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Using Frequent Pattern Mining to Find Co-mutated Genes in Breast Cancer Zachary Stanfield 4/7/2015.

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Presentation on theme: "Using Frequent Pattern Mining to Find Co-mutated Genes in Breast Cancer Zachary Stanfield 4/7/2015."— Presentation transcript:

1 Using Frequent Pattern Mining to Find Co-mutated Genes in Breast Cancer Zachary Stanfield 4/7/2015

2 Project Idea and Theory Mutations of two genes in the same pathway rarely provide a significant growth advantage for tumor cells. 1 Two mutated genes in the same pathway are then mutually exclusive in patients By contrast, mutations of genes in different biological pathways may be collaborative in providing a growth advantage for tumor cells. 1 Mutated genes in different pathways are then co-occurring in patients Idea: Use frequent pattern mining on clinical mutation data to find mutations that co-occur

3 AND Start with two pathways A and B A A B B Patient Set 1Patient Set 2 (Patient Set 1) ∩ (Patient Set 2) = Ø 123123 (A1,B2): S > minsupp (A3,B1): S > minsupp (A1 or A3,B2 or B1) has strong support Then these pairs of co- mutated genes represent a 2 pathway knockout

4 Data TCGA Breast Caner clinical mutation data: 772 patients and 10720 mutated genes (88% of which occur in 5 or less patients) ……….......... Mutated Genes Patients Mutated Genes........................ Transform Data Raw Data

5 Method Apply the Apriori algorithm to find co-occurring mutations Patients are the “transactions” and mutated genes are the “items” Use the hypergeometric test to find significantly mutually exclusive mutated genes Find small sets of these mutually exclusive mutated genes and show that genes in frequent itemsets each occur in a different mutually exclusive set. Annotate the mutated genes to show that the mutually exclusive ones share a pathway/function while the co-occurring gene sets are those in two cancer-related pathways

6 Apriori Algorithm Itemsets with support greater than minsupp are added to L i and these are then used to generate larger itemsets that are checked for minsupp Support = P(A U B)

7 Initial Results Largest frequent itemset size is 2 99 gene mutation pairs minsupp = 0.01 (co-mutated in 7 or more patients) With 95% confidence, there are 36 pairs of significantly mutually exclusive genes

8 Next Steps Generate sets of mutually exclusive mutations For all frequent itemsets, find the ME set in which each gene occurs Show that these set pairs have high support in the data Annotate (Gene Ontology) these ME mutation sets and each unique gene in the frequent itemsets to show pairs of cancer-related pathways are co-mutated in patients

9 Questions?

10 References 1.Systematic Interpretation of Comutated Genes in Large-Scale Cancer Mutation Profiles. Yunyan Gu, 2010. http://mct.aacrjournals.org/content/9/8/2186.full http://mct.aacrjournals.org/content/9/8/2186.full 2.http://webdocs.cs.ualberta.ca/~zaiane/courses/cmput499/slides/Le ct10/sld054.htmhttp://webdocs.cs.ualberta.ca/~zaiane/courses/cmput499/slides/Le ct10/sld054.htm

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