DECIPHERING SUBCLONAL DIVERSITY IN EARLY TUMORS: A NOVEL IMAGING R-NN CYTOMETRY APPROACH TO MAP GENETIC PROFILES AND MARKER EXPRESSIONS

Tumor heterogeneity is a major issue in cancer diagnosis and therapy due to the difficulty of identifying subclonal variation at an early stage. The goal of this work is to establish a new approach that combines deep learning with high-resolution imaging to identify subclonal heterogeneity in early-stage tumors. Using a Fast R-CNN model to classify subclones and Imaging Mass Cytometry (IMC) to validate the result is a systematic way to detect and visualize the heterogeneity of subclonal populations. The Fast R-CNN had a very high F1-score of 0. 92, which proves the high efficiency of the algorithm in identifying subclonal regions. IMC also provided a strong dual-method validation by associating the genetic markers with the aforementioned subclones computationally. These findings not only extend knowledge of early tumor evolution but also have implications for precision oncology, where therapies can be tailored according to the subclonal architecture. As such, the study shows the possibility of this hybrid approach, but the small sample size and the use of annotated data point to the need for more research. The broader implications of this approach are that this could be applied to other cancer types, which would mean that there is a new approach to the development of multi-cancer detection platforms and personalized treatment. Future work will be directed to the enlargement of datasets, the improvement of models, and the investigation of tumor dynamics over time. The findings of this research provide a solid base for the diagnosis and treatment of cancer, which is a major improvement in the study of oncology.