bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract A2-33: Molecular profiling of patient-derived xenograft models across cancers
Authors: Kan Zhengyan , Rosfjord Edward , Hardwick James , Ding Ying , Zheng Xianxian , Fernandez Julio , Shi Stephanie , Ozeck Mark , Wang Hui , Troche Gabriel , Upeslacis Eric , Jackson-Fisher Amy , Ching Keith , Deng Shibing , Tao Xie , Chionis John , Lira Maruja , Li Xiaorong , Tsaparikos Konstantinos , Lappin Patrick , Vizcarra Pamela , Shields David , Lucas Judy , Rejto Paul 
Published: November 2015

Abstract
Patient-Derived Xenograft (PDX) provides important preclinical model for pharmacological testing of oncology drug candidates. Molecular profiling of PDX tumors contributes to many areas of drug discovery from target discovery to development of clinical biomarker hypotheses and clinical trial design. We established a work flow to perform genomic and histopathology analyses of large numbers of PDX tumor models being made available for Pfizer internal research. To date we have generated whole-genome sequencing (WGS), whole-exome sequencing (WES) and whole transcriptome sequencing (RNA-Seq) data on PDX models spanning six cancer types including colon, pancreatic and breast cancers. Bioinformatics pipelines were developed to quantify gene expression and detect genetic alterations including mutation, copy number variations and gene fusions. A controlled evaluation study demonstrated that in silico classification of NGS reads into human/mouse origins is more effective than laboratory-based methods for removing mouse tissue contamination. Comparative analyses of molecular profiles from PDX and primary tumors of the same cancer origins suggest that important patterns of gene expression are retained by PDX models. An integrative genomic classifier was developed using the random forest algorithm, trained on primary tumor data, and shown to identify PDX cancer subtypes with high accuracy.
Citation Format: Zhengyan Kan, Edward Rosfjord, James Hardwick, Ying Ding, Xianxian Zheng, Julio Fernandez, Stephanie Shi, Mark Ozeck, Hui Wang, Gabriel Troche, Eric Upeslacis, Amy Jackson-Fisher, Keith Ching, Shibing Deng, Xie Tao, John Chionis, Maruja Lira, Xiaorong Li, Konstantinos Tsaparikos, Patrick Lappin, Pamela Vizcarra, David Shields, Judy Lucas, Paul Rejto. Molecular profiling of patient-derived xenograft models across cancers. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A2-33.
Abstract A2-35: Discovery of differentiation therapeutics using a systems biology approach
Authors: Li Yulin , Seita Jun , Felsher Dean , Dill David 
Published: November 2015

Abstract
Accurately assessing the changes in cellular differentiation status in response to drug treatments or genetic manipulations is crucial for understanding the tumorigenesis process and developing novel therapeutics for human cancer. Here we have developed a novel systems biology approach, called the Lineage Maturation Index (LMI), to define the changes in differentiation state of cancer cells based on their gene expression profiles. We have been focusing on hematopoietic malignancies, but this approach is applicable to many human cancer types. We define the expression profile of N genes within a cell to be a point in an N-dimensional space. A lineage vector is derived from a progenitor cell (such as a hematopoietic stem cell) to a mature cell (such as a granulocyte) based on the expression of these N genes. By projecting the expression profile of an unknown cell onto the lineage vector, an LMI can be computed for the differentiation state of this specific cell. Upon drug treatments or genetic manipulations, a shift in LMI from stem cells towards mature cell types indicates differentiation. We have confirmed that the LMI approach can track the differentiation of normal hematopoietic cells. We have further shown that differentiation is a major consequence of various targeted therapies against leukemia irrespective of the driving oncogenes. Most importantly, we have used this approach to analyze the Connectivity Map HL60 leukemia data set for drugs that can induce leukemia cells to differentiate. In addition to drugs known to have differentiation activities (such as all-trans retinoic acid and etoposide), we discovered that several novel drugs (such as dihydroergotamine, mebendazole, and clonidine) have potent differentiation activities in leukemia cells. Currently, we are expanding our study into multiple types of solid tumors, such as breast, prostate, and colon cancers. Our LMI approach is highly robust in characterizing differentiation of the cancer cells and discovering novel differentiation therapies for human cancer.
Citation Format: Yulin Li, Jun Seita, Dean Felsher, David Dill. Discovery of differentiation therapeutics using a systems biology approach. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A2-35.
Abstract A2-40: TOMA OS-seq™: A high efficiency targeted resequencing strategy for detecting gene amplifications, rearrangements, indels, and point mutations in tumor DNA isolated from FFPE solid tumors and plasma
Authors: Bouhlal Yosr , McKenzie Alexander , So Austin P. 
Published: November 2015

Abstract
Critical genetic errors, namely specific mutations, are required for the development and maintenance of cancer. Molecular diagnostics that leverage the breadth of next-generation sequencing (NGS) to identify this mutational landscape are therefore becoming an important clinical tool in the precision treatment of cancer. However, widespread adoption of NGS technologies in the clinic is hampered by the complexity of the NGS workflow, and the resulting limits in analytical capabilities due to noise introduced by poor step-wise yields and bias arising from extensive application of PCR.
To address these deficiencies, we introduce TOMA OS-Seq™, a complete targeted resequencing workflow designed specifically for the requirements of the clinical laboratory. Based on oligo-selective sequencing, a panel of 96 genes - each with diagnostic or prognostic value - can be sequenced end-to-end from as little as 1 ng of input material, enabling the analysis of DNA purified from fine-needle aspirates and plasma. Utilizing a minimal number of high efficiency processing steps to create a targeted library, this simple workflow minimizes the requirement for PCR amplification, preserving the quantitative relationship among the loci interrogated in the original sample. TOMA OS-Seq™ is therefore capable of detecting of copy number alterations, in addition to rearrangements/fusions, insertions/deletions, and single nucleotide variants.
Citation Format: Yosr Bouhlal, Alexander McKenzie, Austin P. So. TOMA OS-seq™: A high efficiency targeted resequencing strategy for detecting gene amplifications, rearrangements, indels, and point mutations in tumor DNA isolated from FFPE solid tumors and plasma. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A2-40.
Abstract A2-42: Identifying clinically important somatic mutations through a knowledge-based approach
Authors: Ainscough Benjamin J. , Griffith Malachi , Kunisaki Jason , Coffman Adam , McMichael Joshua F. , Eldred James M. , Walker Jason R. , Fulton Robert S. , Wilson Richard K. , Griffith Obi L. , Mardis Elaine R. 
Published: November 2015

Abstract
Large-scale tumor sequencing projects, like The Cancer Genome Atlas (TCGA), have implicated thousands of somatic mutations in cancer. These initiatives have incentivized many improvements in somatic variant detection. However, we have observed that important pathogenic variants are often missed due to stringent filtering, tumor heterogeneity, tumor contamination of normal, low tumor purity, alignment challenges, and other issues. These idiosyncrasies can impede variant detection algorithms from reliably calling even the most clinically relevant variants. To rescue this missed variation we devised a knowledge based variant identification strategy. We mined the literature and other variant databases for pathogenic variation and assembled them into an integrated Database of Curated Mutations(DoCM - www.docm.info). The DoCM contains 488 variants across 63 genes implicated in 34 cancer types. We developed an algorithm to identify any pathogenic variant signal, for all variants in the DoCM, in aligned sequence data. As a proof of principle, we applied this approach to four cancer types sequenced by TCGA: acute myeloid leukemia (AML), breast cancer, ovarian carcinoma, and uterine corpus endometrial carcinoma. Obvious sequencing and alignment errors, like variants in homopolymer runs, were excluded from subsequent analysis by manual review. Across these four TCGA projects, which includes 1,840 individuals, 1,757 clinically relevant variants were identified, 1,223 of which had not been previously reported in TCGA studies. To validate this approach, custom capture probes were designed for all of the DoCM variants, new libraries constructed and deep sequencing performed on 96 tumor and matched normal samples from the AML and breast cancer TCGA projects. Following this strategy, we were able to confirm the rescue of clinically relevant somatic mutations that were missed in the original TCGA analysis. We propose a knowledge-driven variant detection approach be considered as standard practice to avoid false-negative calls of events likely to be clinically relevant.
Citation Format: Benjamin J. Ainscough, Malachi Griffith, Jason Kunisaki, Adam Coffman, Joshua F. McMichael, James M. Eldred, Jason R. Walker, Robert S. Fulton, Richard K. Wilson, Obi L. Griffith, Elaine R. Mardis. Identifying clinically important somatic mutations through a knowledge-based approach. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A2-42.
Abstract A2-44: Stellaris® RNA fluorescence in situ hybridization (RNA FISH) for the detection of long non coding RNA biomarkers
Authors: Orjalo Arturo V. , Johansson Hans E. 
Published: November 2015

Abstract
Long non-coding RNAs (lncRNAs) play important roles in all the central aspects of the gene expression cascade with wide ranging effects on cell proliferation and differentiation. As such, lncRNAs are highly promising additions to many cancer biomarkers portfolios and enable more precise diagnostic staging. This, in turn, provides a clearer prognosis and treatment options of different cancers. Direct visualization and quantification of lncRNAs in single cells and in tissue are thus emerging as essential tools for both basic and clinical research. Interestingly, studies have demonstrated that transcript levels are more strongly correlated to clinical traits than the corresponding protein levels. Because RNA biomarkers have the potential to contribute to the development of targeted cancer therapies, there is also an obvious need for a robust and dependable methodology to discover and validate these RNAs.
RNA fluorescence in situ hybridization (RNA FISH) provides a powerful means to detect RNA biomarkers in single cells, while still maintaining tissue morphology. Significant advances in RNA FISH technology, such as the hybridization of pools of singly labeled fluorescent 20-mer oligonucleotides to the RNA target, now afford specific and sensitive multiplex detection of RNA to yield information on the RNA's distinct spatial distribution within cells and tissue.
Data is presented on the design of probe sets against multiple disease relevant pre-lncRNAs and against the introns of their precursors, as well as on the simultaneous dual-channel imaging of the pre-lncRNAs and mature lncRNAs. Because the absolute majority of post-transcriptional processing, including splicing of mRNAs and lncRNAs, occurs co-transcriptionally, the location of introns can be used as a proxy nuclear location for the encoding gene. We find mature lncRNAs to locate at or close to the site of transcription, but also well away from it. Next, we present data on emerging non-coding RNA biomarkers in breast and prostate cancer tissue and in cell lines utilizing RNA FISH.
In summary, the results and tools presented here will contribute to advancing the current capabilities of the detection and treatment of specific cancers, as well as in the continued discovery and development of cancer drug candidates.
Citation Format: Arturo V. Orjalo, Jr., Hans E. Johansson. Stellaris® RNA fluorescence in situ hybridization (RNA FISH) for the detection of long non coding RNA biomarkers. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A2-44.
Abstract A2-47: Informatics framework for clustering and deriving gene signatures for prognostic stratification of cancer patients
Authors: Jung Segun , Bi Yingtao , Davuluri Ramana V. 
Published: November 2015

Abstract
Stratification of cancer patients into different molecular groups is essential for developing targeted therapies. High-throughput technologies, such as microarrays and next-generation sequencing, have been extensively used for generating multi-omics data. Indeed, The Cancer Genome Atlas (TCGA) consortium, one of the most comprehensive and popular databases of cancer, has been accumulating large volumes of invaluable data for more than 30 cancer types, offering unprecedented opportunity to attain new insights in cancer biology. Despite the technological advances, analyzing, integrating and translating the gene signatures across different platforms remains a computationally challenging task.
Here, we developed a novel computational framework that integrates genomic and clinical data to stratify cancer patients into different molecular subgroups and predict clinically applicable phenotypes, such as survival. Application of this user-friendly framework derives platform-independent isoform-level gene signatures that can be translated from high-dimensional platforms (e.g., RNA-Seq) to clinically adaptable low-dimensional molecular assays (e.g., RT-PCR) for prognostic stratification. We applied the pipeline on two TCGA lung cancer datasets—Lung Adenocarcinoma (LUAD) and Lung Squamous Cell Carcinoma (LUSC). Using independent test data, we achieved about 93% (LUAD) and 98% (LUSC) classification accuracy using less than 70 isoform-level gene signature. The proposed informatics platform is applicable to other cancer types in TCGA data portal.
Citation Format: Segun Jung, Yingtao Bi, Ramana V. Davuluri. Informatics framework for clustering and deriving gene signatures for prognostic stratification of cancer patients. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A2-47.
Abstract A2-54: DNA methylation-dependent transcription regulatory networks elucidate dynamics of transcription regulatory circuitry in cancers
Authors: Liu Yu , Liu Yang , Xiao Zhengtao , Yang Xuerui 
Published: November 2015

Abstract
Context-dependent DNA methylation plays a critical role in regulating gene transcription, thereby serving as an important epigenetic marker or regulator in many biological activities such as pluripotency, development, and complex diseases such as cancer. However, previously in most of the de novo reconstructions of cancer type-specific transcription regulatory networks (TRN), DNA methylation has rarely been taken into account as a significant factor in regulating transcription factor activity and controlling gene expression. The present study was set to systematically assess the involvement of DNA methylation in transcription regulatory circuitry in cancer. We took advantages of the multi-dimensional profiling data of DNA methylations and gene expressions in tumor samples of different cancers in The Cancer Genome Atlas (TCGA) consortium, and developed an integrative analysis pipeline based on conditional mutual information theory, to quantify the cooperative regulatory effects of CpG site methylations and transcription factors on gene expressions, in a genome-wide scale. Our analysis showed that DNA methylation and transcription factors indeed control gene expressions often in duet. To map the interplay between these two major defining factors of gene expression, DNA Methylation-dependent Transcription Regulatory Network (MeTRN), the first of its kind, was assembled for each of 19 major cancer types in TCGA. MeTRN provides a comprehensive view specifically on DNA methylation-dependent transcription events in each cancer type, and has been largely validated by public ChIP-seq and DNaseI-seq data. Comparison of these networks across cancer types showed that context-specificity of transcriptional circuits can be largely attributed to the context-dependent nature of DNA methylation patterns. Therefore, by assembling the cooperative effects of transcription factors and DNA methylations on gene transcription, MeTRN recapitulates an epigenetic scheme that implements dynamics of transcription regulatory circuitry across cancers via context-dependent DNA methylation marks. Supported by extensive cross-validations and experimental evidence, this epigenetically marked and dynamic transcription regulatory circuitry serves as a new basis for further mechanistic studies of gene expression dysregulations in cancers.
Citation Format: Yu Liu, Yang Liu, Zhengtao Xiao, Xuerui Yang. DNA methylation-dependent transcription regulatory networks elucidate dynamics of transcription regulatory circuitry in cancers. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A2-54.
Abstract A2-60: Phospho-proteomic analysis of MEK inhibition in mutant NRAS melanoma
Authors: Vu Ha Linh , Davies Michael A. , Aplin Andrew E. 
Published: November 2015

Abstract
Melanoma is a devastating form of skin cancer with limited therapeutic options. Fifteen to twenty percent of melanoma patients have an activating mutation in NRAS, a small GTPase. NRAS has several downstream effectors, the most well-characterized is RAF leading to activation of MEK/ERK. While there are no FDA-approved targeted therapies for melanoma patients with a primary mutation in NRAS, one form of targeted therapy that has been explored is MEK inhibition. Phase I to III studies of MEK inhibitors in mutant NRAS melanomas have shown disappointing clinical efficacy, the reasons for which are unclear. In 3D systems that mimic the dermal microenvironment, we found a heterogeneous response in our panel of mutant NRAS melanoma cells to the allosteric MEK inhibitor, trametinib. To explore the underlying basis of this heterogeneity, we utilized a high-throughput reverse-phase protein array (RPPA) platform to identify compensatory signaling alterations. The RPPA analysis of phospho-proteomic changes in NRAS mutant melanoma in response to trametinib indicated a compensatory increase in AKT signaling. Interestingly, we detected a decrease in mitogen inducible gene 6 (MIG6), a negative regulator of ERBB receptors. The overexpression of MIG6 led to a negative regulation of EGFR (ERBB1) and ERBB2, and subsequent decrease in AKT activation. Conversely, the depletion of MIG6 increased EGFR and ERBB2 signaling. Either depletion of MIG6 or addition of EGF increased migration of NRAS mutant cells treated with trametinib, an invasive phenotype that may mitigate the negative effects of trametinib on cell growth. Our results indicate a potential role for the combined inhibition of EGF/ERBB receptors and MEK in NRAS mutant melanoma patients.
Citation Format: Ha Linh Vu, Michael A. Davies, Andrew E. Aplin. Phospho-proteomic analysis of MEK inhibition in mutant NRAS melanoma. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A2-60.
Abstract PR14: Nucleation of transcriptional super-enhancers at tumor oncogenes
Authors: Abraham Brian J. , Kwiatkowski Nicholas , Weintraub Abraham S. , Hnisz Denes , Hannett Nancy , Young Richard A. 
Published: November 2015

Abstract
Transcriptional super-enhancers drive expression of oncogenes in many cancers and are being targeted with novel transcriptional and epigenetic therapeutics (1,2,3,4). Super-enhancers are acquired in cancers through multiple mechanisms, including DNA translocation of an extant super-enhancer and focal amplification. We recently discovered a novel mechanism by which super-enhancers are nucleated in T cell acute lymphoblastic leukemias (T-ALLs) (5). In this case, a small, monoallelic insertion creates a DNA binding site for a master transcription factor protein, which binds and recruits additional factors to nucleate the super-enhancer, which in turn drives high levels of the TAL1 transcription factor. We describe here a method for unbiased identification of similar genomic insertions that nucleate potentially oncogenic regulatory elements in cancers. This approach uses data from genome-wide ChIP-Seq studies that map locations of enhancer-binding proteins to identify sequences missing from reference genomes. We have found many additional genomic insertions in many additional cancers. I will describe new insights into the regulation of cancer that occur due to nucleation of novel regulatory elements.
1 Hnisz, Abraham, Lee, et al., Cell 2013
2 Loven, Hoke, Lin, et al., Cell 2013
3 Kwiatkowski, et al., Nature, 2014
4 Chipumoro, et al., Cell, 2014
5 Mansour et al., Science 2014
Citation Format: Brian J. Abraham, Nicholas Kwiatkowski, Abraham S. Weintraub, Denes Hnisz, Nancy Hannett, Richard A. Young. Nucleation of transcriptional super-enhancers at tumor oncogenes. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr PR14.
Indignation, Ideologies, and Armed Mobilization: Civil War in Italy, 1943–45
Authors: Costalli Stefano , Ruggeri Andrea 
Published: November 2015

Abstract
Ideas shape human behavior in many circumstances, including those involving political violence. Yet they have usually been underplayed in studies of the causes of armed mobilization. Likewise, emotions have been overlooked in most analyses of intrastate conflict. A mixed-methods analysis of Italian resistance during the Fascist regime and the Nazi occupation (1943–45) provides the opportunity to theorize and analyze empirical evidence on the role of indignation and radical ideologies in the process of armed mobilization. These nonmaterial factors play a crucial role in the chain that leads to armed collective action. Indignation is a push factor that moves individuals away from accepting the status quo. Radical ideologies act as pull factors that provide a new set of strategies against the incumbent. More specifically, detachment caused by an emotional event disconnects the individual from acceptance of the current state of social relations, and individuals move away from the status quo. Ideologies communicated by political entrepreneurs help to rationalize the emotional shift and elaborate alternative worldviews (disenchantment), as well as possibilities for action. Finally, a radical ideological framework emphasizes normative values and the conduct of action through the “anchoring” mechanism, which can be understood as a pull factor attracting individuals to a new status.