RarecyteFinder: A Bench-to-Bits Toolkit for Label-Free, Whole-Spectrum Analysis of Rare Disseminated Tumor Cells in Liquid and Tissue Biopsies - Project Summary/Abstract Disseminated tumor cells (DTCs), encompassing circulating tumor cells (CTCs) in blood and other body fluids, and isolated tumor cells (ITCs) in peritumoral tissues and lymph nodes (LNs), are vital in the metastatic process of cancer. Despite their rarity, DTCs, as metastatic precursors, play a critical role in metastatic colonization in distant organs and are essential in cancer staging, prognosis, and clinical management. In the field of liquid biopsies, CTCs, ctDNAs, and tumor-derived exomes have emerged as significant materials for cancer diagnosis and monitoring therapeutic responses. CTCs, in particular, are unique for encapsulating comprehensive molecular information, thus serving as key proxies for primary tumors and offering insights into tumor metastasis mechanisms. However, the study of CTCs and ITCs faces challenges, especially in detection and analysis, due to their extreme rarity and the absence of a generic, accurate method for their unbiased detection. Traditional methods, often relying on epithelial markers or morphological characteristics, suffer from low sensitivity and high false-positive rates. This problem is compounded in ITCs embedded in the complex cellular matrix of peritumoral tissues and LNs, making their identification and retrieval for molecular profiling challenging. Therefore, an effective, unbiased approach for identifying and analyzing these rare DTCs across various solid tumor types is urgently needed. To address this, we propose RarecyteFinder, a bench-to-bits, label-free method for the unbiased identification and in-depth molecular analysis of DTCs in diverse liquid and tissue biopsy specimens. Leveraging genome-wide copy number alterations as a robust marker, RarecyteFinder aims to overcome the current limitations of DTC study, such as low sensitivity and high false-positive rates, and to provide accurate identification and comprehensive molecular insights into DTCs. The method will also enable precise tracing of DTCs’ tissue-of-origin and tumor type (TOTT), identifying their targetable vulnerabilities and predicting drug responses. Our preliminary data have already demonstrated RarecyteFinder's potential, and we now aim to develop it into a fully functional prototype through three synergistic aims: 1) Advanced development and validation for CTC analysis in blood samples from various cancers, including systematic validation with clinical biospecimens and improving TOTT tracing; 2) Development and validation of a Signature/Drug Target Mapper (SDTM) module, employing bioinformatic and machine learning models to resolve transcriptome signatures, drug sensitivities, and targetable vulnerabilities of DTCs; 3) Extension of RarecyteFinder to include rare ITCs in peritumoral tissues and LNs to enhance understanding of ITCs in tumor progression and metastasis.
