Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Two-hybrid screening wikipedia , lookup
DNA repair protein XRCC4 wikipedia , lookup
Genetic code wikipedia , lookup
Vectors in gene therapy wikipedia , lookup
Non-coding DNA wikipedia , lookup
Biochemistry wikipedia , lookup
Biosynthesis wikipedia , lookup
Artificial gene synthesis wikipedia , lookup
Secreted frizzled-related protein 1 wikipedia , lookup
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 – – – – 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