PPT-Talk outline Brief history of gene-expression profiling for cancer type classification

Current commercially available tests development and performance Clinical application Problems and limitations How DNA sequencing and mutation profiling can potentially help Talk outline. The problem to be solved (an example). Hauf, J., Zimmermann, F.K., M. ü. ller, S., 2000. Simultaneous genomic over expression of seven glycolytic enzymes in the yeast Saccharomyces cerevisiae. Ezyme. Microbiol. Technol.. CpG. Island . landscape (part 2). Héctor. Corrada Bravo. CMSC858P Spring 2012. (many slides courtesy of Rafael Irizarry). How do we measure DNA methylation?. Microarray Data. One question…. Where do we measure? . http://. www.cbioportal.org/index.do. Web resource for exploring, visualizing, and analyzing . multidimentional. cancer genomics data. cBioPortal. : Purpose and Advantages. reduces. . molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. Presented by: Bryan Shaw. Background. TIL are a part of tumors and are well know to have major roles in cancer biology. TIL are vital in tumor growth, cancer progression and response to therapy.. Figure 1. Current and emerging techniques for evaluating TIL composition in solid tumors.. How do we regulate the expression of our genes? . Involved in gene expression. DNA regulatory sequences. Regulatory genes. Small regulatory proteins (. RNAs. ). Regulatory sequences. Stretches of DNA that interact with regulatory proteins to control transcription.. 1
Class name – VIH. Course name - ZOO-Biotech. (Rohit). DNA MICROARRAY. Introduction. As we know that for molecular characterisation of any gene/genome, nucleotide sequences are required.. Understanding the genome function by analysing the genes and the ways the genes expressed by the genome have a key importance.. Quantity and quality of material (10-30% cases have insufficient tissue/RNA) . Limitations of GEP Tests. “Off-label” CUP . tumours. . . - Potential for false positives if tested . tumour. not in classifier . Cancer. Class Discovery and Class Prediction . by Gene Expression Monitoring. T. . R. . Golub. , D. K. . Slonim. & . Others. 1999 . Big Picture in 1999. The Need for Cancer Classification. Cancer . Systematic . In . silico. . analysis using public database. Sung Hwan Lee. 1,4. , Baek Gil . Kim. 2,3. , . Ho Kyoung Hwang. 1,4. , Woo . Jung . Lee. 1,4. , Chang . Moo Kang. 1,4**.  . 1. Department . ” data, and data resources. Anthony Gitter. Cancer Bioinformatics (BMI 826/CS 838). January . 22, . 2015. What computational analysis contributes to cancer research. Predicting driver alterations. Defining properties of cancer (sub)types. 20 18 , Vol. 9 http://www. jcancer .org 22 49 J J o o u u r r n n a a l l o o f f C C a a n n c c e e r r 201 8 ; 9 ( 1 3 ) : 2249 - 2 265 . doi: 10.7150/ jca . 24744 Review Gene Expression Detecti Eur J Breast Health 2017; 13: 168-74 ABSTRACTBreast cancer is a heterogeneous disease. e purpose of adjuvant therapy for early-stage breast cancer is to provide maximum benet with minimum si 11635 . Northpark. Drive, Suite 250, Wake Forest, NC 27587. Gene . expression profiling . for . targeted cancer . treatment. Luminita. . Castillos. 1. , PhD, MBA, Francisco Castillos. 1. , III, MD and Anton Yuryev.