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Machine Learning–Based Multi-Omics Integration to Robustly Predict Survival and Oncogenic Pathway Activation in Common Female Cancers.

Project Description

This cross-College studentship aims to develop a machine-learning approach for accurately identifying oncogenic pathway activation and predicting survival in patients with breast and endometrial cancer. Breast and endometrial cancers are the two most common cancers in women in the UK, with a significant impact on lives and healthcare. Genetically, both diseases are highly heterogeneous with complex aetiologies, making prognosis and prediction of relapse challenging.

Recent pan-cancer analyses of the Cancer Genome Atlas (TCGA) using machine learning identified aberrant Ras pathway activation (including NF1 loss) in patients who were Ras wild-type, thereby highlighting potential responders to MEK inhibitors who would otherwise not have been considered for this line of therapy1. Furthermore, a multi-omic integration of TCGA data in hepatocellular carcinomas was able to robustly predict survival2. Based on these recent findings, this studentship will develop novel machine-learning methods and investigate their use for detecting and matching advanced cancer patients to targeted therapies and predicting patient relapse. Overall, we hypothesise that deep learning of multi-omic datasets (namely somatic mutations, CNVs and transcriptomics) can highlight aberrant pathway activity in breast and endometrial cancers, predict which patients are likely to relapse and identify additional cohorts likely to respond to targeted therapies.

Interviews will be held 17th May 2019

Entry requirements

Applicants are required to hold/or expect to obtain a UK Bachelor Degree 2:1 or better in a relevant subject. The University of Leicester English language requirements apply where applicable.

How to apply

Please apply via:

Project / Funding Enquiries

Dr David Guttery: E-mail – ; Telephone – 01162523181
Dr Huiyu Zhou: E-mail – ; Telephone - 01162525295
Application enquiries to

Funding Notes

3.5 year MRC IMPACT DTP studentship


1. Way GP, Sanchez-Vega F, La K, et al. Machine Learning Detects Pan-cancer Ras Pathway
Activation in The Cancer Genome Atlas. Cell Rep 2018; 23(1): 172-80 e3.
2. Chaudhary K, Poirion OB, Lu L, Garmire LX. Deep Learning-Based Multi-Omics Integration
Robustly Predicts Survival in Liver Cancer. Clinical cancer research: an official journal of the American Association for Cancer Research 2018; 24(6): 1248-59.

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