Clinical OMICS

JUL-AUG 2019

Healthcare magazine for research scientists, labs, pathologists, hospitals, cancer centers, physicians and biopharma companies providing news articles, expert interviews and videos about molecular diagnostics in precision medicine

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Page 19 of 51

18 Clinical OMICs July/August 2019 SPONSORED CONTENT 18 Clinical OMICs July/August 2019 Tissue Phenomics An Essential Role in Immuno-oncology Camille Mojica Rey A major area of focus for cancer research is the search for informative biomarkers to allow stratification of patients in clinical research and the eventual development of companion diagnostics for personalized therapies. But that search is limited by the lack of sensitivity and consistency in conventional immunohistochemistry (IHC) assays used by pathologists to evaluate solid tumor tissue samples—not to mention the scarcity of good quality biopsy samples in early-stage clinical studies. Until now, IHC has worked well to diagnose cancer, determine the stage and grade of a tumor, and identify cell type and origin. It has also been useful in monitoring treatment efficacy. Personalized medicine, however, requires IHC to act as a robust assay. That's where high-performance computing comes in. Researchers at Definiens have developed a method of applying advanced computing tools to solid tumor image analysis, pioneering a new field called tissue phenomics. Tissue phenomics is the discipline of mining tissue images to identify patterns that are related to clinical outcome, providing potential prognostic and predictive value. It utilizes artificial intelligence (AI)—such as machine learning (ML) and deep learning (DL)—to quantify tumor pathology, setting the stage for tissue-based companion diagnostics. "Tissue phenomics goes beyond conventional IHC by allowing the inclusion of spatial information, which is critical to understanding the landscape of the cancer," says Ralf Huss, M.D., Definiens' chief medical officer. Automated tumor image analysis has the ability to find unique features that, after further investigation, might turn out to be clinically relevant. "These are features we could not detect otherwise, that the pathologist just has not been trained to read from images," says Huss. In addition to discovering novel features, tissue phenomics provides useful information about whether immune cells are in close relationship with cancer cells. Proximity determines whether an immune cell recognizes a cancer cell and kills it or signals to other cells. "This relational information is what tissue phenomics technology is able to provide," Huss says. Definiens recognizes that the true power of tissue phenomics will only be realized when its data are combined with other data. Ongoing discussions in the field about which types of data are better than others are counterproductive; the key to making sense of the available data is integration. "Tissue phenomics is part of the 'omics field," says Ingrid Braenne, Ph.D., who leads Definiens' exploratory data science group. "The whole 'omics field is moving towards the multi-omics approach, where we combine all the different 'omics [technologies] to get a detailed profile of the tumor microenvironment." Based on latest literature, the six IO-Panel categories (left) are defined by distinct immune profiles (right). It is also possible to assess the heterogene- ity of the tumor interactions with a regional profiling (middle). In the future, the goal is to support treatment decisions based on this categorization.

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