Novel Multi-Omics Integration and Machine Learning Tools to Understand Heterogeneous Systems of Ovarian Cancers

Lead Research Organisation: Imperial College London
Department Name: Dept of Surgery and Cancer


My work focuses on the integration of CT imaging data (radiomics) with metabonomics, trancriptomics and genomics data. One of the biggest challenges with ovarian cancer is chemotherapy resistance. We aim to enhance the current clinical understanding of ovarian cancer by investigating this disease through various omics lenses. Specifically, we are interested in how multi-omics profiles can predict disease outcome.

In this project, the plan is to investigate ovarian cancer with a top down phenotype-to-genomics approach. This is facilitated by our collection of a completely novel a multi-locale, and multi-omics ovarian cancer cohort. In parallel with this lab component, we are developing a variety of pre-processing and machine learning tools to enhance the reproducibility and validity of radiomics, transcriptomics, genomics and metabonomics results. A large multi-omics ovarian cancer cohort combined with a robust analysis, in turn, will yield insightful results across the board; from clinic to bioinformatician.

On a biological level, we will collect omics data from various ovarian cancerous and non-cancerous tissue sites. This includes biochemical analysis of ovarian cancer tissue samples and plasma samples. Along with these human samples, we also have obtained corresponding time-series enhanced contrast CT scans. We seek to understand how tissue molecular aetiology is reflected in plasma omics profiles, and radiomics profiles.

This novel multi-omics ovarian cancer cohort will be analysed by employing traditional machine learning and deep learning techniques. Novel machine learning and deep learning approaches, that are specifically designed for this type of multi-omics cohort will also be developed. We plan to promote a 'gold standard' radiomics analysis of ovarian cancer, and a novel framework for multi-omics integration.

The overarching aim is to identify a variety of makers that are indicative of disease malignancy and chemotherapy resistance. Ultimately, we want ovarian cancer patients to get the best available therapy at the correct time.


10 25 50