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Your search keyword '"Sarah Kerns"' showing total 14 results
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14 results on '"Sarah Kerns"'

2. Impact of chemotherapy for breast cancer on leukocyte DNA methylation landscape and cognitive function: a prospective study

Author

Song Yao, Qiang Hu, Sarah Kerns, Li Yan, Adedayo A. Onitilo, Jamal Misleh, Kelley Young, Lianlian Lei, Javier Bautista, Mostafa Mohamed, Supriya G. Mohile, Christine B. Ambrosone, Song Liu, and Michelle C. Janelsins

Subjects
DNA methylation, Chemotherapy, Breast cancer, Cognitive function, Medicine, Genetics, QH426-470
Abstract

Abstract Background Little is known about the effects of chemotherapeutic drugs on DNA methylation status of leukocytes, which may be predictive of treatment benefits and toxicities. Based on a prospective national study, we characterize the changes in leukocyte DNA methylome from pre- to post-chemotherapy (approximately 4 months apart) in 93 patients treated for early stage breast cancer and 48 matched non-cancer controls. We further examined significant methylation changes with perceived cognitive impairment, a clinically significant problem related to cancer and chemotherapy. Results Approximately 4.2% of the CpG sites measured using the Illumina 450K methylation array underwent significant changes after chemotherapy (p

Published
2019
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3. Fine-mapping of prostate cancer susceptibility loci in a large meta-analysis identifies candidate causal variants

Author

Tokhir Dadaev, Edward J. Saunders, Paul J. Newcombe, Ezequiel Anokian, Daniel A. Leongamornlert, Mark N. Brook, Clara Cieza-Borrella, Martina Mijuskovic, Sarah Wakerell, Ali Amin Al Olama, Fredrick R. Schumacher, Sonja I. Berndt, Sara Benlloch, Mahbubl Ahmed, Chee Goh, Xin Sheng, Zhuo Zhang, Kenneth Muir, Koveela Govindasami, Artitaya Lophatananon, Victoria L. Stevens, Susan M. Gapstur, Brian D. Carter, Catherine M. Tangen, Phyllis Goodman, Ian M. Thompson, Jyotsna Batra, Suzanne Chambers, Leire Moya, Judith Clements, Lisa Horvath, Wayne Tilley, Gail Risbridger, Henrik Gronberg, Markus Aly, Tobias Nordström, Paul Pharoah, Nora Pashayan, Johanna Schleutker, Teuvo L. J. Tammela, Csilla Sipeky, Anssi Auvinen, Demetrius Albanes, Stephanie Weinstein, Alicja Wolk, Niclas Hakansson, Catharine West, Alison M. Dunning, Neil Burnet, Lorelei Mucci, Edward Giovannucci, Gerald Andriole, Olivier Cussenot, Géraldine Cancel-Tassin, Stella Koutros, Laura E. Beane Freeman, Karina Dalsgaard Sorensen, Torben Falck Orntoft, Michael Borre, Lovise Maehle, Eli Marie Grindedal, David E. Neal, Jenny L. Donovan, Freddie C. Hamdy, Richard M. Martin, Ruth C. Travis, Tim J. Key, Robert J. Hamilton, Neil E. Fleshner, Antonio Finelli, Sue Ann Ingles, Mariana C. Stern, Barry Rosenstein, Sarah Kerns, Harry Ostrer, Yong-Jie Lu, Hong-Wei Zhang, Ninghan Feng, Xueying Mao, Xin Guo, Guomin Wang, Zan Sun, Graham G. Giles, Melissa C. Southey, Robert J. MacInnis, Liesel M. FitzGerald, Adam S. Kibel, Bettina F. Drake, Ana Vega, Antonio Gómez-Caamaño, Laura Fachal, Robert Szulkin, Martin Eklund, Manolis Kogevinas, Javier Llorca, Gemma Castaño-Vinyals, Kathryn L. Penney, Meir Stampfer, Jong Y. Park, Thomas A. Sellers, Hui-Yi Lin, Janet L. Stanford, Cezary Cybulski, Dominika Wokolorczyk, Jan Lubinski, Elaine A. Ostrander, Milan S. Geybels, Børge G. Nordestgaard, Sune F. Nielsen, Maren Weisher, Rasmus Bisbjerg, Martin Andreas Røder, Peter Iversen, Hermann Brenner, Katarina Cuk, Bernd Holleczek, Christiane Maier, Manuel Luedeke, Thomas Schnoeller, Jeri Kim, Christopher J. Logothetis, Esther M. John, Manuel R. Teixeira, Paula Paulo, Marta Cardoso, Susan L. Neuhausen, Linda Steele, Yuan Chun Ding, Kim De Ruyck, Gert De Meerleer, Piet Ost, Azad Razack, Jasmine Lim, Soo-Hwang Teo, Daniel W. Lin, Lisa F. Newcomb, Davor Lessel, Marija Gamulin, Tomislav Kulis, Radka Kaneva, Nawaid Usmani, Chavdar Slavov, Vanio Mitev, Matthew Parliament, Sandeep Singhal, Frank Claessens, Steven Joniau, Thomas Van den Broeck, Samantha Larkin, Paul A. Townsend, Claire Aukim-Hastie, Manuela Gago-Dominguez, Jose Esteban Castelao, Maria Elena Martinez, Monique J. Roobol, Guido Jenster, Ron H. N. van Schaik, Florence Menegaux, Thérèse Truong, Yves Akoli Koudou, Jianfeng Xu, Kay-Tee Khaw, Lisa Cannon-Albright, Hardev Pandha, Agnieszka Michael, Andrzej Kierzek, Stephen N. Thibodeau, Shannon K. McDonnell, Daniel J. Schaid, Sara Lindstrom, Constance Turman, Jing Ma, David J. Hunter, Elio Riboli, Afshan Siddiq, Federico Canzian, Laurence N. Kolonel, Loic Le Marchand, Robert N. Hoover, Mitchell J. Machiela, Peter Kraft, The PRACTICAL (Prostate Cancer Association Group to Investigate Cancer-Associated Alterations in the Genome) Consortium, Matthew Freedman, Fredrik Wiklund, Stephen Chanock, Brian E. Henderson, Douglas F. Easton, Christopher A. Haiman, Rosalind A. Eeles, David V. Conti, and Zsofia Kote-Jarai

Subjects
Science
Abstract

Prostate cancer (PrCa) involves a large heritable genetic component. Here, the authors perform multivariate fine-mapping of known PrCa GWAS loci, identifying variants enriched for biological function, explaining more familial relative risk, and with potential application in clinical risk profiling.

Published
2018
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11. Two graphs walk into a bar: Readout-based measurement reveals the Bar-Tip Limit error, a common, categorical misinterpretation of mean bar graphs

Author

Sarah Kerns and Jeremy Wilmer

Subjects
education.field_of_study, Theoretical computer science, Bar (music), Computer science, Bar chart, Population, Conflation, Bar-Tip Limit error, Measure (mathematics), Sensory Systems, Article, law.invention, Interpretation (model theory), Ophthalmology, law, Data Interpretation, Statistical, DDoG measure, data visualization, Limit (mathematics), education, bar graph of means, MAGI principles, Categorical variable
Abstract

How do viewers interpret graphs that abstract away from individual-level data to present only summaries of data such as means, intervals, distribution shapes, or effect sizes? Here, focusing on the mean bar graph as a prototypical example of such an abstracted presentation, we contribute three advances to the study of graph interpretation. First, we distill principles for Measurement of Abstract-Graph Interpretation (MAGI principles) to guide the collection of valid interpretation data from viewers who may vary in expertise. Second, using these principles, we create the Draw Datapoints on Graphs (DDoG) measure, which collects drawn readouts (concrete, detailed, visuospatial records of thought) as a revealing window into each person’s interpretation of a given graph. Third, using this new measure, we discover a common, categorical error in the interpretation of mean bar graphs: the Bar-Tip Limit (BTL) error. The BTL error is an apparent conflation of mean bar graphs with count bar graphs. It occurs when the raw data are assumed to be limited by the bar-tip, as in a count bar graph, rather than distributed across the bar-tip, as in a mean bar graph. In a large, demographically diverse sample, we observe the BTL error in about one in five persons; across educational levels, ages, and genders; and despite thoughtful responding and relevant foundational knowledge. The BTL error provides a case-in-point that simplification via abstraction in graph design can risk severe, high-prevalence misinterpretation. The ease with which our readout-based DDoG measure reveals the nature and likely cognitive mechanisms of the BTL error speaks to the value of both its readout-based approach and the MAGI principles that guided its creation. We conclude that mean bar graphs may be misinterpreted by a large portion of the population, and that enhanced measurement tools and strategies, like those introduced here, can fuel progress in the scientific study of graph interpretation.

Published
2021

14. Common genetic variation associated with increased susceptibility to prostate cancer does not increase risk of radiotherapy toxicity

Author

Mahbubl, Ahmed, Leila, Dorling, Sarah, Kerns, Laura, Fachal, Rebecca, Elliott, Matt, Partliament, Barry S, Rosenstein, Ana, Vega, Antonio, Gómez-Caamaño, Gill, Barnett, David P, Dearnaley, Emma, Hall, Matt, Sydes, Neil, Burnet, Paul D P, Pharoah, Ros, Eeles, and Catharine M L, West

Subjects
Male, Principal Component Analysis, genetic variants, Prostatic Neoplasms, Genetics and Genomics, Middle Aged, prostate cancer, Polymorphism, Single Nucleotide, White People, Humans, Genetic Predisposition to Disease, Radiation Injuries, late toxicity, Germ-Line Mutation, radiotherapy, Aged, Genome-Wide Association Study
Abstract

Background: Numerous germline single-nucleotide polymorphisms increase susceptibility to prostate cancer, some lying near genes involved in cellular radiation response. This study investigated whether prostate cancer patients with a high genetic risk have increased toxicity following radiotherapy. Methods: The study included 1560 prostate cancer patients from four radiotherapy cohorts: RAPPER (n=533), RADIOGEN (n=597), GenePARE (n=290) and CCI (n=150). Data from genome-wide association studies were imputed with the 1000 Genomes reference panel. Individuals were genetically similar with a European ancestry based on principal component analysis. Genetic risks were quantified using polygenic risk scores. Regression models tested associations between risk scores and 2-year toxicity (overall, urinary frequency, decreased stream, rectal bleeding). Results were combined across studies using standard inverse-variance fixed effects meta-analysis methods. Results: A total of 75 variants were genotyped/imputed successfully. Neither non-weighted nor weighted polygenic risk scores were associated with late radiation toxicity in individual studies (P>0.11) or after meta-analysis (P>0.24). No individual variant was associated with 2-year toxicity. Conclusion: Patients with a high polygenic susceptibility for prostate cancer have no increased risk for developing late radiotherapy toxicity. These findings suggest that patients with a genetic predisposition for prostate cancer, inferred by common variants, can be safely treated using current standard radiotherapy regimens.

Published
2015

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