Abstract Titles
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All accepted abstracts will be available in the Journal for ImmunoTherapy of Cancer (JITC).
Abstract Titles
Onsite Posters
Onsite posters will be presented in the Poster Hall at the Walter E. Washington Convention Center in Washington, D.C. All odd numbered posters will be presented on Friday, Nov. 12, 2021. Even numbered posters will be presented Saturday, Nov. 13, 2021.
ePosters
ePosters will be on display on the SITC 2021 virtual meeting platform from 7 a.m. EST on Friday, Nov. 12, 2021 until the virtual meeting platform is closed on Jan. 9, 2022.
| # | Type | Title | Authors | Category | Keywords |
|---|---|---|---|---|---|
| 818 | Poster Presentation | Using deep learning approaches with mIF images to enhance T cell identification for tumor -Automation of infiltrating lymphocytes (TILs) scoring on H&E images | Abu Bakr Azam, M.Engg; Yu Qing Chang, MB BCh BAO; Matthew Leong Tze Ker, MBBS; Denise Goh, BBiomedSc (Hons); Jeffrey Chun Tatt Lim, BSc.; Mai Chan Lau, PhD; Benedict Tan, PhD; Lihui Huang, PhD; Joe P. Yeong, MD, PhD; Yiyu Cai, Assoc Prof. | Machine Learning, Artificial Intelligence, and Computational Modeling | Biomarkers;Immunoscore;T cell;Tumor infiltrating lymphocytes (TILs) |
| 819 | Poster Presentation | Radiomic Markers Associated with Clinical Benefit in Advanced Uveal Melanoma Patients with Radiographic Progression on Tebentafusp | Volkan Beylergil, MD; Laura Collins; Lawrence H. Schwartz, MD; Thomas Eche; Binsheng Zhao; Richard D. Carvajal; Shaad E. Abedin, MD, FACP; Laurent Dercle, MD | Machine Learning, Artificial Intelligence, and Computational Modeling | Bioinformatics;Bispecifics;T cell;Targeted therapy;Tumor antigens |
| 820 | Poster Presentation | Machine learning significantly improves neoantigen-HLA predictions utilizing > 26,000 data points from the PACTImmuneTM Database | Vinnu Bhardwaj, PhD; Amin Momin; Jonathan Johnston; Elizabeth Speltz; Tyler Borrman; Stefanie Mandl; Olivier Dalmas, Ph.D.; Zheng Pan, PhD; Ashish Kheterpal; Eric Stawiski | Machine Learning, Artificial Intelligence, and Computational Modeling | Adoptive immunotherapy;Bioinformatics;Neoantigens;Solid tumors;T cell;Tumor antigens |
| 821 | Poster Presentation | Machine Learning Models Can Quantify CD8 Positivity in Lymphocytes in Melanoma Clinical Trial Samples | Benjamin Glass, MS; S Adam Stanford-Moore; Diksha Meghwal; Nishant Agrawal; Mary Lin; Cyrus Hedvat; George Lee, PhD; Scott Ely; Michael Montalto; Ilan Wapinski; Vipul Baxi, MS; Andrew Beck | Machine Learning, Artificial Intelligence, and Computational Modeling | Solid tumors;T cell;Tumor microenvironment |
| 822 | Poster Presentation | GraphITE: Unsupervised Graph Embeddings Approach to Multiplex Immunofluorescence Image Exploration Reveals New Insights into NSCLC and HNSCC Tumor Microenvironment | Michael J. Surace, PhD; Helen K. Angell, PhD, BSc; Christopher Innocenti; Zhenning Zhang; Isabelle Gaffney; Andreas Spitzmüller; Balaji Selvaraj; Khan Baykaner | Machine Learning, Artificial Intelligence, and Computational Modeling | Biomarkers;Monocyte/Macrophage;Myeloid cells;T cell;Tumor infiltrating lymphocytes (TILs);Tumor microenvironment;Tumor stroma |
| 823 | Poster Presentation | Spatial analysis of tumor-infiltrating lymphocytes correlates with the response of metastatic colorectal cancer patients treated with vactosertib in combination with pembrolizumab | Tae Won Kim, MD; Keun-Wook Lee, MD; Joong Bae Ahn, MD; Young Suk Park, MD; Gahee Park, PhD; Kyunghyun Paeng; Chan-Young Ock, MD, PhD; Hyejoo Park, PhD; Jiyeon Ryu, PhD; Bitna Oh, MD; Bo-Kyoung Kim, MD; Sunjin Hwang, MD; Ki Baik Hahm, MD; Seong-Jin Kim, PhD | Machine Learning, Artificial Intelligence, and Computational Modeling | Biomarkers;Checkpoint blockade;Clinical study;Clinical trial;Immune monitoring;T cell;Tumor infiltrating lymphocytes (TILs);Tumor microenvironment |
| 824 | Poster Presentation | High quality neoantigens are immunoedited in long-term pancreatic cancer survivors | Zachary Sethna, PhD; Marta Luksza, PhD; Luis A. Rojas; Kevin Soares, MD; Joanne Leung, PhD; Jayon Lihm; David Hoyos; Anton Dobrin; Rajya Kappagantula; Alvin Makohon-Moore; Amber Johns; Antony Gill; Masataka Amisaki, MD, PhD; Pablo Guasp, PhD; Abderezak Zebboudj, PhD; Rebecca Yu; Adrienne Kaya Chandra; Zagaa Odgerel; Michel Sadelain; Erin Patterson; Christine Iacobuzio-Donahue, PhD, MD; Benjamin D. Greenbaum, PhD; Vinod Balachandran, MD | Machine Learning, Artificial Intelligence, and Computational Modeling | Antigen presenting cells;Autoimmunity;Bioinformatics;Immune tolerance;Neoantigens;Systems biology;T cell;Tumor antigens |
| 825 | Poster Presentation | High-dimensional Image Cytometry Reveals Spatially Organized Tumor-immune Microenvironment in Hepatocellular Carcinoma | Haoyang Mi, MS; Aleksander S. Popel, Ph.D.; Mark Yarchoan, MD | Machine Learning, Artificial Intelligence, and Computational Modeling | Bioinformatics;Biomarkers;Checkpoint blockade;Clinical trial;Immune contexture;Monocyte/Macrophage;Systems biology;T cell;Targeted therapy;Tumor microenvironment |
| 826 | Poster Presentation | Establishing the preclinical/translational PK/PD relationship for BT7480, a Nectin-4/CD137 Bicycle tumor-targeted immune cell agonist™ (Bicycle TICA™) | Hitesh Mistry; Fernando Ortega; Johanna Lahdenranta; Punit Upadhyaya, PhD; Kristen Hurov; Phil Jeffrey; CHRISTOPHE D. CHASSAGNOLE, PhD | Machine Learning, Artificial Intelligence, and Computational Modeling | Bispecifics;Solid tumors;Systems biology;T cell;Targeted therapy;Tumor antigens;Tumor infiltrating lymphocytes (TILs) |
| 827 | Poster Presentation | Streamlining design of safe and effective TCR therapies with AI | Mikolaj Mizera, PhD; Anna Sanecka-Duin, PhD, MS; Maciej Jasinski, PhD; Paulina Król; Giovanni Mazzocco, M.Sc.; Victor Murcia Pienkowski, PhD; Alexander Myronov; Iga Niemiec; Piotr Skoczylas, M.Sc.; Slawomir Stachura, PhD; Piotr Stepniak; Daniel Wojciechowski; Lukasz Grochowalski; Oskar Gniewek; Jan Kaczmarczyk, PhD; Agnieszka Blum, PhD, MD | Machine Learning, Artificial Intelligence, and Computational Modeling | Adoptive immunotherapy;Bioinformatics;Clinical study;Immune monitoring;Immune toxicity;Neoantigens;T cell;T cell lineages;Targeted therapy;Tumor antigens |
| 828 | Poster Presentation | Quantifying perivascular immune cells in the stroma of human triple negative breast tumors using deep learning spatial analytics | Anna Juncker-Jensen, PhD; Nicholas Stavrou, MS; Mohammed R. Moamin; Mate L. Nagy; Richard J. Allen; Angela Cox; Claire Lewis | Machine Learning, Artificial Intelligence, and Computational Modeling | Immune contexture;Immune suppression;Monocyte/Macrophage;Myeloid cells;Regulatory T cell (Treg cell);Solid tumors;T cell;Tumor infiltrating lymphocytes (TILs);Tumor microenvironment;Tumor stroma |
| 829 | Poster Presentation | Spatial arrangement and density of tumor-infiltrating lymphocytes (TILs) predicts response to immunotherapy in head and neck squamous cell carcinoma patients | Reetoja Nag, PhD; Germán Corredor; Vidya S. Viswanathan; Pingfu Fu, PhD; James Lewis Jr.; Jay Wasman; Theodoros N. Teknos; Monaliben Patel, MD; Quintin Pan; Anant Madabhushi, PhD | Machine Learning, Artificial Intelligence, and Computational Modeling | Biomarkers;Tumor infiltrating lymphocytes (TILs) |
| 830 | Poster Presentation | Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes reveals Immune-excluded phenotype related to APOBEC signature and clonal evolution of cancer | Chan-Young Ock, MD, PhD; Sanghoon Song, MS; Gahee Park, PhD; Changhee Park, MD; Soo Ick Cho, MD, PhD; Seunghwan Shin, MD; Yoojoo Lim, MD; Wonkyung Jung, MD; Heon Song; Jeongun Ryu; Minuk Ma; Seonwook Park, PhD; Sergio Pereira, PhD; Donggeun Yoo, PhD; Kyunghyun Paeng | Machine Learning, Artificial Intelligence, and Computational Modeling | Biomarkers;Immune suppression;Immune tolerance;Systems biology;Tumor evasion;Tumor infiltrating lymphocytes (TILs);Tumor microenvironment;Tumor stroma |
| 831 | Poster Presentation | Exact Shapley values for explaining complex machine learning based molecular tests of checkpoint inhibitors: potential utility for patients, physicians, and translational research | Heinrich Roder, DPhil; Lelia Net, PhD; Joanna Roder, PhD; Thomas Campbell, PhD; Mark McCleland, PhD; Wei Zou, PhD; Minu Srivastava, PhD; David Shames, PhD; Laura Maguire, PhD; Robert Georgantas III, PhD | Machine Learning, Artificial Intelligence, and Computational Modeling | Bioinformatics;Biomarkers;Checkpoint blockade;Proteomics;Solid tumors |
| 832 | Oral Presentation | Deep learning reveals predictive sequence concepts within immune repertoires to immunotherapy | John-William Sidhom, M.D., PhD.; Petra B. Ross-Macdonald, PhD; Megan Wind-Rotolo, PhD; Andrew M. Pardoll, MD, PhD; Alexander Baras, M.D., Ph.D. | Machine Learning, Artificial Intelligence, and Computational Modeling | Bioinformatics;Biomarkers;Checkpoint blockade;Immune monitoring;Solid tumors;T cell;Tumor infiltrating lymphocytes (TILs);Tumor microenvironment |
| 833 | Poster Presentation | A scalable deep learning framework for rapid automated annotation of histologic and morphologic features from large unlabeled pan-cancer H&E datasets | David Soong, PhD.; Anantharaman Muthuswamy, PhD; Clifton Drew, PhD; Nora Pencheva; Maria Jure-Kunkel; Kate Sasser, PhD; Hisham Hamadeh, PhD; Suzana Couto; Brandon Higgs | Machine Learning, Artificial Intelligence, and Computational Modeling | Bioinformatics;Immune contexture;Solid tumors;Tumor microenvironment |
| 834 | Poster Presentation | A Robust Deep Learning Approach for Precisely Segmenting Cells in Multiplex Tissue Images | Daniel Winkowski, PHD; Jeni Caldara; Brit Boehmer; Regan Baird, PhD | Machine Learning, Artificial Intelligence, and Computational Modeling | Immune suppression;Inflammation;Monocyte/Macrophage;NK/NKT cell;Regulatory T cell (Treg cell);T cell lineages;Tumor infiltrating lymphocytes (TILs);Tumor microenvironment;Tumor stroma |
Presented By:
