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Activity Number: 388 - Statistical and Computational Advances in Cancer Genomics with Application to Precision Medicine
Type: Invited
Date/Time: Tuesday, July 31, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #326839
Title: Predicting Cancer Outcomes from Genomics Data
Author(s): Peter Campbell*
Companies: Wellcome Trust Sanger Institute
Keywords: Cancer; Genomics; Precision Oncology

One route to learning how genetic changes modulate cancer outcomes is the creation of large clinical-genomic knowledge banks. We have compiled such knowledge banks from thousands of patients across several tumour types. Key observations to emerge include:  Many driver mutations correlate with clinical features at first presentation, including stage and conventional risk factors for outcome;  With sufficiently large knowledge banks of matched genomic and clinical data, it is possible to generate predictions of future disease course that are personally tailored to a given patient's cancer;  These predictive models generally outperform current standard prognostic schemes;  Personalised patient predictions require new methods for data visualisation and presentation;  Knowledge banks can provide decision support for challenging therapeutic conundrums;  To gain the accuracy of prediction required for supporting individual patient decisions, power calculations indicate that knowledge banks will require sample sizes of thousands;  There remain statistical challenges in making "out-of-cohort" predictions from a knowledge bank to a real-world patient.

Authors who are presenting talks have a * after their name.

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