 2019), but its effect on HER3 is unknown. The ability of PACAP to cause tyrosine phosphorylation of the EGFR, HER2 and HER3 was investigated. Using a panel of 17 non-small cell lung cancer (NSCLC) cell lines, mRNA for EGFR, HER2, HER3 was detected in 94%, 88%, and 100% of the cell lines, respectively. Addition of 100 nM PACAP but not vasoactive intestinal peptide to A549 cells increased EGFR, HER2 and HER3 tyrosine phosphorylation by 420, 240 and 190%, respectively. The increase is EGFR tyrosine phosphorylation was blocked by gefitinib or PACAP(6-38), a PAC1 antagonist. The increase in HER2 tyrosine phosphorylation caused by PACAP was inhibited by Herceptin or PACAP(6-38). The increase in HER3 tyrosine phosphorylation caused by PACAP was inhibited by the HER3 blocking antibody mAb3481 or PACAP(6-38). Immunoprecipitation experiments indicated the PACAP addition to A549 cells resulted in the formation of EGFR/HER2 and HER2/HER3 heterodimers, whereas addition of EGF resulted in EGFR/HER2 heterodimers and addition of the HER3 agonist neuregulin-1 resulted in HER2/HER3 heterodimers. Addition of N-acetyl-cysteine (antioxidant), Tiron (superoxide scavenger) or diphenylene iodonium (Nox/Duox inhibitor) impaired the ability of PACAP to cause EGFR, HER2 or HER3 transactivation. The results indicate that reactive oxygen species are essential for PACAP to cause EGFR, HER2 or HER3 transactivation in non small cell lung cancer cells.
Citation Format: Terry W. Moody, Lingaku Lee, Robert T. Jensen. GPCRs for pituitary adenylate cyclase activating polypeptide regulate transactivation of HER3 in non-small cell lung cancer cells [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2075.
Abstract 2087: Bringing advanced analytics experts to the data: Report of an interdisciplinary machine learning workshop
Authors: Stahl-Toyota Sophia , Glocker Katrin , Perez Analie Pascoe , Knurr Alexander , Denker Alexander , Ückert Frank 
Published: September 2020

Abstract
Gaining insights from medical and molecular omics data with advanced statistical learning methods requires the collaboration of experts with foundational knowledge of the data contents and context as well as computational and statistical background. The purpose of the workshop was to derive the key components that are necessary to apply machine learning methods to non-public real-world clinical, genomic and transcriptomic data from heterogeneous sources in a cross-institutional collaborative effort. Machine learning experts were invited to the premises of the institution that held a comprehensive collection of patient data. During the preparation phase, regular virtual meetings took place in order to discuss decisions regarding the research questions to analyze, transformations of the raw file formats and the technical infrastructure. The interdisciplinary group of experts decided to focus on models that would assign patients matching therapy classes based on their molecular variation profile. During the workshop phase, the experts spent several days at the same location working solely on the analysis in question. Here we present the lessons learned from conducting such an interdisciplinary workshop. The key components can be divided into a preparatory and a workshop phase. The first phase lays the foundation for the collaboration, including the formalities, discussion of the data and the applicable methods. It is especially important to involve all domains in decisions regarding the data preprocessing steps. As an example, a method might have particular requirements considering the data dimensions, which is information that the data experts need to know in order to aid with the meaningful reduction of the degrees of freedom, by e.g. aggregation or filtering. Sharing anonymized sample files that represent the data format in a realistic manner and sample scripts for testing the infrastructure helps all parties to set up the analysis pipelines efficiently. In the second phase, the intense working atmosphere allowed for quick iterations of feature engineering, model training and result evaluation. In the common case that a dataset is not yet publicly sharable, we demonstrate that it is a feasible option to invite analysts to the institution holding the data. Additionally, the focused atmosphere that is created by a limited-time workshop setting is advantageous to the motivation of the collaborators.
Citation Format: Sophia Stahl-Toyota, Katrin Glocker, Analie Pascoe Perez, Alexander Knurr, Alexander Denker, Frank Ückert. Bringing advanced analytics experts to the data: Report of an interdisciplinary machine learning workshop [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2087.
Abstract 2093: DeepRePath: Finding prognostic features of tumor cell on the histopathologic image of lung cancer using explainable deep convolutional neural network
Authors: Ko Yoon Ho , Hong Soon Auck , Kim Tae-Jung , Hong Ji Hyung , Chun Sang Hoon , Kim Seoree , An Ho Jung , Park Sungsoo , Shim Won Sang 
Published: September 2020

Abstract
Introduction
Histopathological images in high-powered contain quantitative information for tumor cell morphology, but humans could not sufficiently extract prognostic features of tumor cell morphology associated with tumor recurrence of patient. In this study, we developed and trained the DeepRePath model, a deep convolutional neural network (CNN), on histopathological images to predict the recurrence of resected lung adenocarcinoma (LUAD).
Methods
A total of 244 haematoxylin and eosin stained histopathology slides of resected LUAD patients were used to train and validate the DeepRePath model. We manually segmented a cancer region in a LUAD tissue slide and captured to an image. All images were captured at x400 magnification and split into 13,329 image patches. We trained the DeepRePath model by transfer learning using 13,329 image patches from 244 LUAD patients.
Results
95 out of 244 patients developed recurrence after surgical resection. The DeepRePath model showed an average area under the curve (AUC) of 0.78 in 5-fold cross validation on the training cohort. We plan to employ external validation to evaluate the proposed model. In addition, using gradient-weighted class activation mapping (Grad-CAM), we confirmed that DeepRePath identified and used individual tumor cells with atypia to predict the recurrence of the patient.
Conclusions
DeepRePath model using transfer learning and CNN can accurately predict recurrence after curative resection of LUAD based on atypia of tumor cell.
Citation Format: Yoon Ho Ko, Soon Auck Hong, Tae-Jung Kim, Ji Hyung Hong, Sang Hoon Chun, Seoree Kim, Ho Jung An, Sungsoo Park, Won Sang Shim. DeepRePath: Finding prognostic features of tumor cell on the histopathologic image of lung cancer using explainable deep convolutional neural network [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2093.
Abstract 2109: Amplicon design algorithm for single cell targeted DNA sequencing using machine learning
Authors: Wang Shu , Gulati Saurabh , Kim Dong , Sahu Sombeet , Parikh Saurabh , Li Nianzhen , Manivannan Manimozhi , Beard Nigel 
Published: September 2020

Abstract
Background: To realize the promise of precision medicine for cancer, assessing genetic variation present in rare cells and understanding the role that these cells play in the evolution of tumor progression is essential. High throughput single cell DNA targeted sequencing enables detection of rare mutations in cells and identification of subclones defined by co-occurrence of mutations. The big challenge with multiplex sequencing at single cell level is the non-uniform amplification of the targeted regions during PCR. This results in inadequate coverage of mutations of interest in the panel and hence makes genotyping challenging. To address this challenge, we developed a machine learning engine to optimize amplicon design for uniform amplification by making reliable performance prediction.
Methods: Multiple panels with various sizes were designed with amplicons spanning a wide range of design properties such as amplicon GC, length, secondary structure prediction, primer specificity. These panels were synthesized and processed through Tapestri single cell DNA platform. The tested amplicons are classified into low-performer, OK-performer and high flyer based on their normalized reads-per-cell value. Design properties and property distribution of the amplicons and the panel are the features. We used random forest classifier to calculate feature importance and analyzed the range of the top features for each class and their significance of variance between classes. These ranges were then used as parameters in the assay design pipeline. Next, we train machine learning models with performance data to develop a performance prediction engine.
Results: To test the performance of the design pipeline with new parameters, we designed a small (31), medium (128) and large (287) amplicon panel. Multiple runs were conducted for each panel with different cell types. We were able to achieve high panel performance of 97%, 92% and 88% across the three panels. The new parameters resulted in ~10-20% improvement in panel uniformity. We are working on further optimizing the performance prediction engine by using different ML classification models with K-fold cross validation, training using larger group of amplicons and optimizing features using combinations of properties.
Citation Format: Shu Wang, Saurabh Gulati, Dong Kim, Sombeet Sahu, Saurabh Parikh, Nianzhen Li, Manimozhi Manivannan, Nigel Beard. Amplicon design algorithm for single cell targeted DNA sequencing using machine learning [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2109.
Abstract 2139: A seven lncRNA based risk score system for predicting the recurrence and prognosis of ovarian cancer patients
Authors: Yang Haojie , Han Changpeng 
Published: September 2020

Abstract
Objective: Ovarian cancer (OC) has the third highest incidence and highest mortality among all gynecological tumors. This study was performed in order to establish a risk score system for OC patients.
Methods: The RNA-seq data of OC (the training set) was extracted from TCGA database, and the GSE32062 (the validation set) data was selected from GEO database. Using the limma package, differentially expressed RNAs between the recurrence and non-recurrence groups were identified. For constructing a risk score system, the independent prognosis-correlated lncRNAs were selected using the survival package. Then, the independent clinical prognostic factors were identified using survival package, and the nomogram survival model was built using the rms package. Furthermore, enrichment analysis was conducted using the DAVID tool, and a lncRNA-mRNA co-expression network was constructed using Cytoscape software.
Results: There were 550 DERs (497 mRNAs and 53 lncRNAs) between the recurrence and non-recurrence samples. After identification of 321 DERs significantly related to survival prognosis, seven independent prognosis-correlated lncRNAs (MORC2-AS1, LINC00189, MYCNOS, SNHG7, DSCR9, TCL6, and RBM26-AS1) were further selected for constructing the risk score system. Subsequently, the nomogram survival model based on independent clinical prognostic factors (including age, tumor recurrence, and RS model status) was established. There were five overlapping pathways (including Jak-STAT and Wnt signaling pathways and other pathways in cancer) between the searched and the enriched pathways. Additionally, the seven independent prognosis-correlated lncRNAs were involved in the co-expression network, which was directly correlated with OC.
Conclusion: The risk score system and nomogram survival model based on lncRNAs (MORC2-AS1, LINC00189, MYCNOS, SNHG7, DSCR9, TCL6, and RBM26-AS1) may be valuable for predicting the prognosis of OC.
Citation Format: Haojie Yang, Changpeng Han. A seven lncRNA based risk score system for predicting the recurrence and prognosis of ovarian cancer patients [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2139.
Abstract 2178: Expansion of tumor-specific tumor-infiltrating lymphocytes (TIL) from head and neck tumors
Authors: Nagle Luz , Weber Amy Mackay , Hall MacLean , Beatty Matthew , Wadsworth J. Trad , McMullen Caitlin , Patel Krupal , Vorwald Kathryn , Chung Christine , Pilon-Thomas Shari 
Published: September 2020

Abstract
Background: Until recently, treatment options for patients with head and neck cancer include a combination of surgery, chemotherapy and radiation. Although the combined use of these treatment options modestly improves survival, combination therapy increases toxicity. Overall survival of these treatment regimens for patients with advanced head and neck cancer is just over 10 months. Even with the newly approved and vastly more tolerable use of anti-PD-1 immunotherapy, survival is only extended by 3-5 months. In comparison, adoptive cell therapy (ACT) using tumor-infiltrating lymphocytes (TIL) has improved the median overall survival in patients with metastatic melanoma to 52 months. The goal of this study was to test the feasibility of expanding tumor-reactive TIL from head and neck tumors for the development of a clinical trial.
Methods: Primary tumors were resected from 20 head and neck cancer patients. Tumors were minced into small fragments (1-3 mm3) and plated in high dose IL-2. After 4 weeks, expanded TIL were phenotyped by flow cytometry and tested for tumor reactivity by IFN-gamma production after co-culture of TIL with autologous tumor digest. IFN-gamma levels were determined by ELISA. Reactive TIL were expanded using a rapid expansion protocol (REP).
Results: The mean expansion of TIL per fragment was 1.96 × 106. From 20 primary tumors, seven were contaminated and discarded. TIL were successfully expanded from 11/13 (85%) evaluable tumors. The phenotype of expanded TIL consisted of CD3− CD56+ NK cells and CD3+ T cells. Tumor-specific reactivity was analyzed for seven samples. TIL expanded from three (43%) tumors were predominantly CD8+ and were reactive against autologous tumor as determined by IFN-gamma ELISA. Expansion of TIL during REP resulted in an average 600-fold increase in cell numbers (range of 61 - 1,774 fold) and led to the depletion of NK cells and an increase of CD3+ T cells.
Conclusions: In this study, we have demonstrated the feasibility of growing tumor-reactive TIL from head and neck tumors. This study raises the potential for the implementation of ACT with TIL for the treatment of head and neck cancer patients.
Citation Format: Luz Nagle, Amy Mackay Weber, MacLean Hall, Matthew Beatty, J. Trad Wadsworth, Caitlin McMullen, Krupal Patel, Kathryn Vorwald, Christine Chung, Shari Pilon-Thomas. Expansion of tumor-specific tumor-infiltrating lymphocytes (TIL) from head and neck tumors [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2178.