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.

The ultra-bright femtosecond X-ray pulses provided by X-ray Free Electron Lasers (XFELs) open capabilities for studying the structure and dynamics of a wide variety of biological and inorganic systems beyond what is possible at synchrotron sources. Although the structure and chemistry at the catalytic sites have been studied intensively in both biological and inorganic systems, a full understanding of the atomic-scale chemistry requires new approaches beyond the steady state X-ray crystallography and X-ray spectroscopy at cryogenic temperatures. Following the dynamic changes in the geometric and electronic structure at ambient conditions, while overcoming X-ray damage to the redox active catalytic center, is key for deriving reaction mechanisms. Such studies become possible by using the intense and ultra-short femtosecond X-ray pulses from an XFEL, where sample is probed before it is damaged. We have developed methodology for simultaneously collecting X-ray diffraction data and X-ray emission spectra, using an energy dispersive spectrometer, at ambient conditions, and used this approach to study the room temperature structure and intermediate states of the photosynthetic water oxidizing metallo-protein, photosystem II. Moreover, we have also used this setup to simultaneously collect the X-ray emission spectra from multiple metals to follow the ultrafast dynamics of light-induced charge transfer between multiple metal sites. A Mn-Ti containing system was studied at an XFEL to demonstrate the efficacy and potential of this method.