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Effective response to immunotherapy relies on dynamic interactions between tumor and immune cells in the tumor microenvironment. A critical step towards improving current therapeutic strategies in immuno-oncology (IO) is to dissect the immune response to cancer and to study the composition and heterogeneity of the TME.
TORONTO (PRWEB) May 09, 2019
In a live webinar upcoming on Wednesday, May 29, 2019 examples will be shown for how histopathologic multiplexed immunohistochemistry (IHC) benefits from artificial intelligence and is used to:
Single biomarkers, as PD-L1 IHC tests, have improved our understanding and prediction of patient’s immune response, but they are still limited in generating insights. Although PD-L1 expression can be used to predict a more favorable response to IO treatment, a significant percentage of patients with high expression of PD-L1 still do not respond. Hence, the question is whether a combination of markers can improve prediction and patient stratification.
The presenter will show how Definiens used automated image analysis on NSCLC biopsies to improve prediction of response to anti-PD-L1 therapy using a combination of PD-L1 and CD8.
Having shown that a combination of two markers improve prediction, she will in the second part of the webinar focus on complex patterns and demonstrate how a set of markers can be used to group patients based on biomarker profiles. The first example will show how machine learning guided feature selection from seven biomarkers improved the recurrence prediction in Prostate Cancer patients with intermediate Gleason Scores (Gleason Score: 6 – 9).
The second example will demonstrate a study using seven multiplexed immune markers to segregate NSCLC patients into six categories. The Definiens standardized marker panel (IO-Panel) consist of a combination of validated multiplex IHC assays. Based on an optimized image and data analysis protocol, the IO-Panel is designed to stratify patients into knowledge-based immune categories ranging from hot, intermediate to cold.
Identification of tissue based spatial patterns supports biological hypothesis generation and further helps us to improve our understanding of the complex immune responses to cancer. In the last part of this webinar, we will focus on the quantification of tumor heterogeneity and show how to apply artificial intelligence to identify global and local TME patterns and to profile patients based on these spatial features.
Join guest speaker Ingrid Braenne, Ph.D., Team Manager Exploratory Data Science at Definiens AG to learn and discuss how artificial intelligence and multiplexing of biomarkers can be used to extract biological insights from digital histopathology slides. Gain insights on how to profile patients based on the TME and thereby unlock a comprehensive view on the tumor immune heterogeneity. Learn how to improve the development of predictive biomarkers to effectively aid your clinical development in oncology.
For more information or to register for this event, visit Using AI and Multiplex Biomarker Analysis for Deeper Insights into the Tumor Microenvironment.
Xtalks, powered by Honeycomb Worldwide Inc., is a leading provider of educational webinars to the global life science, food and medical device community. Every year thousands of industry practitioners (from life science, food and medical device companies, private & academic research institutions, healthcare centers, etc.) turn to Xtalks for access to quality content. Xtalks helps Life Science professionals stay current with industry developments, trends and regulations. Xtalks webinars also provide perspectives on key issues from top industry thought leaders and service providers.
For the original version on PRWeb visit: https://www.prweb.com/releases/using_ai_and_multiplex_biomarker_analysis_for_deeper_insights_into_the_tumor_microenvironment_new_webinar_hosted_by_xtalks/prweb16298179.htmNEXT ARTICLE
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