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European Biopharmaceutical Review
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Biomarkers are expected to have a major impact on the way cancer is diagnosed, treated and managed. New software offers the potential to analyse and visualise the genomic signatures of cancer cells, paving the way for swifter, more effective biomarker discovery.
Research into molecular biology has helped to identify a large number of genes associated with human disease during the last decade, and is helping scientists to unpick the fundamental biology of major illnesses. Complex gene expression experiments in particular are helping to support this process, as they are able to create a global picture of cellular function by measuring the activity (often called the ‘expression’) of tens of thousands of genes at once.
For example, a strategic research initiative currently underway in Sweden is taking an in-depth look at the role of biomarkers in cancer research. A biomarker is any characteristic that can be objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention.
Although the term biomarker is relatively new, these characteristics have been used in pre-clinical research and clinical diagnosis for a considerable time. For example, blood pressure is now often used to determine the risk of stroke. It is also widely known that cholesterol values can act as a biomarker (and risk indicator) for coronary and vascular disease, that C-reactive protein (CRP) is a biomarker for infl ammation, and prostate-specific antigen (PSA) for prostate cancer.
When examined in molecular terms, these biomarkers describe the subset of markers that might be discovered using genomics, proteomics technologies or imaging technologies. Viewed in this way, biomarkers could have a major role to play in medicinal biology, as they can help with early diagnosis, disease prevention, drug target identification, patient stratification, drug response and more. As such, gene-based biomarkers are widely considered to be an effective way of studying human disease.
Using Biomarkers within Medicinal Biology
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.
When identified at an early stage, biomarkers can provide an important tool for diagnosing disease types and stages, predicting the outcomes of different therapies, and monitoring pharmaceutical development. As such, a number of international research projects are now focusing on different tumours in order to pursue the identification and validation of biomarkers as both diagnostic and therapeutic targets, and also to facilitate the identification of cancer subpopulations based on clinical behaviour and treatment response in tumours.
In fact, researchers around the world are currently using cutting-edge omics platforms, extensive biobanks, and on going collaboration to identify and analyse new biomarkers related to cancer, along with the complex networks that these biomarkers inhabit. As a result, these research teams hope to have a major impact on the way cancer is diagnosed, treated and managed in the next five to 10 years.
The Role of Genomic Signatures
To use a very simple analogy, genomic signatures can be used to identify cancer cells in the way that a flag can identify a particular country. Although each fl ag is composed of several distinct colours and shapes, when these items are put together and arranged in a certain way, they form a distinctive pattern – or signature – that helps to distinguish one flag from another.
When studying genetic data, researchers are essentially looking for recognisable patterns like these. However, they are faced with the challenge of trying to extract these ‘shapes’ from huge arrays of genes, proteins and/or RNA molecules.
What comes out of this analysis is an incredible, almost impossible to imagine, amount of data. As such, it has become increasingly difficult to identify which genes are relevant, and to what degree, especially when working with tens of thousands of data points being generated by hundreds of different patients.
To make matters even more challenging, research groups working in this area typically consist of a collection of highly trained specialists, each of whom has a unique technical skill. As a result, each individual person on the research team – whether a pathologist, molecular biologist and/or biostatistician – is often so specialised that none of them fully understands exactly what his or her colleagues are doing.
Also, whenever a study involves such an enormous amount of genetic information, there is bound to be a number of confounding factors that distort the data. As such, when scientists are working with tens of thousands of genes, the ability to remove this ‘noise’ is very important, so that they can be sure that they’re working with the most reliable data possible.
Software Supports Easier Analysis
Despite these challenges, it is absolutely essential for scientists working in this area to capture, explore and analyse this vast amount of data effectively, since this information is vital if they are to apply their findings to real-world conditions.
Fortunately, the latest software in this area is now helping to accelerate and facilitate the understanding of both the context and relationships of the information contained within large data sets by displaying them graphically, in real time. The simplicity of this interaction now makes it possible for researchers to work with powerful and statistical analysis in entirely new ways. Not only that, but faster analysis means that scientists often have more time to test more creative theories, which in turn leads to better research results.
However, the high dimensional nature of many data sets makes direct visualisation impossible, as the human brain can only process a maximum of three dimensions. As such, the solution is to work with data dimension reduction techniques such as PCA – a method that can be used to project high dimensional data down to lower dimensions.
When using PCA to reduce the dimensions of such valuable data, it’s important not to lose too much information in the process. PCA works well in this regard, as it is a wellestablished technique for reducing the dimensionality of data, while keeping as much variation as possible.
By using PCA in this way, specialist software can then be used to plot the lower dimension data produced via PCA onto a two-dimensional computer screen, so that full-colour 3D images can be rotated and examined with the naked eye more easily.
Computer software can then be used to manipulate the different PCAplots – interactively and in real time – complete with all annotations and other integrated links, as well as a number of powerful statistical functions such as false discovery rates (FDR) and p-values. By receiving all of this data in a highly comprehensible graphical format (3D), scientists are able to make decisions that are based on information that they can understand much more easily. As a result, this level of data visualisation can produce results in seconds, even if the researcher isn’t a trained statistician.
At the same time, sophisticated data analysis software also makes it much easier to make a qualified judgment about the amount of ‘noise’ present, so that researchers can see true patterns as they emerge, and test and re-test a number of different hypotheses very quickly, in rapid succession.
In fact, with key actions and plots now displayed within a fraction of a second, scientists can increasingly perform the research they want and find the results they need instantly – without the wait. This approach has helped to open up new ways of working with the analysis and, as a consequence, has helped to bring the biologists back into the analysis phase, which means that bioinformaticians and biostatisticians are free to focus on their own areas of interest and expertise.
Can Data Visualisation Help to Reveal New Biomarkers?
If the data analysed has batch effects, or is paired with unwanted dependencies, these variables can be removed with just a few mouse clicks, so that the results can be delivered and analysed in real time. It’s therefore now possible to investigate the output of large clinical trials very quickly, and test different hypotheses and explore alternative scenarios within seconds. As a result, scientists looking for new biomarkers can now drastically shorten their analysis time when attempting to identify relevant structures in their data.
In many cases, scientists begin their research by coding any interesting factors (and confounding factors) into a single file. At this stage, researchers can then import this data and look at the pattern of samples in order to search for both anticipated and non-anticipated sub-patterns.
Then, after examining these sub-patterns using the coded factors that they had identified earlier, the team can look for any significant differences by using statistical tests, as well as kNN visualisation, randomisation and permutation tools. With this approach, researchers are able to make a decision on which variables to trust, annotate any significant variables that have been found, and then export them for functional analysis using additional software tools.
The Future
It’s an accepted fact that the incidence of cancer increases with age, which means that the clinical management of cancer will remain a major challenge for the scientific and medical communities for years to come. It will therefore be vital to identify new strategies for the management and treatment of cancer, and to study the key role of biomarkers in this area.
Over the last three years, a major effort has been made to develop sophisticated software that is not only extremely powerful in this regard, but which can also help scientists to explore and analyse the high-dimensional data sets interactively and in real time. With this approach, researchers are now able to analyse and explore extremely large data sets – even those with more than 100 million data samples – on a regular computer.
More important still, the latest, most intuitive software in this field now allows the actual researchers involved – the people with the most biological insight – to study the data and to look for patterns and structures, without having to be a statistics or computer expert. This new generation of software can help to provide unprecedented insight for a wide range of cancer research programmes, and will play an invaluable role in the ongoing study of biomarkers.
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