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Computational Methods of Researching Cancer Treatments

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Computational Methods of Researching Cancer Treatments Health & Medicine Dr Benjamin Haibe-Kains ︱ ARTIFICIAL INTELLIGENCE AND genomics to improve prediction recognised that, due to the high costs CANCER RESEARCH of patient survival and response to of clinical trials, the existing clinical data Artiicial Intelligence and their underlying therapies. During his graduate training related to given treatment and cancer Computational machine learning algorithms could be at the Université Libre de Bruxelles in subtypes were extremely scarce. Given of particular value for research exploring Belgium, he worked on developing that machine learning usually requires complex diseases, when trying to identify predictors of survival in breast cancer a large sample size to avoid artefactual effective pharmacological treatments for patients based on high dimensional gene discoveries, it was time to investigate methods of researching them. expression (messenger RNA) data. He “preclinical models”, which are cancer continued his research as a postdoctoral cells derived from patient tumours that Cancer, one of the leading causes of fellow at the Dana-Farber Cancer one can replicate ininitely. These models cancer treatments death worldwide, is a perfect example of Institute/Harvard School of Public Health, therefore provide a fantastic advantage this. Scientists have not yet been able to leveraging a large collection of data to compared to clinical trials as the same identify a systematic treatment for cancer develop “gene expression signatures” to cancer cells can be tested with multiple Artiicial Intelligence (AI) and machine learning algorithms have the he computational analysis of big that is successful in curing the disease in robustly identify molecular subtypes of therapies to assess which one is the most potential to bring substantial advances in the ields of research exploring data is set to bring huge advances many of its most aggressive subtypes. breast and ovarian cancers. eficient, something impossible to do complex diseases and trying to identify effective treatments. Dr Benjamin Tin terms of the methods used to Cancer derives from an uncontrolled with patients. Haibe-Kains, working at The Princess Margaret Cancer Centre in Toronto, carry out medical and basic research. A division of abnormal cells in a given Using large collections of cancer has spent over a decade developing machine learning tools and databases number of researchers worldwide have part of the body, which can invade and molecular data and machine learning Dr Haibe-Kains’ laboratory invested most that could help scientists gain a better understanding of different sub-types already started developing algorithms destroy surrounding healthy tissue algorithms, Dr Haibe-Kains and of its resources in compiling and curating of cancer and their response to pharmacological treatments, in order to that can analyse large amounts of and organs. It is an extremely complex his collaborators identiied several the largest anticancer drug screens identify more effective drugs for individual patients. biological and medical data, identifying disease, with more than 200 subtypes, “prognostic biomarkers”, that are in preclinical models. Such screens new hypotheses for future investigation. each of which is contain not only the A particular ield of medical research often diagnosed and Dr Benjamin Haibe-Kains explores genomic make-up that could highly beneit from the use treated differently. of the cancer cells, of machine learning algorithms is that of As cancer arises from the potential of machine learning but also the way exploring cancer genomics and trying to aberrations in the these cells react to determine the best treatments for different genomic materials algorithms, bioinformatics and chemical treatments; subtypes of the disease. of the cells, scientists these complex data have developed computational genomics to improve are referred to as THE VALUE AND CHALLENGES OF sophisticated prediction of cancer patients’ survival pharmacogenomics. BIG DATA proiling These efforts Throughout the years, scientists have technologies to and response to therapies resulted in the collected vast amounts of data from measure these development experiments aiming to achieve a aberrations and use them to personalise computational models using speciic of PharmacoDB1, a web-application better understanding of the molecular therapies. Still, the most common molecular features to predict the allowing researchers to quickly access the dynamics behind complex diseases. treatments for cancer are chemotherapy, probability of survival of cancer patients pharmacogenomic data to investigate However, the complexity of such data using drugs to kill the most proliferative treated with standard-of-care treatments. the possible associations between makes it dificult for human researchers cells, and radiotherapy, using high energy He says: “Amongst many discoveries, genomic aberrations and drug response. to run in-depth analyses and extract the X-rays. These treatments can sometimes my research unravelled the landscape of relevant information. This is where the be successful in reducing or eradicating cancer pathway activities associated with As part of his current research, Dr use of high performance computers, cancerous cells, yet they can be highly patients’ survival in each of the molecular Haibe-Kains is testing machine learning coupled with the right programs could toxic and are not tailored toward the subtypes, allowing me to further improve algorithms on the database, to try be of great help. In the last decades, speciic set of genomic aberrations that my molecular prognostic models.” and pinpoint predictors of treatment computer scientists have worked hard make each tumour unique. reaction. Paired with the right machine to develop machine learning algorithms EFFECTIVE TREATMENTS FOR learning algorithms, PharmacoDB could – programs allowing machines to Artiicial Intelligence tools could assist INDIVIDUAL PATIENTS be used to develop an AI tool assisting quickly analyse large amounts of data researchers by analysing the complex In 2012, Dr Haibe-Kains started his own in selection of the most effective and “learn” models useful to identify genomic make-up of each individual independent laboratory, broadening treatments for each individual cancer the relevant pieces of information and tumour to develop accurate predictors his ield of research to explore ways in patient. Dr Haibe-Kains’ laboratory also make predictions. These algorithms can of treatment response. This would in turn which machine learning algorithms could leveraged these valuable data to address then be leveraged to develop artiicial help to identify more effective treatments be used to predict therapy response another important issue in cancer intelligence (AI) tools that can assist for individual patients, a major step in patients. With big players like IBM research: how to classify drugs based humans to analyse data that are beyond towards personalised medicine. announcing its Watson initiative to on their mechanism of action? Although our reach. As they become increasingly develop AI to assist oncologists in their biologists, chemists and pharmacologists advanced and sophisticated, AI tools are COMPUTATIONAL METHODS FOR treatment decision process, it was an teamed up to develop a large portfolio of opening up a new world of possibilities CANCER RESEARCH exciting opportunity for Dr Haibe-Kains drugs with high anticancer potential, it is for big data analysis, by transforming the Throughout his career, Dr Benjamin to extend the biomarker discovery unclear for many of these drugs how they way in which studies and investigations Haibe-Kains has explored the potential beyond patients’ survival, and to make actually inhibit the growth of cancer cells. are conducted, leading to important of machine learning algorithms, personalised medicine a reality. However, Dr Haibe-Kains and his team developed discoveries in a much shorter time. bioinformatics and computational Dr Haibe-Kains and his team quickly the Drug Network Fusion (DNF), a www.researchoutreach.org www.researchoutreach.org Behind the Bench Dr Benjamin Haibe-Kains E: [email protected] T: +1 416 581 8626 W: http://www.pmgenomics.ca/bhklab/ https://www.linkedin.com/in/benhaibekains/ https://twitter.com/bhaibeka Research Objectives Research Society, Ministry of Economic postdoctoral fellowship at the Dana-Farber Dr Haibe-Kains’ research focuses Development/Ministry of Research and Cancer Institute and Harvard School of on the computational integration of Innovation of Ontario, Ontario Institute for Public Health (USA). high dimensional molecular data to Cancer Research simultaneously analyse multiple facets of Contact carcinogenesis. Bio Benjamin Haibe-Kains, PhD Dr Benjamin Haibe-Kains is Scientist at Princess Margaret Cancer Research Tower, Funding the Princess Margaret Cancer Centre Floor 11, Room 310 Princess Margaret Cancer Foundation, and Assistant Professor in the Medical 101 College Street Canadian Institutes of Health Research, Biophysics and Computer Science Toronto Terry Fox Research Institute, Stand Up To departments of the University of ON M5G 1L7 Cancer Canada, Cancer Research Society, Toronto (Canada). He earned his PhD in Canada Natural Sciences and Engineering Research Bioinformatics at the Université Libre de Council of Canada, Canadian Cancer Bruxelles (Belgium), before conducting a for the scientiic community. With these better schedule the series of medical Q&A large amounts of pharmacogenomic data appointments
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