{"id":52411,"date":"2016-11-02T08:23:57","date_gmt":"2016-11-02T18:23:57","guid":{"rendered":"http:\/\/www.hawaii.edu\/news\/?p=52411"},"modified":"2020-03-13T12:19:34","modified_gmt":"2020-03-13T22:19:34","slug":"uh-cancer-center-researcher-awarded-4-5-million-to-study-breast-and-other-cancers","status":"publish","type":"post","link":"https:\/\/www.hawaii.edu\/news\/2016\/11\/02\/uh-cancer-center-researcher-awarded-4-5-million-to-study-breast-and-other-cancers\/","title":{"rendered":"UH<\/abbr> Cancer Center researcher awarded $4.5 million to study breast and other cancers"},"content":{"rendered":"Reading time: <\/span> 2<\/span> minutes<\/span><\/span>
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Lana Garmire<\/figcaption><\/figure>\n

University of Â鶹´«Ã½<\/span> Cancer Center<\/a> Assistant Professor Lana Garmire<\/a><\/strong> was awarded two new National Institutes of Health (NIH<\/abbr>) grants totaling $4.5 million to use Big Data to study preeclampsia, breast cancer and acute leukemia.<\/p>\n

“Â鶹´«Ã½<\/span> is a fertile ground to do big data research. The state has a diversified population for genomics research, and state of the art high performance computing facilities for large-scale computation,” said Garmire. “Both projects can make huge impacts on understanding the fundamentals of diseases to help cancer patients.”<\/p>\n

“These awards represent a great honor for a scientist like Garmire, who is becoming recognized as a leader in Big Data and team-centered science,” said Randall Holcombe<\/strong>, UH<\/abbr> Cancer Center director. “This area of research has become critically important for the analysis of large sets of data.”<\/p>\n

Identify biomarkers for preeclampsia and breast cancer risks<\/h2>\n

The $3 million Research Project Grant (R01) from National Institute of Child Health and Development will use Big Data to study preeclampsia and breast cancer. Currently there is no cure for preeclampsia and it is one of the leading cases of maternal and fetal deaths according to the World Health Organization. Garmire and collaborators will identify:<\/p>\n

    \n
  • Biomarkers for preeclampsia and breast cancer risks<\/li>\n
  • Biomarkers and molecular links that explain why preeclampsia mothers and their children have lower risks of breast cancers.<\/li>\n
  • Why certain ethnic groups such as Asians have a higher risk of preeclampsia.<\/li>\n<\/ul>\n

    Garmire will use local sample resources from Â鶹´«Ã½<\/span> Biorepository and Kapiʻolani<\/span> Medical Center, which is based on the multi-ethnic populations in Â鶹´«Ã½<\/span>.<\/p>\n

    Research personalized treatments<\/h2>\n

    The $1.4 million R01 from National Library of Medicine is the continuation of a $980,000 K01 award from the Big Data to Knowledge Initiative at NIH<\/abbr>. Garmire and collaborators will develop an integrative bioinformatics platform to study the complexity of tumor cells of individual patients. The study pushes the limit of technology boundaries by:<\/p>\n

      \n
    • Developing tools that reveal the genomics profile of a cancer patient to develop more personalized treatments.<\/li>\n
    • Applying big data (bioinformatics) on acute leukemia patients to find key genes and sequence of events that respond better to therapies.<\/li>\n<\/ul>\n

      Garmire’s co-investigators are at University of California San Diego, Yale and UH<\/abbr>.<\/p>\n

      —By Nana Ohkawa<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

      Lana Garmire’s research will identify biomarkers for preeclampsia and breast cancer risks and develop personalized treatments.<\/p>\n","protected":false},"author":16,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[30],"tags":[218,169,9],"class_list":["post-52411","post","type-post","status-publish","format-standard","hentry","category-research","tag-cancer","tag-uh-cancer-center","tag-uh-manoa","entry","no-media"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t