bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 4242: Ethnic disparities in the risk of cardiac death following cancer diagnosis: A population-based study
Authors: Gad Mohamed M. , Saad Anas M. , Al-Husseini Muneer J. , Abushouk Abdelrahman I. , Ahmed Haitham M. 
Published: August 2018

Abstract
Background: Cancer-related cardiac complications and mortality have been well studied. However, the disparities among racial and ethnic groups have not been addressed on a large scale. In this study, we aimed to investigate the racial and ethnic disparities associated with cardiac death in cancer patients.
Methods: Data were collected from The Surveillance, Epidemiology and End-Results program of the National Cancer Institute. We defined an "event" as death due to a cardiac cause within ten years after cancer diagnosis, and observed patients who experienced the event during that period. Then, we calculated the Observed/Expected (O/E) ratio and the excess risk per 10,000 person-years to estimate the change of risk following the diagnosis in different ethnicities, when compared to the general population.
Results: We reviewed 4,671,989 cancer patients, of whom 163,255 died due to a cardiac cause within the ten years following cancer diagnosis. The overall risk of cardiac death increased significantly in this period with an O/E ratio of 1.16 and an excess risk of 11.66 per 10,000. The sample was divided into 2 groups: Whites with a total O/E ratio of 1.11, and non-Whites with a total O/E ratio of 1.38. When examined for the risk in the first 5 years after diagnosis, Whites and non-Whites demonstrated significant increase in the risk with O/E of 1.12 and 1.39, respectively. However, when examined for the long-term risk more than 5 years after diagnosis, only non-Whites had a significant increase with an O/E of 1.21, while Whites with a cancer diagnosis had the same risk of cardiac death as Whites without cancer diagnosis with an O/E of 1.00. The sample was then further subdivided according to ethnicity into non-Hispanic Whites, Blacks, American Indians (or Alaska natives), Asians (or Pacific islanders), and Hispanics. All groups showed a significantly increased risk of cardiac death following a cancer diagnosis (O/E=1.13, 1.36, 2.33, 1.44, and 1.05, respectively).
Conclusion: Non-White patients with a prior cancer diagnosis had a significantly higher long-term risk of cardiac death compared to noncancer patients. Accordingly, careful attention should be paid to the cardiovascular status assessment of those patients to mitigate the added risk of cardiac mortality due to cancer-related morbidities. Further studies are warranted to determine why cardiac event risk increases at a discrepantly higher rate in minority patients with a cancer diagnosis.
Citation Format: Mohamed M. Gad, Anas M. Saad, Muneer J. Al-Husseini, Abdelrahman I. Abushouk, Haitham M. Ahmed. Ethnic disparities in the risk of cardiac death following cancer diagnosis: A population-based study [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 4242.
Abstract 4254: Associations of sleep, quality of life and energy level with breast cancer mortality and recurrence: The Shanghai Breast Cancer Survival Study
Authors: Zhu Jingjing , Bao Pingping , Cai Hui , Shi Liang , Zheng Ying , Zheng Wei , Shu Xiao-ou 
Published: August 2018

Abstract
Background: Sleep disorder is highly prevalent among breast cancer patients. Whether sleep disorder is associated with breast cancer prognosis has rarely been investigated. The objective of this study is to prospectively assess the association of self-reported sleep difficulty and sleep quality with total mortality and breast-cancer specific mortality/recurrence in the Shanghai Breast Cancer Survival Study.
Methods: This study included 4,219 women with stage I-III breast cancer diagnosed at an age between 20 to 75 years. Information on sleep attributes was collected at approximately 18 months and 36 months after breast cancer diagnosis. Information on disease recurrence and survival status was obtained via in-person follow-up and via record linkage with vital statistics registry data. Cox proportional hazard model was used to derive hazard ratios (HR) and confidence intervals (CIs) for all-cause mortality and breast cancer-specific mortality/recurrence. Stratified analyses according to self-reported overall quality of life, depression and energy level assessed at 18 months were also conducted.
Results: During a median follow-up of 9.8 years (range: 0.1-12.2 years), 757 deaths due to any causes and 672 breast cancer recurrences or related deaths were recorded after the 18-month post-diagnosis survey. Sleep difficulty and low sleep quality were not significantly associated with total or breast-cancer specific mortality/recurrence. The respective HRs were 0.98 (95% CI: 0.79, 1.22) and 1.01 (95% CI: 0.77, 1.31) for total mortality and 1.04 (95% CI: 0.82, 1.31) and 0.93 (95% CI: 0.69, 1.26) for breast cancer-specific mortality/recurrence. On the other hand, low quality of life, depression and low energy level were significantly associated with increased total mortality, with HRs of 3.19 (2.45, 4.15), 1.61 (1.21, 2.14) and 1.53 (1.23, 1.91), respectively. These three factors did not modify the association of sleep measurements with breast cancer outcomes (all P-interactions &gt; 0.10).
Conclusions: In a large population-based cohort study, we did not observe any significant association of sleep difficulty and poor sleep quality with increased risk of death and recurrence in breast cancer patients. Instead, low quality of life and energy level were found to be associated with disease prognosis and thus they could be important indicators for targeting population with poor breast cancer prognosis.
Citation Format: Jingjing Zhu, Pingping Bao, Hui Cai, Liang Shi, Ying Zheng, Wei Zheng, Xiao-ou Shu. Associations of sleep, quality of life and energy level with breast cancer mortality and recurrence: The Shanghai Breast Cancer Survival Study [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 4254.
Abstract 4261: Pathways of metastatic bladder cancer from a longitudinal patient data set
Authors: Mason Jeremy M. , Hasnain Zaki , Miranda Gus , Gill Karanvir , Newton Paul K. , Gill Inderbir S. , Kuhn Peter 
Published: August 2018

Abstract
Bladder cancer (BCa), the 6th commonest cancer in the U.S., is highly lethal when metastatic. Sites and patterns of patient-specific metastatic spread are deemed random and unpredictable. Whether BCa metastatic patterns can be quantified and predicted more accurately is unknown. We used a prospective, longitudinal dataset of 3,505 BCa patients who underwent definitive treatment following diagnosis and were continuously enrolled (1971-2016; mean follow-up 6.13 years). Metastases developed in 30% (n=1,040) of patients, with 5-year survival rate of 18%, compared to 72% in those without metastases (n=2,465). The three commonest metastatic sites at time-of-first progression were pelvis (n=283; 27.2%), bone (n=274; 26.3%), and lung (n=247; 23.8%). We illustrate metastatic pathway progression as color-coded, circular, tree-ring diagrams (primary in the center; metastatic sites outwards). Markov chain modeling, denoting nodes as potential anatomic metastatic sites, indicated higher probability of spread from the bladder to pelvis (15.6%), bone (15.6%), and lung (13.9%), categorizing 4 of 10 anatomical sites as ‘spreaders' and 2 as ‘sponges'. We created a dynamical, data-visualization, web-platform that displays temporal, spatial and Markov modeling figures with predictive capability. For contrasting subgroups, this platform indicated differences in transition probabilities, rank-ordering of metastatic sites, and spreader/sponge node classification. Spatiotemporal patterns of BCa metastasis and sites of spread indicate underlying organotropic mechanisms in the prediction of response. This recognition opens the possibility of organ site-specific therapeutic targeting in the oligo-metastatic disease setting. In the precision medicine era, visualization of complex, time-resolved clinical data will enhance management of post-operative, metastatic BCa patients.
Citation Format: Jeremy M. Mason, Zaki Hasnain, Gus Miranda, Karanvir Gill, Paul K. Newton, Inderbir S. Gill, Peter Kuhn. Pathways of metastatic bladder cancer from a longitudinal patient data set [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 4261.
Abstract 4265: Computational models of the role of base excision repair in sensitization of cancer cells to 5-fluorodeoxyuridine
Authors: Qian Mengdi , Almasan Alexandru , Gürkan-Çavusoglu Evren 
Published: August 2018

Abstract
The purpose of this study is to computationally analyze the effect of inhibition of base excision repair (BER) in the response of cancer cells to treatment with 5-fluorodeoxyuridine. 5-fluorouracil (5-FU) and its metabolite 5-fluorodeoxyuridine (5-FdU) are standard treatments for different solid tumors and especially for colon cancer. In the literature, it is shown that the depletion of uracil DNA glycosylase (UDG), one of the enzymes that initiate the BER pathway, enhanced the cytotoxicity of 5-FdU, making UDG a potential target to enhance efficacy of chemotherapeutic agents. It is also shown that the activity of UDG is significantly higher in colon cancer cells compared to normal tissue. We have developed a computational model of the BER pathway to capture the BER dynamics in colon cancer cell lines after treatment with 5-FdU. Our model is composed of a series of ordinary differential equations where the parameters are the kinetic rate constants and the enzyme concentrations. We have used the data from the literature to initially parameterize our models. We have then used least square estimation to find the parameters that will capture the BER time course for colon cancer cells after 5-FU treatment using the experimental data from the literature. We have performed sensitivity and identifiability analyses to determine the parameters that are reliably estimated. The experimental data from the literature showed that the depletion of UDG resulted in incorporation of uracil and 5-FU in the DNA. We have computationally replicated this effect by decreasing the protein concentration of UDG in the models and simulating the amount of remaining 5-FU in the DNA after BER with reduced activity has taken place. We have studied the depletion effect for different protein concentrations of UDG and the results have shown that the accumulation of 5-FU in these colon cancer cells correlate with the level of UDG. The suppression of UDG activity has the potential to enhance the outcome of treatment with 5FdU, and we will use the developed computational models to quantitatively optimize these effects in our future work.
Citation Format: Mengdi Qian, Alexandru Almasan, Evren Gürkan-Çavusoglu. Computational models of the role of base excision repair in sensitization of cancer cells to 5-fluorodeoxyuridine [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 4265.
Abstract 4266: Computational exploration of mechanistic determinants of ADC pharmacokinetics using QSP modeling strategies
Authors: Wilkins A. Katharina , Matteson Andrew , Gruenbaum Lore , Park Jennifer , Burke John M. , Apgar Joshua F. 
Published: August 2018

Abstract
The pharmacokinetics of antibody drug conjugate (ADC) therapeutics typically show a discrepancy between the PK of total antibody (conjugated and unconjugated antibody) and that of conjugated antibody, carrying one or more payload molecules. This discrepancy is often attributed strictly to deconjugation (Kamath, 2014), however recent evidence suggests that the underlying mechanisms may be more complex. This work employs a computational quantitative systems pharmacology (QSP) approach that generates a hypotheses to better understand the impact of drug antibody ratio (DAR) and the resulting changes in molecular properties on overall PK and relative payload disposition as observed in preclinical and clinical studies. Our work establishes the benefit of using computational models to design novel ADCs and to optimize the discovery and development of existing ADCs to better enable clinical trial design.
References: Kamath, A. V., and Iyer, S. (2014). Preclinical Pharmacokinetic Considerations for the Development of Antibody Drug Conjugates. Pharmaceutical Research, 32(11), 3470-3479.
Citation Format: A. Katharina Wilkins, Andrew Matteson, Lore Gruenbaum, Jennifer Park, John M. Burke, Joshua F. Apgar. Computational exploration of mechanistic determinants of ADC pharmacokinetics using QSP modeling strategies [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 4266.
Abstract 427: High-quality whole-genome sequencing of FFPE samples
Authors: Bailey Shannon T. , Lund Jim , Wang Hao , Cui Weiren , Hao Chen , Sun Hongye , Gulcher Jeffrey R. 
Published: August 2018

Abstract
Next-generation sequencing analysis of formalin-fixed, paraffin-embedded (FFPE) samples has the potential to lead to major advances in cancer treatment and prevention. However, whole-genome sequencing of these samples has remained challenging due to the difficult process of isolating high-quality DNA and distinguishing true variant calls from artifacts. Previous studies have attempted to overcome these challenges by using whole-exome sequencing or smaller exon panels at high coverage depths to analyze FFPE samples. While these approaches have produced useful results, they have had varying degrees of success, tend have coverage biases due to probe capture issues, and are limited to only a fraction of the genome. Thus, there has not been widespread adoption of using FFPE samples for next-generation sequencing.
In an attempt to improve on the process of next-generation sequencing of FFPE samples, we first developed an efficient DNA extraction method that produces high quantities of high-quality DNA. Whole-genome sequencing of DNA generated by this method demonstrated uniform high coverage that was comparable to DNA prepared from fresh frozen samples at the same sequencing depth. This result is in contrast with data generated from DNA isolated by a traditional FFPE extraction method, which had lesser sequencing coverage even with twice the sequencing depth. Further analysis of the data generated using our method demonstrated similar numbers of mutations called and mutational signatures without artifact contamination.
We extended this analysis to a large cancer cohort consisting of over 1,000 FFPE samples in tumor normal pairs that were up to 6 years old. Whole-genome sequencing of these samples demonstrate that there was an average of 98% of the genome covered at 20X or greater using our method with no drop off for the older samples. Mutational analyses of these samples demonstrate similar findings as that previously published using TCGA datasets.
In summary, our study of greater than 1,000 samples is the largest report of whole-genome sequencing of FFPE samples to date. Our results demonstrate the feasibility of using our method to assay tissues stored in archival tissue biobanks, analyze patient samples for clinical trial recruitment, or examining sections as part of normal clinical workflows, all of which hold the potential for greatly benefiting human health.
Citation Format: Shannon T. Bailey, Jim Lund, Hao Wang, Weiren Cui, Chen Hao, Hongye Sun, Jeffrey R. Gulcher. High-quality whole-genome sequencing of FFPE samples [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 427.
Abstract 4270: Success of T-cell immunotherapy predicted by a mathematical model of the adaptive immune system and evolving cancer cells
Authors: George Jason Thomas , Levine Herbert 
Published: August 2018

Abstract
It is now well-established that a patient's adaptive immune system plays an important role in identifying and eliminating cancer cells in much the same way that intracellular pathogens are cleared during infection. From a therapeutic standpoint, the adaptive immune system is unique in that it can co-evolve alongside a developing tumor. At present, the dynamics and survival probability of a mutating threat in the presence of an adaptive immune system has yet to be quantified. Here, we consider a foundational population model of a foreign invader faced with innate or adaptive immune pressure. We find that optimal growth strategies depend on whether or not the invading threat may mutate as well as the mode of immune detection. Our early model predicts that decreases in thymic output seen in advanced age may substantially affect the ability to recognize a growing population of cancer cells and recapitulates general features found in age-dependent AML incidence. This mathematical framework is able to predict the success of recent immunotherapeutic strategies, including personalized neoantigen vaccines. Our model serves as an attempt at modeling stochastic cancer evolution alongside an adaptive immune compartment.
Citation Format: Jason Thomas George, Herbert Levine. Success of T-cell immunotherapy predicted by a mathematical model of the adaptive immune system and evolving cancer cells [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 4270.
Abstract 4273: Identification of RNA-seq shape abnormality
Authors: Choi Hyo Young , Hayes David N. , Marron James S. 
Published: August 2018

Abstract
A substantial proportion of human genes differ in function through different forms of shape changes in read coverage of RNA-seq. For example, tumor-suppressor genes lose their function through various changes in expression such as aberrant splicing, frameshift indel, large deletions, or overexpression of noncoding RNAs. Although previous studies have examined mutational effect on such shape changes, it has been observed that a large fraction of shape aberration occurs in the absence of mutations. We hypothesize that read coverage shapes would show rich information on various forms of abnormality in RNA-seq data, which may have been missed in current mutation callers. We have developed a statistical method to systematically detect abnormal RNA-seq samples using read coverage data alone independently of mutational analysis. We model the underlying mechanism possibly generating aberrant shapes by multiple unknown mixture distributions, recasting the problem as high-dimensional latent variables framework. Based on this underlying mechanism, we normalize the read coverage to adjust different library sizes, extract the latent information in a robust way, and determine the cases that are strongly involved in abnormality. This approach allows us to detect not only local changes in expression such as alternative splicing events and frameshift indel but also landscape changes such as fusion and a wide range of deletions. This methodology can be applied genome-widely to detect key genes with strong shape aberrations, prioritizing genes for further investigation. We analyzed 522 TCGA head and neck squamous cell carcinomas RNA-seq tumor samples. At several known tumor-suppressor genes, we identified the cases with novel structural changes including alternative splicing, intragenic deletion, and fusion with/without mutations reported as well as the cases with no evidence of changes despite presence of mutations known for altering shapes. Notably, some of the identified shape changes in TP53 and CDKN2A were confirmed as being the outcomes of missing genetic variants near splice sites (exon-intron junctions). The genome-wide study with carefully chosen significance level provided a set of key genes with strong evidence of shape abnormality including TP53, CDKN2A, and FAT1 that are known for the most alternative splicing events. We also analyzed how often such shape changes arise with or without certain mutations in genome-wide scans. To conclude, our results provide a new statistical framework for various forms of RNA-seq shape changes and a tool for systematic discovery of such abnormal samples, and give insights into mutational effect on shape aberration.
Citation Format: Hyo Young Choi, David N. Hayes, James S. Marron. Identification of RNA-seq shape abnormality [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 4273.
Abstract 4280: Improving the sensitivity of wide targeted cancer gene panels via novel genome analysis tools
Authors: Kermani Bahram G. , Roberts Evans L. , Boyle Theresa A. , Magliocco Anthony M. 
Published: August 2018

Abstract
In the recent years, wide targeted gene panels have gained popularity, due to their ability to interrogate many genes at the same time. In cancer, particularly, there is a set of ~600 genes known as Cancer Gene Census for which mutations have been causally implicated. Many of the wide cancer panels, therefore, target a subset of these genes. As targeted panels widen, the average coverage of them reduces, if the total budget of the DNA reads remains constant. Therefore, wide panels usually have a more modest coverage, as compared to narrow panels. The reduction in coverage, along with the biases that exist in targeted enrichment approaches make a challenging environment for genome assembly pipelines. We argue that, in such systems, false negatives are prone to increase to non-trivial values, primarily due to: 1) inefficiencies in the enrichment methods, especially if they are hybridization-based; 2) sporadic insufficiency of coverage; 3) biases in the analysis pipelines; 4) filtering operations that take place, in attempt to reduce false positives. As much of the losses are due to the dependency of the assays and the pipelines to the reference genome, we have approached this issue by designing a tandem set of tools: 1) A hybrid-denovo genome assembly pipeline, which has significantly less bias to the reference genome, as compared to the conventional align-to-reference methods. This pipeline can be used in lieu of or in combination with the conventional pipelines; 2) An in-silico verification (ISV) tool that provides the experts with a multifaceted visual and textual information, to gauge the evidence (or lack thereof) for the variants of interest. While not meant to necessarily be a replacement to wet validation, ISV provides the ability to identify the errors that may otherwise seep through the system. In order to show feasibility of this approach, a small set of FFPE TruSight Tumor 170 samples including cases of lung and breast cancer was used. The comparisons were done for all RefSeq exonic bases on a hereditary cancer gene subset. Our preliminary analysis using this data shows that by using our proprietary genome analysis pipeline and ISV tool, the sensitivity of the reported variants could be improved from ~80% to ~98% or higher, without impairing the specificity. If we consider intronic bases which have reasonable coverage, including those involved in splicing, the sensitivity improvements could be even larger.
Citation Format: Bahram G. Kermani, Evans L. Roberts, Theresa A. Boyle, Anthony M. Magliocco. Improving the sensitivity of wide targeted cancer gene panels via novel genome analysis tools [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 4280.