Abstract 6087: Cytomics-on-chip and AI-driven predictive analysis platform for early detection of epithelial cancers

Abstract

Abstract Cancer is the 2nd leading cause of death (over 605,000 people) in the US, at an expense of over $200B, with 1 in 3 people projected to have cancer during their lifetime per CDC. Despite the significant impact of early detection and screening on prognosis, only some cancers are diagnosed at an early stage. Carcinomas, comprising >80% of cancer incidence, allow ease in cytology sample access chairside, due to the lesions’ epithelial presentation. This presents a unique opportunity for early detection and screening in epithelial cancers. In low-resource healthcare settings, from clinical examination to the long, tedious and expensive diagnostic journey for cancers & pre-cancerous lesions, can lead to missed, delayed or over diagnosis scenarios. This affects treatment initiation and potentially outcome. To facilitate early intervention, there is compelling need to develop accurate and effective minimally invasive screening platforms. Recent advances in the -omics disciplines, microfluidics and AI tools are starting to reveal promising signatures of early disease detection, with potential to drastically improve screening and diagnostic systems. We are developing a novel application of the cytomics-on-chip platform, for enabling chairside, quantitative screening of suspicious epithelial lesions. The biosensor module involves 1. a single use, cytomics platform employing a cartridge with cellular array and high specificity biomarker reagents, that allows single cell molecular imaging to be completed in a portable analyzer. 2. a microfluidics module that allows cytomorphometric measurements to be completed. 3. The results generated are utilized to train machine learning algorithms to detect cyto-signatures and provide an intuitive result that may be utilized by health care practitioners in clinical-decision making. The first cell-based point-of-care oncology tool has recently been validated with high accuracy (99.3%), sensitivity and specificity, in a multi-site prospective clinical study. Here we demonstrate a pilot study towards development of a smart single cell cytomics-on-chip platform for prompt cytomorphometric and biomarker characterization towards diagnosis of urothelial, anal and cervical cancers, and dysplasia lesions, utilizing brush/pap and fresh urine samples. This has potential for continuous quantitative indexing, for disease categorization. As cancers become more pervasive, improved early detection/screening methods that are accurate, cost effective, easy to implement during routine clinical practice, and providing minimal discomfort to the patient, are urgently needed, improving confidence in clinicians’ decisions. Citation Format: Kritika Srinivasan Rajsri, Michael P. McRae, Nicolaos J. Christodoulides, Khaled Algashaamy, Monica T. Garcia-Buitrago, Fei Chen, Fang-Ming Deng, Jennifer S. Smith, John T. McDevitt. Cytomics-on-chip and AI-driven predictive analysis platform for early detection of epithelial cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6087.