Visualising Complex Data Results from 100,000 Cancer Patients
News Dec 12, 2012
Earlier this week it was announced that up to 100,000 patients with cancer and rare diseases in England will have their entire genetic code sequenced, with the UK Prime Minister outlining that £100m has been set aside for the project over the next three to five years.
This is welcome news as for decades, biomedical scientists have been looking for new ways to diagnose cancers at an early, curable stage and also to select the optimal therapy for individual patients. At the moment, cancer treatments are effective in only some of the patients undergoing therapy, and many cancer patients are still being diagnosed too late, once the cancer is already too far advanced. Despite these challenges, researchers are now exploring how unique biomarkers could help to improve the outcome for people with cancer by enhancing detection and treatment approaches.
However as with the 100,000 sample announced, mapping this kind of genetic data and performing genome sequencing in an attempt to try and personalise medical diagnosis and care, leads to enormous amounts of data being produced in order to classify patients into groups e.g. by distinctive biomarkers.
In addition, once you get to the clinical stage by taking DNA or blood samples even more data is produced and needed to be understand before patients can be grouped. Fortunately, in recent years there have been major developments in analysis software that can handle DNA mapping on this scale, helping to structure patients into groups and identifying which biomarkers should be used.
Chinese researchers have developed interfacially polymerized porous polymer particles for low- abundance glycopeptide separation. These polymer particles - with hydrophilic-hydrophobic heterostructured nanopores - can separate low-abundance glycopeptides from complex biological samples with high-abundance background molecules efficiently.