Single Sample Expression-Anchored Mechanisms Predict Survival in Head and Neck Cancer Yang et al. 2012 Presented by Yves A. Lussier MD PhD The University of Chicago
Head and Neck Squamous Cell cancer A group of similar cancers Spread to lymph nodes Strongly related to environmental risk factors Alcohol, smoking, UV lights, viruses ~40,000 new cases in the US per year
Hard to predict recurrence Early detection is crucial Computational goals Use gene expression data for patient classification and recurrence survival Improve interpretability of the classifiers by
integrating pathway information Keep classification performance high Standardize across multi datasets Sample analysis Input: an expression profile of a sample A vector of real values for each patient
Step 1: rank the genes Step 2: calculate a score for each gene Rank of gene g in sample s
Total number of ranked genes Gene set features Gene sets: GO terms or KEGG pathways
Define the normalized centroid (NC) of a gene set to be the average weights of the genes Calculate the NC score for each gene set and its complement Gene set features Calculate the NC score for each gene set and
its complement Gene set features The score of a gene set is the difference between the two Which results in the FAIME profile for each
sample FAIME summary Differential pathways Differential analysis for each pathway\GO feature
Healthy FAIME scores Sick FAIME scores Get empirical p-values by shuffling
the labels (1000 repeats) Analysis overview 1 Data used
8 GE HNSCC datasets 3 for training 5 for validation Controls can be
Samples from an independent healthy individuals Paired samples sample from distant uninvolved site Paired samples samples from the margins of the tumor Data used
Preprocessing Download raw data (cel files) from ArrayExpress Use MAS5 to get the expression matrix Probe -> genes Keep the probe with the largest inter-quantile range Remove genes with negative average expression across samples
Affys MAS5 implementation should produce non negative values Comment: preprocessing is not sample based! Data used
Stability of FAIME scores Dataset A 22 HNSCC 22 independent controls Boxplot for each sample
Values are pathway scores Reproducibility tests Compared methods 1. FAIME 1. Pathway p-value calculated using Z-scores
2. Hypergeometric enrichment analysis 1. Differential genes (from the original papers) 3. GSEA (actually, they used GSA) 4. CORG (Lee et al. 2008; Ideker lab) Test the overlap after running the methods on datasets A-C
Reproducibility tests Reproducibility across methods Goal: test if FAIMEs significant pathways cover the standard analysis Idea: use the standard analysis (GSEA, or HG) as gold
standard Given FAIMEs pathways calculate precision-recall curves based on pathway sizes For a threshold k calculate precision and recall considering pathways with > k genes Reproducibility across methods
Validation on independent datasets Use the pathways and GO terms that were stable in datasets A-C 57 features Test their ability to classify new datasets
D: n=35; E:n=91 Classification: just cluster the samples to two groups (next slides) Average linkage hierarchical clustering Validation on independent datasets
Extra-cellular events Nuclear events, cell cycle No Errors Metabolism
Survival analysis For datasets E and F a follow-up study is given for the cancer patients Cluster the HNSCC samples to two groups Used the CLARA algorithm
Compare the sample clusters based on the survival data (recurrence free survival) Survival analysis Additional dataset (supplement)
Survival analysis: comments The same flow with GSEA or Mean-G: survival plots are not significant (p>0.07) In the publication of dataset E another HNSCC specific clinical test (called HPV) did not produce significant separation.
Prostate cancer (Chen et al. 2013, BMC Medical Genomics) Analyzed three case-control datasets Validation: survival analysis on an independent dataset
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