A Multi-PCA Approach to Glycan Biomarker Discovery using Mass Spectrometry Profile Data Anoop Mayampurath, Chuan-Yih Yu Info-690 (Glycoinformatics) Final Project Presentation

Background [1] Kyselova et al. “Alterations in the Serum Glycome Due to Metastatic Prostate Cancer “ Journal of Proteome Research, 2007, 6:

[2] Tang et. al “Identification of N-Glycan Serum Markers Associated with Hepatocellular Carcinoma from Mass Spectrometry Data” Journal of Proteome Research, 2009, Article ASAP [3] Ressom et. al “Analysis of MALDI-TOF Mass Spectrometry Data for Discovery of Peptide and Glycan Biomarkers of Heptacelluar Carcinoma, Journal of Proteome Research, 2008, 7:603

Objective Given a set of N mass spectra(disease and healthy), develop an algorithm that identifies “significant” spectra and glycan peaks ▫From the significant glycan peaks  Nature of regulation between disease and healthy  Study of effects such as fucosylation and linkage ▫From the significant spectra  A smaller set of spectra m << N that help in analysis  Glycan annotation  Check for overlapping glycans What is meant by “significant”? ▫Elements that exhibit coherent patterns and large variation between disease and healthy Datasets ▫151 MALDI TOF mass spectra : 73 cancer, 78 normal

Data Processing - MultiNGlycan

Details ▫Background subtraction ▫Peak Picking ▫Identification of common glycans across all 151 spectra ▫Filtering using Fit Coefficient cutoff > 0.5  30% of spectra has glycan fit coefficient greater that 0.5, then retain A Nxp matrix X is obtained (N : number of glycans, p: number of spectra)

Multi-PCA algorithm Perform PCA Perform inner-product Sort glycans by inner product (which measure correlation) Shave off 10% of glycans with the lowest inner product score Repeat [4] Hastie et. al ‘‘Gene shaving’ as a method for identifying distinct sets of genes with similar expression patterns’, Genome Biology 2000, 1(2):1-21

Multi-PCA Algorithm X Sort by inner product, shave of 10% of glycans -The algorithm was iterated until 10 glycan values were acquired. The glycans are supposed to be coherent in intensity changes while having high variance between cancer and no cancer - We also switched dimensions to shave off spectra. The algorithm was iterated until we got 6 spectra

Results Mass value Total Intensity

Filtered out Not present in original composition file

Mass value Total Intensity

Significant Spectra No overlapping glycans were found

Future Directions Fragmentation of glycans to study effect of linkage among glycans Glycan microarray More detail on overlapping glycans (substitute single score by combined score) Orthogonalize the data to see other patterns.

Acknowledgements Prof. Haixu Tang, School of Informatics & Computing Prof. Yehia Mechref, Dept of Chemistry