Pathology in Cancer Research Task Force at the Annual Meeting
Program Committee Chair: Marcia R. Cruz-Correa, University of Puerto Rico Comprehensive Cancer Center, San Juan, Puerto Rico
Join us in New Orleans for the AACR Annual Meeting 2022, which is the focal point of the cancer research community, where scientists, clinicians, other health care professionals, survivors, patients, and advocates gather to share the latest advances in cancer science and medicine. From population science and prevention; to cancer biology, translational, and clinical studies; to survivorship and advocacy; the AACR Annual Meeting highlights the work of the best minds in cancer research from institutions all over the world.
Sessions from the Pathology in Cancer Research Task Force
Predictive Biomarkers for Immunotherapy (ED003)
April 8, 2022, 5 – 6:30 p.m.
New Orleans Theater B, Convention Center
Chairperson: Janis Marie Taube, Johns Hopkins University School of Medicine, Baltimore, Maryland
This session will focus on the best method for prediction of response to immunotherapy in the adjuvant setting. Immunotherapy is highly effective in a subset of patients, in some cancers more than others. Therefore, the prediction of response is critical to enrich the patient population in both the neoadjuvant and advanced disease settings to determine who requires additional therapy and who can stop treatment. This session will address ways to determine risk vs. benefit as well as the difference between prognostic vs. predictive biomarkers. Both tissue-based and blood-based biomarkers will be addressed.
Introduction to biomarkers in the adjuvant setting: Predictive vs. prognostic
David L. Rimm, Yale University, New Haven, Connecticut
Tissue-based biomarkers in the neoadjuvant/adjuvant setting
Janis Marie Taube
Peripheral blood ‘omics’ in the adjuvant setting
Tomas Kirchoff, NYU Langone Health, New York, New York
Pathology for Cancer Researchers (ED048)
April 9, 2022, 8 – 9:30 a.m.
Room 343-345, Convention Center
Chairperson: Jiaoti Huang, Duke University School of Medicine, Durham, North Carolina
In this session, three expert tumor pathologists will discuss the following topics: 1. How tumors are pathologically diagnosed, classified, graded and staged; 2. Recent advances in machine learning and A.I. provide new tools to turn pathology slides into large, PathOMICS datasets. In this presentation, we will explore different ways by which PathOMICS data are generated and used for cancer detection, grading and prognosis. Using prostate cancer as an example, the discussion will focus on clinical applications of pathology A.I. and on the integration of PathOMICS and transcriptomics data; 20223. Tissue based genomic studies can be powerful tools for studying and understanding molecular pathogenesis, therapeutic targeting and drug resistance mechanisms in cancer. This presentation will focus on prostate cancer precursor and advanced lesions to showcase molecular pathology-based tools and approaches used to inform, guide and complement such genomic approaches.
Tumor pathology: Diagnosis, classification, grading and staging
PathOMICS: Next generation pathology for cancer research
Beatrice S. Knudsen, University of Utah, Salt Lake City, Utah
How molecular pathology tools lead to novel insights and inform tissue based genomics in cancer
Angelo Michael DeMarzo, Johns Hopkins University School of Medicine, Baltimore, Maryland
Next Generation Pathology: From Histopathology to Artificial Intelligence (ME37)
April 12, 2022, 7 – 7:45 a.m.
Room 208-210, Convention Center
The increasingly complex resources and depth of knowledge that pathologists have acquired in the last two decades have mostly leveraged more accurate molecular and imaging methods, which in turn increasingly correlated with response to therapy and clinical outcome. Recent advancements in deep learning focus on informative histological patterns through a hierarchical learning process, providing robust predictions in cancer and creating risk profiles signatures. In addition, novel imaging technologies, such as 3D microscopy, tumor scanning microscopy, and multiplex immunophenotyping have come to the forefront. The analysis of these complex data require the design and implementation of machine learning workflows, which lower the barrier of entry to this work for both data scientists and pathologists. The repertoire being added to oncologic research by pathologists is incremental and leverages the depth of knowledge acquired for over a century by our discipline.
Next generation pathology: From histopathology to artificial intelligence
Massimo Loda, Weill Cornell, New York, New York
Digital and Computational Histopathology: Taking Cancer Diagnostics to the Cutting Edge (SY29)
April 12, 2022, 12:30 – 2 p.m.
Room 343-345, Convention Center
Chairperson: Anil Vasdev Parwani, The Ohio State University, Columbus, Ohio
Significant technical advancement in the ability to use whole slide imaging (WSI) to digitize large numbers of slides automatically, rapidly and at high resolution is impacting cancer diagnostics. Pathologists can now view, analyze and share WSI with oncologists and even patients. Many laboratories currently using WSI have made headway at integrating their digital slides with laboratory information system (LIS), and started streamlining their “digital workflow” with the intent of moving to a “slideless” laboratory. We are also witnessing an increased use of computational pathology and artificial intelligence tools in cancer diagnostics. Together, these technologies are transforming cancer diagnostics and have the potential to change the practice patterns in the years to come. This symposium will focus on where we are with this transformation journey and what are some of the future directions in using digital and computational pathology for cancer diagnostics.
Advancing cancer diagnosis: Impact of digital pathology and computational pathology tools
Anil Vasdev Parwani
The application of Artificial Intelligence in pathology and issues related to deployment, clinical validation, and regulation
Liron Pantanowitz, University of Michigan, Ann Arbor, Michigan
Tissue imaging, diagnostic reports and molecular data: Multi-modal cancer representation
Hamid Tizhoosh, University of Waterloo, Waterloo, Ontario, Canada