From single cells to populations: generalized pseudotime analysis to identify patient trajectories from cross-sectional data in cancer genomics

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Cancer continually evolves at the genetic level through the acquisition of mutations that subsequently lead to the reprogramming of normal cellular activity and ultimately abnormal function. The evolution of cancer in each patient is unique, even when they are of the same type, although they will share some core similarities. In order to understand how cancers evolve over time, it would be ideal to conduct studies where individual patients are followed over time and samples of tumours continually obtained to understand the molecular changes that are ongoing. In practice, this is both logistically impossible and unethical as multiple invasive surgeries to obtain biopsies would be both costly and distressing to patients and treatment cannot be withheld to enable prospective monitoring of the disease. The most practical clinical studies involve obtaining a single tumour biopsy from a patient (or multiple biopsies collected during the same surgery) for a large number of patients. This cross-sectional profile across a random patient population would not give us direct information about how the disease of individual patients evolve but we could combine all the information across the patients to identify sub-groups of individuals who appear to have similar disease trajectories. That is, suppose we have two patients, one at an advanced stage of disease with many mutations and another who presents at a relatively earlier disease stage, and both share a similar set of core mutations. The molecular status of the advanced patient could be an indicator of the future molecular profile of the early stage patient, if left untreated. This project proposes to develop novel statistical machine learning algorithms to apply such logic and rationale to integrate molecular profiles obtained from whole genome sequencing analysis of patients in a cross-sectional study to identify and learn temporal information that is not directly observed but may leave tell-tale clues behind.We will apply these algorithms to the national Genomics England 100,000 Genomes Projects which seeks to sequence tens of thousands of cancer genomes across a range of cancer type over the next few years. The project will give insight into how cancers evolves and importantly provide a means of developing prognostic indicators that are based on molecular information to tell us the severity of a patient's disease and their likely trajectories.