Molecular Biology of Cancer AND Cancer Informatics (omics) David Boone

Outline What is cancer and how is it different from other diseases? Major players in tumor initiation (Understand one before many) – Oncogenes – Tumor suppressors Types of mutations The many – informatics – arrays – sequencing – How to read a heatmap Personalized Medicine/cancer genomics WORKS – examples from breast cancer. Summary Molecular biology of cancer Cancer Informatics (omics)

Goals Understand why cancer treatment is difficult. Learn how changes in genome and/or expression can affect cell proliferation/death. – looking at signaling schematic be able to identify what a certain mutation might do. Define oncogenes and tumor suppressors. Define two ways of gathering ‘omics’ data Be able to analyze heat maps and Kaplan Meier Curve Describe how informatics is impacting personalized medicine.

Outline What is cancer and how is it different from other diseases? Major players in tumor initiation (Understand one before many) – Oncogenes – Tumor suppressors Types of mutations The many – informatics – arrays – sequencing – How to read a heatmap Personalized Medicine/cancer genomics WORKS – examples from breast cancer. Summary Molecular biology of cancer Cancer Informatics (omics)

How is cancer different from a bacterial infection? Term used to describe 100s or 1000s of distinct neoplastic disorders caused by your own cells growing out of control.

Normal

Cancer

Metastasis Spreading of cancer from one organ to another

The many steps of Metastasis occur through clonal selection and the accumulation of different mutations Fidler 2003 nature reviews cancer

Outline What is cancer and how is it different from other diseases? Major players in tumor initiation (Understand one before many) – Oncogenes – Tumor suppressors Types of mutations The many – informatics – arrays – sequencing – How to read a heatmap Personalized Medicine/cancer genomics WORKS – examples from breast cancer. Summary Molecular biology of cancer Cancer Informatics (omics)

Structure = function DNA RNAPROTEIN

The Central Dogma of Molecular Biology Structure is very important – replication – transcription/translation info to create proteins DNA RNA Protein 23 chromosomes 3.2 billion base pairs ~20000 genes (1.5% of genome) DNA

Question If all of our cells (mostly) have identical DNA sequences that are densely packed in nuclei then how do we have the different types of cells and organs necessary for human life? Regulation of gene expression

Central Dogma No T’s replaced with U’s Translation start Translation stop

Important genes in cancer Oncogenes- genes that encode for proteins that have the potential to cause cancer. – like a gas pedal they make the cells divide more frequently or survive when they shouldn’t. – turned on by activating mutations, amplifications, and overexpression. – ex. c-Myc, IGF1R, Ras (growth factors, signaling molecules, transcription factors)

IGF1R is an important oncogene in BC – Can you pick out any other potential oncogenes? Survival

Important genes in cancer Tumor Suppressors- genes that encode for proteins that prevent tumor development. – like a break pedal they prevent proliferation and initiate cell death if there are problems like DNA damage. They keep proliferation and oncogenes in check. – Stop the cell cycle, induce apoptosis, DNA repair, etc. – turned off by inactivating mutations, deletions, and lack of expression. – ex. p53, BRCA1/2, PTEN, RB

Myc (oncogene) is one of the most frequent amplifications in BC. p53 (tumor suppressor) is one of the most frequently mutated or lost genes in BC. too much

Outline What is cancer and how is it different from other diseases? Major players in tumor initiation (Understand one before many) – Oncogenes – Tumor suppressors Types of mutations The many – informatics – arrays – sequencing – How to read a heatmap Personalized Medicine/cancer genomics WORKS – examples from breast cancer. Summary Molecular biology of cancer Cancer Informatics (omics)

Genetic alterations (All mutations are not equal) Point mutations

Frameshift mutation K A* DNA RNA Protein

Genetic alterations Point mutations Duplications or amplifications Myc

Genomic alterations Point mutations Duplications or amplifications Deletions p53

Genomic alterations Point mutations Duplications or amplifications Deletions Insertions/ Inversions

Genomic alterations Point mutations Duplications or amplifications Deletions Insertions/ Inversions Translocations

Expression alterations outside of the genome Overexpression. – ex. Myc (high mitogenic signaling results in high expression of unmutated Myc. Epigenetic silencing – ex. methylation

Outline/Summary Molecular Biology of Cancer What is cancer and how is it different from other diseases? Major players in tumor initiation (Understand one before many) – Oncogenes – Tumor suppressors Types of mutations The many – informatics – arrays – sequencing – How to read a heatmap Personalized Medicine/cancer genomics WORKS – examples from breast cancer. Summary Molecular biology of cancer Cancer Informatics (omics)

Outline What is cancer and how is it different from other diseases? Major players in tumor initiation (Understand one before many) – Oncogenes – Tumor suppressors Types of mutations The many – informatics – arrays – sequencing – How to read a heatmap Personalized Medicine/cancer genomics WORKS – examples from breast cancer. Summary Molecular biology of cancer Cancer Informatics (omics)

Now we know a lot about individual genes, but how do we study global genetic or expression changes? Microarrays – Hybridization based (complementary base pairing) – Comparative Genome Hybridization (CGH) or SNP arrays for known DNA variants and copy number changes. – mRNA Expression arrays for RNA expression. Next-Gen sequencing – Sequence based – can be used for DNA or RNA – can detect mutations, copy number changes, and expression differences.

Microarrays Used for: 1)Gene expression 2)Copy number changes whole transcript expression array covers 28,869 well- annotated genes with 764,885 distinct probes. affy 6.0 SNP chip- 906,000 SNPs and ~ 1,000,000 ADVANTAGES: Relatively cheap Analyze all known genes Analysis is relatively easy DISADVANTAGES Cannot detect novel genes, mutations, or copy number changes

Next-gen sequencing Advantages- -No limitation on novel detections -simultaneously discover expression or copy number changes AND mutations. Disadvantages: -expensive -analysis is complex and difficult (but not for iBRIC scholars!) Human Genome Project took ~13 years and cost ~3 billion dollars. 3billion bases Now in a few weeks for less than a few thousand dollars, we can sequence a genome.

The sequencing revolution $1000 genome Human genome project 15 years $3,000,000, genomes 1 day $45,000

The Cancer Genome Atlas (TCGA) The Cancer Genome Atlas (TCGA) is a comprehensive and coordinated effort to accelerate the understanding of the molecular basis of cancer through the application of genome analysis technologies, including large- scale genome sequencing. 33 cancers 275 million dollars patients 2700 publications since started in 2006 RNA expression (RNAseq and microarray) Exome sequencing (mutation) Whole genome (for some) Copy number Methylation miRNA expression

ENCODE (encyclopedia of DNA elements) – The human genome project or our generation Build a comprehensive parts list of functional elements in the human genome.

Outline What is cancer and how is it different from other diseases? Major players in tumor initiation (Understand one before many) – Oncogenes – Tumor suppressors Types of mutations The many – informatics – arrays – sequencing – How to read a heatmap Personalized Medicine/cancer genomics WORKS – examples from breast cancer. Summary Molecular biology of cancer Cancer Informatics (omics)

HOW DO WE MAKE SENSE OF IT ALL?!?!

Pattern recognition/Logic Problems

Personalized medicine Bayer

Breast Cancer- IT WORKS!! Historically classified based on tumor size, nodal involvement, invasion, histology, etc. More recently genomics and transcriptomics have provided clues to what drives tumor initiation and progression and has the potential to divide patients into separate groups that might respond to different therapies. Sørlie T et al. PNAS 2001;98:

Gene expression patterns of 85 experimental samples representing 78 carcinomas, three benign tumors, and four normal tissues, analyzed by hierarchical clustering using the 476 cDNA intrinsic clone set. ER- ER+ basalHER2+Normal basal like luminal A luminal B Finding Patterns!!! Gene Expression Patterns Reveal Novel Breast Cancer Sub-types patient gene highlow

Overall and relapse-free survival analysis of the 49 breast cancer patients, uniformly treated in a prospective study, based on different gene expression classification. Sørlie T et al. PNAS 2001;98: ©2001 by National Academy of Sciences Personalized medicine WORKS!!!!

Gene expression use in the clinic Mammaprint – approved by FDA – 70 gene signature for patients with node negative and ER+ tumors – compared to conventional classification in ~300 patients 87 would have been treated differently. 67 were determined high risk by conventional methods and were given chemo but by Mammaprint were classified low risk. Followed patients for 10 yrs and Mammaprint was more accurate Oncotype PAM50 All good for deciding chemo vs. endocrine therapy, but still need classifications for ER- tumors

Summary Cancer is different than other diseases because it is really 100s or 1000s of diseases and is your own cells gone bad. Oncogenes – cause cancer – hyperactive – mutation, amplification Tumor Suppressors- prevent cancer – Prevent cell cycle progression – Induce cell death – DNA repair – lost or turned off – mutation, deletion, methylation Different types of alterations – Somatic mutation – amplification – deletion – methylation Global analysis (omics) – Microarray – Sequencing Personalized Medicine – Biomarker – How to read a heatmap and KM curve

12 highlow 345 A

Sample Preparation Lyse cells DNA RNA cDNA Histone modifications/TF Binding ChIPseq RNAseq WGS ChIPseq

Library Preparation Or cDNA

Cluster Generation and Sequencing Illumina Sequencing by synthesis

Data Analysis RNAseq Additionally may require 1) transcript assembly and 2) estimation of abundance

Problem After performing RNAseq analysis on a matched pair of tumor and normal tissue from a single patient you find that a novel gene is expressed 4 times higher in the tumor sample. Your collaborator conclude that this gene is transcribed at higher rates in tumors. – What are alternative interpretations? Think of caveats to RNAseq.

RNAseq caveats Rnaseq is a steady state measurement – RNA abundance is a result of transcription and degradation RNAseq is the measurement of the average expression in the population. – Doesn’t tell you expression in individual cells. Instead demonstrates expression in the pool of cells and perhaps different cell types. RNA temporally and spatially regulated.

Other sequencing caveats. Aligning to the ‘reference genome’ 38 th main alignment 20 th main release Still have regions of unknown location

Up to 50% of human genome is non-unique Problem for aligning Many isoforms share exons Other sequencing caveats..Repetitive regions and shared exons