Melanoma and Big Data

Introduction

All cancers are different. Some, arise from relatively well-understood changes (or mutations) in a patient’s genetic code. Melanoma, on the other hand, appears very differently in different people, making the task of understanding how the cancer works much harder.

By gaining a greater understanding of the characteristics of those people more susceptible to melanoma makes it easier to ensure those with a high risk are informed and advised. It is equally a focus to understand the biology of melanoma development, in the hope of identifying treatments in the future.

Interest and activities

For this study, very large numbers of tissue samples have been collected from patients along with detailed information on their disease and lifestyle. We aim to associate the genetic alterations we see in their specific cancers to their lifestyle (for example smoking, or sunny holidays), family history and clinical data (such as vitamin D levels). For one of these studies, we have collected 356 DNA samples from patients and performed Next-Generation Sequencing (NGS) on each one. This technique allows us to read the genomic sequence of their cancer samples. We have then looked for any regions of their genome that are lost or appear multiple times. These copy number variations have been linked to many cancer types including melanoma, and we hope to use our data to help work out the role they play in cancer formation and spread.

By looking at the patterns, causes, and effects of health the study has confirmed that sunbathing on sunny holidays as a key risk behaviour and identified regular lower intensity weekend sun exposure as protective.

The research group is a genetic epidemiology research group so the second aim is to identify susceptibility genes and how they interact with sun exposure. There are two main approaches to identifying genes which influence risk of melanoma;

1. Family studies: studying families where multiple people been diagnosed with melanoma to identify rare genetic variation associated with a high risk of melanoma (“high penetrance”) and

2. Studies of unrelated persons: those with a diagnosis of melanoma (“cases”) and persons with the same ethnic background and of a similar age and gender distribution without a diagnosis of melanoma (“controls”) to identify genetic variation with more modest differences in risk.

Melanoma incidence is continuing to increase in most western countries, and the fastest recent increase has been amongst older people (>60 years) and especially in men. Age increases the risk of death from melanoma. Death is also more likely in men than women. As the incidence is rising in older men, then it seems likely that death rates will actually increase for melanoma rather than decrease, in the next period due to population growth.

The generation of these data sets is a very computationally-intensive problem. We have generated vast amounts of data for this project (over 16 Terabytes and counting). This has taken over 45 CPU weeks of computation time. This has been made possible by the high-performance computing facilities provided by the MRC Medical Bioinformatics Centre at LIDA.

Researchers

Professor Eva Morris - Epidemiology and Biostatistics, Institute of Leeds Medical Research (LIMR)