bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 5544: New technologies to probe the systems glycobiology of cancer
Authors: Rudd Pauline M. , Hilliard Mark , Muniyappa Mohankumar , O'Flaherty Roisin , Saldova Radka 
Published: July 2017

Abstract
A robotic 384 well based platform was developed to release and label the human serum N-glycome. Improved separation of the glycan pool, based on hydrophilic interaction UPLC chromatography combined with mass spectrometry (Waters Corporation) and computer assisted data interpretation (NIBRT Glycobase) enabled us to build a database after assigning the detailed structures of more than 170 N-glycans.
The aim of the project was to use these technologies and databases to link glycosylation changes in individual patients’ serum with features of cancer and with changes in a range of other –omics data acquired from the same patients.
Alterations in glycosylation in various breast cancers were mapped to changes in the serum glycomes and aligned with genetic, transcriptomic and proteomic data. Pathway analysis showed strong associations between these glycan changes and the –omics data. This revealed that many of the glycan changes are directly associated with pathways involved in cancer metastasis.
Our next aim was to demonstrate the feasibility of collecting personalised data from individual patients from each of the –omics analyses to build up a dataset of the changing glycosylation over time. This opens up the possibility of linking these data to explore pathways of disease and, in particular, nodal points where the patient can no longer compensate for the effects of an altered pathway that is leading to disease.
To this end, single glycoproteins from 8 controls and 26 ovarian cancer patients were sequentially purified from 5ul of serum on affinity plates and the released glycans were analysed. Rather than the conventional way of looking at markers which compare a patients’ data with averages, the control can now be the blood of the patient themselves taken at an earlier time point.
The automation, sensitivity, quantitation and resolution of the new technology platforms coupled with dedicated software open up new possibilities for precision medicine and early intervention based on the biochemical profile of the patient.
References:
Association of N-glycosylation with breast carcinoma and systemic features using high-resolution quantitative UPLC. Saldova R, Asadi Shehni A, Haakensen VD, Steinfeld I, Hilliard M, Kifer I, Helland A, Yakhini Z, Børresen-Dale AL, Rudd PM. J Proteome Res. 2014 May 2;13(5):2314-27.
Serum N-glycan analysis in breast cancer patients–Relation to tumour biology and clinical outcome.
Haakensen VD, Steinfeld I, Saldova R, Shehni AA, Kifer I, Naume B, Rudd PM, Børresen-Dale AL,Yakhini Z. Mol Oncol. 2016 Jan;10(1):59-72.

Citation Format: Pauline M. Rudd, Mark Hilliard, Mohankumar Muniyappa, Roisin O'Flaherty, Radka Saldova. New technologies to probe the systems glycobiology of cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5544. doi:10.1158/1538-7445.AM2017-5544
Abstract 5545: Integrated system-level analyses of androgen receptor variant networks to identify novel prostate cancer-relevant genes
Authors: Magani Fiorella , Bray Eric R. , Zhao Ning , Peacock Stephanie , Burnstein Kerry L. 
Published: July 2017

Abstract
Unbiased high-throughput approaches provide a global view of the massive genetic and transcriptomic changes that occur in and potentially drive cancer. Such approaches permit the discovery of novel markers and therapeutic targets. Current treatments for advanced prostate cancer (PC) focus on inhibiting the androgen receptor (AR). However, PC inevitably progresses to a stage termed castration-resistant prostate cancer (CRPC), which is incurable. In CRPC, C-terminal truncated, constitutively active AR splice variants (such as AR-V7) play key transcription-regulatory roles resulting in treatment resistance and disease progression. However, designing high-affinity drugs to target the amino terminus of AR and AR variants is a major challenge due to the intrinsic disorganized structure of this region. Thus there is an imperative need to identify novel AR-V7 hub genes in PC that may serve as therapeutic targets. We performed an extensive and highly robust gene expression meta-analysis on PC patient samples. We defined gene modules correlated with disease progression using a powerful systems biology approach termed Weighted Gene-Co-expression Network Analysis (WGCNA). Further, we mapped the AR-V7 interactome for the first time using a novel high-throughput synthetic genetic array screening in yeast, known as Yeast Augmented Network Analysis (YANA). YANA was performed by crossing yeast expressing AR-V7 with a large collection of yeast strains lacking non-essential genes, and identifying those genes that caused a change in yeast growth (fitness). Human orthologs of the identified yeast genes were used to build an AR-V7 functional gene network. Finally, we combined the results from our independent system-level analyses with experimental data to identify hub genes that are upregulated in PC patients, regulated by AR-V7, and also functionally interact with AR-V7. The identified genes not only include select genes previously linked to PC, such as members of the cyclin and topoisomerase families, but also genes that have not been previously linked to AR-V7 activity or PC progression. Moreover, our gene expression signature predicts a higher risk of PC recurrence after primary treatment in patients. In sum, we show here an unbiased and novel gene discovery strategy using bioinformatics in concert with experimental approaches to identify new candidate genes in CRPC that may lead to prognostic markers and future targeted therapies.
Citation Format: Fiorella Magani, Eric R. Bray, Ning Zhao, Stephanie Peacock, Kerry L. Burnstein. Integrated system-level analyses of androgen receptor variant networks to identify novel prostate cancer-relevant genes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5545. doi:10.1158/1538-7445.AM2017-5545
Abstract 5554: Metabolic reprogramming in non-small cell lung cancer: a precision oncology approach
Authors: Tavassoly Iman , Iyengar Ravi 
Published: July 2017

Abstract
Lung cancers are among the most common invasive cancers worldwide and annually lead to high mortality and morbidity. Genomic alterations have been known to control the evolution of hallmarks of cancer in a dynamic way. These molecular alterations combined with epigenomic and post-genomic modifications contribute to formation of these neoplasms. Multiplicity of these changes has made development of personalized therapeutic regimens for these cancers a complex problem. Metabolic reprogramming is one of the main mechanisms in progression of cancers. There have been efforts to model the metabolic reprogramming in cancer using metabolic networks of cancer cells, but there has been no computational framework to model these metabolic transitions in cancer for precision and personalized medicine. We have combined computational, mathematical and experimental methodologies to develop a platform for precision oncology in non-small cell lung cancer (NSCLC) by in silico models of metabolic switches. Our integrative analysis of genomic data from NSCLC has led to discovery of genomic signatures controlling metabolic reprogramming in NSCLC with KRAS mutations. This discovery was proved in vivo and in vitro using drugs blocking different metabolic pathways. We have shown that NSCLC cells and tumors which carry KRAS mutations and have these genomic signatures are addicted to the pentose phosphate pathway (PPP). We have verified and proved the predictive value of these genomic signatures using Patient Derived Xenograft (PDX) tumor models of NSCLC. We are developing a mathematical and computational framework to model these metabolic switches. Our platform is capable of using genomic data from a cell line or tumor to determine the metabolic dependency of them quantitatively and predict the optimized personalized treatments for modulating metabolic pathways aiming to control cancer progression.
Citation Format: Iman Tavassoly, Ravi Iyengar. Metabolic reprogramming in non-small cell lung cancer: a precision oncology approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5554. doi:10.1158/1538-7445.AM2017-5554
Abstract 5555: Integrative network variomics reveals complex genotype to phenotype relationships in cancer
Authors: Sahni Nidhi , Yi Song 
Published: July 2017

Abstract
Proteins interact with other macromolecules in complex cellular networks for signal transduction and biological functions. Recently a deluge of genomic information becomes available from patients stricken by a variety of cancer types. In cancer, genetic aberrations have been traditionally thought to abolish the entire gene function. It has been increasing appreciated that each genomic mutation of a gene could have a subtle but unique effect on protein function or network rewiring, contributing to diverse phenotypic consequences across cancer patient populations. In this study, we designed an integrative multi-omics platform using systems biology approaches to functionally assess the effects of cancer genetic variants. We compared a broad spectrum of mutation classes for a wide range of mechanistic effects on gene function in the context of signaling networks. Our results reveal a functional landscape of context-specific signaling network perturbations underlying cancer genetic heterogeneity at the base-pair resolution. Such information is critical for understanding the complex pleiotropic effect of cancer genes, and provides a possible link between genotype and phenotype in cancer. Furthermore, this study suggests new perspectives in targeted therapy and precision medicine.
Citation Format: Nidhi Sahni, Song Yi. Integrative network variomics reveals complex genotype to phenotype relationships in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5555. doi:10.1158/1538-7445.AM2017-5555
Abstract 5558: Deconstruction of a human tumor microenvironment
Authors: Balkwill Frances R. , Pearce Oliver M. , Delaine-Smith Robin , Maniati Eleni , Nichols Sam , Wang Jun , Bessant Conrad , Knight Martin 
Published: July 2017

Abstract
The purpose of this study was to understand the relationships between the molecular mechanisms of disease progression and higher-order features such as tissue stiffness, extent of disease and cellularity in the tumor microenvironment, TME. Using samples of human high-grade serous ovarian cancer metastases, ranging from normal to heavily diseased, we identified molecular components of the TME using transcriptomic and proteomic analysis. We integrated these data against higher-order features of the tissue, namely the biomechanics, cellularity, and disease score. We then used bioinformatics and multivariate statistics to identifying components of the TME that best model the higher-order features. For the first time, we revealed the complexity of extracellular matrix remodeling during metastases development, defining patterns of extracellular-matrix associated genes and proteins that predicted both extent of disease and tissue modulus. This allowed us to identify a core group of twenty-two matrix-associated molecules that modeled the dynamic process of tissue remodeling during tumor progression. We used these data to generate a ‘matrix index’, a quantitative measure of the gene expression of the twenty-two molecules. In cancer transcriptomic databases, this matrix index had prognostic significance in thirteen solid cancers including high-grade serous ovarian cancer, even after multivariate analysis. We conclude that there may be a common host matrix response to human solid cancers.
Citation Format: Frances R. Balkwill, Oliver M. Pearce, Robin Delaine-Smith, Eleni Maniati, Sam Nichols, Jun Wang, Conrad Bessant, Martin Knight. Deconstruction of a human tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5558. doi:10.1158/1538-7445.AM2017-5558
Abstract 5562: Integrating evolutionary dynamics into treatment of metastatic castrate-resistant prostate cancer: a pilot multidisciplinary study
Authors: Zhang Jingsong , Cunningham Jessica J. , Brown Joel S. , Gatenby Robert A. 
Published: July 2017

Abstract
Background: Prior studies have investigated “intermittent therapy” to delay hormone resistance in prostate cancer but did not explicitly include quantitative analysis of the evolutionary dynamics. We hypothesize that evolution-informed strategies may prolong time to progression but require a multidisciplinary effort to simulate intratumoral Darwinian dynamics and design a clinically feasible treatment protocol.
Methods: We investigate intratumoral evolutionary dynamics during treatment of metastatic castrate resistant prostate cancer (mCRPC) with abiraterone, which blocks CYP17A1 autosynthesis of testosterone from endocrine precursors. We build a mathematical model assuming three competing phenotypes: (i) TP cells express CYP17A1 and produce testosterone; (ii) T+ cells require exogenous androgen; and (iii) T- cells are androgen-independent and resistant to abiraterone. Model predictions were tested in a pilot clinical trial.
Results: Mathematical Model: Computer simulations demonstrate continuous maximum dose abiraterone treatment produces competitive release of resistant T- cells. However, limited treatment designed to maintain residual TP and T+ populations and suppress proliferation of T- cells was predicted to prolong response while lowering the required drug dose. Clinical Trial: In a pilot clinical trial, 11 men with asymptomatic mCRPC were treated with abirateone according to an evolution-informed, patient-specific algorithm based on the modeling results. Pre-treatment biopsies (available for 3 patients) demonstrated the predicted tumor subpopulations. Cycles of response and regrowth similar to model simulations were observed with cycle lengths varying from 3 months to &gt; 1 year. Over a median follow up period of 18 months, 10 subjects remain responsive to Abiraterone without PSA or radiographic progression. Median time to PSA progression significantly (P&lt;0.001) exceeds the 11.1 months historical control. As predicted by the simulations, tumor control required significantly less abiraterone with average cumulative dose less than 40% of standard of care (P&lt;0.001).
Conclusion: Integration of mathematical models and Darwinian first principles into trial design, may allow patient-specific modifications to prolong response while lowering drug doses in mCRPC.
Citation Format: Jingsong Zhang, Jessica J. Cunningham, Joel S. Brown, Robert A. Gatenby. Integrating evolutionary dynamics into treatment of metastatic castrate-resistant prostate cancer: a pilot multidisciplinary study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5562. doi:10.1158/1538-7445.AM2017-5562
Abstract 5564: The interplay between etiology-dependent genomic and epigenetic signatures: The paradigm of liver cancer
Authors: Haviv Izhak , Perez Shira , Lavi-Itzkovitz Anat , Kaspi Antony , Tover Ana , El-Osta Assam , Yaari Gur , Stemmer Salomon , Gal-Tanamy Meital 
Published: July 2017

Abstract
The evolutionary theory assumes that occurrence of mutations in cancer is random. However, recent studies suggest that passenger mutations are not randomly scattered in cancer genomes and that chromatin organization dictate mutations profiles. Hepatocellular carcinoma (HCC) serves as a model of diverse spectrum of cancers, since it is induced by a number of well-known etiological agents, mainly Hepatitis C virus (HCV) and Hepatitis B virus (HBV). This provides a unique opportunity to test whether different etiological agents dictate distinct mutational landscapes across the genome through a unique epigenetic signature. The HCC-specific genomic mutations were studied in 80 HCC biopsies from three etiology groups - HBV, HCV, or other. To explore the mutational signature we performed target high-resolution sequencing that enables the detection of low-frequency passenger mutations. By using this strategy we identified novel distinct etiology-dependent regional mutations signature in specific genes and pathways in HCC that was not detected previously by exome sequencing of liver tumors. These observations suggest that viral infection modulate somatic mutations causing HCC. To explore the link between genomic signature and genome wide chromatin organization we studied the epigenetic changes occur following HCV infection. Epigenetic analysis unraveled known and novel pathways that are controlled by the virus such as Hepatic lipid metabolism, cell motility, cell cycle and immunity. Invers correlation between high mutation rate and enrichment of chromatin modifications associated with active transcription provides a link between etiology and cancer genome. Our novel approach offers a perspective into the mechanisms that shape mutational signature development in cancer.
Note: This abstract was not presented at the meeting.
Citation Format: Izhak Haviv, Shira Perez, Anat Lavi-Itzkovitz, Antony Kaspi, Ana Tover, Assam El-Osta, Gur Yaari, Salomon Stemmer, Meital Gal-Tanamy. The interplay between etiology-dependent genomic and epigenetic signatures: The paradigm of liver cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5564. doi:10.1158/1538-7445.AM2017-5564
Abstract 5568A: A network based analysis for the treatment of triple negative breast cancer
Authors: Fleisher Brett A. , Ait-Oudhia Sihem 
Published: July 2017

Abstract
Background: Triple-negative breast cancer (TNBC) is a complex heterogeneous disease characterized by the absence of three hallmark receptors: human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), and progesterone receptor (PR). Compared to other breast cancer subtypes, TNBC is more aggressive, has a higher prevalence in African Americans, and more frequently affects younger patients. Currently, TNBC lacks clinically-accepted targets for tailored therapy, warranting the need for candidate biomarkers.
Methods: BiomarkerBase, a biomarker knowledgebase developed by Amplion uses a comprehensive list of synonyms to identify biomarkers registered in the records of clinical trials via the government website clinicaltrials.gov. Under each of the registered breast cancer biomarkers (with the exceptions of HER2, ER, and PR), sub-searches were conducted for clinical trials that explicitly used TNBC (or the full name, triple negative breast cancer) in the title of the biomarkers’ studies. Of note, most trials in this review were completed phase II or III. The selected candidate biomarkers were classified into four FDA biomarker categories (surrogate, prognostic, predictive, or pharmacodynamic (PD)) as well as by location (blood/plasma, cell surface, cytoplasm, or nucleus). Current literature about the biomarkers was further examined using PubMed. There, we sought to find papers that studied how the biomarkers may interact. These markers were further organized into a “cellular protein network” that demonstrates potential connectivity.
Results: Blood biomarkers include VEGF/VEGFR and IL-8; cell surface biomarkers include EGFR, IGFBP, c-Kit, c-Met, and PD-L1; cytoplasm biomarkers include PIK3CA/AKT/mTOR-related metabolites (GPC, PCh, Lactate/ glucose), pPKM2, F1,6BP, SPHK1, STAT1/IRF2BP2, S6, p4E-BP1, SUV39H1, PTEN, and ALDH1; and nucleus biomarkers include BRCA1, the glucocorticoid receptor (GR), TP53, and Ki67. Table 1 provides an overview of the examined surrogate, prognostic, predictive, and PD biomarkers in TNBC in addition to current literature found in PubMed that provides support for their potential use.
Conclusion: To date, there are no clinically-validated biomarkers for TNBC, which has hindered the development of tailored therapy for both chemosensitive and refractory TNBC. Before validation and use of these biomarkers occurs, stringent-clinical criteria must be met first. The list and cellular protein network provided is an inventory and reference point for promising biomarkers for breakthrough, targeted therapies in TNBC.
Citation Format: Brett A. Fleisher, Sihem Ait-Oudhia. A network based analysis for the treatment of triple negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5568A. doi:10.1158/1538-7445.AM2017-5568A
Abstract 557: SPOP mutant subclass prediction and its impact on prostate cancer prognosis
Authors: Liu Deli 
Published: July 2017

Abstract
SPOP mutations define a distinct and key molecular class of prostate cancer, but there is no signature to identify SPOP mutant subclass based on transcriptional data, and its impact on prognosis and clinical outcomes of prostate cancer patients is unknown.
We developed the SPOP mutant transcriptional signature with high sensitivity and specificity based on transcriptional data. After we predicted SPOP mutant subclass from a large cohort project with transcriptional signature, we studied its association with clinicopathologic variables and prognosis in prostate cancer patients. On univariate analysis, SPOP mutant subclass was associated with lower preoperative serum prostate-specific antigen, extraprostatic extension and older patients (p&lt;0.05). On multivariate analysis, SPOP mutant subclass was associated with metastasis (p&lt;0.05). Combined SPOP mutant status and CAPRA-S models have slightly better prediction for metastasis. On Kaplan-Meier analysis, we found the significant association of SPOP mutant subclass with better patient outcomes of metastasis, and patients without radiation therapy with even better metastatic outcomes.
The SPOP mutant subclass could be predicted by using our SPOP mutant signature purely based on transcriptional data. Clinical and prognosis difference were found among SPOP mutant subclass.
Citation Format: Deli Liu. SPOP mutant subclass prediction and its impact on prostate cancer prognosis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 557. doi:10.1158/1538-7445.AM2017-557