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Cancer Genome Analysis
02-­‐715 Advanced Topics in Computa8onal Genomics Cancer Progression
Tumors
•  Cancer cells –  Reproduce in defiance of the normal restraints on cell growth and division –  Invade and colonize territories normally reserved for other cells •  Types of cancers –  Carcinomas: cancers arising from epithelial cells –  Sarcomas: cancers arising from connec8ve 8ssue or muscle cells –  Leukemias and lymphomas: cancers derived from white blood cells and their precursors Development of Cancer Cells
•  Agents that trigger carcinogenesis –  Chemical carcinogens (causes local DNA altera8ons) –  Radia8on such as x-­‐rays (causes chromosome breaks and transloca8ons), UV light (causes DNA base altera8ons) –  Viruses: Hepa88s-­‐B, Hepa88s-­‐C virus for liver cancer Carcinogenesis
•  Stages of progression in the development of cancer of the epithelium of the uterine cervix. Metastasis
Pathways of Tumorigenesis
Cancer-Causing Genes
•  Oncogenes –  Muta8ons that confer gain of func8ons to oncogenes can promote cancer –  Muta8ons with growth-­‐promo8ng effects on the cell –  OXen heterozygous •  Tumor suppressor genes –  Muta8ons that confer loss of func8on can contribute to cancer –  Typically homozygous •  DNA maintenance genes –  Indirect effects on cancer development by not repairing DNA or correc8ng muta8ons Mutations in Tumor Suppressor Genes
Mutations in Oncogenes
Replication of DNA Damages
Driver and Passenger Mutations
•  Driver muta8ons – 
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Causally implicated in oncogenesis Gives growth advantage to cancer cells posi8vely selected in the microenvironment of the 8ssue E.g., muta8ons that de-­‐ac8vate tumor suppressor genes •  Passenger muta8ons –  Soma8c muta8ons with no func8onal consequences –  Does not give growth advantage to cancer cells Identifying Driver Mutations
•  Typically involves sequencing tumor DNA and the matched normal DNA •  Comparison with reference genome and other known DNA polymorphisms to filter out benign muta8ons •  Signatures of driver muta8ons –  Frequently observed muta8ons across tumors are likely to be driver muta8ons. But, what about tumor heterogeneity? –  Muta8ons that cluster in subset of genes (e.g., oncogenes). Passenger muta8ons are more randomly distributed across genomes Challenges
•  Soma8c muta8ons in both genomes (SNP, CNVs, indels, chromosomal rearrangement etc.) and epigenomes can be posi8vely selected (drivers) •  Different cancer types have different rates of muta8ons. Mutator phenotype may or may not be present. •  Infrequently occurring driver muta8ons are hard to iden8fy. Challenges
•  Computa8onal challenges unique to cancer genome analysis –  Sequence alignment and assembly can be significantly more challenging because of highly rearranged chromosomes and high varia8on across cancer genomes –  Soma8c muta8on calling is more challenging •  the impurity of the sample –  Normal genomes have allele copies of 0, 1, or 2 –  Cancer genomes can have allele copies of frac8ons of 0, 1, or 2 •  Most soma8c muta8ons are rare Breast Cancer Genomes and Subtypes
Comprehensive molecular portraits of human breast tumours. Nature 490, 61–70. 2012. Sorting Intolerant to From Tolerant (SIFT)
•  A tool that uses sequence homology to predict whether an amino acid subs8tu8on affects protein func8on •  Assuming that important amino acids are conserved in the protein family, changes at well-­‐conserved posi8ons tend to be predicted as deleterious. •  Given a protein sequence, –  choose related proteins –  obtains an alignment of these proteins with the query –  Based on the amino acids appearing at each posi8on in the alignment, calculate the probability that an amino acid at a posi8on is tolerated condi8onal on the most frequent amino acid being tolerated. •  Classifies a subs8tu8on into tolerated or deleterious ones SIFT: predic8ng amino acid changes that affect protein func8on. Nucl. Acids Res. (2003) 31 (13): 3812-­‐3814. PolyPhen
•  SoXware for predic8ng damaging effects of missense muta8ons. –  Predic8on based on •  Eight sequence based features •  Three structure-­‐based features –  Naïve-­‐Bayes classifier –  Train dataset 1 •  Posi8ve examples: 3,155 damaging alleles annotated in the UniProt database as causing human Mendelian diseases and affec8ng protein stability or func8on •  Nega8ve examples: 6,321 differences between human proteins and their closely related mammalian homologs –  Train dataset 2 •  Posi8ve examples: 13,032 human disease-­‐causing muta8ons from UniProt •  Nega8ve examples: 8,946 human nonsynonymous SNPs without annotated involvement in disease. A method and server for predic8ng damaging missense muta8ons. Nature Methods 7, 248 -­‐ 249 (2010) PolyPhen Features
•  Black: candidates, blue: selected PolyPhen
•  predic8ng cancer driver/passenger muta8ons with PolyPhen Summary
•  Understanding the gene8cs of cancer –  Both germline polymorphisms and soma8c muta8ons can contribute to trigger tumorigenesis –  Determine driver and passenger muta8ons •  OXen frequently occurring muta8ons are declared as driver muta8ons •  SIFT and PolyPhen for evalua8ng the func8onal effects of muta8ons