bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 3842: Potent antitumor activity of the HSP90 inhibitor SNX-2112 in type 1 papillary renal cell carcinoma
Authors: Pahwa Roma , Giri Abhigya , Sourbier Carole , Thomas Craig , Linehan W Marston , Neckers Len , Srinivasan Ramaprasad 
Published: August 2018

Abstract
Papillary renal-cell carcinoma (pRCC) is the second most common form of kidney cancer, accounting for 15 to 20% of all cases. There are currently no treatment options of proven benefit for patients with advanced pRCC. The type 1 subset of pRCC tumors are associated with gain of chromosome 7 and activating mutations of MET that could lead to aberrant MET pathway activation resulting in cell proliferation and anti-apoptotic signaling. While MET kinase inhibitors are active in some patients with type 1 pRCC, primary and acquired resistance is common, prompting us to investigate possible therapeutic alternatives. Heat shock protein 90 (Hsp90), which stabilizes numerous oncogenic proteins, including MET and several downstream mediators of MET activity, is an important regulator of cell proliferation and survival, and has attracted wide interest as a novel target for cancer therapy. Hsp90 inhibitors were also identified as molecules of interest based on antitumor activity against patient derived type 1 pRCC cell lines following quantitative high throughput screening (qHTS) with small molecule libraries. We hypothesized that inhibition of Hsp90 might provide a novel therapeutic approach for patients with pRCC tumors with MET pathway activation. SNX-2112 was chosen as a candidate Hsp90 inhibitor for further evaluation. Our data demonstrate that inhibition of the Hsp90 pathway is lethal to pRCC cell lines with MET pathway activation, and that treatment with SNX-2112 leads to the degradation of MET/pMET and its downstream signaling intermediates p-AKT, AKT, p-ERK. Furthermore, SNX-2112 leads to inhibition of critical downstream transcriptional targets of AKT and ERK, including VEGF, c-Myc and BIRC5. SNX-2112 has potent anti-tumor activity in vitro, inhibiting proliferation, clonogenicity and 3D-anchorage independent colony formation of MET activated pRCC cell lines. These data demonstrate that Hsp90 inhibition is a promising therapeutic strategy in Type 1 pRCC and is worthy of further preclinical and clinical evaluation.
Citation Format: Roma Pahwa, Abhigya Giri, Carole Sourbier, Craig Thomas, W Marston Linehan, Len Neckers, Ramaprasad Srinivasan. Potent antitumor activity of the HSP90 inhibitor SNX-2112 in type 1 papillary renal cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3842.
Abstract 3862: Empowering research on ubiquitin and ubiquitin-like protein modification cascade using recombinant enzyme systems
Authors: Yao Eric , Lai Shenshen , Yan Jun 
Published: August 2018

Abstract
Protein modifications by the covalent attachment of ubiquitin (Ub) or ubiquitin-like proteins (UBLs) such as SUMO, ISG15 or NEDD8 participate in many critical cellular processes. These include cell-cycle regulation, DNA repair, endocytosis, mitophagy, oncogenesis, post-membrane-protein trafficking, antiviral pathways and most notably, proteasomal degradation of target proteins. Ubiquitination and modification by UBLs share a similar catalytic cascade which requires the sequential action of three classes of enzymes: E1 activating enzymes, E2 conjugating enzymes and E3 ligases. While Ub/UBLs are activated by E1 through an ATP-dependent thioester bond formation and transferred to E2 from E1, the exact mechanism of how they are subsequently attached to a protein substrate depends on the type of E3's containing characteristic catalytic domain - RING, HECT or RBR. It has been generally accepted that E3's mediate substrate specificity and E2's are the main determinants for selection of the lysine to construct the modifier chains, which thereby directly control the cellular fate of the substrate. Recent research has linked dysregulation of the Ub/UBLs modification system to numerous diseases including cancer, immunological disorders and neurodegeneration. The high substrate specificity provided by combinations of over 30 E2's and over 600 E3's in the human genome makes these enzymes emerging drug targets. In response to a growing market demand, SignalChem has developed an extensive array of products encompassing enzymes, Ub/UBL modifiers and substrates in the ubiquitination, SUMOylation, ISGylation and NEDDylation processes. With Promega's AMP-GloTM reagents and an optimized assay protocol, we have identified and validated a variety of functionally competent combinations of these enzyme components. With the established protocol, each enzyme in the catalytic cascade has been assessed for their specific activity towards generation of free AMP. In addition, inhibition profiles of all three enzymes have been obtained using the assay system, further demonstrating their potential to be used in high-throughput screening to identify potential lead compounds for drug discovery and development programs.
Citation Format: Eric Yao, Shenshen Lai, Jun Yan. Empowering research on ubiquitin and ubiquitin-like protein modification cascade using recombinant enzyme systems [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3862.
Abstract 387: Glutamine metabolism reprogramming in adipocyte-tumor microenvironment promotes triple-negative breast cancer growth
Authors: Liu Yang , Zhao Tingli , Li Zhengzheng , Wang Lai , Yang Mei , Yuan Shengtao , Sun Li 
Published: August 2018

Abstract
OBJECTIVE
Adipocytes are the most abundant stromal partners in breast tissue. However, the crosstalk between breast cancer cells and adipocytes has been given less attention compared to cancer-associated fibroblasts. We try to explain whether cancer associate adipocytes (CAAs) in microenvironment can lead to metabolic remodeling of triple-negative breast cancer, promoted cancer proliferation, migration and the potential mechanism.
METHOD
A Transwell co-culture model in vitro and orthotopic mouse model of human breast tumor formation in vivo were used to study the crosstalk between adipocytes and breast cancer cells, and then using clinical breast cancer sample analysis to study the mechanism of glutamine reprograming.
RESULTS
We find that while cocultivated adipocytes with breast cancer cells, it exhibited enhanced oxygen consumption rate (OCR) and ATP, accompanied by increasing glucose and glutamine uptake. Interestingly, the metabolic way is significantly different between phenotype-specific breast cancer cells. Compared with non-triple-negative breast cancer cells, triple-negative breast cancer cells (MDA-MB-231, MDA-MB-468) are more dependent on glutamine. We show that cancer associate adipocytes (CAAs) promote growth of breast cancer cells through key enzymes such as GLUL/GLS/GDH and transporters such as SLC1A5/SLC38A1/SLC38A2 related to glutamine metabolism in vitro. Using cytokine antibody microarrays, IL-6/IL-8/ leptin/TIPM2 is secreted by CAAs and is required to enhance breast cancer cells malignancy in vitro. Consistently, the correlation of inflammatory cytokines and adipokines with prognosis is analyzed by the Kaplan-Meier plotter, the data suggest that inflammatory cytokines and adipokines is associated with glutamine metabolism in triple-negative breast cancer microenvironment.
CONCLUSIONS
These results suggest that adipocytes promote proliferation of triple-negative breast cancer via metabolism reprogramming potentially by inducing inflammatory cytokines and adipokines upregulation of cancer associate adipocytes (CAAs).
Citation Format: Yang Liu, Tingli Zhao, Zhengzheng Li, Lai Wang, Mei Yang, Shengtao Yuan, Li Sun. Glutamine metabolism reprogramming in adipocyte-tumor microenvironment promotes triple-negative breast cancer growth [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 387.
Abstract 388: p16/MTAP status as a model for investigating gene-environment interactions in metabolism
Authors: Sanderson Sydney M. , Mikhael Peter , Dai Ziwei , Locasale Jason W. 
Published: August 2018

Abstract
While the consequences of tumor suppressor gene deletions have been extensively characterized, little is known about how concurrent deletions of neighboring genes can influence the interaction of cancer cells with the environment. Using a panel of tissue-matched cancer cell lines exhibiting either wild-type or homozygous deletions of the p16 locus, metabolomics analysis showed that concurrent deletion of the proximal gene encoding the methionine salvage enzyme methylthioadenosine phosphorylase (MTAP) is associated with a subtle but reproducible global shift in metabolite levels. However, MTAP status does not appear to be predictive of metabolic responsiveness to restriction of key nutrients that feed into and out of the methionine cycle. Of particular interest is the finding that cell lines exhibit remarkable heterogeneity in responsiveness to nutrient availability, seemingly independent of MTAP status or tissue of origin. Similarly, MTAP-deleted cell lines exhibit variable global metabolic reprogramming in response to acute ectopic expression of MTAP, and maintain their heterogeneous responsiveness to nutrient restriction. These findings illustrate the exceptional metabolic diversity across cancer cell lines despite similar culture conditions, demonstrating the usefulness of examining a variety of cell lines in studies of cancer cell metabolism.
Citation Format: Sydney M. Sanderson, Peter Mikhael, Ziwei Dai, Jason W. Locasale. p16/MTAP status as a model for investigating gene-environment interactions in metabolism [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 388.
Abstract 3886: A large cancer pharmacogenomics combination screen powering crowd-sourced advancement of computational drug synergy predictions
Authors: Dry Jonathan R. , Menden Michael P. , Bulusu Krishna , Guinney Justin , Saez-Rodriguez Julio 
Published: August 2018

Abstract
This abstract has been withheld from publication due to its inclusion in the AACR Annual Meeting 2018 Official Press Program. It will be posted online following its presentation.
Citation Format: Jonathan R. Dry, Michael P. Menden, Krishna Bulusu, Justin Guinney, Julio Saez-Rodriguez. A large cancer pharmacogenomics combination screen powering crowd-sourced advancement of computational drug synergy predictions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3886.
Abstract 3894: DPYD-Varifier, a computational model to identify 5-FU toxicity-associated DPYD variants
Authors: Shrestha Shikshya , Zhang Cheng , Jerde Calvin R. , Li Hu , Offer Steven M. , Diasio Robert B. 
Published: August 2018

Abstract
5-Fluorouracil (5-FU) is a cornerstone therapy for the treatment of several aggressive tumors. However, nearly 1/3 of patients receiving 5-FU experience severe (grade 3+) adverse 5-FU toxicity. Genetic variations in dihydropyrimidine dehydrogenase (DPD; DPYD gene) are major contributors of 5-FU toxicity. With continued progress in next-generation sequencing technologies, the number of reported genetic variants is substantially increasing; to date, there are more than 450 reported missense variants in the DPYD gene. Only a handful of those variants, including *2A, I560S, D949V, and rs75017182, have been established as predictors of toxicity. Because most of the remaining variants are unstudied, mainly due to their rare occurrence and unequal racial distribution, it is challenging to conduct statistically powered clinical association studies. Therefore, in vitro and computational methods to identify potential toxicity-associated variants have major appeal. The primary goal of the current study was to develop a highly accurate gene-specific in silico classifier to predict deleterious DPYD variants. For this purpose, using a recombinant cellular model, we characterized 69 missense DPYD variants reported in online databases. We then utilized DPD activity data from the 69 variants in combination with data from 87 previously characterized variants (see Offer, SM et al. Cancer Research 2013 & 2014) to develop the DPYD variant classifier model (DPYD-Varifier). DPD protein-specific biochemical and structural features and amino acid conservation scores were integrated into the model's random forest classifier-based algorithm. DPYD-Varifier showed improved accuracy (85% accurate) compared to other in silico tools. Additionally, the model provided mechanistic insights into alterations of DPD function conferred by DPYD variants. In general, deleterious variants were located in closer proximity to important binding/coordinating sites of the DPD protein. Additionally, DPYD-Varifier allowed us to estimate the cumulative allele frequency of all reported deleterious variants in various geographical/racial groups, which can substantially add to our knowledge of DPD deficiency and 5-FU toxicity observed in these populations. In conclusion, the integration of in vitro and accurate computational data from our study permitted classification of relevant DPYD variants, which could be useful to more effectively individualize 5-FU therapy in the future.
Citation Format: Shikshya Shrestha, Cheng Zhang, Calvin R. Jerde, Hu Li, Steven M. Offer, Robert B. Diasio. DPYD-Varifier, a computational model to identify 5-FU toxicity-associated DPYD variants [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3894.
Abstract 3896: A big data approach to predicting context specific synergistic drug combinations
Authors: Gilvary Coryandar M. , Madhukar Neel , Elemento Olivier 
Published: August 2018

Abstract
Combination therapies for various cancers have been shown to increase efficacy, lower toxicity and escape resistance. However, systematically interrogating all possible synergistic therapies is experimentally unfeasible due to the sheer volume of possible combinations. Computational approaches have proven to be an invaluable tool within pharmacogenomics and have helped with prioritizing the development of perspective therapeutics as well as matching the right drugs with the right patients. Here we apply a novel big data approach in the evaluation and ultimately the prediction of drug synergy by using the recently released NCI-ALMANAC, the largest publically available synergistic drug efficacy dataset to date. First, to better understand drug combinations, we distinguished between those that were synergistic and adverse and evaluated various drug similarity metrics for all pairs. We found that certain features showed significant differences between adverse and synergistic drug combinations, such as post-treatment transcriptional effects similarity (D = 0.17, p &lt; 0.001) and chemical structure similarity (D = 0.25, p &lt; 0.001). By exploiting these significant similarities and dissimilarities and incorporating cell line specific data we developed a machine learning model to predict context specific drug synergy and achieved significant predictive performance (AUC = 0.823). We find that our model can be used to both identify novel synergistic drug pairs, as well as find novel indications for known drug combinations by identifying new sensitive cell lines. Moreover, the interpretability of our model allows for the interrogation of features for a deeper understanding of why certain combinations are predicted synergistic. In addition to identifying the cancer types and subtypes a combination therapy would be most synergistic within, we set out to identify the molecular indication for highly synergistic pairs. Specifically, we systematically identified candidate predictive biomarkers which could be used to stratify patient cohorts. Overall, our model and methodology can expedite the development and expansion of combination therapeutics, which can help battle acquired resistance and increase therapeutic efficacy. The thorough understanding of specific combination efficacy dependencies allows for a true precision medicine application of these therapeutics.
Citation Format: Coryandar M. Gilvary, Neel Madhukar, Olivier Elemento. A big data approach to predicting context specific synergistic drug combinations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3896.
Abstract 3897: Pharmacogenomic landscape of long non-coding RNAs in human cancers
Authors: Nath Aritro , Huang R. Stephanie 
Published: August 2018

Abstract
Extensive efforts in recent large-scale drug screens have enabled identification of protein-coding genes (PCGs) as biomarkers for anti-cancer agent sensitivity, but information on the role of long-non coding RNAs (lncRNAs) in drug response is sparse. Lower abundance, difficulty in expression profiling and limited functional knowledge have so far hindered global analysis of lncRNAs in drug screens. However, lncRNAs represent nearly 60% of the human genome (vs. 3% for PCGs) and contain a vast majority of pharmacogenomic SNPs. Thus, it is critically important to evaluate the role of lncRNAs in drug sensitivity. Here, we perform a comprehensive reanalysis of large-scale drug screens using novel computational methods to create a landscape of pharmacogenomic interactions of lncRNAs in human cancers.
We developed an algorithm to accurately impute the lncRNA transcriptome using PCG expression. This approach allowed us to generate lncRNA transcriptome in high throughput drug screening data sets and enabled systematic evaluation of lncRNAs for hundreds of drugs. For example, we applied our method to the Genomics of Drug Sensitivity in Cancer (GDSC) study, where sensitivity to 265 anti-cancer agents were quantified in 963 cancer cell lines. Sparse regression analysis showed lncRNA transcriptome were similar in accuracy at predicting drug response compared to PCGs (P = 0.9), indicating lncRNAs may be equally important biomarkers.
Subsequently, we identified drug-specific lncRNA biomarkers using ANOVA while adjusting for tissue type. Our analysis recapitulated literature-verified lncRNAs associated with drug response, e.g. HOTAIR expression with cisplatin resistance (FDR &lt; 0.05). In addition, we identified several compelling novel lncRNA associations for specific drugs along with previously uncharacterized multi-drug response predictors.
We next identified lncRNAs that may serve as biomarkers for drug response while controlling for known cancer functional events. As an example, we analyzed EGFR-inhibitors (erlotinib, gefitinib, pelitinib) while controlling for EGFR somatic point mutations and amplification status and identified an anti-sense lncRNA overlapping UGT1A locus that may be a potential regulator of erlotinib metabolism. These findings reveal important lncRNAs that predict response independent of known cancer functional events and are currently being evaluated in non-small cell lung cancer patients.
In conclusion, our comprehensive analysis has generated unprecedented insights into the role of lncRNAs in anti-cancer drug response and will be an invaluable resource for researchers studying drug response in various cancer types.
Citation Format: Aritro Nath, R. Stephanie Huang. Pharmacogenomic landscape of long non-coding RNAs in human cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3897.
Abstract 39: LCN2, CD133 expressions in breast cancer spinal bone metastasis and CD151, TWIST1, EPHA2 in lung metastasis: Prognostic value and organ-specific relapse
Authors: Maiti Aparna , Hait Nitai C. 
Published: August 2018

Abstract
Purpose: Breast cancer most often recurs and metastasizes to the distal organs that had their primary tumors surgically excised. Due to heterogeneous nature of the breast cancer, metastasis organotropism has poorly understood. In this study, we assessed the specific cancer-related gene expression changes occurring with metastatic breast cancer recurrence to distant organs comparing with non-metastatic breast cancer. Experimental design: We develop a clinically relevant metastatic syngeneic mouse model using two cell lines 4T1-luc2 that can only metastasize to lung and 4T1.2-luc2 metastasized to bone and lung. After the primary mammary tumor surgically excised from mice, we monitor mice using IVIS imaging for metastatic tumor lesion development in the distant organs followed by MRI scan to detect spinal bone metastases. Animals were sacrificed to collect spines, bones, and lung lesions for molecular analysis. We also validate the gene expression data using the RNA-seq cohort data from the Roswell Park Cancer Institute patient samples with advanced breast cancer bone metastases and matched non-metastatic breast cancer.
Results: Our cell lines, RNA-seq data analysis suggested that there are at least 50 genes which are statistically highly expressed in 4T1.2-luc2 compared to 4T1-luc2 cells. Out of which only a few genes are well established for cancer metastasis biology, include ANGPTL77, SERPINE2, TSPAN11, ESM1, LCN2 which are expressed more than 8 fold in 4T1.2-luc2 compared to 4T1-luc2 cells. To our surprise, we found that most of these genes are differentially expressed when metastasized to distant organ. Our data revealed that LCN2 (7 fold) and CD133 (above 20 fold) are overexpressed in the spine and bone compared to the primary or lung met lesions formed by 4T1.2-luc2. Conversely, LCN2 and CD133 genes are downregulated in breast cancer lung metastasis tissues, whereas CD151, EPHA2, and TWIST1 genes are highly overexpressed in lung metastatic lesion compared to primary, bone or spine. Further, the RNA-seq data of patient samples explained that LCN2, CD133 expressions are significantly higher (8 out of 10 patients) in advanced breast cancer bone metastases compared with matching non-metastatic breast cancer. Conclusion: Our data suggested that LCN2, CD133 would be a prognostic marker for breast cancer spinal bone metastasis, whereas CD151, TWIST1, EPHA2 would be a prognostic marker for lung metastasis and organ-specific relapse.
Citation Format: Aparna Maiti, Nitai C. Hait. LCN2, CD133 expressions in breast cancer spinal bone metastasis and CD151, TWIST1, EPHA2 in lung metastasis: Prognostic value and organ-specific relapse [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 39.