ISCB-Asia/SCCG 2012, session on cancer genome informatics


Yinyin Yuan
Division of Molecular Pathology, Institute of Cancer Research, London

Quantifying breast cancer intra-tumor heterogeneity to correct and complement genomics

Abstract

Solid tumors are complex tissues composed of a mixture of cancer and normal cells, which complicates the interpretation of their molecular profiles. Normal cell contamination can dilute cancer cell information and tissue architecture is generally not reflected in molecular assays.

To address these challenges, we developed a computational approach based on standard Haematoxylin and Eosin-stained sections and demonstrated its power in a discovery cohort of 323 breast tumors and an independent validation cohort of 241 tumors. First, to deconvolute cellular heterogeneity and detect subtle genomic aberrations, we introduced an algorithm based on tumor cellularity to increase the comparability of copy-number profiles between samples. Second, we demonstrated that a predictor for survival integrating image-based and molecular features significantly outperforms classifiers based on single data types. Third, we described and validated a novel, independent prognostic factor based on quantitative analysis of spatial patterns between stromal cells, which are not detectable by molecular assays. Our quantitative methods refine and complement molecular assays of tumor samples and could benefit all large-scale cancer studies.

Biography

Yinyin Yuan, Ph.D., was trained as a computer scientist, finishing a 5-year BSc degree within 4 years (2003) at the University of Science and Technology of China, before obtaining her MSc (2005) and PhD (2009) at the University of Warwick. At Warwick she became interested in studying genetic regulation in plant disease by leveraging statistical analysis tools originally developed for other disciplines such as economics.

In June 2012, she joined the Division of Molecular Pathology at the Institute of Cancer Research in London in 2012 as the leader of the Computational Pathology and Integrative Genomics team.