The majority of cancer treatments end in failure due to Intra-Tumor Heterogeneity (ITH). ITH in cancer is represented by clonal evolution where different sub-clones compete with each other for resources under conditions of Darwinian natural selection. Predicting the growth of these sub-clones within a tumour is among the key challenges of modern cancer research. Predicting tumor behavior enables the creation of risk profiles for patients and the optimisation of their treatment by therapeutically targeting sub-clones more likely to grow. Current research efforts in this space are focused on mathematical modelling of population genetics to quantify the selective advantage of sub-clones, thus enabling predictions of which sub-clones are more likely to grow. These tumor evolution models are based on assumptions which are not valid for real-world tumor micro-environment. Furthermore, these models are often fit on a single instance of a tumor, and hence prediction models cannot be validated. This paper presents an alternative approach for predicting cancer evolution using a data-driven machine learning method. Our proposed method is based on the intuition that if we can capture the true characteristics of sub-clones within a tumor and represent it in the form of features, a sophisticated machine learning algorithm can be trained to predict its behavior. The work presented here provides a novel approach to predicting cancer evolution, utilizing a data-driver approach. We strongly believe that the accumulation of data from microbiologists, oncologists and machine learning researchers could be used to encapsulate the true essence of tumor sub-clones, and can play a vital role in selecting the best cancer treatments for patients.

The biggest clinical challenge in oncology is the fact the tumours change over time, progressing from benign to malignant, becoming metastatic, and developing treatment resistance1,2. This occurs through a process of clonal evolution involving cancer cells and their microenvironment3. Intra-tumour heterogeneity (ITH), or the genetic and phenotypic variation of cancer cells within the same tumour, is the natural consequence of this evolutionary process. ITH is also a key factor contributing to the lethal outcome of cancer, as it provides the substrate of phenotypic variation upon which adaptation can occur4. A fundamental question in oncology is therefore: can we predict a cancer's next evolutionary "step"?