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Statistical Approaches to Personalised Medicine in Breast Cancer

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Abstract: Breast cancer is the leading cause of cancer in women, with roughly 1.4 million new cases diagnosed each year worldwide. We have previously shown that, as a result of different molecular processes, breast cancer is a collection of at least ten distinct diseases that have diverse prognoses. We present here a statistical model that accounts for different types of relapse (loco-regional and distant), competing causes of death (cancer death or other causes), clinical covariates, and specific baseline hazards for different molecular subgroups. We show that a multistate model for breast cancer recurrence combined with proper patient stratification can be very useful to make informed decisions about clinical management of the patients. In particular, intermediate events like loco-regional relapse change completely the pattern of disease-specific survival and suggest different courses of action for each breast cancer subtype. Using the complete patient history of recurrences, we show that every disease subtype can metastasise into any organ, although there are some preferences for specific sites. Finally, we study the pattern of consecutive recurrences in each subtype, the number and speed of each metastasis site, the risk of death for each of them. We show that our model produces baseline risks that can be used to measure the prognostic power of biomarkers or to predict the additional benefit of chemotherapy. We also comment on the integration of different layers of molecular information in the context of a personalised breast cancer program.

This talk is part of the Computational and Systems Biology series.

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