Genetic Engineering & Biotechnology News

SEP15 2017

Genetic Engineering & Biotechnology News (GEN) is the world's most widely read biotech publication. It provides the R&D community with critical information on the tools, technologies, and trends that drive the biotech industry.

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26 | SEPTEMBER 15, 2017 | | Genetic Engineering & Biotechnology News with varied definitions. Overall, it represents a holistic, yet mathematical means for diagnosis that incorporates multiple sources of data (e.g., pathological, radiological, clinical, and molecular data and laboratory findings) to derive clinically actionable knowledge. The field is poised to become a global game-changer. The continuing progress, regulatory issues, and future of the field was discussed at the recent Compu- tational Pathology Symposium, held as part of the 29th Eu- ropean Congress of Pathology 2017 meeting in Amsterdam, the Netherlands. Global Game-Changer Identifying cancer subtypes and predicting response to treatment are driving forces of precision medicine for cancer diagnostics and therapeutics. Mining the details of the intri- cate architecture of tumor cells requires more than the naked eye of a skilled pathologist. The disciplines of radiomics and pathomics help elaborate those features and measurements. The sophisticated algorithms employed by radiomics and pathomics extracts large amounts of quantitative features from medical images and high-resolution tissue images, re- spectively. "Both approaches allow us to analyze features and mea- surements and understand more about disease," explains Anant Madabhushi, Ph.D., professor and director, Center for Computational Imaging and Personalized Diagnostics, Department of Biomedical Engineering, Case Western Re- serve University. "For example, we improve our predictions of treatment response, survival rate, recurrence possibilities, and disease progression." According to Dr. Madabhushi, these relatively new fields will change existing diagnosis paradigms: "Current therapies often use drugs that stimulate the immune system, but that only works about 20% of the time. There is a huge unmet need to know who will respond, especially in the current en- vironment for healthcare. Clearly, the costs of treatments are, unfortunately, driven by expensive drugs that may not work. Therefore, tools that predict response more accurately will improve patient outcome, as well as costs." Dr. Madabhushi says the ultimate goal is to do more with less: "The idea is to take routinely acquired data and maxi- mize knowledge gleaned using computational tools. Further, this approach may soon positively impact global healthcare. "Given the availability of internet access, digitized patho- logical tests may be rapidly sent to the cloud for review by skilled pathologists. Thus, for example, a slide made from the breast cancer of any woman in the world could be com- prehensively analyzed by extracting subvisual information to computationally identify subtleties, allowing the identifica- tion of patients most likely to achieve a response." Open-Source Platform for Digital Pathology While quantitative image analysis in digital pathology can vastly improve the speed, objectivity, and reproducibility of whole-slide analysis and biomarker interpretation, a major challenge is developing, validating, and sharing novel algo- rithms. Peter Bankhead, Ph.D., senior image analyst at Philips, developed digital pathology algorithms as a postdoctoral re- searcher at Queen's University, Belfast, Ireland, to support the molecular pathology research program. What resulted was an open-source platform called QuPath. Dr. Bankhead notes that molecular-pathology research has two major difficulties: "The images are huge, and the analysis is complex. For example, a whole-slide scan of a large tissue sample could be up to 40 GB in size uncom- pressed, and contain millions of cells—each of which needs to be identified, classified, and quantified. I wrote QuPath to give me the tools I needed to work with this kind of data effectively." QuPath now encompasses many tools designed to meet the growing need for a user-friendly, extensible, open- source solution for digital pathology and whole-slide image analysis. Dr. Bankhead explains, "On one level, QuPath is designed for users who do not need to be experts in image analysis—including pathologists. QuPath provides essential features commonly needed in pathology applications (e.g., for identifying tissue, detecting cells, and making measure- ments). But there is also a lot of advanced functionality, such as the ability to interactively train classifiers to distinguish be- Big Data from Images of Tiny Tissue Samples Digitized and computationally analyzed images of microscopy specimens could help pathologists identify cancer subtypes, allowing clinicians to better predict treatment responses. In this image, provided by Bristol- Myers Squibb, a non-small cell lung carcinoma FFPE sample is shown stained with PD-L1 immunohisto- chemistry. Courtesy of Anant Madabhushi, Ph.D., Case Western Reserve University. Translational Medicine Feature Continued from page 1

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