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News Article | November 21, 2016
Site: www.acnnewswire.com

Development could pave way for improved therapeutics for patients with immune disorders Researchers in Singapore have artificially generated new mouse blood and immune cells from skin cells. This is a significant first step towards the eventual goal: the engineering of new human blood cells from skin cells or other artificial sources. One of the major challenges of regenerative medicine is to manufacture new blood and immune cells for patients in need. This development could lead to a robust source of new blood or immune cells becoming available to treat patients with immune disorders and other such diseases, or those who require blood transfusions. While there were previous efforts to generate new mouse blood cells from skin cells, the yielded cells could last only two weeks once injected back into mice. In contrast, the artificially skin-derived blood cells in this study can last for multiple months in mice. Published in scientific journal Nature Communications, this study was led by researchers from A*STAR's Genome Institute of Singapore (GIS) and Institute of Molecular and Cell Biology (IMCB). To date, the researchers have identified a cocktail of four factors that can convert mouse skin cells into different types of blood cells. By introducing the four factors that are normally active in blood cells into skin cells, they could artificially 'rewrite' skin cells to adopt features of blood cells. "On the face of it, skin cells and blood cells couldn't be more different from one another. We have been interested in whether it might be possible to rewrite the identity of cells, specifically to turn skin into blood," said the study's first author Dr Cheng Hui, who initiated this project as a postdoctoral fellow at GIS. "This is not only of practical importance for regenerative medicine in terms of potentially yielding a source of new blood or immune cells, but it is also interesting from a fundamental biological perspective that two very different cells - like skin and blood - can be interconverted," added Dr Kyle Loh, currently an investigator and instructor at Stanford Institute for Stem Cell Biology & Regenerative Medicine, Stanford University School of Medicine, and a member of the project team as a former GIS intern. GIS Executive Director Prof Ng Huck Hui said, "This development could be a potential game-changer for regenerative medicine. If researchers are able to extend what they did with the mice to human cells in the foreseeable future, it can translate into tangible benefits for the patients in need." Notes to Editor: The research findings described in this media release can be found in the scientific journal Nature Communications, under the title, "Reprogramming Mouse Fibroblasts into Engraftable Myeloerythroid and Lymphoid Progenitors" by Hui Cheng1,*, Heather Yin-Kuan Ang1,*, Chadi A EL Farran2,3, Pin Li1, Haitong Fang2, Tongming Liu1, Say Li Kong1, Michael Lingzi Chin1, Weiyin Ling1, Edwin Kok Hao Lim1, Hu Li4, Tara Huber1, Kyle M. Loh5, Yuin-Han Loh2,3 & Bing Lim1,6 1 Stem Cell and Regenerative Biology Group, Genome Institute of Singapore, Singapore 138672 2 Epigenetics and Cell Fates Laboratory, Institute of Molecular and Cell Biology, Singapore 138673 3 Department of Biological Sciences, National University of Singapore, 14 Science Drive 4, Singapore 117543 4 Center for Individualized Medicine, Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA 5 Department of Developmental Biology, Stanford Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA 6 Current address: Translational Medicine Research Center, Merck Research Laboratories, Singapore 138665 *Equal contribution; Correspondence: and About A*STAR's Genome Institute of Singapore (GIS) The Genome Institute of Singapore (GIS) is an institute of the Agency for Science, Technology and Research (A*STAR). It has a global vision that seeks to use genomic sciences to achieve extraordinary improvements in human health and public prosperity. Established in 2000 as a centre for genomic discovery, the GIS will pursue the integration of technology, genetics and biology towards academic, economic and societal impact. The key research areas at the GIS include Human Genetics, Infectious Diseases, Cancer Therapeutics and Stratified Oncology, Stem Cell and Regenerative Biology, Cancer Stem Cell Biology, Computational and Systems Biology, and Translational Research. The genomics infrastructure at the GIS is utilised to train new scientific talent, to function as a bridge for academic and industrial research, and to explore scientific questions of high impact. For more information about GIS, please visit www.gis.a-star.edu.sg About the Agency for Science, Technology and Research (A*STAR) The Agency for Science, Technology and Research (A*STAR) is Singapore's lead public sector agency that spearheads economic oriented research to advance scientific discovery and develop innovative technology. Through open innovation, we collaborate with our partners in both the public and private sectors to benefit society. As a Science and Technology Organisation, A*STAR bridges the gap between academia and industry. Our research creates economic growth and jobs for Singapore, and enhances lives by contributing to societal benefits such as improving outcomes in healthcare, urban living, and sustainability. We play a key role in nurturing and developing a diversity of talent and leaders in our Agency and Research Institutes, the wider research community and industry. A*STAR oversees 18 biomedical sciences and physical sciences and engineering research entities primarily located in Biopolis and Fusionopolis. For more information on A*STAR, please visit www.a-star.edu.sg For media queries and clarifications, please contact: Joyce Ang Senior Officer, Office of Corporate Communications Genome Institute of Singapore, A*STAR Tel: +65 6808 8101 Email:


Home > Press > Unmasking new drivers of stomach cancer: Study advances understanding of stomach cancer progression and could lead to new therapeutic targets and improved clinical outcomes Abstract: Scientists have uncovered a new class of master control elements in stomach cancer called “super-enhancers”, which control critical cancer genes and proteins required for stomach tumours to survive and grow. The team’s generation of this unprecedented and comprehensive catalogue of stomach cancer super-enhancers is an important milestone for the community. Currently, stomach cancer is the fifth most common cancer worldwide and the third leading cause of global cancer death[1]. Most gastric cancers are diagnosed late, and the disease is often resistant to current clinical treatments. By studying super-enhancers in stomach tumours, the team was able to shed light on how these elements impact the risk of stomach cancer development and progression of the disease. For example, stomach cancer patients whose tumours exhibited high numbers of super-enhancers exhibited a significantly poorer survival rate. Selective activation of these super-enhancers could explain why certain groups of people are at risk of developing the disease. These tissue-specific super-enhancers also represent a previously untapped reservoir of cancer vulnerability, acting to bridge oncogenic signalling to tissue-specific features of malignancy. Thus, studying the mechanisms driving the development of super-enhancers in stomach cancer may lead to novel therapies. Specifically, the team also identified two DNA-binding proteins as drivers for the formation of tumour-specific super-enhancers. These may serve as potential drug targets for stomach cancer. The researchers were able to make this finding using a highly sensitive, “made-in-Singapore” technology, called Nano-ChIPseq. Developed at A*STAR’s Genome Institute of Singapore (GIS), Nano-ChIPseq enables the study of epigenomic changes in clinical samples obtained directly from stomach cancer patients, rather than laboratory cultured cell lines. Unlike DNA which is stable and unchanging, epigenomic instructions are strongly influenced by factors such as food, infectious agents, and chemicals, suggesting that they can be modified by environmental risk factors. The team is now working to commercialise the technology and develop data technologies, which will enable scientists to revolutionise cancer therapeutics. “Future work in our lab will investigate how such super-enhancers can be disrupted by drugs, which will open up new avenues for cancer therapy,” said the study’s corresponding author Prof Patrick Tan, Deputy Executive Director of A*STAR’s Biomedical Research Council and an associate faculty member at the GIS. “We hope that our findings will encourage the scientific community to embrace our technology as a means to rapidly uncover master control elements that are highly relevant to diseases and clinical outcomes. This effort may eventually change the way stomach and other cancers are managed, which will enhance the clinical outcomes of cancer patients.” Published in scientific journal Nature Communications, this study was led by GIS, in collaboration with Duke-NUS Medical School, Cancer Science Institute of Singapore at the National University of Singapore (NUS), Singapore General Hospital (SGH), Weatherall Institute of Molecular Medicine at Oxford University, and the Singapore Gastric Cancer Consortium (SGCC). Commercialisation of the Nano-ChIPseq platform is currently supported through gap funding awarded by ETPL Pte Ltd, the commercialisation arm of A*STAR. "I felt something awesome will be coming when I heard about the new method, Nano-ChIPseq. The study suggests that extensive alterations in gene regulation, and not genes themselves, explain deep mysteries of gastric cancer, which are known to exhibit small numbers of mutations and deep involvement of bacterial infection, an environmental factor. Naturally, development of novel therapeutic strategies must take account of these findings," said Dr Toshikazu Ushijima, Chief of Division of Epigenomics at the National Cancer Center Research Institute in Japan, and a member of the SGCC Scientific Advisory Board. GIS Executive Director Prof Ng Huck Hui added, “This is a remarkable technological breakthrough for the community. As we constantly work to find better treatments for cancer, it is also important that we find more efficient ways to study how epigenomic changes can drive the formation of cancerous cells from healthy cells. A greater understanding about molecular changes in diseases can potentially lead to early therapeutic intervention and improved care for the patients. 1Cancer Therapeutics and Stratified Oncology, Genome Institute of Singapore, 60 Biopolis Street, Genome #02-01, Singapore 138672, Singapore. 2Cancer and Stem Cell Biology Program, Duke-NUS Graduate Medical School, 8 College Road, Singapore 169857, Singapore. 3NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, 5 Lower Kent Ridge Road, Singapore 119074, Singapore. 4Cancer Science Institute of Singapore, National University of Singapore, 14 Medical Drive, #12-01, Singapore 117599, Singapore. 5Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, 2 Medical Drive #04-01, Singapore 117597, Singapore. 6Department of Human Genetics, Genome Institute of Singapore, 60 Biopolis Street, Genome #02-01, Singapore 138672, Singapore. 7Medical Research Council (MRC) Molecular Haematology Unit, Weatherall Institute of Molecular Medicine, Oxford University, Oxford OX3 9DS, UK. 8Department of Upper Gastrointestinal & Bariatric Surgery, Singapore General Hospital, Singapore 169608, Singapore. 9Division of Surgical Oncology, National Cancer Centre Singapore, 11 Hospital Drive, Singapore 169610, Singapore. 10Department of General Surgery, Singapore General Hospital, Singapore 169608, Singapore. 11Department of Medical Oncology, Yonsei University College of Medicine, Seoul 120-752, South Korea. 12SingHealth/Duke-NUS Institute of Precision Medicine, National Heart Centre Singapore, Singapore 168752, Singapore. 13Laboratory of Cancer Epigenome, Department of Medical Sciences, National Cancer Centre, 11 Hospital Drive, Singapore 169610, Singapore. 14School of Biological Sciences, Nanyang Technological University, Singapore 637551, Singapore. 15Cellular and Molecular Research, National Cancer Centre, 11 Hospital Drive, Singapore 169610, Singapore. *These authors contributed equally to this work. About Genome Institute of Singapore, A*STAR The Genome Institute of Singapore (GIS) is an institute of the Agency for Science, Technology and Research (A*STAR). It has a global vision that seeks to use genomic sciences to achieve extraordinary improvements in human health and public prosperity. Established in 2000 as a centre for genomic discovery, the GIS will pursue the integration of technology, genetics and biology towards academic, economic and societal impact. The key research areas at the GIS include Human Genetics, Infectious Diseases, Cancer Therapeutics and Stratified Oncology, Stem Cell and Regenerative Biology, Cancer Stem Cell Biology, Computational and Systems Biology, and Translational Research. The genomics infrastructure at the GIS is utilised to train new scientific talent, to function as a bridge for academic and industrial research, and to explore scientific questions of high impact. For more information about GIS, please visit www.gis.a-star.edu.sg About the Agency for Science, Technology and Research (A*STAR) ​ The Agency for Science, Technology and Research (A*STAR) is Singapore's lead public sector agency that spearheads economic oriented research to advance scientific discovery and develop innovative technology. Through open innovation, we collaborate with our partners in both the public and private sectors to benefit society. As a Science and Technology Organisation, A*STAR bridges the gap between academia and industry. Our research creates economic growth and jobs for Singapore, and enhances lives by contributing to societal benefits such as improving outcomes in healthcare, urban living, and sustainability. We play a key role in nurturing and developing a diversity of talent and leaders in our Agency and Research Institutes, the wider research community and industry. A*STAR oversees 18 biomedical sciences and physical sciences and engineering research entities primarily located in Biopolis and Fusionopolis. For more information on A*STAR, please visit www.a-star.edu.sgFor more information about GIS, please visit www.gis.a-star.edu.sg For more information, please click If you have a comment, please us. Issuers of news releases, not 7th Wave, Inc. or Nanotechnology Now, are solely responsible for the accuracy of the content.


News Article | November 15, 2016
Site: www.eurekalert.org

Knowing the likely course of cancer can influence treatment decisions. Now a new prediction model published today in Lancet Oncology offers a more accurate prognosis for a patient's metastatic castration-resistant prostate cancer. The approach was as novel as the result - while researchers commonly work in small groups, intentionally isolating their data, the current study embraces the call in Joe Biden's "Cancer Moonshot" to open their question and their data, collecting previously published clinical trial data and calling for worldwide collaboration to evaluate its predictive power. That is, researchers crowdsourced the question of prostate cancer prognosis, eventually involving over 550 international researchers and resulting in 50 computational models from 50 different teams. The approach was intentionally controversial. "Scientists like me who mine open data have been called 'research parasites'. While not the most flattering name, the idea of leveraging existing data to gain new insights is a very important part of modern biomedical research. This project shows the power of the parasites," says James Costello, PhD, senior author of the paper, investigator at the University of Colorado Cancer Center, assistant professor in the Department of Pharmacology at the CU School of Medicine, and director of Computational and Systems Biology Challenges within the Sage Bionetworks/DREAM organization. The project was overseen as a collaborative effort between 16 institutions, led by academic research institutions including CU Cancer Center, open-data initiatives including Project Data Sphere, Sage Bionetworks, and the National Cancer Institute's DREAM Challenges, and industry and research partners including Sanofi, AstraZeneca, and the Prostate Cancer Foundation. Challenge organizers made available the results from five completed clinical trials. Teams were challenged to connect a deep set of clinical measurements to overall patient survival, organizing their insights into novel computational models to better predict patient survival based on clinical data. "The idea is that if a patient comes into the clinic and has these measurements and test results, can we put this data in a model to say if this patient will progress slowly or quickly. If we know the features of patients at the greatest risk, we can know who should receive standard treatment and who might benefit more from a clinical trial," Costello says. The most successful of the 50 models was submitted by a team led by Tero Aittokallio, PhD, from the Institute for Molecular Medicine Finland, FIMM, at University of Helsinki, and professor in the Department of Mathematics and Statistics at University of Turku, Finland. "My group has a long-term expertise in developing multivariate machine learning models for various biomedical applications, but this Challenge provided the unique opportunity to work on clinical trial data, with the eventual aim to help patients with metastatic castration-resistant prostate cancer," Aittokallio says. Basically, the model depended on not only groups of single patient measurements to predict outcomes, but on exploring which interactions between measurements were most predictive - for example, data describing a patient's blood system composition and immune function were only weakly predictive of survival on their own, but when combined became an important part of the winning model. The model used a computational learning strategy technically referred to as an ensemble of penalized Cox regression models, hence the model's name ePCR. This model then competed with 49 other entries, submitted by other teams working independently around the world. "Having 50 independent models allowed us to do two very important things. First when a single clinical feature known to be predictive of patient survival is picked out by 40 of the 50 teams, this greatly strengthens our overall confidence. Second, we were able to discover important clinical features we hadn't fully appreciated before," Costello says. In this case, many models found that in addition to factors like prostate-specific antigen (PSA) and lactate dehydrogenase (LDH) that have long been known to predict prostate cancer performance, blood levels of an enzyme called asparate aminotransferease (AST) is an important predictor of patient survival. This AST is an indirect measure of liver function and the fact that disturbed levels of AST are associated with poor patient performance implies that studies could evaluate the role of AST in prostate cancer. "The benefits of a DREAM Challenge are the ability to attract talented individuals and teams from around the world, and a rigorous framework for the assessment of methods. These two ingredients came together for our Challenge, leading to a new benchmark in metastatic prostate cancer," says paper first author, Justin Guinney, PhD, director of Computational Oncology for Sage Bionetworks located at Fred Hutchinson Cancer Research Center. "A goal of the Project Data Sphere initiative is to spark innovation - to unlock the potential of valuable data by generating new insights and opening up a new world of research possibilities. Prostate Cancer DREAM Challenge did just that. To witness cancer clinical trial data from Project Data Sphere be used in research collaboration and ultimately help improve patient care in the future is extremely rewarding!" says Liz Zhou, MD, MS, director of Global Health Outcome Research at Sanofi. The goal now is to make the ePCR model publicly accessible through an online tool with an eye towards clinical application. In fact, the National Cancer Institute (NCI) has contracted the winning team to do exactly this. Soon, when patients face difficult decisions about the best treatment for metastatic castration-resistant prostate cancer, ePCR tool could be an important piece of the decision-making process. Challenge winners and results can be found on the Prostate Cancer DREAM Challenge homepage. The clinical trial data can be found at Project Data Sphere. The research article describing this work can be found at The Lancet Oncology. Additional papers that describe individual team methods can be found in the DREAM Channel at F1000Research. The University of Colorado Cancer Center, located at the Anschutz Medical Campus, is Colorado's only National Cancer Institute-designated comprehensive cancer center, a distinction recognizing its outstanding contributions to research, clinical trials, prevention and cancer control. CU Cancer Center's clinical partner University of Colorado Hospital is ranked 15th by US News and World Report for Cancer and the CU Cancer Center is a member of the prestigious National Comprehensive Cancer Network®, an alliance of the nation's leading cancer centers working to establish and deliver the gold standard in cancer clinical guidelines. CU Cancer Center is a consortium of more than 400 researchers and physicians at three state universities and three institutions, all working toward one goal: Translating science into life. For more information visit Coloradocancercenter.org and follow CU Cancer Center on Facebook and Twitter. Founded in 2006 by A. Califano (Columbia University) and Gustavo Stolovitzky (IBM Research) the Dialogue on Reverse Engineering Assessment and Methods (DREAM) Challenges Initiative poses fundamental questions about systems biology and translational medicine. Designed and run by a community of researchers from a variety of organizations, the DREAM challenges invite participants to propose solutions -- fostering collaboration and building communities in the process. Expertise and institutional support are provided by Sage Bionetworks, along with the infrastructure to host challenges via their Synapse platform. Together, the leaders of the DREAM Challenges Initiative share a vision allowing individuals and groups to collaborate openly so that the "wisdom of the crowd" provides the greatest impact on science and human health. More information is available at: http://dreamchallenges. . Project Data Sphere, LLC, an independent, not-for-profit initiative of the CEO Roundtable on Cancer's Life Sciences Consortium (LSC), operates the Project Data Sphere® platform. Launched in April 2014, the Project Data Sphere platform provides one place where the cancer community can broadly share, integrate, analyze and discuss historical patient-level comparator arm data sets (historical patient-level cancer phase III) from multiple providers, with the goal of advancing research. With its broad-access approach, the initiative brings diverse minds and technology together to help unleash the full potential of existing clinical trial data and speed innovation by generating collective insights that may lead to improved trial design, disease modeling and beyond. The platform currently contains 27,600 patient lives of data; 9,400 of those are across a wide spectrum of prostate cancer populations. In order to ensure that researchers can realize the full potential of this data, PDS teamed with CEO Roundtable on Cancer Member, SAS Institute Inc. SAS, a leader in data and health analytics, developed and hosts the site and provides free state-of-the-art analytic tools to authorized users within the Project Data Sphere environment. Sage Bionetworks is a nonprofit biomedical research organization, founded in 2009, with a vision to promote innovations in personalized medicine by enabling a community-based approach to scientific inquiries and discoveries. Sage Bionetworks strives to activate patients and to incentivize scientists, funders and researchers to work in fundamentally new ways in order to shape research, accelerate access to knowledge and transform human health. It is located on the campus of the Fred Hutchinson Cancer Research Center in Seattle, Washington and is supported through a portfolio of philanthropic donations, competitive research grants, and commercial partnerships. More information is available at http://www. .


News Article | November 16, 2016
Site: www.prweb.com

Knowing the likely course of cancer can influence treatment decisions. Now a new prediction model published today in Lancet Oncology offers a more accurate prognosis for a patient's metastatic castration-resistant prostate cancer. The approach was as novel as the result – while researchers commonly work in small groups, intentionally isolating their data, the current study embraces the call in Joe Biden's "Cancer Moonshot" to open their question and their data, collecting previously published clinical trial data and calling for worldwide collaboration to evaluate its predictive power. That is, researchers crowdsourced the question of prostate cancer prognosis, eventually involving over 550 international researchers and resulting in 50 computational models from 50 different teams. The approach was intentionally controversial. "Scientists like me who mine open data have been called 'research parasites'. While not the most flattering name, the idea of leveraging existing data to gain new insights is a very important part of modern biomedical research. This project shows the power of the parasites," says James Costello, PhD, senior author of the paper, investigator at the University of Colorado Cancer Center, assistant professor in the Department of Pharmacology at the CU School of Medicine, and director of Computational and Systems Biology Challenges within the Sage Bionetworks/DREAM organization. The project was overseen as a collaborative effort between 16 institutions, led by academic research institutions including CU Cancer Center, open-data initiatives including Project Data Sphere, Sage Bionetworks, and the National Cancer Institute's DREAM Challenges, and industry and research partners including Sanofi, AstraZeneca, and the Prostate Cancer Foundation. Challenge organizers made available the results from five completed clinical trials. Teams were challenged to connect a deep set of clinical measurements to overall patient survival, organizing their insights into novel computational models to better predict patient survival based on clinical data. "The idea is that if a patient comes into the clinic and has these measurements and test results, can we put this data in a model to say if this patient will progress slowly or quickly. If we know the features of patients at the greatest risk, we can know who should receive standard treatment and who might benefit more from a clinical trial," Costello says. The most successful of the 50 models was submitted by a team led by Tero Aittokallio, PhD, from the Institute for Molecular Medicine Finland, FIMM, at University of Helsinki, and professor in the Department of Mathematics and Statistics at University of Turku, Finland. "My group has a long-term expertise in developing multivariate machine learning models for various biomedical applications, but this Challenge provided the unique opportunity to work on clinical trial data, with the eventual aim to help patients with metastatic castration-resistant prostate cancer," Aittokallio says. Basically, the model depended on not only groups of single patient measurements to predict outcomes, but on exploring which interactions between measurements were most predictive – for example, data describing a patient's blood system composition and immune function were only weakly predictive of survival on their own, but when combined became an important part of the winning model. The model used a computational learning strategy technically referred to as an ensemble of penalized Cox regression models, hence the model's name ePCR. This model then competed with 49 other entries, submitted by other teams working independently around the world. "Having 50 independent models allowed us to do two very important things. First when a single clinical feature known to be predictive of patient survival is picked out by 40 of the 50 teams, this greatly strengthens our overall confidence. Second, we were able to discover important clinical features we hadn't fully appreciated before," Costello says. In this case, many models found that in addition to factors like prostate-specific antigen (PSA) and lactate dehydrogenase (LDH) that have long been known to predict prostate cancer performance, blood levels of an enzyme called asparate aminotransferease (AST) is an important predictor of patient survival. This AST is an indirect measure of liver function and the fact that disturbed levels of AST are associated with poor patient performance implies that studies could evaluate the role of AST in prostate cancer. "The benefits of a DREAM Challenge are the ability to attract talented individuals and teams from around the world, and a rigorous framework for the assessment of methods. These two ingredients came together for our Challenge, leading to a new benchmark in metastatic prostate cancer," says paper first author, Justin Guinney, PhD, director of Computational Oncology for Sage Bionetworks located at Fred Hutchinson Cancer Research Center. “A goal of the Project Data Sphere initiative is to spark innovation – to unlock the potential of valuable data by generating new insights and opening up a new world of research possibilities. Prostate Cancer DREAM Challenge did just that. To witness cancer clinical trial data from Project Data Sphere be used in research collaboration and ultimately help improve patient care in the future is extremely rewarding!” says Liz Zhou, MD, MS, director of Global Health Outcome Research at Sanofi. The goal now is to make the ePCR model publicly accessible through an online tool with an eye towards clinical application. In fact, the National Cancer Institute (NCI) has contracted the winning team to do exactly this. Soon, when patients face difficult decisions about the best treatment for metastatic castration-resistant prostate cancer, ePCR tool could be an important piece of the decision-making process. Challenge winners and results can be found on the Prostate Cancer DREAM Challenge homepage. The clinical trial data can be found at Project Data Sphere. The research article describing this work can be found at The Lancet Oncology. Additional papers that describe individual team methods can be found in the DREAM Channel at F1000Research. About University of Colorado Cancer Center The University of Colorado Cancer Center, located at the Anschutz Medical Campus, is Colorado’s only National Cancer Institute-designated comprehensive cancer center, a distinction recognizing its outstanding contributions to research, clinical trials, prevention and cancer control. CU Cancer Center’s clinical partner University of Colorado Hospital is ranked 15th by US News and World Report for Cancer and the CU Cancer Center is a member of the prestigious National Comprehensive Cancer Network®, an alliance of the nation’s leading cancer centers working to establish and deliver the gold standard in cancer clinical guidelines. CU Cancer Center is a consortium of more than 400 researchers and physicians at three state universities and three institutions, all working toward one goal: Translating science into life. For more information visit Coloradocancercenter.org and follow CU Cancer Center on Facebook and Twitter. About the DREAM Challenges Initiative Founded in 2006 by A. Califano (Columbia University) and Gustavo Stolovitzky (IBM Research) the Dialogue on Reverse Engineering Assessment and Methods (DREAM) Challenges Initiative poses fundamental questions about systems biology and translational medicine. Designed and run by a community of researchers from a variety of organizations, the DREAM challenges invite participants to propose solutions — fostering collaboration and building communities in the process. Expertise and institutional support are provided by Sage Bionetworks, along with the infrastructure to host challenges via their Synapse platform. Together, the leaders of the DREAM Challenges Initiative share a vision allowing individuals and groups to collaborate openly so that the “wisdom of the crowd” provides the greatest impact on science and human health. More information is available at: http://dreamchallenges.org/. About the Project Data Sphere Initiative Project Data Sphere, LLC, an independent, not-for-profit initiative of the CEO Roundtable on Cancer's Life Sciences Consortium (LSC), operates the Project Data Sphere® platform (http://www.ProjectDataSphere.org). Launched in April 2014, the Project Data Sphere platform provides one place where the cancer community can broadly share, integrate, analyze and discuss historical patient-level comparator arm data sets (historical patient-level cancer phase III) from multiple providers, with the goal of advancing research. With its broad-access approach, the initiative brings diverse minds and technology together to help unleash the full potential of existing clinical trial data and speed innovation by generating collective insights that may lead to improved trial design, disease modeling and beyond. The platform currently contains 27,600 patient lives of data; 9,400 of those are across a wide spectrum of prostate cancer populations. In order to ensure that researchers can realize the full potential of this data, PDS teamed with CEO Roundtable on Cancer Member, SAS Institute Inc. SAS, a leader in data and health analytics, developed and hosts the site and provides free state-of-the-art analytic tools to authorized users within the Project Data Sphere environment. About Sage Bionetworks Sage Bionetworks is a nonprofit biomedical research organization, founded in 2009, with a vision to promote innovations in personalized medicine by enabling a community-based approach to scientific inquiries and discoveries. Sage Bionetworks strives to activate patients and to incentivize scientists, funders and researchers to work in fundamentally new ways in order to shape research, accelerate access to knowledge and transform human health. It is located on the campus of the Fred Hutchinson Cancer Research Center in Seattle, Washington and is supported through a portfolio of philanthropic donations, competitive research grants, and commercial partnerships. More information is available at http://www.sagebase.org.


News Article | September 6, 2016
Site: www.biosciencetechnology.com

A new technique invented at MIT can precisely measure the growth of many individual cells simultaneously. The advance holds promise for fast drug tests, offers new insights into growth variation across single cells within larger populations, and helps track the dynamic growth of cells to changing environmental conditions. The technique, described in a paper published in Nature Biotechnology, uses an array of suspended microchannel resonators (SMR), a type of microfluidic device that measures the mass of individual cells as they flow through tiny channels. A novel design has increased throughput of the device by nearly two orders of magnitude, while retaining precision. The paper’s senior author, MIT professor Scott Manalis, and other researchers have been developing SMRs for nearly a decade. In the new study, the researchers used the device to observe the effects of antibiotics and antimicrobial peptides on bacteria, and to pinpoint growth variations of single cells among populations, which has important clinical applications. Slower-growing bacteria, for instance, can sometimes be more resistant to antibiotics and may lead to recurrent infections. “The device provides new insights into how cells grow and respond to drugs,” says Manalis, the Andrew (1956) and Erna Viterbi Professor in the MIT departments of Biological Engineering and Mechanical Engineering and a member of the Koch Institute for Integrative Cancer Research. The paper’s lead authors are Nathan Cermak, a recent Ph.D. graduate from MIT’s Computational and Systems Biology Program, and Selim Olcum, a research scientist at the Koch Institute. There are 13 other co-authors on the paper, from the Koch Institute, MIT’s Microsystems Technology Laboratory, the Dana-Farber Cancer Institute, Innovative Micro Technology, and CEA LETI in France. Manalis and his colleagues first developed the SMR in 2007 and have since introduced multiple innovations for different purposes, including to track single cell growth over time, measure cell density, weigh cell-secreted nanovesicles, and, most recently, measure the short-term growth response of cells in changing nutrient conditions. All of these techniques have relied on a crucial scheme: One fluid-filled microchannel is etched in a tiny silicon cantilever sensor that vibrates inside a vacuum cavity. When a cell enters the cantilever, it slightly alters the sensor’s vibration frequency, and this signal can be used to determine the cell’s weight. To measure a cell’s growth rate, Manalis and colleagues could pass an individual cell through the channel repeatedly, back and forth, over a period of about 20 minutes. During that time, a cell can accumulate mass that is measurable by the SMR. But while the SMR weighs cells 10 to 100 times more accurately than any other method, it has been limited to one cell at a time, meaning it could take many hours, or even days, to measure enough cells. The key to the new technology was designing and controlling an array of 10 to 12 cantilever sensors that act like weigh stations, recording the mass of a cell as it flows through the postage-stamp-sized device. Between each sensor are winding “delay channels,” each about five centimeters in length, through which the cells flow for about two minutes, giving them time to grow before reaching the next sensor. Whenever one cell exits a sensor, another cell can enter, increasing the device’s throughput. Results show the mass of each cell at each sensor, graphing the extent to which they’ve grown or shrunk. In the study, the researchers were able to measure about 60 mammalian cells and 150 bacteria per hour, compared to single SMRs, which measured only a few cells in that time. “Being able to rapidly measure the full distribution of growth rates shows us both how typical cells are behaving, an­­d also lets us detect outliers — which was previously very difficult with limited throughput or precision,” Cermak says. One comparable method for measuring masses of many individual cells simultaneously is called quantitative phase microscopy (QPM), which calculates the dry mass of cells by measuring their optical thickness. Unlike the SMR-based approach, QPM can be used on cells that grow adhered to surfaces. However, the SMR-based approach is significantly more precise. “We can reliably resolve changes of less than one-tenth of a percent of a cancer cell’s mass in about 20 minutes. This precision is proving to be essential for many of the clinical applications that we’re pursuing,” Olcum says. In one experiment using the device, the researchers observed the effects of an antibiotic, called kanamycin, on E. coli. Kanamycin inhibits protein synthesis in bacteria, eventually stopping their growth and killing the cells. Traditional antibiotic tests require growing a culture of bacteria, which could take a day or more. Using the new device, within an hour the researchers recorded a change in rate in which the cells accumulate mass. The reduced recording time is critical in testing drugs against bacterial infections in clinical settings, Manalis says: “In some cases, having a rapid test for selecting an antibiotic can make an important difference in the survival of a patient.” Similarly, the researchers used the device to observe the effects of an antimicrobial peptide called CM15, a relatively new protein-based candidate for fighting bacteria. Such candidates are increasingly important as bacteria strains become resistant to common antibiotics. CM15 makes microscopic holes in bacteria cell walls, such that the cell’s contents gradually leak out, eventually killing the cell. However, because only the mass of the cell changes and not its size, the effects may be missed by traditional microscopy techniques. Indeed, the researchers observed the E. coli cells rapidly losing mass immediately following exposure to CM15. Such results could lend validation to the peptide and other novel drugs by providing some insight into the mechanism, Manalis says. The researchers are currently working with members of the Dana Farber Cancer Institute, through the MIT/DFCI Bridge program, to determine if the device could be used to predict patient response to therapy by weighing tumor cells in the presence of anticancer drugs. Marc Kirschner, a professor and chair of the Department of Systems Biology at Harvard Medical School, who was not involved in the study, said the new microfluidics device will open up new avenues for studying the “physiology and pharmacology of cell growth. … Since growth is related to proliferation and to the stress a cell is under, it is a natural feature to study, but it has been difficult before this method.” “The technical problems to get this working were significant and it is still incredible for me to think that they pulled this off,” Kirschner adds. “I expect that when it is … into biology labs it will be useful for many problems in cancer, metabolism, cell death, and cell stress.” The research was sponsored, in part, by the U.S. Army Research Office, the Koch Institute and Dana Farber/Harvard Cancer Center Bridge Project, the National Science Foundation, and the National Cancer Institute.


News Article | September 6, 2016
Site: www.chromatographytechniques.com

A new technique invented at MIT can precisely measure the growth of many individual cells simultaneously. The advance holds promise for fast drug tests, offers new insights into growth variation across single cells within larger populations, and helps track the dynamic growth of cells to changing environmental conditions. The technique, described in a paper published in Nature Biotechnology, uses an array of suspended microchannel resonators (SMR), a type of microfluidic device that measures the mass of individual cells as they flow through tiny channels. A novel design has increased throughput of the device by nearly two orders of magnitude, while retaining precision. The paper’s senior author, MIT professor Scott Manalis, and other researchers have been developing SMRs for nearly a decade. In the new study, the researchers used the device to observe the effects of antibiotics and antimicrobial peptides on bacteria, and to pinpoint growth variations of single cells among populations, which has important clinical applications. Slower-growing bacteria, for instance, can sometimes be more resistant to antibiotics and may lead to recurrent infections. “The device provides new insights into how cells grow and respond to drugs,” says Manalis, the Andrew (1956) and Erna Viterbi professor in the MIT departments of Biological Engineering and Mechanical Engineering and a member of the Koch Institute for Integrative Cancer Research. The paper’s lead authors are Nathan Cermak, a recent PhD graduate from MIT’s Computational and Systems Biology Program, and Selim Olcum, a research scientist at the Koch Institute. There are 13 other co-authors on the paper, from the Koch Institute, MIT’s Microsystems Technology Laboratory, the Dana-Farber Cancer Institute, Innovative Micro Technology, and CEA LETI in France. Manalis and his colleagues first developed the SMR in 2007 and have since introduced multiple innovations for different purposes, including to track single cell growth over time, measure cell density, weigh cell-secreted nanovesicles, and, most recently, measure the short-term growth response of cells in changing nutrient conditions. All of these techniques have relied on a crucial scheme: One fluid-filled microchannel is etched in a tiny silicon cantilever sensor that vibrates inside a vacuum cavity. When a cell enters the cantilever, it slightly alters the sensor’s vibration frequency, and this signal can be used to determine the cell’s weight. To measure a cell’s growth rate, Manalis and colleagues could pass an individual cell through the channel repeatedly, back and forth, over a period of about 20 minutes. During that time, a cell can accumulate mass that is measurable by the SMR. But while the SMR weighs cells 10 to 100 times more accurately than any other method, it has been limited to one cell at a time, meaning it could take many hours, or even days, to measure enough cells. The key to the new technology was designing and controlling an array of 10 to 12 cantilever sensors that act like weigh stations, recording the mass of a cell as it flows through the postage-stamp-sized device. Between each sensor are winding “delay channels,” each about five centimeters in length, through which the cells flow for about two minutes, giving them time to grow before reaching the next sensor. Whenever one cell exits a sensor, another cell can enter, increasing the device’s throughput. Results show the mass of each cell at each sensor, graphing the extent to which they’ve grown or shrunk. In the study, the researchers were able to measure about 60 mammalian cells and 150 bacteria per hour, compared to single SMRs, which measured only a few cells in that time. “Being able to rapidly measure the full distribution of growth rates shows us both how typical cells are behaving, an­­d also lets us detect outliers — which was previously very difficult with limited throughput or precision,” Cermak says. One comparable method for measuring masses of many individual cells simultaneously is called quantitative phase microscopy (QPM), which calculates the dry mass of cells by measuring their optical thickness. Unlike the SMR-based approach, QPM can be used on cells that grow adhered to surfaces. However, the SMR-based approach is significantly more precise. “We can reliably resolve changes of less than one-tenth of a percent of a cancer cell’s mass in about 20 minutes. This precision is proving to be essential for many of the clinical applications that we’re pursuing,” Olcum says. In one experiment using the device, the researchers observed the effects of an antibiotic, called kanamycin, on E. coli. Kanamycin inhibits protein synthesis in bacteria, eventually stopping their growth and killing the cells. Traditional antibiotic tests require growing a culture of bacteria, which could take a day or more. Using the new device, within an hour the researchers recorded a change in rate in which the cells accumulate mass. The reduced recording time is critical in testing drugs against bacterial infections in clinical settings, Manalis says. “In some cases, having a rapid test for selecting an antibiotic can make an important difference in the survival of a patient.” Similarly, the researchers used the device to observe the effects of an antimicrobial peptide called CM15, a relatively new protein-based candidate for fighting bacteria. Such candidates are increasingly important as bacteria strains become resistant to common antibiotics. CM15 makes microscopic holes in bacteria cell walls, such that the cell’s contents gradually leak out, eventually killing the cell. However, because only the mass of the cell changes and not its size, the effects may be missed by traditional microscopy techniques. Indeed, the researchers observed the E. coli cells rapidly losing mass immediately following exposure to CM15. Such results could lend validation to the peptide and other novel drugs by providing some insight into the mechanism, Manalis says. The researchers are currently working with members of the Dana Farber Cancer Institute, through the MIT/DFCI Bridge program, to determine if the device could be used to predict patient response to therapy by weighing tumor cells in the presence of anticancer drugs. Marc Kirschner, a professor and chair of the Department of Systems Biology at Harvard Medical School, who was not involved in the study, said the new microfluidics device will open up new avenues for studying the “physiology and pharmacology of cell growth. … Since growth is related to proliferation and to the stress a cell is under, it is a natural feature to study, but it has been difficult before this method.” “The technical problems to get this working were significant and it is still incredible for me to think that they pulled this off,” Kirschner adds. “I expect that when it is … into biology labs it will be useful for many problems in cancer, metabolism, cell death, and cell stress.”


News Article | September 5, 2016
Site: news.mit.edu

A new technique invented at MIT can precisely measure the growth of many individual cells simultaneously. The advance holds promise for fast drug tests, offers new insights into growth variation across single cells within larger populations, and helps track the dynamic growth of cells to changing environmental conditions. The technique, described in a paper published in Nature Biotechnology, uses an array of suspended microchannel resonators (SMR), a type of microfluidic device that measures the mass of individual cells as they flow through tiny channels. A novel design has increased throughput of the device by nearly two orders of magnitude, while retaining precision. The paper’s senior author, MIT professor Scott Manalis, and other researchers have been developing SMRs for nearly a decade. In the new study, the researchers used the device to observe the effects of antibiotics and antimicrobial peptides on bacteria, and to pinpoint growth variations of single cells among populations, which has important clinical applications. Slower-growing bacteria, for instance, can sometimes be more resistant to antibiotics and may lead to recurrent infections. “The device provides new insights into how cells grow and respond to drugs,” says Manalis, the Andrew (1956) and Erna Viterbi Professor in the MIT departments of Biological Engineering and Mechanical Engineering and a member of the Koch Institute for Integrative Cancer Research. The paper’s lead authors are Nathan Cermak, a recent PhD graduate from MIT’s Computational and Systems Biology Program, and Selim Olcum, a research scientist at the Koch Institute. There are 13 other co-authors on the paper, from the Koch Institute, MIT’s Microsystems Technology Laboratory, the Dana-Farber Cancer Institute, Innovative Micro Technology, and CEA LETI in France. Manalis and his colleagues first developed the SMR in 2007 and have since introduced multiple innovations for different purposes, including to track single cell growth over time, measure cell density, weigh cell-secreted nanovesicles, and, most recently, measure the short-term growth response of cells in changing nutrient conditions. All of these techniques have relied on a crucial scheme: One fluid-filled microchannel is etched in a tiny silicon cantilever sensor that vibrates inside a vacuum cavity. When a cell enters the cantilever, it slightly alters the sensor’s vibration frequency, and this signal can be used to determine the cell’s weight. To measure a cell’s growth rate, Manalis and colleagues could pass an individual cell through the channel repeatedly, back and forth, over a period of about 20 minutes. During that time, a cell can accumulate mass that is measurable by the SMR. But while the SMR weighs cells 10 to 100 times more accurately than any other method, it has been limited to one cell at a time, meaning it could take many hours, or even days, to measure enough cells. The key to the new technology was designing and controlling an array of 10 to 12 cantilever sensors that act like weigh stations, recording the mass of a cell as it flows through the postage-stamp-sized device. Between each sensor are winding “delay channels,” each about five centimeters in length, through which the cells flow for about two minutes, giving them time to grow before reaching the next sensor. Whenever one cell exits a sensor, another cell can enter, increasing the device’s throughput. Results show the mass of each cell at each sensor, graphing the extent to which they’ve grown or shrunk. In the study, the researchers were able to measure about 60 mammalian cells and 150 bacteria per hour, compared to single SMRs, which measured only a few cells in that time. “Being able to rapidly measure the full distribution of growth rates shows us both how typical cells are behaving, an­­d also lets us detect outliers — which was previously very difficult with limited throughput or precision,” Cermak says. One comparable method for measuring masses of many individual cells simultaneously is called quantitative phase microscopy (QPM), which calculates the dry mass of cells by measuring their optical thickness. Unlike the SMR-based approach, QPM can be used on cells that grow adhered to surfaces. However, the SMR-based approach is significantly more precise. “We can reliably resolve changes of less than one-tenth of a percent of a cancer cell’s mass in about 20 minutes. This precision is proving to be essential for many of the clinical applications that we’re pursuing,” Olcum says. In one experiment using the device, the researchers observed the effects of an antibiotic, called kanamycin, on E. coli. Kanamycin inhibits protein synthesis in bacteria, eventually stopping their growth and killing the cells. Traditional antibiotic tests require growing a culture of bacteria, which could take a day or more. Using the new device, within an hour the researchers recorded a change in rate in which the cells accumulate mass. The reduced recording time is critical in testing drugs against bacterial infections in clinical settings, Manalis says: “In some cases, having a rapid test for selecting an antibiotic can make an important difference in the survival of a patient.” Similarly, the researchers used the device to observe the effects of an antimicrobial peptide called CM15, a relatively new protein-based candidate for fighting bacteria. Such candidates are increasingly important as bacteria strains become resistant to common antibiotics. CM15 makes microscopic holes in bacteria cell walls, such that the cell’s contents gradually leak out, eventually killing the cell. However, because only the mass of the cell changes and not its size, the effects may be missed by traditional microscopy techniques. Indeed, the researchers observed the E. coli cells rapidly losing mass immediately following exposure to CM15. Such results could lend validation to the peptide and other novel drugs by providing some insight into the mechanism, Manalis says. The researchers are currently working with members of the Dana Farber Cancer Institute, through the the Koch Institute and Dana Farber/Harvard Cancer Center Bridge Project, to determine if the device could be used to predict patient response to therapy by weighing tumor cells in the presence of anticancer drugs. Marc Kirschner, a professor and chair of the Department of Systems Biology at Harvard Medical School, who was not involved in the study, said the new microfluidics device will open up new avenues for studying the “physiology and pharmacology of cell growth. … Since growth is related to proliferation and to the stress a cell is under, it is a natural feature to study, but it has been difficult before this method.” “The technical problems to get this working were significant and it is still incredible for me to think that they pulled this off,” Kirschner adds. “I expect that when it is … into biology labs it will be useful for many problems in cancer, metabolism, cell death, and cell stress.” The research was sponsored, in part, by the U.S. Army Research Office, the Koch Institute and Dana Farber/Harvard Cancer Center Bridge Project, the National Science Foundation, and the National Cancer Institute.


News Article | February 28, 2017
Site: www.eurekalert.org

Pennsylvania's congressional district maps are almost certainly the result of gerrymandering according to an analysis based on a new mathematical theorem on bias in Markov Chains developed by Carnegie Mellon University and University of Pittsburgh mathematicians. Their findings are published in the Feb. 28 online early edition of the Proceedings of the National Academy of Sciences (PNAS). Markov chains are algorithms which can generate a random object by starting from a fixed object and evolving in a stepwise fashion, making small random changes at each step. Markov chains have numerous applications, and are used to model things like thermodynamic processes, chemical reactions, economic and financial phenomena, protein folding and DNA sequences. To evaluate gerrymandering of congressional districts, a Markov Chain can, in principle, be used to compare the characteristics of the current districting map with a typical districting of the same state by generating truly random districtings as points of comparison. However, one of the limitations of Markov chains is that there is often no way to determine how long the chains need to run in order to achieve a truly random sample. Without knowing the upper limit, researchers must assume that they've run the algorithm long enough for their resulting assumptions to be valid. In the PNAS paper, University of Pittsburgh Assistant Professor of Computational and Systems Biology Maria Chikina and Carnegie Mellon Professor of Mathematical Sciences Alan Frieze and Assistant Professor of Mathematical Sciences Wesley Pegden prove a theorem that can use a Markov Chain to show that a sample is nonrandom, without generating random samples from the Markov Chain itself. This allows researchers to use the Markov chain to rigorously demonstrate bias in the congressional districting maps of the state of Pennsylvania without having to make unproven assumptions on the time required to generate samples from the Markov Chain. The researchers began with a current map of Pennsylvania's congressional districts, and applied a Markov chain that incorporated geometric constraints on districts that would be used to create random districting maps. Those factors included ensuring roughly equal populations in each district, border continuity, and constraining the ratio of perimeter to area. The researchers ran the chain, which changed the map in random steps. Statistical properties of the map were found to change rapidly with small random changes to the initial map, which, according to their theorem, would be extremely unlikely to happen by chance. "There is no way that this map could have been produced by an unbiased process," said Pegden. While the new method doesn't provide a new tool for drawing congressional district maps, it does provide a rigorous test to detect that existing maps were created in a biased fashion, and researchers may find applications in the many other fields where Markov Chains are used. The research was supported by the National Institutes of Health (MH10900901A1, HG00854003), the National Science Foundation (DMS1362785, CCF1522984, DMS1363136), the Simons Foundation and the Sloan Foundation.


News Article | November 17, 2016
Site: www.sciencedaily.com

Knowing the likely course of cancer can influence treatment decisions. Now a new prediction model published in Lancet Oncology offers a more accurate prognosis for a patient's metastatic castration-resistant prostate cancer. The approach was as novel as the result -- while researchers commonly work in small groups, intentionally isolating their data, the current study embraces the call in Joe Biden's "Cancer Moonshot" to open their question and their data, collecting previously published clinical trial data and calling for worldwide collaboration to evaluate its predictive power. That is, researchers crowdsourced the question of prostate cancer prognosis, eventually involving over 550 international researchers and resulting in 50 computational models from 50 different teams. The approach was intentionally controversial. "Scientists like me who mine open data have been called 'research parasites'. While not the most flattering name, the idea of leveraging existing data to gain new insights is a very important part of modern biomedical research. This project shows the power of the parasites," says James Costello, PhD, senior author of the paper, investigator at the University of Colorado Cancer Center, assistant professor in the Department of Pharmacology at the CU School of Medicine, and director of Computational and Systems Biology Challenges within the Sage Bionetworks/DREAM organization. The project was overseen as a collaborative effort between 16 institutions, led by academic research institutions including CU Cancer Center, open-data initiatives including Project Data Sphere, Sage Bionetworks, and the National Cancer Institute's DREAM Challenges, and industry and research partners including Sanofi, AstraZeneca, and the Prostate Cancer Foundation. Challenge organizers made available the results from five completed clinical trials. Teams were challenged to connect a deep set of clinical measurements to overall patient survival, organizing their insights into novel computational models to better predict patient survival based on clinical data. "The idea is that if a patient comes into the clinic and has these measurements and test results, can we put this data in a model to say if this patient will progress slowly or quickly. If we know the features of patients at the greatest risk, we can know who should receive standard treatment and who might benefit more from a clinical trial," Costello says. The most successful of the 50 models was submitted by a team led by Tero Aittokallio, PhD, from the Institute for Molecular Medicine Finland, FIMM, at University of Helsinki, and professor in the Department of Mathematics and Statistics at University of Turku, Finland. "My group has a long-term expertise in developing multivariate machine learning models for various biomedical applications, but this Challenge provided the unique opportunity to work on clinical trial data, with the eventual aim to help patients with metastatic castration-resistant prostate cancer," Aittokallio says. Basically, the model depended on not only groups of single patient measurements to predict outcomes, but on exploring which interactions between measurements were most predictive -- for example, data describing a patient's blood system composition and immune function were only weakly predictive of survival on their own, but when combined became an important part of the winning model. The model used a computational learning strategy technically referred to as an ensemble of penalized Cox regression models, hence the model's name ePCR. This model then competed with 49 other entries, submitted by other teams working independently around the world. "Having 50 independent models allowed us to do two very important things. First when a single clinical feature known to be predictive of patient survival is picked out by 40 of the 50 teams, this greatly strengthens our overall confidence. Second, we were able to discover important clinical features we hadn't fully appreciated before," Costello says. In this case, many models found that in addition to factors like prostate-specific antigen (PSA) and lactate dehydrogenase (LDH) that have long been known to predict prostate cancer performance, blood levels of an enzyme called asparate aminotransferease (AST) is an important predictor of patient survival. This AST is an indirect measure of liver function and the fact that disturbed levels of AST are associated with poor patient performance implies that studies could evaluate the role of AST in prostate cancer. "The benefits of a DREAM Challenge are the ability to attract talented individuals and teams from around the world, and a rigorous framework for the assessment of methods. These two ingredients came together for our Challenge, leading to a new benchmark in metastatic prostate cancer," says paper first author, Justin Guinney, PhD, director of Computational Oncology for Sage Bionetworks located at Fred Hutchinson Cancer Research Center. "A goal of the Project Data Sphere initiative is to spark innovation -- to unlock the potential of valuable data by generating new insights and opening up a new world of research possibilities. Prostate Cancer DREAM Challenge did just that. To witness cancer clinical trial data from Project Data Sphere be used in research collaboration and ultimately help improve patient care in the future is extremely rewarding!" says Liz Zhou, MD, MS, director of Global Health Outcome Research at Sanofi. The goal now is to make the ePCR model publicly accessible through an online tool with an eye towards clinical application. In fact, the National Cancer Institute (NCI) has contracted the winning team to do exactly this. Soon, when patients face difficult decisions about the best treatment for metastatic castration-resistant prostate cancer, ePCR tool could be an important piece of the decision-making process.


NovogeneAIT Singapore and the Genome Institute of Singapore Forge Public-Private Partnership to Establish Whole Genome Sequencing Centre in Singapore Novogene, a leading commercial provider of genomic services and solutions with cutting edge next-generation sequencing and bioinformatics expertise; AITbiotech Pte Ltd, a Singapore biotechnology company; and the Genome Institute of Singapore (GIS) announced today that NovogeneAIT Genomics Singapore (NovogeneAIT) - a new joint venture between Novogene and AITbiotech - will establish a joint whole genome sequencing (WGS) centre at Biopolis, Singapore. The new centre will provide Illumina HiSeq X based whole genome sequencing and bioinformatics analysis of human, plant and animal samples for biomedical and agricultural researchers. The centre will devote a major portion of its sequencing capability to support public research projects and empower super scale sequencing initiatives in Singapore and the region. In addition, NovogeneAIT will collaborate with GIS to develop new applications of next-generation sequencing, such as WGS solutions for cancer diagnosis and stratified cancer treatment. "I am very excited and pleased to announce this significant new initiative with the Genome Institute of Singapore," stated Dr. Ruiqiang Li, CEO of Novogene. "The centre is the first major project for NovogeneAIT and is an important milestone for our company. We look forward to providing high-quality sequencing services in Singapore and to advancing important research initiatives that can benefit humanity." "We are delighted to work with a local biotech company," said Prof. Ng Huck Hui, Executive Director of GIS. "Such public-private partnerships will prove to be highly beneficial as it leverages the strengths of both parties to advance genomic science and medicine in Singapore, as well as to create successful local biotech companies." About Novogene Corporation Novogene is a leading provider of genomic services and solutions with cutting edge NGS and bioinformatics expertise and one of the largest sequencing capacities in the world. Novogene utilizes scientific excellence, a commitment to customer service and unsurpassed data quality to help our clients realize their research goals in the rapidly evolving world of genomics. With 1,300 employees, multiple locations around the world, 43 NGS related patents, and over 200 publications in top tier journal such as Nature and Science, the company has rapidly become a world-leader in NGS services. For more information, visit http://en.novogene.com. NovogeneAIT, a newly formed joint venture between Novogene and AITbiotech announced in September 2016, provides Illumina HiSeq X based NGS services to the Association of Southeast Asia Nations (ASEAN) and other Asian regions. About AITbiotech AITbiotech is a leading Genomic Services and MDx company based in Singapore. Founded by Alex Thian in 2008, it has a core molecular services and R&D laboratory in Singapore managed by a team of experienced biotechnologists. It provides a complete suite of Genomic Services including Capillary Sequencing, Next-generation Sequencing Services, Bioinformatics Services and customized molecular services to the research, healthcare and biomedical industries in Singapore and Asia. AITbiotech is also an ISO 13485 certified company which manufactures and distributes its own line of real-time PCR pathogen detection assays branded as abTESTM in the Asian and European markets. For more information, please visit our website: www.aitbiotech.com. About A*STAR's Genome Institute of Singapore (GIS) The Genome Institute of Singapore (GIS) is an institute of the Agency for Science, Technology and Research (A*STAR). It has a global vision that seeks to use genomic sciences to achieve extraordinary improvements in human health and public prosperity. Established in 2000 as a centre for genomic discovery, the GIS will pursue the integration of technology, genetics and biology towards academic, economic and societal impact. The key research areas at the GIS include Human Genetics, Infectious Diseases, Cancer Therapeutics and Stratified Oncology, Stem Cell and Regenerative Biology, Cancer Stem Cell Biology, Computational and Systems Biology, and Translational Research. The genomics infrastructure at the GIS is utilised to train new scientific talent, to function as a bridge for academic and industrial research, and to explore scientific questions of high impact. For more information about GIS, please visit www.gis.a-star.edu.sg Media contacts: Mr Alex Thian AITbiotech +65 6778 6822 www.aitbiotech.com Joyce Peng, Ph.D. Global Marketing Director and General Manager Novogene Corporation +1-626-222-5584 Joyce Ang Senior Officer, Office of Corporate Communications Genome Institute of Singapore, A*STAR +65 6808 8101

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