Abstract 4918: Artificial intelligence-assisted macrophage identification in tumor biopsies
| Autor: | Kelsey Weigel, Will Paces, Elliott Ergon, Jeni Caldara, Kile McFadden, Cris Luengo, Roberto Gianani, Bharathi Vennapusa |
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| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | Cancer Research. 79:4918-4918 |
| ISSN: | 1538-7445 0008-5472 |
| DOI: | 10.1158/1538-7445.am2019-4918 |
| Popis: | Understanding response to immunotherapy requires accurate and complete characterization of tumor-associated immune cells in order to fully contextualize the immuno-oncology biomarker expression. Current standard practices surrounding enumeration of biomarker-positive immune cells using image analysis necessitate a dual-labeling approach combining the biomarker of interest and immune cell identification assays. Machine Learning (ML) may be used to distinguish different tissue types in a biopsy (e.g. tumor vs non-tumor), or to identify different cell types (e.g. macrophages vs other cells). A machine learning algorithm obtains statistics for a specific tissue class or cell type based on a training set, given by “ground truth” examples. The algorithm then generalizes from the given examples to “learn” the ability to find the tissue or cell type on the rest of the digital scan of the tissue slide, or other slide scans. Here we specifically describe ML methods for macrophage identification in digital scans of cancer tissue slides. The described methods can be used independently or in combination and are both based upon the use of artificial intelligence-based computational tissue analysis (cTA®) algorithms. These technologies can recognize macrophages independently of traditional IHC or IF dual-labeling identification methods. This same methodology may also be applicable for other specific subsets of morphologically distinct immune cells. The first strategy consists of training the cTA algorithm according to pathologist identification of macrophages, without a macrophage-specific staining (e.g., CD68). In this case, the slides are stained only with a PD-L1 assay, and pathologists establish the “ground truth” to teach the ML classifier. The second strategy consists of using CD68 labeling to identify macrophages to teach the ML classifier. Upon successful training and performance testing, the developed macrophage classifier can potentially be applied to new specimens without relying on pathologist annotation or IHC or IF dual-labeling for macrophage recognition. In summary, these two novel approaches demonstrate how ML could be used for characterization of critical immune cells such as macrophages in immuno-oncology tissue evaluations, without the reliance on additional biomarkers to specifically identify the cells. In this study, we evaluated the approach in the context of standard PD-L1 IHC as is used in the Companion Diagnostic (CDx) setting. Successful ML cTA strategies may be applied to other CDx assays to gain additional information about immune cell identification and quantification while only relying on simple monoplex assays. Importantly, these ML strategies would not require a change to the existing clinical practices where higher-level multiplexing would be needed to achieve similar outcomes. Citation Format: Kelsey Weigel, Will Paces, Elliott Ergon, Jeni Caldara, Kile McFadden, Cris Luengo, Roberto Gianani, Bharathi Vennapusa. Artificial intelligence-assisted macrophage identification in tumor biopsies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4918. |
| Databáze: | OpenAIRE |
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