In this study, we aim to use scRNAseq and spatial transcriptomics (ST) data to predict the interactions between cancer and immune pathways that contribute to tumor growth. The central hypothesis is that machine learning algorithms can learn a low-dimensional feature space that is predictive of tumor growth from these data types. To test this hypothesis, we will apply machine-learning techniques to large-scale scRNAseq and ST data obtained from human tumor samples. Our goal is to identify the key pathways that drive tumor growth and discover novel tumor-immune interactions that contribute to tumor development. Our approach addresses the need to find new tumor-immune interactions that enable tumor development. The method is designed to be free of biases, as it does not depend on prior knowledge of pathways or gene collections, but instead relies solely on sequencing data. The result will be a set of predictions that can be used to generate testable biological hypotheses and can provide a quantitative ordering of the interactions found. The significance of this study lies in its potential to provide a new framework for the ab initio prediction of multicellular gene programs. The proposed approach will allow us to gain deeper insights into the complex interactions between cancer and immune pathways that drive tumor growth. The results of this study have the potential to have a major impact on the field of cancer research. The ability to predict tumor-immune interactions will provide a new avenue for therapeutic development and could lead to new ways to target cancer and improve patient outcomes In conclusion, this study represents a significant step forward in the understanding of tumor growth and its interactions with the immune system. By leveraging scRNAseq and ST data, and applying machine learning algorithms, we hope to gain new insights into the mechanisms of tumor growth and the interactions between cancer and immune pathways.