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Pancreatic Neoplasms: HELP
Articles by Anna Goldenberg
Based on 2 articles published since 2009
(Why 2 articles?)
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Between 2009 and 2019, Anna Goldenberg wrote the following 2 articles about Pancreatic Neoplasms.
 
+ Citations + Abstracts
1 Article Sensitive tumour detection and classification using plasma cell-free DNA methylomes. 2018

Shen, Shu Yi / Singhania, Rajat / Fehringer, Gordon / Chakravarthy, Ankur / Roehrl, Michael H A / Chadwick, Dianne / Zuzarte, Philip C / Borgida, Ayelet / Wang, Ting Ting / Li, Tiantian / Kis, Olena / Zhao, Zhen / Spreafico, Anna / Medina, Tiago da Silva / Wang, Yadon / Roulois, David / Ettayebi, Ilias / Chen, Zhuo / Chow, Signy / Murphy, Tracy / Arruda, Andrea / O'Kane, Grainne M / Liu, Jessica / Mansour, Mark / McPherson, John D / O'Brien, Catherine / Leighl, Natasha / Bedard, Philippe L / Fleshner, Neil / Liu, Geoffrey / Minden, Mark D / Gallinger, Steven / Goldenberg, Anna / Pugh, Trevor J / Hoffman, Michael M / Bratman, Scott V / Hung, Rayjean J / De Carvalho, Daniel D. ·Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada. · Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada. · Memorial Sloan Kettering Cancer Center, New York, NY, USA. · Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada. · Genome Technologies, Ontario Institute for Cancer Research, Toronto, Ontario, Canada. · UMR_S 1236, Univ Rennes 1, Inserm, Etablissement Fran├žais du sang Bretagne, Rennes, France. · Department of Biochemistry and Molecular Medicine, UC Davis Comprehensive Cancer Center, Sacramento, CA, USA. · Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada. · Fred Litwin Centre for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada. · Department of Surgery, Toronto General Hospital, Toronto, Ontario, Canada. · Department of Computer Science, University of Toronto, Toronto, Ontario, Canada. · Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada. rayjean.hung@lunenfeld.ca. · Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada. rayjean.hung@lunenfeld.ca. · Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada. ddecarv@uhnresearch.ca. · Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada. ddecarv@uhnresearch.ca. ·Nature · Pubmed #30429608.

ABSTRACT: The use of liquid biopsies for cancer detection and management is rapidly gaining prominence

2 Article Unsupervised detection of genes of influence in lung cancer using biological networks. 2011

Goldenberg, Anna / Mostafavi, Sara / Quon, Gerald / Boutros, Paul C / Morris, Quaid D. ·Banting and Best Department of Medical Research, University of Toronto, Toronto, ON, Canada. goldenberg@utoronto.ca ·Bioinformatics · Pubmed #21965819.

ABSTRACT: MOTIVATION: Lung cancer is often discovered long after its onset, making identifying genes important in its initiation and progression a challenge. By the time the tumors are discovered, we only observe the final sum of changes of the few genes that initiated cancer and thousands of genes that they have influenced. Gene interactions and heterogeneity of samples make it difficult to identify genes consistent between different cohorts. Using gene and gene-product interaction networks, we propose a principled approach to identify a small subset of genes whose network neighbors exhibit consistently high expression change (in cancerous tissue versus normal) regardless of their own expression. We hypothesize that these genes can shed light on the larger scale perturbations in the overall landscape of expression levels. RESULTS: We benchmark our method on simulated data, and show that we can recover a true gene list in noisy measurement data. We then apply our method to four non-small cell lung cancer and two pancreatic cancer cohorts, finding several genes that are consistent within all cohorts of the same cancer type. CONCLUSION: Our model is flexible, robust and identifies gene sets that are more consistent across cohorts than several other approaches. Additionally, our method can be applied on a per-patient basis not requiring large cohorts of patients to find genes of influence. Our approach is generally applicable to gene expression studies where the goal is to identify a small set of influential genes that may in turn explain the much larger set of genome-wide expression changes.