bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 918: Development of a platform for programmable in vivo oncogene activation and screening using CRISPRa technology
Authors: Thege Fredrik Ivar , Rupani Dhwani N. , Barathi Bhargavi B. , Maitra Anirban , Rhim Andrew D. , Wörmann Sonja M. 
Published: June 2022

Abstract
Conventional genetically engineered mouse models (GEMMs) are time consuming, laborious and offer limited spatio-temporal control. We have developed a streamlined platform for in vivo gene activation using CRISPR activation (CRISPRa) technology. Our model system allows for flexible, sustained and timed activation of one or more target genes, in vitro or in vivo, using single or pooled lentiviral guides. Using Myc and Yap1 as model oncogenes, we implemented this platform to study the effect of oncogene activation on the tumorigenic potential of primary pancreatic organoids, as well as in an autochthonous model of lung adenocarcinoma. We found that Myc-activation in pancreatic organoids increased their tumorigenic potential and resulted in significantly shorter survival relative to controls when transplanted orthotopically. In vivo Myc activation in the lung accelerated tumor progression and resulted in significantly shorter overall survival relative to non-targeted tumor controls. Furthermore, we found that Myc-activation drives the acquisition of an immune suppressive “cold” tumor microenvironment. Through cross-species validation of our results, we were able to link MYC to a previously described, immunosuppressive transcriptomic subtype in patient tumors, thus identifying a patient cohort that may benefit from combined MYC/immune-targeted therapies. Our work demonstrates how CRISPRa can be used for rapid functional validation of putative oncogenes and may allow for the identification and evaluation of potential metastatic and oncogenic drivers through competitive screening.
Citation Format: Fredrik Ivar Thege, Dhwani N. Rupani, Bhargavi B. Barathi, Anirban Maitra, Andrew D. Rhim, Sonja M. Wörmann. Development of a platform for programmable in vivo oncogene activation and screening using CRISPRa technology [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 918.
Abstract 946: Utility of intestinal organoids to model serrated colon cancer in vitro
Authors: Premsrirut Prem K. , Wang Huaien , Reino Eduardo J. Garcia , Breen Isabella , Vasileva Ana A. 
Published: June 2022

Abstract
Serrated colorectal cancer (CRC) is an aggressive and treatment-resistant form of colorectal carcinogenesis that accounts for one-fourth of the patients with CRC1. At the molecular level, serrated CRC is characterized by epigenetic modifications termed the CpG Island Methylator Phenotype (CIMP) and the presence of somatic mutations that activate the mitogen-activated protein kinase (MAPK) pathway1,2,3. The BRAFV600E is an early genetic change in serrated polyps, but it is not sufficient to drive tumor formation. Combinations of mutations and epigenetic modifications in P16INK4A, MLH1, ZNFR3, RNF43, and TGFBR2 are often seen in most patients4. Therefore, a combinatorial approach to develop BrafV600E-driven serrated CRC organoids harboring a variety of the above mutants will enable in vitro studies of the tumorigenic potential of each combination as well as allow engraftment into animals. In turn, the latter will allow for applying preclinical in-vivo pharmacology modalities in vitro, thereby bypassing the time required to create a specific knock-in/out strain for each genetic modification desired. Here, we describe a pipeline to directly genetically manipulate specific organoid systems in vitro. We first established organoid cultures from the small and large intestine of unique Tet-inducible RNAi mice where efficient gene knock-down can contribute to microsatellite instability. Using CRISPR/Cas9, we then edited these to generate CRC organoid models containing the BrafV600E mutation in combination with knock outs or knock-ins of cell cycle regulators and negative regulators of the Wnt pathway. These deficiencies mimic the progressive mutational changes upon acquisition BrafV600E. Our data lay the groundwork for future use of RNAi technology in combination with sequential gene editing for in vitro pharmacology and modeling diseases with complex genetic architecture both in vitro and in vivo.
Citation Format: Prem K. Premsrirut, Huaien Wang, Eduardo J. Garcia Reino, Isabella Breen, Ana A. Vasileva. Utility of intestinal organoids to model serrated colon cancer in vitro [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 946.
Abstract 990: Functional genomic screen of kinases in high-grade serous ovarian cancer metastasis
Authors: Kotnik Emilee Nicole , Lomonosova Elena , Wilke Daniel , Longmore Gregory , Fuh Katherine C. 
Published: June 2022

Abstract
High-Grade Serous Ovarian Cancer (HGSC) is a highly metastatic cancer with the majority of patients presenting in advanced stages. Currently there are limited targeted treatment options for patients, especially for women with high metastatic tumor burden. In order to improve patient outcomes, we have aimed to identify and characterize kinases that may regulate the steps of metastasis in HGSC. Kinases in particular serve as actionable targets because we can design inhibitors of the kinases and their ligands. Several kinases, such as tyrosine kinases and aurora kinases, have previously been implicated in ovarian cancer and seem to influence tumor cell metastasis. Our lab performed a functional genomic screen on 719 kinases in a tumor cell attachment and proliferation assay. In this screen, we cultured primary fibroblasts from patient omentum and plated them on top of a collagen and fibronectin matrix, to mimic the tumor microenvironment. We used siRNA transfection reagents, which were plated atop the fibroblasts along with GFP-labeled ovarian cancer cells that were then incubated with the other cell layers for 72 hours. Based on the florescence intensities, we calculated median average deviations (MAD) values normalized to controls to be a measure of ovarian cancer cell attachment and proliferation. This screen was replicated in 3 ovarian cancer cell lines, including OVCAR8, ES2, and A2780, across 9 panels for 2 libraries. Both libraries had 2 distinct siRNAs for each kinase, in total 4 siRNAs per kinase. From this initial screen, we have identified 20 candidate kinases to validate based on having significantly negative MAD values in at least two cells lines in the screen. Candidate kinases have been validated by knock-down in two ovarian cancer cell lines, ES2 and OVCAR8, and assessed on their ability to migrate in a wound-healing assay. The top kinases will be further studied for their ability to regulate the steps of tumor cell metastasis and the mechanisms that allow them to promote metastasis. Our study aims to identify actionable targets that influence these steps of metastasis, so we can develop drugs that target metastatic tumors and serve as maintenance therapies to help prevent further metastasis, mediate chemoresistance, and improve patient outcomes.
Citation Format: Emilee Nicole Kotnik, Elena Lomonosova, Daniel Wilke, Gregory Longmore, Katherine C. Fuh. Functional genomic screen of kinases in high-grade serous ovarian cancer metastasis [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 990.
Abstract 991: OVO-like genes are master regulators of the epithelial fate in breast cancer
Authors: Garcia Diana Drago , Yarden Yosef 
Published: June 2022

Abstract
The OVO-like genes are evolutionary conserved C2H2 zinc finger transcription factors (TFs) with SNAG repressor domains involved in the maintenance of epithelial homeostasis. From the three Ovo-like family genes identified in mammals, Ovol-like1 (Ovol1) and Ovol-like2 (Ovol2) are the most studied members with multiple reports pinpointing relevant roles in development and disease. In cancer, one of the hallmarks of invasive carcinoma involves the loss of E-cadherin expression by the repression of SNAG transcription factors. Interestingly, contrary to the other SNAG domain family members the OVO-like genes regulate epithelial cell fate. We performed data analysis in breast cancer cell lines and patient data obtained from GEO, CCLE, TCGA and METABRIC to assess the potential of the OVO-like genes as relevant regulators of gene expression in breast carcinoma. In addition, we stablished breast cancer models with inducible expression or repression of the OVO-like genes. We also analyzed the cellular systems we generated using naïve high-throughput methods such as RNA-Seq. Likewise, we studied the effect of OVO-like genes on tumor progression and metastasis using inducible expression systems in both xenograft and syngeneic breast cancer models. We observed that Ovol1 and Ovol2 are constitutively expressed in human breast cancer cell lines, and they are upregulated due to chromosomal gains in breast cancer patients. We observed how the introduction of Ovol1 and Ovol2 into mesenchymal-like breast cancer cell lines modified their transcriptome in a way that promoted expression of epithelial marker genes (e.g., Amphiregulin) and downregulated mesenchymal markers (e.g., Vimentin). In addition, introducing Ovo-like genes in mesenchymal-like cells greatly affected their viability and function. Surprisingly, we observed a similar effect by downregulating the Ovo-like genes in epithelial-like cells. We propose a model where a common epigenetic machinery is shared between structurally related transcription factors with opposing effects, such as OVO-like proteins and other SNAG-containing transcription factors, such as Snail and Slug, such that the abundance of the transcription factors and their co-repressors defines the state of the system as either epithelial or mesenchymal.
Citation Format: Diana Drago Garcia, Yosef Yarden. OVO-like genes are master regulators of the epithelial fate in breast 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 991.
Abstract CT231: Digital phenotyping in young breast cancer patients treated with neoadjuvant chemotherapy
Authors: Delrieu Lidia , Hamy Anne-Sophie , Coussy Florence , Kassara Amyn , Asselain Bernard , Antero Juliana , De Villèle Paul , Dumas Elise , Forstmann Nicolas , Guérin Julien , Hotton Judicael , Jouannaud Christelle , Milder Maud , Leopold Armand , Sedeaud Adrien , Soibinet Pauline , Toussaint Jean-François , Vercamer Vincent , Laas Enora , Reyal Fabien 
Published: June 2022

Abstract
Introduction: Breast cancer (BC) has particular characteristics in young women, with diagnosis at more advanced stages, a poorer prognosis and highly aggressive tumors. In NeoFit, we will use an activity tracker to identify and describe various digital profiles (heart rate, physical activity, and sleep patterns) in women below the age of 45 years on neoadjuvant chemotherapy for BC.Methods/Analysis: NeoFit is a prospective, national, multicenter, single-arm open-label study. It will include 300 women below the age of 45 years treated with neoadjuvant chemotherapy for BC. Participants will be asked to wear a Withing Steel HR activity tracker round the clock for 12 months. The principal assessments will be performed at baseline, at the end of neoadjuvant chemotherapy and at 12 months. We will evaluate clinical parameters, such as toxicity and the efficacy of chemotherapy, together with quality of life, fatigue, and parameters relating to lifestyle and physical activity. The women will complete REDCap form questionnaires via a secure internet link.Discussion: In this study, the use of an activity tracker will enable us to visualize changes in the lifestyle of young women on neoadjuvant chemotherapy for BC, over the course of a one-year period. This exploratory study will provide crucial insight into the digital phenotypes of young BC patients on neoadjuvant chemotherapy and the relationship between these phenotypes and the toxicity and efficacy of treatment. This trial will pave the way for interventional studies involving sleep and physical activity interventions.
Citation Format: Lidia Delrieu, Anne-Sophie Hamy, Florence Coussy, Amyn Kassara, Bernard Asselain, Juliana Antero, Paul De Villèle, Elise Dumas, Nicolas Forstmann, Julien Guérin, Judicael Hotton, Christelle Jouannaud, Maud Milder, Armand Leopold, Adrien Sedeaud, Pauline Soibinet, Jean-François Toussaint, Vincent Vercamer, Enora Laas, Fabien Reyal. Digital phenotyping in young breast cancer patients treated with neoadjuvant chemotherapy [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 CT231.
Abstract CT546: A phase 2, multicenter study to evaluate the efficacy and safety of TACE sequential tislelizumab as adjuvant therapy in patients with HCC at high risk of recurrence after curative resection
Authors: Liang Tingbo , Zhang Zongli , Zheng Jinfang , Wang Yijun , Zhang Jialin , Li Bo , Ma Tao , Xu Yunfei , Wu Changxiong , Sun Quan , Meng Yiman , Yang Xiaoli 
Published: June 2022

Abstract
Background: Surgical resection is one of the most important radical treatment in hepatocellular carcinoma (HCC). However, the 5-year recurrence rate of HCC after surgery is up to 70%, hampering further improvement in the prognosis of HCC patients. Risk factors for recurrence include microvascular invasion (MVI), multiple lesion and solitary tumor ≥ 5 cm, etc. Postoperative adjuvant treatment with transarterial chemoembolization (TACE) can reduce the recurrence rate and improve overall survival (OS) for patients with high risk of recurrence to some extent. TACE treatment promotes the release of tumor related antigen and the recruitment of immune cells by killing tumor cells, thereby promoting the anti-tumor immune response; it may have a potential synergistic effect with PD-1 blockade. Currently, there is no ongoing study on TACE combined with immunotherapy as postoperative adjuvant therapy for HCC. This is an open-label, multicenter, phase II study to evaluate the efficacy and safety of TACE sequential tislelizumab as adjuvant therapy in HCC patients with high risk of recurrence after curative resection (NCT04981665).
Trial design: Eligible patients are ≥18 y, ECOG PS 0 or 1, Child-Pugh class A, diagnosed with HCC confirmed by histopathology or cytology, with no prior systematic treatment and locoregional therapy for HCC, and have received a curative resection with high risk of recurrence fulfilling one of the following criteria: (1) a solitary tumor &gt; 5 cm, (2) solitary tumor &gt; 2 cm and ≤ 5 cm with MVI, (3) 2-3 lesions. 50 pts will receive TACE treatment once (4±1 w) after curative resection, then followed by tislelizumab 200 mg IV Q3W (5±2 d). Tislelizumab will be administered until disease recurrence, intolerable toxicity, death, withdrawal of consent or completion of a maximum of 17 cycles of tislelizumab. Primary endpoint is 2-year recurrence-free survival rate (2-year RFS rate). Secondary endpoints include RFS, time to recurrence, OS, 1-year RFS rate, 1-year and 2-year OS rate, and safety. Exploratory endpoints include biomarkers related to the response and prognosis of adjuvant TACE sequential tislelizumab therapy.
Citation Format: Tingbo Liang, Zongli Zhang, Jinfang Zheng, Yijun Wang, Jialin Zhang, Bo Li, Tao Ma, Yunfei Xu, Changxiong Wu, Quan Sun, Yiman Meng, Xiaoli Yang. A phase 2, multicenter study to evaluate the efficacy and safety of TACE sequential tislelizumab as adjuvant therapy in patients with HCC at high risk of recurrence after curative resection [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 CT546.
Abstract LB151: Deep learning - based protein therapeutics design to treat HER2+ cancers
Authors: Liang Ziwei , Murray Tom , Bluestone Max , Zoumadakis Chris , Martin Luke , Vlasenko Alexey , LaFrence Jeffrey , Lazar John , Denecke Matthias , Longo David 
Published: June 2022

Abstract
Background: HER2 amplification and activation occurs in approximately 15~25% of all breast cancers and is further indicated in other cancer types with high un-met medical needs, such as cholangiocarcinoma, ovarian and esophageal cancer. Further improvement of current HER2 targeting therapeutics on safety and efficacy remains to be desired. Deep learning - based computational approach, such as AlphaFold, has been successfully applied to the understanding of protein structure. However, its applications to protein therapeutic design remain largely underdeveloped. Method: We have developed an end-to-end protein design engine Apollo, which can be used to design target-binding protein therapeutics in silico. Apollo uses recurrent neural networks (RNNs), transformers and graph convolution neural networks (GCNs) to represent proteins, protein structures and protein-protein interactions. This is further improved by multi-head and hierarchical attention layers. Using generative AI, Apollo can either generate putative binding proteins de novo, or to optimize protein lead molecules. Candidate proteins are evaluated in silico for binding affinity and specificity, secondary and tertiary structure, isoelectric point, solubility, and other drug-like properties. Generated designs are iteratively improved by Apollo via a reinforcement learning loop. Results: Apollo has achieved high prediction accuracy by in silico evaluations, using metrics such as AUC and the confusion matrices. Using Apollo, we then designed mini protein binders of 50~100 aa to target HER2 extracellular domain (ECD). These binders can be produced with high yield and purity using mammalian protein expression systems. From de novo generation, the designed candidates are shown to bind to human HER2 with EC50 in the nanomolar range by ELISA. From optimizations based on lead molecules, the designed candidates have shown improved production yield and thermostability while maintaining binding affinity. Conclusions: We have shown the proof-of-concept success of using machine learning to design protein therapeutics.
Citation Format: Ziwei Liang, Tom Murray, Max Bluestone, Chris Zoumadakis, Luke Martin, Alexey Vlasenko, Jeffrey LaFrence, John Lazar, Matthias Denecke, David Longo. Deep learning - based protein therapeutics design to treat HER2+ cancers [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 LB151.
Abstract LB161: Whole mammogram image improves breast cancer prediction
Authors: Jiang Shu , Colditz Graham A. 
Published: June 2022

Abstract
To efficiently capture data from mammographic breast images and classify long term risk of breast cancer, we developed methods that use the extensive existing data that are currently ignored in the context of breast cancer risk stratification. More than 20 studies support texture features add value to risk prediction beyond breast density. However, the entire mammogram imaging data has a high dimension of pixels (~13 million per image), greatly exceeding the number of women in a cohort. We apply functional principal component analysis methods to predict 5-years breast cancer incidence using baseline mammograms. We applied these methods onto women participating in the Joanne Knight Breast Health Cohort which is comprised of over 10,000 women undergoing repeated mammography screening at Siteman Cancer Center and followed since 2010. All women had baseline mammogram at entry, provided a blood sample and completed a risk factor questionnaire. Mammograms are all using the same technology (Hologic). During follow-up through October 2020, we identified 246 incident breast cancer cases (pathology confirmed) and matched them to controls from the perspective cohort based on month of mammogram and age at entry. In a baseline model we controlled for age, menopause, BMI, and mammographic breast density (BIRADs). We then added the full image (characterized by the FPC) to the base model and further compared the AUC of the new model vs the base model using the likelihood ratio test. AUC is validated with internal 10-fold cross validation. The AUC for 5-year breast cancer risk classification increased significantly from a median of 0.61 (sd 0.09 for estimated AUCs across 10-fold internal validation) for the baseline model to 0.70 (0.10) when the full image is added, p &lt; 0.001. We conclude that using full mammogram images for breast cancer risk prediction captures additional information on breast tissue characteristics that relate to cancer risk, and improves prediction classification. This prediction algorithm can run efficiently in real time (in seconds) with processing of digital mammograms. Thus, this model can be easily implemented in mammography screening services and other clinical settings to guide real-time risk stratification to improve precision prevention of the leading cancer in women world-wide. Further analysis will quantify the value of adding other breast cancer risk factors, including polygenic risk scores. Addition of repeated mammogram images over time should further increase classification performance. This approach has the potential to improve risk classification by using data already available for the vast majority of women already having repeated screening mammograms.
Citation Format: Shu Jiang, Graham A. Colditz. Whole mammogram image improves breast cancer prediction [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 LB161.
Abstract LB177: Widespread hypertranscription in aggressive human cancer
Authors: Zatzman Matthew , Fuligni Fabio , Ripsman Ryan , Suwal Tannu , Edward Lisa-Monique , Denroche Rob , Jang Gun Ho , Notta Faiyaz , Gallinger Steven , Selvanathan Saravana P. , Toretsky Jeffrey , Hellmann Matthew D. , Tabori Uri , Huang Annie , Shlien Adam 
Published: June 2022

Abstract
Cancers are often defined by the dysregulation of specific transcriptional programs; however, the importance of global transcriptional changes is less understood. Hypertranscription is the genome-wide increase in RNA output. Hypertranscription’s prevalence, underlying drivers and prognostic significance are undefined in primary human cancer. This is due in part to limitations of expression profiling methods, which assume equal RNA output between samples. Here, we developed a computational method to directly measure hypertranscription in 7,494 human tumors, spanning 31 cancer types. Hypertranscription is ubiquitous across cancer, especially in aggressive disease. It defines patient subgroups with worse survival, even within well-established subtypes. Our data suggest that loss of transcriptional suppression underpins the hypertranscriptional phenotype. Single-cell analysis reveals hypertranscriptional clones, which dominate transcript production regardless of their size. Finally, patients with hypertranscribed mutations have improved response to immune checkpoint therapy. Our results provide fundamental insights into gene dysregulation across human cancers and may prove useful in identifying patients that would benefit from novel therapies.
Citation Format: Matthew Zatzman, Fabio Fuligni, Ryan Ripsman, Tannu Suwal, Lisa-Monique Edward, Rob Denroche, Gun Ho Jang, Faiyaz Notta, Steven Gallinger, Saravana P. Selvanathan, Jeffrey Toretsky, Matthew D. Hellmann, Uri Tabori, Annie Huang, Adam Shlien. Widespread hypertranscription in aggressive human 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 LB177.