bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 5284: Recurrent Middle Eastern differentiated thyroid carcinoma has worse outcome than persistent disease
Authors: Parvathareddy Sandeep K. , Siraj Abdul K. , Annaiyappanaidu Padmanaban , Siraj Nabil , Ahmed Saeeda O. , Qadri Zeeshan , Azam Saud , Al-Kuraya Khawla S. 
Published: June 2023

Abstract
Background: Differentiated Thyroid Cancer (DTC) is the most common endocrine malignancy. Despite its excellent prognosis, recurrent and persistent disease remain major challenges after initial treatment. Although most studies have considered persistence and recurrent as a single entity, emerging studies to differentiate recurrent and persistent disease are controversial and study on recurrent and persistent DTC from Middle Eastern ethnicity is lacking.
Methods: We retrospectively analyzed 1822 patients who underwent surgery ± I131 treatment for DTC with a median age of 38.9 years and median follow-up of 90 months.
Results: We found a similar prevalence rate of persistent and recurrent disease (16.4% vs. 16.6%) in Middle Eastern DTC patients. Relative to patients with persistent disease, patients with recurrent disease were significantly older (median age 36.1 vs. 45.8 years; p &lt; 0.0001), were more likely to have ATA high risk tumors (p &lt; 0.0001) and had a higher rate of distant metastasis (p &lt; 0.0001). However, structural disease had an almost similar frequency in both recurrent and persistent disease (72.2% vs 73.3%). On multivariant logistic regression analysis, both T status and N status are independent predictors for both recurrent and persistent disease. However, older age, extrathyroidal extension and the presence of distant metastasis were independent predictors of only recurrent disease. In addition, patients with recurrent disease had significantly worse overall survival (OS; p &lt; 0.0001) and cancer specific survival (CSS; p &lt; 0.0001), which remained significant in multivariate Cox proportional hazard model (Hazard ratio = 2.72; 95% confidence interval = 1.20 - 7.01; p = 0.0153 for OS and Hazard ratio = 5.09; 95% confidence interval = 2.10 - 15.24; p = 0.0001 for CSS).
Conclusion: Although persistent and recurrent disease in Middle Eastern DTC have similar frequencies, recurrent disease had worse outcome compared to persistent disease. This has great potential clinical relevance to differentiate recurrene from persistence for therapeutic and follow-up approach for improving outcomes of DTC patients from Middle Eastern ethnicity.
Citation Format: Sandeep K. Parvathareddy, Abdul K. Siraj, Padmanaban Annaiyappanaidu, Nabil Siraj, Saeeda O. Ahmed, Zeeshan Qadri, Saud Azam, Khawla S. Al-Kuraya. Recurrent Middle Eastern differentiated thyroid carcinoma has worse outcome than persistent disease. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5284.
Abstract 5353: Graph representation learning of tumor topology from spatial imaging data
Authors: Ceglia Nicholas , Freeman Samuel S. , Pourmaleki Maryam , McPherson Andrew , Shah Sohrab P. 
Published: June 2023

Abstract
Recently, novel technologies have enabled spatially resolved multiplexed protein profiling of tumors; however, there is an unmet need for unbiased detection of spatial structures within the tissue. By encoding multiplexed images as a graph, we produce an efficient and well-studied graph representation that allows for the application of sophisticated machine learning methods, particularly graph neural networks (GNNs) and graph autoencoders (GAE). While GNNs have recently become popular tools in the analysis of spatial data, current GNN methods are limited by production of cell-level representations rather than graph-level representations. To understand patterns of local tissue structure and protein expression, we implemented a generalizable GAE framework that summarizes marker intensity and morphological values with the local topology of neighboring cells. In this way, each local cellular graph is represented as a single point in the lower dimensional space. We show that our framework generates low-dimensional features that reflect both cellular attributes and tissue structure. We demonstrate that clustering local structure in 318 fields of view from 22 tumor lesions across 7 patients with in-transit melanoma captured using multiplexed immunofluorescence (mpIF) can produce spatial motifs predictive of response to intralesional interleukin-2 (IL-2) immunotherapy. Additionally, we applied our framework to mpIF data of 146 high grade serous ovarian cancer tumors across 50 patients. We identify clusters with higher CD8+ T cell infiltration that associate with homologous recombination deficiency. Our method can be readily applied to various spatially resolved multiplexed imaging platforms, and the resulting embeddings provide a compressed representation of subgraphs that can be used in further classification or regression analysis of patient- and/or image-level histological, clinical, or genomic labels.
Citation Format: Nicholas Ceglia, Samuel S. Freeman, Maryam Pourmaleki, Andrew McPherson, Sohrab P. Shah. Graph representation learning of tumor topology from spatial imaging data. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5353.
Abstract 5365: Extracting novel knowledge from scientific literature to build a web portal for cancer researchers to keep up with the latest scientific discoveries
Authors: Pan Feng , Zhang Yuan , Sui Xin , Sun Donghu , Chung Menghan , Zhang Jinfeng 
Published: June 2023

Abstract
Millions of new papers are published in biomedical sciences every year. In many disciplines, it has become impossible to read all the published new papers to learn what is happening in the frontier of a particular area. This gap is widening as the publishing speed has been accelerating in recent years. To address this challenge, one can convert unstructured text data into a structured form, which can then support highly accurate information retrieval, information integration, and automated knowledge discovery. A plausible approach for such a task is to use named entity recognition (NER) and relation extraction (RE) methods to identify important biological entities and extract their relations to construct knowledge graphs (KGs). The LitCoin Natural Language Processing (NLP) Challenge was recently organized by NCATS of NIH and NASA to spur innovation by rewarding the most creative and high-impact uses of biomedical text to create KGs. In addition to entities and relations, the manually annotated LitCoin dataset also contains the annotations of relations being new discoveries or background knowledge. Our team participated in the challenge and ranked first place. The novelty prediction model of our pipeline has achieved an F1 score of 0.90. We have applied our model to all the PubMed abstracts published previously and the newly published ones to extract the novel discoveries in each article. A web portal has been created to allow scientists to view the latest discoveries in cancer research. The web portal is updated daily with versatile visualization tools for cancer researchers to quickly grasp the latest discoveries in a particular area. It also offers powerful functions to explore the existing literature and make sophisticated inferences about causal and indirect relationships.
Citation Format: Feng Pan, Yuan Zhang, Xin Sui, Donghu Sun, Menghan Chung, Jinfeng Zhang. Extracting novel knowledge from scientific literature to build a web portal for cancer researchers to keep up with the latest scientific discoveries. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5365.
Abstract 5366: Constructing the largest-scale biomedical knowledge graph using all PubMed articles and its application in automated knowledge discovery
Authors: Zhang Yuan , Pan Feng , Sui Xin , Sun Donghu , Chung Menghan , Zhang Jinfeng 
Published: June 2023

Abstract
The number of biomedical publications is growing at an accelerated speed. This ever-increasing amount of scientific literature has made reading all the published articles regularly impossible even for a very specific research area. A solid grasp of existing literature is essential for coming out with novel and plausible scientific ideas. To bridge the gap between the published scientific findings and our incapability of manually processing them, we need to convert the unstructured text into structured form to enable automated methods to use the structured, machine-readable information to generate novel hypotheses, which can then be manually validated. A plausible approach for converting unstructured text into structured form is to use named entity recognition (NER) and relation extraction (RE) methods to identify the biological entities and extract their relations to construct knowledge graphs (KGs). KGs can link concepts within existing research to allow researchers to find connections that may have been difficult to discover without them. The LitCoin Natural Language Processing (NLP) Challenge was recently organized by NCATS of NIH and NASA to spur innovation by rewarding the most creative and high-impact uses of biomedical, publication-free text to create KGs. Our team participated in the challenge and ranked first place. Using the pipelines developed for the LitCoin NLP challenge, we have constructed the largest-scale biomedical KG using all PubMed articles. We further develop advanced deep-learning methods to predict new links from the constructed KG. We demonstrate the power of this new framework using several examples important for drug discovery.
Citation Format: Yuan Zhang, Feng Pan, Xin Sui, Donghu Sun, Menghan Chung, Jinfeng Zhang. Constructing the largest-scale biomedical knowledge graph using all PubMed articles and its application in automated knowledge discovery. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5366.
Abstract 5390: Robust prediction of pan-cancer immune checkpoint blockade response using machine learning
Authors: Chang Tiangen , Dhruba Saugato R. , Cao Yingying , Morris Luc G. , Ruppin Eytan 
Published: June 2023

Abstract
Immune checkpoint blockade (ICB) has revolutionized our approach to cancer treatment. However, the response rate is still low. With the accumulation of data, efforts to use these data to build machine learning predictors of ICB response are rising. However, as most datasets are still quite small, pertaining machine learning models may often ‘overfit’ the data, i.e., have a much weaker performance on independent test data than on the data they were learned upon. Here we analyzed ~2000 patient samples from multiple cohorts across 16 solid tumor types. Based on 10 genomic & clinical features, we built different machine learning models, comparing their performance in terms of the Area Under ROC Curve (AUC) and importantly, in terms of the AUC difference between training vs validation sets, using a standard cross validation procedure. As a result, the Linear LASSO Regression (LLR) model outperformed all other models by having the highest AUC on the validation set (0.74) and notably, the smallest AUC difference between training and validation sets (0.02). In contrast, these two AUC values for the FDA approved tumor mutational burden (TMB) biomarker are 0.63 and 0.03. The highest predictive features were, receiving chemotherapy before ICB treatment or not, TMB, cancer type, blood albumin level, blood neutrophil-to-lymphocyte ratio, and patient age. The LLR score also consistently predicted overall survival and progression-free survival across all individual cancer types. More importantly, ICB response probability increased near-monotonically from 0% to &gt; 43% with the LLR score, which is an important feature for patient exclusion. In contrast, ICB response probability is ~25% when TMB = 0 and not always higher with higher TMB. In summary, the LLR model identifies key features predicting pan-cancer ICB response and survival. The use of combined genomic and clinical features holds potential to further facilitate clinical ICB patient stratification beyond TMB.
Citation Format: Tiangen Chang, Saugato R. Dhruba, Yingying Cao, Luc G. Morris, Eytan Ruppin. Robust prediction of pan-cancer immune checkpoint blockade response using machine learning. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5390.
Abstract 54: Regulation of luminal epithelial cell identity in the pre-tumorigenic mammary gland
Authors: Breindel Jerrica , Gross Kayla M. , Kuperwasser Charlotte 
Published: June 2023

Abstract
The transcription factor MYC is frequently altered in a large portion of human breast cancers; the gene is amplified in ~15% of breast cancers and overexpressed in another 30-50%. Mouse mammary tumor models driven by overexpression of MYC, such as MMTV-Myc, also exhibit a rapid onset and high frequency of mammary tumors indicating that MYC overexpression plays an important role in driving breast carcinogenesis. However, the role of MYC in breast cancer initiation and subtype determination is not well understood. In the normal mammary gland, MYC is a critical regulator of and is essential for alveolar luminal epithelial cell differentiation during pregnancy. Aberrant MYC expression can promote improper alveolar development and milk production and is a potent inducer of cellular proliferation. Based on these known functions of MYC during development, we hypothesized that its overexpression may induce early changes in epithelial cell differentiation leading to pre-tumorigenic shifts in cell lineages that contribute to the formation of luminal breast tumors. To investigate this, we analyzed the mammary glands of MMTV-Myc (MYC) mice prior to any signs of tumor formation. As early as 5 weeks of age, MYC overexpression led to alterations at both the histological and cellular levels. Interestingly, pre-tumorigenic MYC glands phenotypically resembled mid-pregnant glands of WT mice. Specifically, pre-tumorigenic MYC glands demonstrated budding along the mammary ducts and expansion of a Sca1−/CD49b− luminal epithelial cell subset not typically seen in nulliparous mice. RNA-seq was performed on luminal epithelial cells isolated from WT or pre-cancerous MYC mammary glands to define the luminal differentiation states upon MYC overexpression. While there was an overlap in twenty-nine genes that control milk production and cellular metabolism between pre-cancerous MYC luminal cells and those from mid-pregnant WT mice, luminal cells from MYC mice also exhibited a unique fingerprint of forty-one genes that overlap with cell states found in breast cancer. These uniquely expressed luminal cell genes may be important for MYC-regulated tumorigenesis. Ongoing and future experiments will investigate the role of specific MYC-target genes in mammary gland development versus tumor formation to clarify the role of MYC in these divergent processes.
Citation Format: Jerrica Breindel, Kayla M. Gross, Charlotte Kuperwasser. Regulation of luminal epithelial cell identity in the pre-tumorigenic mammary gland [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 54.
Abstract 5404: Predicting protein receptor status from H&E-stained images in breast cancer
Authors: Lee Geongyu , Kim Chungyeul , Kwak Tae-Yeong , Kim Sun Woo , Chang Hyeyoon 
Published: June 2023

Abstract
Correct prediction of cancer molecular subtype plays a very important role in determining the treatment of cancer patients. In determining the molecular subtype of breast cancer, protein receptors such as estrogen receptors (ER), progesterone receptors (PR), and human epidermal growth factor receptor type 2 (HER2) are used as key factors. These protein receptors also play an important role in determining the treatment method or predicting the prognosis of breast cancer. To confirm this, a test should be performed by immunohistochemical (IHC) staining. In this study, we investigated the morphological relationship between the molecular subtypes and hematoxylin and eosin (H&E) stained whole slide images (WSIs) without IHC stained images, and performed an experiment to evaluate that the protein receptors can be predicted by these morphological features. The TCGA-BRCA data were utilized in this study. There were 728 cases out of the total 1097 cases where the IHC status for ER, PR, and HER2 was either positive or negative. Each individual case is scanned using different scanners and magnifications. In some cases with multiple slides, we use only the first scanned image. The entire WSI was randomly split into 3:1:1, and used for training, tuning, and test, respectively. Individual WSIs was tiled into 1024 × 1024 patches for this study. The multi-task deep learning model predicts the protein receptor status of individual patches. In this study, a confidence measure was added to remove the uncertainty of the deep learning model. When learning WSIs, patches of less than 70% based on these confidence scores did not affect WSIs. Specifically, when learning morphological features, we used strong augmentation such as grayscale, gaussian blur, color jitter, and posterization to ensure that predictions were not biased by color alone. Based on individual patches, ER accuracy of 76%, PR accuracy of 65%, and HER2 accuracy of 79% are shown. As a result of prediction by majority voting on WSI images through patch predicted results, ER accuracy of 74.6% PR accuracy of 66%, and HER2 accuracy of 76.6% Through this study, it was confirmed that the sufficiently trained deep learning model predicted ER, PR, and HER2, which are important factors of molecular subtype, relatively well. Through this, it was found that there was some correlation between morphological characteristics and molecular subtypes in H&E stained WSIs. If more data are collected through future experiments, a molecular subtype prediction model can be developed.
Citation Format: Geongyu Lee, Chungyeul Kim, Tae-Yeong Kwak, Sun Woo Kim, Hyeyoon Chang. Predicting protein receptor status from H&E-stained images in breast cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5404.
Abstract 5456: Application of a multiplex urinalysis test for predicting treatment response in patients with BCG unresponsive bladder cancer: a pilot study
Authors: Furuya Hideki , Murakami Kaoru , Pagano Ian , Chen Runpu , Sun Yijun , Kim Nari , Kadel Edward E. , Wong Cheryl V. , Davarpanah Nicole , Rosser Charles J. 
Published: June 2023

Abstract
Introduction and Objective: Recently, we reported that OncuriaTM, a multiplex urinalysis test, could be predictive of BCG treatment response. Furthermore, high-grade BCG unresponsive bladder cancer have limited treatment options. Such patients are offered either radical cystectomy or systemic therapy. One of the systemic therapies include immune-oncology agents, e.g., PD-1 or PD-L1 inhibitors. In this study, we tested the performance of OncuriaTM in a BCG unresponsive cohort to determine if it could predict response to a PDL1 inhibitor.
Methods: OncuriaTM data was evaluated in voided urine samples obtained from a prospectively collected cohort of 18 subjects who have BCG-unresponsive NMIBC with treatment of atezolizumab monotherapy or atezolizumab BCG combination (provided by Genentech, NCT02792192). The urine samples were collected prior to treatment in both arms. The OncuriaTM test, which measures 10 cancer-associated biomarkers was performed in an independent clinical laboratory. Predictive models were previously developed using supervised learning and cross-validation analyses. Model performance was validated using ROC curves.
Results: Pre-treatment urinary concentrations of MMP9, VEGFA, CA9, SDC1, PAI1, APOE, A1AT, ANG and MMP10 were increased in patients who developed disease recurrence. A combinatorial predictive model of treatment outcome achieved sensitivity and specificity of &gt;90%.
Conclusion: Previous pilot study found that monitoring the urinary levels of a cancer-associated biomarker panel enabled the discrimination of patients who did not respond to intravesical BCG therapy. In this study, we noted the performance of OncuriaTM for the prediction of systemic PDL1 inhibitor treatment response. A limitation of this study includes its small sample size. With further study, the multiplex OncuriaTM test may be applicable for the clinical evaluation of bladder cancer patients who have not previously responded to intravesical BCG treatment and is considering systemic immune-oncology options.
Citation Format: Hideki Furuya, Kaoru Murakami, Ian Pagano, Runpu Chen, Yijun Sun, Nari Kim, Edward E. Kadel, Cheryl V. Wong, Nicole Davarpanah, Charles J. Rosser. Application of a multiplex urinalysis test for predicting treatment response in patients with BCG unresponsive bladder cancer: a pilot study. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5456.