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Inaugural AACR Companion Society Session at the USCAP Annual Meeting 2022 

March 20, 2022, 10:30 a.m. – 12:30 p.m.
Los Angeles Convention Center
Room 515B

The AACR Pathology in Cancer Research Task Force will be hosting the inaugural AACR Companion Society Session at the 2022 USCAP (United States and Canadian Academy of Pathology) Annual Meeting. Join us in Los Angeles for this exciting session. 


Session Chairs: Robin G. Lorenz, Genentech, San Mateo, California and Lawrence D. True, University of Washington, Seattle, Washington

With rapid advances in ‘omics, informatics, computational biology, image analysis, and artificial intelligence, we are on the brink of a truly transformational period where the promise of personalized medicine and precision oncology can be realized. These novel technologies present an immense opportunity to pathologists. This course will provide an overview of the next generation of emerging technologies in cancer diagnostics and highlight the critical role of pathologists and pathologists-in-training in leading this field and moving it forward.

Learning Objectives: Upon completion of this educational activity, learners will be able to:

  1. Compare the ability of current spatial omic platforms to enable biomarker discovery and characterize the tumor microenvironment.
  2. Describe the practical considerations in using whole slide imaging and digital pathology tools in cancer diagnostics.
  3. Discuss how tumor tissue from rapid autopsy specimens can be used to generate immune competent patient-derived models to study response to therapy.
  4. Describe the technologies required for 3D pathology and the opportunities and challenges for integrating 3D datasets with other diagnostic modalities. 

Welcome and Introduction
Massimo Loda, Weill Cornell Medicine, New York, New York

Dr. Loda will welcome the attendees, provide background on AACR’s goals in involving pathologists in cancer research, and introduce educational goals of the session.

Spatial omics: Accelerating insights from cancer tissues using spatial ‘omic technologies 
Jennifer M. Giltnane, Genentech, Inc., South San Francisco, California

Spatial omics combines molecular analysis with spatial mapping of the cells and/or regions in tissues. This topic will cover current platforms for spatial transcriptomics and spatial proteomics, including the pros and cons of each approach to enable biomarker discovery and characterizing the tumor micro environment.

Advancing cancer diagnostics: Applications of informatics, digital pathology, and artificial intelligence
Anil V. Parwani, The Ohio State Wexner Medical Center, Columbus, Ohio

Automated whole slide imaging (WSI) scanners are now rendering diagnostic quality, high-resolution images of entire glass slides, making it possible to integrate imaging into all aspects of pathology diagnostics and informatics-driven workflows. WSI systems are rapidly becoming an integral component of the pathology practice, and certainly will serve as a platform for innovations in cancer diagnostics. This talk will include practical insights into the use of digital pathology and deep learning tools for cancer diagnosis as well as discuss how artificial intelligence tools can be developed to augment cancer diagnostics.

Living tissue pathology: Dissecting high grade serous ovarian cancer therapeutic response using novel patient-derived organoid co-cultures
Sarah J. Hill, Dana Farber Cancer Institute, Boston, Massachusetts

The talk will cover utilization of fresh tumor tissue from surgical and rapid autopsy specimens to generate immune competent patient-derived models for the study of the anti-tumor immune response and general response to all therapy types.  The focus will be in ovarian cancer, but the concept is broadly applicable to all cancer types.

3D Pathology
Nicholas P. Reder, University of Washington, Seattle, Washington

This topic includes high-throughput methods for slide-free three-dimensional (3D) pathological analyses of whole biopsies and surgical specimens. Other topics can include the opportunities and challenges of applying artificial intelligence to 3D imaging data sets and the integration of various other diagnostic modalities including sequencing to samples post-3D imaging.