bluuebunny/crossref_metadata_2025_split huggingface dataset:
Genomically Complex Human Angiosarcoma and Canine Hemangiosarcoma Establish Convergent Angiogenic Transcriptional Programs Driven by Novel Gene Fusions
Authors: Kim Jong Hyuk , Megquier Kate , Thomas Rachael , Sarver Aaron L. , Song Jung Min , Kim Yoon Tae , Cheng Nuojin , Schulte Ashley J. , Linden Michael A. , Murugan Paari , Oseth LeAnn , Forster Colleen L. , Elvers Ingegerd , Swofford Ross , Turner-Maier Jason , Karlsson Elinor K. , Breen Matthew , Lindblad-Toh Kerstin , Modiano Jaime F. 
Published: March 2021

Abstract

Sporadic angiosarcomas are aggressive vascular sarcomas whose rarity and genomic complexity present significant obstacles in deciphering the pathogenic significance of individual genetic alterations. Numerous fusion genes have been identified across multiple types of cancers, but their existence and significance remain unclear in sporadic angiosarcomas. In this study, we leveraged RNA-sequencing data from 13 human angiosarcomas and 76 spontaneous canine hemangiosarcomas to identify fusion genes associated with spontaneous vascular malignancies. Ten novel protein-coding fusion genes, including TEX2-PECAM1 and ATP8A2-FLT1, were identified in seven of the 13 human tumors, with two tumors showing mutations of TP53. HRAS and NRAS mutations were found in angiosarcomas without fusions or TP53 mutations. We found 15 novel protein-coding fusion genes including MYO16-PTK2, GABRA3-FLT1, and AKT3-XPNPEP1 in 11 of the 76 canine hemangiosarcomas; these fusion genes were seen exclusively in tumors of the angiogenic molecular subtype that contained recurrent mutations in TP53, PIK3CA, PIK3R1, and NRAS. In particular, fusion genes and mutations of TP53 cooccurred in tumors with higher frequency than expected by random chance, and they enriched gene signatures predicting activation of angiogenic pathways. Comparative transcriptomic analysis of human angiosarcomas and canine hemangiosarcomas identified shared molecular signatures associated with activation of PI3K/AKT/mTOR pathways. Our data suggest that genome instability induced by TP53 mutations might create a predisposition for fusion events that may contribute to tumor progression by promoting selection and/or enhancing fitness through activation of convergent angiogenic pathways in this vascular malignancy.

Implications:
This study shows that, while drive events of malignant vasoformative tumors of humans and dogs include diverse mutations and stochastic rearrangements that create novel fusion genes, convergent transcriptional programs govern the highly conserved morphologic organization and biological behavior of these tumors in both species.
Abstract IA-11: Combining multiplexed assays of variant effect and modeling to drive variant interpretation
Authors: Fowler Douglas 
Published: March 2021

Abstract
Germline genetic variation can strongly influence lifetime cancer risk and somatic variation can influence prognosis and treatment. However, variants are difficult to interpret, even when they occur in cancer-associated genes. Multiplexed assays of variant effect leverage deep sequencing to enable the experimental assessment of thousands of genetic variants simultaneously. The resulting large-scale variant functional data can be used to drive variant interpretation, empowering the use of genetic information in cancer prevention and treatment. We are developing multiplexed assays of variant effect that can be deployed widely, with the ultimate goal of measuring the effect of most variants at most disease-related genes in the human genome.
Citation Format: Douglas Fowler. Combining multiplexed assays of variant effect and modeling to drive variant interpretation [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr IA-11.
Abstract IA-22: Automated treatment planning and quality assurance in radiation oncology
Authors: Purdie Thomas G. 
Published: March 2021

Abstract
The delivery of radiation for the treatment of cancer is a complicated process that requires both clinical and technical expertise to ensure radiation treatments are safe and effective. Sub-optimal radiation treatments have the potential to result in significant detriment to the patient and several studies have shown radiation treatments, which deviate from established clinical guidelines, result in worse patient outcomes. Therefore, the current radiation treatment process requires substantial multi-disciplinary resources to both generate and verify radiation treatments are of high-quality. In this talk, a previously validated machine learning platform customized for radiation oncology will be presented. The method automatically learns based on data from thousands of previously treated patients which relationships and patterns in radiation oncology image and treatment data and has been applied for automated data mining activities, automated quality assurance to support expedited radiation treatment review, and for radiation dose prediction to develop new radiation treatments by best deciding where dose should be placed and how dose should be delivered without requiring any manual intervention. Therefore, the method can be used to both generate personalized radiation treatments and to quantitatively score radiation treatments for quality and classify radiation treatments that have errors. The automated platform can readily be integrated into current clinical process to improve efficiency in the radiation treatment planning and plan review processes and to better utilize the vast data we have to ensure we are providing patients with highly personalized radiation treatments.
Citation Format: Thomas G. Purdie. Automated treatment planning and quality assurance in radiation oncology [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr IA-22.
Abstract PO-052: Exploring patient derived xenografts based pharmacogenomic data for precision oncology
Authors: Mer Arvind Singh , Haibe-Kains Benjamin 
Published: March 2021

Abstract
Preclinical cancer models play a vital role in oncology research and precision medicine. Patient-derived tumor xenografts (PDXs) are used as reliable preclinical models for studying tumor biology and for testing anti-cancer therapies that are tailored according to genomic characteristics of tumors. Several academic groups, research institutes, and commercial organizations are generating and distributing PDX models. However the distributed nature of PDX model generation and lack of central repository make it challenging to find PDX models with specific characteristics. Furthermore this also hinders meta-analysis (across datasets) of PDX pharmacogenomic data. International consortia and catalogs of PDX models such as PDXNet, EurOPDX and PDXFinder are being developed to standardize PDX associated metadata and facilitate material sharing. Recently we have developed Xenograft Visualization & Analysis (Xeva), an open-source software package in R programming language. Xeva allows PDX growth curve visualization, different response metrics computation and biomarker discovery. Extending to this we have developed XevaDB, a database of PDX drug response and genomic profiles. XevaDB is the first resource to allow concurrent visualization of drug response and associated molecular data such as mutation and copy number alterations. Furthermore XevaDB enables exploration of the tumor growth curve of a PDX model, along with corresponding control. XevaDB contains PDXs from &gt;600 individual patients, spanning across nine different tissue types and &gt;70 drugs. Using XevaDB, we have performed meta-analysis of PDX pharmacogenomic data and have identified 90 pathways significantly associated with response to 53 drugs (FDR &lt; 5%). Our results show that activity of the EGFR signaling pathway is significantly associated with erlotinib response in lung cancer PDXs. We have also found that in PDXs, response to binimetinib is associated with the MAP kinase activation pathway. XevaDB provides a comprehensive resource to search and explore PDX pharmacogenomic data. By combining drug response with genomic data of PDXs, XevaDB allows researchers to quickly find the model of interest and access the data to answer their biological questions. As PDXs based pharmacogenomic datasets continue to expand, XevaDB will facilitate easy access and analysis of this valuable data by the scientific community.
Citation Format: Arvind Singh Mer, Benjamin Haibe-Kains. Exploring patient derived xenografts based pharmacogenomic data for precision oncology [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-052.
Abstract PO-066: Data standardization, integration and meta-analysis of preclinical pharmacogenomics studies for gene expression biomarker discovery
Authors: Smirnov Petr , Haibe-Kains Benjamin 
Published: March 2021

Abstract
The sparsity of predictive biomarkers for drug response remains a major impediment to the clinical success of precision oncology. Over the past decade, massive studies combining in-vitro drug screening with high-throughput molecular profiling of cancer cell lines have been published, with the goal of discovering novel predictive biomarkers for drug response. Unfortunately, inconsistencies in the data, as well as a lack of standards in the field, has effectively siloed the findings of these studies away from the clinical researchers working in precision medicine. In our previous work developing the PharmacoGx/PharmacoDB platform, we have reprocessed and curated the 7 largest pharmacogenomic studies released to date. Together, these studies profiled 760 compounds across ~1600 cell lines, in over 650,000 drug dose response experiments. Here, we present an update of this database, and describe our preliminary findings from a statistical meta-analysis of these data for the discovery of gene expression biomarkers. Our meta-analysis looked for markers consistently associated with drug response across 11 tissue types and 70 compounds found in at least 3 studies in the examined dataset. From these data, we find approximately 4338 consistent associations across studies surviving multiple hypothesis correction, in 1946 genes, across 8 tissues and 34 different drugs. The results show evidence of both drug-mechanism specific markers of response, as well as markers of multi-drug resistance phenotypes. Within lung cancer cell lines, we observe an enrichment of TGF-β signalling related genesets among markers of multi-drug sensitivity and resistance. We discuss our approach to validate these markers using organoid, in vivo and clinical datasets. Finally, we detail tools we are building for researchers to explore our database of putative biomarkers and the data supporting them; focusing on exposing our findings to the clinical precision medicine research community. Ultimately, our goal is to extract a database of high confidence preclinical associations from the consensus of published pharmacogenomic studies. This database would be the first step in translating findings from these massive preclinical studies towards novel clinically actionable biomarkers for use in the pursuit of precision cancer care.
Citation Format: Petr Smirnov, Benjamin Haibe-Kains. Data standardization, integration and meta-analysis of preclinical pharmacogenomics studies for gene expression biomarker discovery [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-066.
Abstract PO-075: Kinotype to phenotype: Perturbed phosphoproteomic state predicts cancer cell growth rates in vitro
Authors: Robson Isaac S. , Berginski Matthew E. , Gomez Shawn M. 
Published: March 2021

Abstract
Numerous aspects of cellular signaling are regulated by the kinome - the network of over 500 protein kinases that guides and modulates information transfer throughout the cell. The key role played by both individual kinases and assemblies of kinases organized into functional subnetworks leads to kinome dysregulation being a key driver of many diseases, particularly cancer. Linked with their role in disease, the druggability of kinases has led to increased interest in the development of kinase inhibitors, with over fifty now having achieved FDA approval. These and other yet to be approved compounds have been used in a variety of contexts, including in screens to test their efficacy as potential therapeutics. Recent proteomics techniques including Kinobeads and multiplexed inhibitor beads linked with mass spectroscopy (MIB/MS) are a relatively new mechanism which affords the ability to assess the state of the protein kinome en masse. When combined with perturbation with targeted kinase inhibitors, the quantification of the dynamic response of the kinome provides a novel platform for the study of cell signaling and potential design of drug therapies. Here, we describe an approach that links the state of the kinome, or kinotype, with a downstream cancer cell phenotype, in this case, cellular growth. Integrating independent perturbation and growth response data sets, we find that the kinotype of a cell has a significant and predictive relationship with cell growth. More specifically, we characterize kinome response to perturbation via Kinobeads within models aimed at predicting cellular growth inhibition rates. Integrating 82 drugs, 237 kinases, and 380 cell lines (both malignant and nonmalignant) into a sparse GLM predicts growth rate inhibition at multiple doses of an unseen small molecule with an r^2 value of 0.624 (Bonferroni corrected p-value of 5.7 × 10^−69), even with an unmatched dataset. We further explore the use of higher-level kinase relationships, i.e., kinome subnetworks, along with manifold learning approaches as a component of these models to better ascertain the potential impact of kinome architecture in cellular response. Together, this systems view of the kinome presents potential opportunities for improved cancer disease classification, the identification of new drug targets, as well as impacting on the broader design of combination therapies for cancer treatment.
Citation Format: Isaac S. Robson, Matthew E. Berginski, Shawn M. Gomez. Kinotype to phenotype: Perturbed phosphoproteomic state predicts cancer cell growth rates in vitro [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-075.
Abstract PR-09: Genetic risk scores for breast cancer based on machine learning analysis of chromosomal-scale length variation
Authors: Ko Charmeine , Toh Christopher , Brody James P. 
Published: March 2021

Abstract
Inherited factors are thought to be responsible for a substantial fraction of breast cancers, but only a small fraction of breast cancers are caused by mutations to known genes, like BRCA1. Most current genetic risk scores for breast cancer focus on the additive effects of single nucleotide polymorphisms (SNPs) found in germ-line DNA. This process ignores epistatic interactions between different genes. We sought to use modern machine learning algorithms to identify complex patterns in germ line DNA that are correlated with the incidence of breast cancer, a strategy which should include epistatic interactions. We looked for these patterns in measurements of chromosomal-scale length variation of germ line DNA. These measurements represents the sum of many insertions, deletions, and copy number variations across the chromosome. We evaluated several different machine learning models, including: a generalized linear model, a distributed random forest, a gradient boosting machine, and a deep learning model. We evaluated these models on two independent large datasets, the Cancer Genome Atlas (TCGA) and the UK Biobank. We found that chromosomal-scale length variation of germline DNA, in combination with machine learning models, can be used to predict whether a woman will develop breast cancer. In the Cancer Genome Atlas (TCGA) dataset, we used a dataset with 968 (cases) women who had been diagnosed with breast cancer and 3715 (controls) women who had not been diagnosed with breast cancer. We found that a gradient boosting algorithm could differentiate between those two classes of women with an AUC=0.73. In the UK Biobank dataset, we identified 1534 (cases) women who had been diagnosed with breast cancer and compared them with a set of 4391 (controls) women who had no report of any type of cancer. In this UK Biobank dataset, we could distinguish between the two classes of women with an AUC =0.83. In conclusion, we found that artificial intelligence algorithms could be used to develop effective genetic risk scores for breast cancer.
Citation Format: Charmeine Ko, Christopher Toh, James P. Brody. Genetic risk scores for breast cancer based on machine learning analysis of chromosomal-scale length variation [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PR-09.
Interception Targets ofAngelica GigasNakai Root Extract versus Pyranocoumarins in Prostate Early Lesions and Neuroendocrine Carcinomas in TRAMP Mice
Authors: Tang Su-Ni , Jiang Peixin , Kim Sangyub , Zhang Jinhui , Jiang Cheng , Lü Junxuan 
Published: March 2021

AbstractWe reported efficacy of Angelica gigas Nakai (AGN) root ethanol extract and equimolar decursin (D)/decursinol angelate (DA) through daily gavage starting at 8 weeks of age (WOA) to male transgenic adenocarcinoma of mouse prostate (TRAMP) mice such that these modalities suppressed precancerous epithelial lesions in their dorsolateral prostate (DLP) to similar extent, but AGN extract was better than the D/DA mixture at promoting the survival of mice bearing prostate neuroendocrine carcinomas to 28 WOA. Here, we compared by microarray hybridization the mRNA levels in pooled DLP tissues and individual neuroendocrine carcinomas to characterize potential molecular targets of AGN extract and D/DA. Clustering and principal component analyses supported distinct gene expression profiles of TRAMP DLP versus neuroendocrine carcinomas. Pathway Enrichment, Gene Ontology, and Ingenuity Pathway Analyses of differential genes indicated that AGN and D/DA affected chiefly processes of lipid and mitochondrial energy metabolism and oxidation-reduction in TRAMP DLP, while AGN affected neuronal signaling, immune systems and cell cycling in neuroendocrine carcinomas. Protein–Protein Interaction Network analysis predicted and reverse transcription-PCR verified multiple hub genes common in the DLP of AGN- and D/DA-treated TRAMP mice at 28 WOA and select hub genes attributable to the non-D/DA AGN components. The vast majority of hub genes in the AGN-treated neuroendocrine carcinomas differed from those in TRAMP DLP. In summary, the transcriptomic approach illuminated vastly different signaling pathways and networks, cellular processes, and hub genes of two TRAMP prostate malignancy lineages and their associations with the interception efficacy of AGN and D/DA.Prevention Relevance:This study explores potential molecular targets associated with in vivo activity of AGN root alcoholic extract and its major pyranocoumarins to intercept precancerous epithelial lesions and early malignancies of the prostate. Without an ethically-acceptable, clearly defined cancer initiation risk reduction strategy available for the prostate, using natural products like AGN to delay formation of malignant tumors could be a plausible approach for prostate cancer prevention.
Prevention of Skin Carcinogenesis by the Non-β-blocking R-carvedilol Enantiomer
Authors: Liang Sherry , Shamim Md Abdullah , Shahid Ayaz , Chen Mengbing , Cleveland Kristan H. , Parsa Cyrus , Orlando Robert , Andresen Bradley T. , Huang Ying 
Published: March 2021

Abstract

Skin cancer is the most common malignancy worldwide and is rapidly rising in incidence, representing a significant public health challenge. The β-blocker, carvedilol, has shown promising effects in preventing skin cancer. However, as a potent β-blocker, repurposing carvedilol to an anticancer agent is limited by cardiovascular effects. Carvedilol is a racemic mixture consisting of equimolar S- and R-carvedilol, whereas the R-carvedilol enantiomer does not possess β-blocking activity. Because previous studies suggest that carvedilol's cancer preventive activity is independent of β-blockade, we examined the skin cancer preventive activity of R-carvedilol compared with S-carvedilol and the racemic carvedilol. R- and S-carvedilol were equally effective in preventing EGF-induced neoplastic transformation of the mouse epidermal JB6 Cl 41-5a (JB6 P+) cells and displayed similar attenuation of EGF-induced ELK-1 activity. R-carvedilol appeared slightly better than S-carvedilol against UV-induced intracellular oxidative stress and release of prostaglandin E2 from the JB6 P+ cells. In an acute UV-induced skin damage and inflammation mouse model using a single irradiation of 300 mJ/cm2 UV, topical treatment with R-carvedilol dose dependently attenuated skin edema and reduced epidermal thickening, Ki-67 staining, COX-2 protein, and IL6 and IL1β mRNA levels similar to carvedilol. In a chronic UV (50–150 mJ/cm2) induced skin carcinogenesis model in mice with pretreatment of test agents, topical treatment with R-carvedilol, but not racemic carvedilol, significantly delayed and reduced skin squamous cell carcinoma development. Therefore, as an enantiomer present in an FDA-approved agent, R-carvedilol may be a better option for developing a safer and more effective preventive agent for skin carcinogenesis.

Prevention Relevance:
In this study, we demonstrated the skin cancer preventive activity of R-carvedilol, the non-β-blocking enantiomer present in the racemic β-blocker, carvedilol. As R-carvedilol does not have β-blocking activity, such a preventive treatment would not lead to common cardiovascular side effects of β-blockers.
How to Reprogram Myeloma-Associated Macrophages: Target IKZF1
Authors: De Sanctis Francesco , Bronte Vincenzo 
Published: March 2021

Abstract
Cancer cells hijack tumor-associated macrophages to sustain their outgrowth. In this issue, Mougiakakos and colleagues identify the IKZF1–IRF4/IRF5 axis as a key molecular pathway regulating macrophage polarization in multiple myeloma. These results suggest targeting IKZF1 could provide a new strategy to reprogram myeloma-associated macrophages toward a tumoricidal and immune-activating phenotype.
See article by Mougiakakos et al., p. 265