bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 5007: Discovery of stromal targets in pancreatic cancer
Authors: Li Shengjun , Park Jeong-Bin , Im Hyung-Jun 
Published: June 2022

Abstract
Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies with limited treatment options. Development of stroma targeting therapeutics for PDAC is gaining attention because PDAC has a unique tumor microenvironment with dense tumor stroma and the tumor stroma is a critical mediator of PDAC progression. Herein, we discovered pancreatic cancer stroma-specific cell surface markers by integrative analysis of single cell RNA sequencing and cell surface marker database.
Methods: Single cell RNA sequencing data of PDAC tissue (n = 3, GSE154778) and normal pancreas tissue (n = 3, GSE131886) was obtained from Gene Expression Omnibus database. Using Seurat package, the data from each sample integrated and the cells were clustered. We selected stroma cell cluster for differential analysis to obtain the genes enriched in PDAC stroma. Then, the intersection of the PDAC stroma specific genes with the gene set of SurfaceomeDB was obtained, which allowed us to obtain the stroma cell surface markers that are highly expressed in PDAC. The marker genes were validated in 1) 36 pairs of PDAC and normal tissue samples from a RNA sequencing data (GSE15471), and 2) 143 PDAC samples in The Cancer Genome Atlas (TCGA), 328 normal pancreatic tissues in Genotype-Tissue Expression (GTEx) database (https://www.gtexportal.org).
Results: The cells from PDAC and normal pancreas were clustered into ductal/tumor, stromal and immune cell clusters according to the expression of cell-type specific marker genes. We identified variably expressed genes between PDAC and normal cells within the stromal cell cluster. Among the PDAC stroma-specific genes, we could obtain 360 cell surface markers using SurfaceomeDB. The top ten PDAC stroma-specific cell surface marker genes were MXRA8, ANTXR1, THY1, SCD1, LY6E, BST2, GPNMB, CDH11, GJB2, and CD55. To validate the results, we compared the expression levels of the top 10 genes between pancreatic cancer tissue and adjacent normal pancreatic tissue. We found that the expression levels of the genes were significantly higher in the cancer tissue compared to the normal pancreatic tissue. We also confirmed that the expression levels of 10 genes were significantly higher in all stages of PDAC compared to normal tissues in a combined RNA sequencing dataset from healthy human pancreatic tissue from GTEx and pancreatic cancer patients from TCGA.
Conclusion: We identified pancreatic cancer stroma-specific cell surface markers using single-cell RNA sequencing data and SurfaceomeDB. The markers can be utilized in development of various pancreatic cancer stroma targeted therapeutics including antibodies, antibody-drug conjugates, peptides, cell therapeutic and nanoparticles.
Citation Format: Shengjun Li, Jeong-Bin Park, Hyung-Jun Im. Discovery of stromal targets in pancreatic cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5007.
Abstract 5056: Automated Gleason grading of digitized frozen section prostate tissue slide images
Authors: Cho Joonyoung , Kwak Tae-Yeong , Kim Sun Woo , Chang Hyeyoon 
Published: June 2022

Abstract
Frozen sections are used in intraoperative consultations which need rapid microscopic analysis and in certain pathological procedures that require fresh tissue. Due to the various artifacts present on frozen section tissue slides, it is difficult to automatically analyze them. We tried automatic analysis of frozen sections.
Two types of segmentation deep learning models detecting Gleason patterns were used. Both models were trained from hematoxylin and eosin (H&E) stained formalin-fixed, paraffin-embedded prostate tissue slide images. Each slide image was reviewed by an experienced pathologist, and all prostate cancer lesions were annotated with corresponding Gleason grades. One model was trained with H&E stained images (RS model), and the other model was trained with Hematoxylin-only (H-only) stained images (H-only model). A color deconvolution method was used to generate H-only stained images from original H&E stained images.
In evaluation, frozen section prostate cancer slide images from The Cancer Genome Atlas (TCGA) were used. To diagnose the slide, we splitted whole slide images (WSI) into patches and analyze those patches to segment the Gleason pattern area. For the WSI result, we reconstructed the result as a whole size heatmap, then we counted the Gleason pattern to diagnose the slide as the prostate grade group.
In detecting malignant cases, the RS model achieved a sensitivity of 98% and the H only model of 97%, respectively. In detecting clinically significant risk cases (grade group 2 or over), sensitivities of 99% and 96% were achieved, respectively. Most of the errors were all false-positive cases. In particular, the RS model mainly had false positives because of the ice crystal artifact, and the H-only model had many false positives for the folded part and the part suspected of being a prostatic intraepithelial neoplasia (PIN). False negatives were common in the compression artifacts for both models.
We confirmed that both models showed high performance in cancer detection. In addition, they showed high accuracy in the classification of high-risk and low-risk groups of cancer. Further performance improvement can be expected with additional training of data related to artifacts or PIN.
Citation Format: Joonyoung Cho, Tae-Yeong Kwak, Sun Woo Kim, Hyeyoon Chang. Automated Gleason grading of digitized frozen section prostate tissue slide images [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5056.
Abstract 5058: Recurrence risk prediction based on automatic histopathologic analysis of breast cancer using whole slide images
Authors: Lee Geongyu , Kim Chungyeul , Kwak Tae-Yeong , Kim Sun Woo , Chang Hyeyoon 
Published: June 2022

Abstract
The RS (Recurrence Score) is the results of a 21-gene test assessing the likelihood of breast cancer recurrence for hormone-positive, HER2-negative, and node-negative breast cancer patients. We conducted an experiment to find out whether there is a correlation between the RS value derived through genetic testing and specific morphological characteristics of breast cancer tissues with the expectation that these morphological features could predict RS values. Deep learning was utilized to assess the morphology of the hematoxylin-eosin (H&E) stained whole slide images (WSIs) of breast cancer. In this study, we used 125 breast cancer cases associated with H&E stained WSIs and RS scores which were divided into 3 risk groups based on the RS values: the low-risk group with 49 cases, the intermediate-risk group with 59 cases, and the high-risk group with 17 cases (low-risk for RS &lt; 18, high-risk for RS &gt; 31, and intermediate-risk for RS in between). On each WSI, an expert pathologist has annotated invasive breast tumor regions. We divided WSIs into 512x512 patches and trained a deep CNN model to classify the patches as benign (including DCIS), low-risk, intermediate-risk, or high-risk. In determining the risk group of each WSI, all patches from the WSI were classified by the model first, and then their majority class was assigned to the WSI. A nested cross validation was adopted to evaluate how well the model generalizes. The performance of the algorithm is shown in Table 1. According to the results, our model was able to distinguish between the low-risk group and the high-risk group to some extent. In particular, there was no case of misclassifying the low-risk group as high-risk. Since these high-risk patients require radiological examination, there is an advantage that the 21-gene test is not required. Although our model has yielded modest results, we believe that future studies through the integration of additional data with other clinical and pathological data will yield better results.
Result Table 21-gene test result (*ground truth) Low Intermediate High Prediction Low 42 14 0 Intermediate 5 44 8 High 2 1 9 Accuracy 0.932 0.776 0.912 Sensitivity 0.857 0.746 0.529 Specificity 0.816 0.803 0.972 PPV 0.851 0.772 0.750
Citation Format: Geongyu Lee, Chungyeul Kim, Tae-Yeong Kwak, Sun Woo Kim, Hyeyoon Chang. Recurrence risk prediction based on automatic histopathologic analysis of breast cancer using whole slide images [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5058.
Abstract 5063: Predicting target driven toxicity for small molecule inhibitors to aid in drug development
Authors: Garcia-Quismondo Manuel , Parikh Mukti , Madhukar Neel , Gilvary Coryandar 
Published: June 2022

Abstract
Adverse events and toxicity concerns account for roughly 50% of clinical trial failures and the majority of drug withdrawals. A large part of these toxicity effects can be explained by a drug’s molecular target, known off targets or a drug’s lack of specificity. Therefore, the understanding of a target’s potential toxicity is crucial in the drug development process, potentially informing therapeutic hypotheses and/or chemical design. Here, we introduce a machine learning model to predict a gene’s likelihood to result in patient toxicity when inhibited, independent of chemical compound information. We used information on drug approvals, discontinuations and withdrawals from the FDA to define “toxic” and “safe” targets, to be used as training data. Our model leveraged preclinical, gene-specific features that can be easily collected such as tissue expression, pathway involvement, and loss of function data, and achieves significant predictive performance (ROC = 0.80). We validated our model’s ability to discriminate between successful and unsuccessful drug candidates based on their predicted target toxicity. Furthermore, our model was able to predict differences in toxicity between gene isoforms, which were in agreement with published in vivo evidence, further highlighting its ability to be applied to guide drug development and lead optimization. By applying our model, the prediction of target-specific toxicity can guide drug screenings, prioritize candidates and avoid costly and dangerous drug failures.
Citation Format: Manuel Garcia-Quismondo, Mukti Parikh, Neel Madhukar, Coryandar Gilvary. Predicting target driven toxicity for small molecule inhibitors to aid in drug development [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5063.
Abstract 5079: Comparative proteome analysis for predicting the prognosis of trastuzumab-based therapy
Authors: Park Juin , Kwon Woo Sun , Kang Sun Kyoung , Kim Tae Soo , Hwang Jihyun , Cho Sang Woo , Kim Kyunggon , Chung Hyun Cheol , Rha Sun Young 
Published: June 2022

Abstract
Trastuzumab, a HER2-targeting antibody, has markedly altered the clinical course of the disease in HER2 + gastric cancer, which is 10-15% of metastatic GC. However, the majority of patients develop resistance within one year of treatment. Therefore, understanding the mechanisms of trastuzumab resistance is urgently needed to overcome trastuzumab resistance. This study explored the characteristics of trastuzumab resistant cell lines through comparative proteome analysis.
We previously reported establishment of four trastuzumab resistant cell lines from HER2 amplified GC cell lines by trastuzumab dose-escalation treatment; YCC-33TR, YCC-38TR, NCI-N87TR, and SNU-216TR. Formalin-fixed paraffin-embedded tissue samples were obtained 10 GC patients prior to trastuzumab therapy. The patients were first stratified into poor and good responders based on whether their tumors had or had not progressed. For analysis of differentially expressed proteins (DEPs), label-free quantitative proteomic profiling was performed using high-resolution LC-MS system. Gene ontology (GO) and KEGG pathway enrichment analyses were conducted using DAVID database.
Consistent with the previous results, we found that HER2 expression in TR cell lines remained as high as parental cell lines, also in proteomic profiling. As a result of proteomic analysis, total 5285 proteins were identified in 4 parental and 4 TR cell lines; 4854 common proteins, while 55 proteins unique to parental cell lines and 58 proteins unique to TR cell lines. Then, we did the hierarchical clustering with these DEPs to identify protein expression patterns. We observed that 129 and 62 proteins were upregulated in parental and TR cell lines, respectively (p&lt;0.05). We identified C5orf51 and CENP-E, poor prognostic factors, were upregulated 200-fold in TR cell lines. Interestingly, we found that HLA-B, ITM2C, and PPP1R3D were highly expressed in TR cell lines and trastuzumab poor responders.
In conclusion, we performed comparative proteome analysis in parental and TR cell lines and investigated the corresponding expression change. It may gain further novel insights into the mechanisms of trastuzuamb resistance.
Citation Format: Juin Park, Woo Sun Kwon, Sun Kyoung Kang, Tae Soo Kim, Jihyun Hwang, Sang Woo Cho, Kyunggon Kim, Hyun Cheol Chung, Sun Young Rha. Comparative proteome analysis for predicting the prognosis of trastuzumab-based therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5079.
Abstract 5109: Development of cfDNA reference standards for methylation-sequencing tests
Authors: Li Bingsi , Su Jing , Xu Jiayue , Zhang Guangliang , Li Xiaoling , Zhou Ya , Wang Hao , Fang Shuai , Zhang Zhihong 
Published: June 2022

Abstract
Introduction: Recently, methylation sequencing has drawn significant attention as it has shown great promise for cancer detection and relapse surveillance. However, as with any low-input sequencing method, quality control is critical for implementation into the clinic, and it is difficult to obtain a sustainable source of patient samples for validation activities. To address these unmet needs, here we describe a design of fit-for-purpose cfDNA Methylation Reference Standards (MRS) to evaluate the performance of cfDNA-based methylation tests for oncology.
Methods: We analyzed the genome-wide methylation landscape of 6 human cancer cell lines, 4 normal human lymphocyte cell lines, and 24 healthy donor cfDNA samples via whole genome bisulfite sequencing (WGBS), the gold standard of methylation analysis. Using cancer cell line genomic DNA cleaved by double-stranded DNA endonuclease (Zymo), we developed a range of cfDNA MRS spanning clinically relevant tumor fractions. DNA fragmentation were validated using the Labchip system (Perkin Elmer) and tumor fractions were independently verified by droplet digital PCR (Biorad).
Results: The MRS series represent surrogates for lung, colorectal, liver, ovarian, pancreatic, and esophageal cancers. They display a size distribution comparable to cfDNA extracted from plasma, and represent a broad range of tumor fractions from 10% to 0.01%. In addition, the assembly accuracy was confirmed by digital droplet PCR (ddPCR) and ultra-deep mutation sequencing.
Conclusions: These results demonstrated that the analytical feature and clinical relevance of MRS provides a versatile tool for assay optimization, validation, and cross-platform comparison.
Citation Format: Bingsi Li, Jing Su, Jiayue Xu, Guangliang Zhang, Xiaoling Li, Ya Zhou, Hao Wang, Shuai Fang, Zhihong Zhang. Development of cfDNA reference standards for methylation-sequencing tests [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5109.
Abstract 5156: Multiplexed EGFR mutation detection with C797S and T790M allelic context using six-color digital PCR
Authors: Yang Wei , Christensen Mari , Chan Jennifer K. , Tsai Julie , Patten Nancy , Tran Ha B. , Hillman Grant R. , Tai Yu Chuan , Bogard Patrick , Litterst Claudia M. , Brophy Victoria H. , Newton Nick , Nelson Christopher D. , Pattje Wouter J. 
Published: June 2022

Abstract
Non-small cell lung cancer (NSCLC) patients harboring activating mutations in epidermal growth factor receptor (EGFR) are benefited from Tyrosine Kinase Inhibitor (TKI) targeted therapy. However, more than 50% of patients receiving the first or second generations of TKI develop a resistance mutation, EGFR-T790M, and they will need to switch to the third-generation TKI (Osimertinib). Upon treatment, a second acquired mutation, EGFR-C797S, leads to the resistance to Osimertinib. If the C797S and T790M mutations are located in trans, a combination of the first and third-generation of TKI(s) may offer some benefit to the patient. The impact of the allelic context to the subsequent treatment is yet to be established and thus an assay identifying C797S and T790M allelic context may serve as a useful research tool. In recent years, digital PCR has emerged to be an ultra-sensitive method in non-invasive identification and monitoring of cancer mutations. However, compared to qPCR and NGS methods, current digital PCR assays are limited by the number of biomarkers that can be incorporated in a single assay and the maximum sample volume that can be screened in one test. In this study, we describe a multiplex digital PCR assay for quantitative detection of EGFR sensitizing and resistance mutations, and identification of C797S and T790M allelic context, using a 6-color digital PCR system that allows high sample volume utilization. The sensitivity and specificity of this multiplex dPCR assay were tested using a panel of contrived cell-free DNA samples. Individual mutations can be detected at as low as 0.2% fractional abundance level. Furthermore, we compared the performance of the multiplex with the 2-color droplet digital PCR Expert Design Assays on the Bio-Rad QX200 ddPCR system, using a commercial multiplex cfDNA reference standard set. The multiplexed EGFR mutation assay on the 6-color Digital LightCycler® System is a promising method for rapid and sensitive monitoring of EGFR mutations in cell-free DNA.
Citation Format: Wei Yang, Mari Christensen, Jennifer K. Chan, Julie Tsai, Nancy Patten, Ha B. Tran, Grant R. Hillman, Yu Chuan Tai, Patrick Bogard, Claudia M. Litterst, Victoria H. Brophy, Nick Newton, Christopher D. Nelson, Wouter J. Pattje. Multiplexed EGFR mutation detection with C797S and T790M allelic context using six-color digital PCR [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5156.
Abstract 5170: Coding and non-coding gene meta-features predict outcome in pediatric rhabdomyosarcomas
Authors: Mitra Anirban P. , Mitra Sheetal A. , Buckley Jonathan D. , Skapek Stephen X. , Hawkins Douglas S. , Triche Timothy J. 
Published: June 2022

Abstract
INTRODUCTION: Pediatric rhabdomyosarcoma (RMS) has varying outcomes, particularly in intermediate-risk disease (IR-RMS) due to the limited ability of clinical staging to accurately risk-stratify a large proportion of patients. This study aimed to identify prognostic signatures in IR-RMS, the clinical subgroup with the most heterogeneous outcomes, which can potentially improve risk stratification compared with routine clinicopathologic metrics. Signature performance was validated on an independent set of RMS patients.
METHODS: Prospectively obtained primary tumor specimens from 80 IR-RMS patients on Children’s Oncology Group clinical trial protocols formed the training set. Tumors from 54 RMS patients across all clinical risk groups formed the validation set. Whole transcriptome profiling was performed using oligonucleotide microarrays employing nearly 1.4 million probe selection regions (PSRs) and used to derive weighted meta-features. Accuracies of protein-coding and non-coding meta-features to predict overall (OS) and event-free (EFS) survival were compared using areas under receiver operating characteristic curves. Associated biological processes were analyzed using curated pathway analysis tools.
RESULTS: PAX-FKHR status was able to predict OS (p=0.041) and EFS (p=0.008) in the validation set, but not in the training set. Histologic subtype followed a similar predictive pattern. Cox regression on over 17,000 coding genes on the training set identified a prognostic 30-coding gene meta-feature (gMF; OS p=0.001, EFS p=0.012). A similar analysis on non-coding transcripts identified a 39-PSR meta-feature (ncMF; OS, EFS p&lt;0.001). Both gMF and ncMF were able to predict OS and EFS (p≤0.023) in the validation cohort. Based on OS, predictive accuracy of ncMF was higher than gMF (96% vs. 71%, p&lt;0.001). Analysis of biological processes using gMF showed enrichment for functions associated with musculoskeletal development and signaling pathways. Similar analysis of non-coding meta-features revealed enrichment for cellular assembly, cell cycle, apoptosis, and cancer-associated functions.
CONCLUSIONS: A non-coding RNA meta-feature was able to better predict outcome in IR-RMS than a coding gene meta-feature, where most standard clinical prognosticators failed. The meta-features were independently validated in IR and non-IR RMS. This suggests that non-coding transcripts can regulate and determine RMS biology and aggressiveness, and be used as novel prognostic indicators.
Citation Format: Anirban P. Mitra, Sheetal A. Mitra, Jonathan D. Buckley, Stephen X. Skapek, Douglas S. Hawkins, Timothy J. Triche. Coding and non-coding gene meta-features predict outcome in pediatric rhabdomyosarcomas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5170.