Author Interviews, Cancer Research, Genetic Research, Personalized Medicine, Technology / 06.01.2016
canSAR Database Analysis Speeds Identification of New Cancer Targets
[caption id="attachment_20449" align="alignleft" width="200"]
Dr. Bissan Al-Lazikini[/caption]
MedicalResearch.com Interview with:
Dr Bissan Al-Lazikani
Team leader in computational biology
The Institute of Cancer Research
London
Medical Research: What is the background for the canSAR database? What are the main uses for the tool?
Dr. Al-Lazikani: Drug discovery is a difficult, time consuming and expensive venture that frequently ends in late stage drug failures - especially in oncology.
As with any complex venture, decisions throughout the drug discovery pipeline can be empowered by having access to the right information at the right time. But for drug discovery this means bringing together billions of experimental data from very diverse areas of science spanning genomics, proteomics, chemistry and more.
We developed canSAR to help guide our own drug discovery efforts by integrating these huge, diverse data and by analysing the data and deriving hidden links and knowledge from them. This means that we can answer questions in minutes that would have taken weeks using previously available public resources. But, more importantly, canSAR analyses and links these data in a way that allows us to derive knowledge that was hidden before. For example, one of the main ways canSAR is used is to help select the best druggable targets for drug discovery. Using canSAR we were able to uncover many druggable cancer proteins that were previously overlooked, and we are delighted to see that several of these proteins are now the subjects of drug discovery and development projects both by us and by others.
We took the decision to make canSAR publicly and freely available because we believe that cancer drug discovery is a vast challenge that requires openness and data sharing worldwide. It has been embraced by the community is being used by tens of thousands of cancer scientists worldwide, both in academia and industry, to generate hypotheses for experiments and select targets for drug discovery.
Dr. Bissan Al-Lazikini[/caption]
MedicalResearch.com Interview with:
Dr Bissan Al-Lazikani
Team leader in computational biology
The Institute of Cancer Research
London
Medical Research: What is the background for the canSAR database? What are the main uses for the tool?
Dr. Al-Lazikani: Drug discovery is a difficult, time consuming and expensive venture that frequently ends in late stage drug failures - especially in oncology.
As with any complex venture, decisions throughout the drug discovery pipeline can be empowered by having access to the right information at the right time. But for drug discovery this means bringing together billions of experimental data from very diverse areas of science spanning genomics, proteomics, chemistry and more.
We developed canSAR to help guide our own drug discovery efforts by integrating these huge, diverse data and by analysing the data and deriving hidden links and knowledge from them. This means that we can answer questions in minutes that would have taken weeks using previously available public resources. But, more importantly, canSAR analyses and links these data in a way that allows us to derive knowledge that was hidden before. For example, one of the main ways canSAR is used is to help select the best druggable targets for drug discovery. Using canSAR we were able to uncover many druggable cancer proteins that were previously overlooked, and we are delighted to see that several of these proteins are now the subjects of drug discovery and development projects both by us and by others.
We took the decision to make canSAR publicly and freely available because we believe that cancer drug discovery is a vast challenge that requires openness and data sharing worldwide. It has been embraced by the community is being used by tens of thousands of cancer scientists worldwide, both in academia and industry, to generate hypotheses for experiments and select targets for drug discovery.
Dr. Deirdre Murray[/caption]
MedicalResearch.com Interview with:
Dr. Deirdre Murray
Senior Lecturer/Consultant Paediatrician
Dept of Paediatrics and Child Health
University College Cork
Clinical Investigations Unit Cork University Hospital
Principal Investigator
Irish Centre for Fetal and Neonatal Translational Research
Medical Research: What is the background for this study? What are the main findings?
Dr. Murray: Everyday in clinic, and in waiting rooms and in restaurants we see parents are handing over their smart phones and iPads to occupy young children. The nature of childhood play is changing rapidly. The exact frequency and the effect of this change in unknown. We wanted to first measure how young children 12-36 months are using touchscreen devices. We asked parents who attended our paediatric unit, both outpatients and short stay inpatients to answer a study specific questionnaire.
We found that of the 82 parents surveyed, 82% of parents owned a touchscreen device, and of these 87% gave their device to their toddler to play with. Thus 71% of toddlers had access to a touchscreen device. This rate was similar across the age range studied (12-36 months). By parental report, 24 months was the median age of ability to swipe (IQR: 19.5–30.5), unlock (IQR: 20.5–31.5) and active looking for touch-screen features (IQR: 22–30.5), while 25 months (IQR: 21–31.25) was the median age of ability to identify and use specific touch-screen features. Overall, 32.8% of toddlers could perform all four skills.
Touchscreen usage was common at a very young age and from 2 years of age toddlers have the ability to interact purposefully with touch-screen technology.
Christie Riemer[/caption]
MedicalResearch.com Interview with:
Christie Riemer
MD Candidate-Class of 2016
Michigan State University
College of Human Medicine
Medical Research: What is the background for this study? What are the main findings?
Response: Online physician rating sites allow patients to recommend, grade, and publicly comment on physician performance. Despite increases in physician rating website popularity, little information exists regarding the online footprint of dermatologists. Many physicians also remain wary of these websites for fear of malicious reviews.
Our study aimed to investigate the patterns of dermatologist online ratings. We found the average ratings for dermatologists were high, >3.5 stars, on the top 5 websites (ZocDoc, Healthgrades, Yelp, RateMDs, and
Dr. McHugh[/caption]
MedicalResearch.com Interview with:
Leo McHugh, Ph.D.
Director, Bioinformatics
Immunexpress
Seattle, Washington
Medical Research: What is the background for this study? What are the main findings?
Dr. McHugh: Sepsis is the leading cause of child mortality in the world, and in developing countries kills more adults than breast cancer, prostate cancer and HIV combined. Approximately 30% of people admitted to ICU have sepsis, and up to 50% of these patients die. It’s a major cost burden also, costing the US health system $17 billion per year. The best way to reduce costs and improve patient outcomes is to detect sepsis early and with confidence, so that appropriate treatments can be applied. Each hour delay in the detection of sepsis has been reported to correspond to an 8% increase in mortality. So the need for a rapid and accurate diagnostic is recognized. Traditional methods rely on detection of the specific pathogen causing the infection, and these methods often take more than 24 hours, and find a pathogen in only 30% of clinically confirmed cases because they’re trying to detect a minuscule amount of pathogen or pathogenic product in the blood. Our approach was to use the host’s own immune system, which is highly tuned to respond to the presence of pathogens. Around 30% of all genes are dysregulated in sepsis, so there is a huge signal base to draw from. The trick with using multi marker host response is to pick out the specific combination of gene expression patterns that cover the broad range of patients that present with sepsis and who may present either early or late in the episode, thus with different gene activation patterns.
This paper describes a simple combination of such genes that can be used to detect sepsis and performs over the full range of patients irrespective of stage of infection or severity of infection. In it’s current format, the test is manual and takes 4-6 hours, and is a great advance on the current tools, however the methods we’ve used are specifically designed to meet requirements to port this assay onto a fully automated Point of Care platform that could deliver a result in under 90 minutes.
Dr. Oktay[/caption]
MedicalResearch.com Interview with:
Kutluk Oktay, MD, PhD.
Professor of Obstetrics & Gynecology, Medicine, and Cell Biology & Anatomy
Director, Division of Reproductive Medicine & Institute for Fertility Preservation
Innovation Institute for Fertility and In Vitro Fertilization
New York Medical College, Valhalla, NY
Medical Research: What is the background for this study? What are the main findings?
Dr. Oktay: Cancer treatments cause infertility and early menopause in a growing number of young women around the world and US. One of the strategies to preserve fertility, which was developed by our team, is to cryopreserve ovarian tissue before chemotherapy and later transplant it back to the patient when they are cured of the cancer and ready to have children. However, success of ovarian transplantation has been limited due to limitation in blood flow to grafts. In this study we described a new approach which seems to improve graft function. The utility of an extracellular tissue matrix and robotic surgery seems to enhance graft function. With this approach both patients conceived with frozen embryos to spare and one has already delivered.
Dr. McDonald[/caption]
MedicalResearch.com Interview with:
Professor John McDonald PhD
Director of its Integrated Cancer Research Center
School of Biology at the Georgia Institute of Technology
Medical Research: What is the background for this study? What are the main findings?
Response: Ovarian cancer is a deadly disease because it cannot be diagnosed at early stages when it can be most effectively effectively treated.
It has long been recognized that there is a great need for an accurate diagnostic test for early stage ovarian cancer.
Until now, efforts to develop a highly accurate way to detect early stage ovarian cancer have been unsuccessful.
We have used a novel approach that integrates advanced methods in analytical chemistry with advanced machine learning algorithms to identify 16 metabolites that collectively can detect ovarian cancer with extremely high accuracy (100% in the samples tested in our study)

















