Abstract A23: True-T – Improved prediction by holistic artificial intelligence-based quantification of T-cell response
Authors: Salto-Tellez Manuel , Makhlouf Yasmine , Craig Stephanie , O'Reilly Paul , Maxwell Perry , James Jacqueline 
Published: December 2022

Abstract
Introduction - Seminal work from Galon et al [PMID: 16371631], found immunohistochemical (IHC) quantification of the immune response to be prognostic in colorectal cancer (CRC). Although numerous systems for scoring T-cell subsets have been published [reviewed in PMID: 35758208] and this type of scoring is acknowledged as a bona fide diagnostic test [PMID: 32320495], it is not commonly used in general diagnostic routine. Our group reported recently that a digital pathology (DP) approach to scoring of CD3/CD4/CD8 in more than 1,500 patients was prognostic of survival in CRC Stage II & III and predictive of chemotherapy response in CRC Stage IV [PMID: 32684627]Hypothesis – An artificial intelligence (AI)-based approach to DP analysis of CD3/CD4/CD8 provides unbiased prognostic and predictive advantage in CRC.Materials & Methods - 3,123 whole slide images from 1,041 patient samples from 4 institutions in the context of the PathLAKE UK DP Consortium (Queens’s University Belfast, University of Oxford, University of Nottingham and University Hospital Coventry and Warwick). These samples represented 4 different clones [LN10 (CD3), 4B12 (CD4), 4B11 (CD8) and SP3 (CD4)] from 3 different companies, and were scanned with the Aperio AT2 scanner. Ninety-six experiments for validation and verification were carried out with open-source AI systems following protocols and pathways described before [PMID: 35626427 & PMID: 34359723].Results – We developed a combined CD3/CD4/CD8 AI scoring tool (True-T) which can quantify CD3/CD4/CD8 expressing cells with an accuracy ranging between 96.94 and 99.26; a sensitivity range of 79.27 to 85.22 and a specificity of 98.96 to 99.40. Using this novel AI method of immune cell classification, previous study findings were replicated across four UK centers using an independent cohort of CRC patients using antibody clones optimized for routine clinical diagnostics (p=0.002; HR: 2.21; 95%CI:1.24-3.94 versus p=0.006; hr: 1.84; 95% ci: 1.54-2.21 in the current and former study [PMID: 32684627] respectively). We now have integrated True-T status into a easy to use graphical user interface (GUI), which includes key determinants of CRC prognosis from within a population-representative cohort (n&gt;600), in order to be able to model the impact of a high or low True-T score with other variables of clinical significance.Conclusion – True-T shows that a holistic AI-based quantification of T-cell response potentially improves prediction of patient prognosis over other in-silico quantitative methods in CRC and can be implemented in routine diagnostics in a seamless manner with an easy-to-use GUI.
Citation Format: Manuel Salto-Tellez, Yasmine Makhlouf, Stephanie Craig, Paul O'Reilly, Perry Maxwell, Jacqueline James. True-T – Improved prediction by holistic artificial intelligence-based quantification of T-cell response [abstract]. In: Proceedings of the AACR Special Conference: Tumor Immunology and Immunotherapy; 2022 Oct 21-24; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2022;10(12 Suppl):Abstract nr A23.