Person:

Mesirov, Jill

The International Society for Computational Biology (ISCB) presents the Sixteenth International Conference on Intelligent Systems for Molecular Biology (ISMB 2008), to be held in Toronto, Canada, July 19–23, 2008. Now in the final phases of scheduling selected presentations, demonstrations, and posters, the organizers are preparing what will likely be recognized as the premier conference on computational biology in 2008. ISMB 2008 (http://www.iscb.org/ismb2008/) will follow the road paved by the ISMB/ ECCB 2007 (http://www.iscb.org/ ismbeccb2007/) in Vienna in the attempt to specifically encourage increased participation from previously under-represented disciplines of computational biology. This conference will feature the best of the computer and life sciences through a variety of core sessions running in multiple parallel tracks, along with single-tracked Keynote Presentations, posters on display throughout the duration of the conference, and an extensive commercial exposition. The first day (July 18) of the meeting is reserved for two-day Special Interest Group (SIG) and Satellite meetings, the second day (July 19) runs SIGs for the first time in parallel with Tutorials and the Student Council Symposium, and for the first time two SIGs are running in parallel with the main ISMB meeting (July 20–23)

Background: Clustering analysis of microarray data is often criticized for giving ambiguous results because of sensitivity to data perturbation or clustering techniques used. In this paper, we describe a new method based on principal component analysis and ensemble consensus clustering that avoids these problems. Results: We illustrate the method on a public microarray dataset from 36 breast cancer patients of whom 31 were diagnosed with at least two of three pathological stages of disease (atypical ductal hyperplasia (ADH), ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC). Our method identifies an optimum set of genes and divides the samples into stable clusters which correlate with clinical classification into Luminal, Basal-like and Her2+ subtypes. Our analysis reveals a hierarchical portrait of breast cancer progression and identifies genes and pathways for each stage, grade and subtype. An intriguing observation is that the disease phenotype is distinguishable in ADH and progresses along distinct pathways for each subtype. The genetic signature for disease heterogeneity across subtypes is greater than the heterogeneity of progression from DCIS to IDC within a subtype, suggesting that the disease subtypes have distinct progression pathways. Our method identifies six disease subtype and one normal clusters. The first split separates the normal samples from the cancer samples. Next, the cancer cluster splits into low grade (pathological grades 1 and 2) and high grade (pathological grades 2 and 3) while the normal cluster is unchanged. Further, the low grade cluster splits into two subclusters and the high grade cluster into four. The final six disease clusters are mapped into one Luminal A, three Luminal B, one Basal-like and one Her2+. Conclusion: We confirm that the cancer phenotype can be identified in early stage because the genes altered in this stage progressively alter further as the disease progresses through DCIS into IDC. We identify six subtypes of disease which have distinct genetic signatures and remain separated in the clustering hierarchy. Our findings suggest that the heterogeneity of disease across subtypes is higher than the heterogeneity of the disease progression within a subtype, indicating that the subtypes are in fact distinct diseases.