# Presentation: 589600

## Applications of Genomic Technologies

**to Improve Recognition, Understanding, and Assessment of Pharmaceutical Actions:**
***A Focus on Integrating Gene Expression Data Sets into Regulatory Practice***

**Frank D. Sistare**
**Office of Testing and Research**
**Center for Drug Evaluation and Research**
**Food and Drug Administration**
***April 9, 2003***

## Presentation Outline

**Overview of the technology and medically related applications of pharmacogenomics relevant to CDER’s responsibilities**
**Concerns and issues (technical, procedural, biological) raised at the drug development, regulatory oversight, patient care interface**
**What CDER is doing to address these issues**

## Slide 3

## PCR (Polymerase Chain Reaction)

**PCR (Polymerase Chain Reaction)**

- Modified from:  http://www.accessexcellence.org/AB/GG/polymerase.html

## Slide 5

## Dual-color Microarray Platforms for Gene Expression Profiling

- cDNA microarray
- spotted oligo microarray
- in situ synthesized oligo microarray

**Dual-color Microarray Platforms for Gene Expression Profiling**

## Anatomy of a GeneChip® Probe Array

**18μm**

**Hybridized Probe Array Image**

**500,000 specific 25mer **
**oligonucleotide probes **

- 1.28cm

**Probe Array**

**Anatomy of a GeneChip****®**** ****Probe Array**

**Notes:**
Current dimension of each “feature” that contains one type of probe in affymetrix array is 18um x 18um. There are millions of copies of this probe in that area.

## GeneChip Product Sales Growth

**GeneChip Product Sales Growth**

- Majority of arrays sold for expression analysis
- Top 20 pharma and most top biotech are customers
- Customers in US, Canada, Europe, Japan, Pacific Rim

**Notes:**
Major expression sellers are Human, Mouse, Rat. Also sell arrays for sequence analysis and genotyping.
Also have systems installed at most leading academic institutions around the world.
Installed Systems = Installed base, not systems sold each year.
Arrays sold = arrays sold each year.

## A Powerful Protein Function Detector:

- <date/time>

**Database**

**10,000s of**
**REPORTERS**

**A Powerful Protein Function Detector:**

**Identifying Changes in the Function of many Proteins Simultaneously**

- PERTURBATION

## The Netherlands Cancer Institute

- Departments of Pathology, Molecular Carcinogenesis, Radiotherapy, Biometrics
- Laura van ‘t Veer, Marc van de Vijver, Guus Hart, Hans Peterse,
- Karin van der Kooy, Dorien Voskuil, Tony van der Velde, Douwe Atsma,
- Anke Witteveen, Ron Kerkhoven, Molecular Pathology, René Bernards
*Emiel Rutgers, Harry Bartelink, Sjoerd Rodenhuis*
- _Rosetta Inpharmatics/Merck and Co.Inc_
- Kirkland WA, USA
- Hongyue Dai, Yudong He, Mao Mao, Matthew Marton, North Creek,
- Tracy Erkkila, Mark Parrish, George Schreiber, Chris Roberts,
- Peter Linsley, Stephen Friend

**A Gene Expression Signature As A Predictor of Survival in Breast Cancer**
- NEJM 347 (12/19/2002): 1999-2009

## Can gene expression profiling be used to improve prediction of clinical outcome?

-  Aim:
- to identify patients at risk to develop distant metastases
- to accurately select for adjuvant therapy (who to treat, avoid over-treatment)

## Unsupervised Clustering Analysis of Breast Cancer Expression Profiles

- BRCA1

- ER

- PR

- Grade 3

- Lymphocytic Infiltrate

- Agioinvasion

**~5000 genes**

**98 Tumor Samples**

**Contains ESR1 and enriched for estrogen-responsive genes**

**Enriched for lymphoid genes**
**(B cells, monocytes)**

##  Supervised Classification Prognosis

- Leave-one-out cross-validation

- 78 tumors

**good**
**signature**

**poor**
**signature**

- threshold set with 10% false negatives
- 91 % sensitivity, 73% specificity

- 70 significant prognosis genes

## Independent validation set of 73 tumors

*patients lymph node negative*

- 70 prognosis genes

- 7% false negatives (n=1)
- 94 % sensitivity, 55% specificity

- 73 tumors

- Patients metastases < 5yrs,
- or disease-free > 5 yrs

## Independent validation set of 114 tumors

*patients lymph node positive*

- 70 prognosis genes

- 7% false negatives (n=2)
- 93 % sensitivity, 49 % specificity

- Patients metastases < 5yrs,
- or disease-free > 5 yrs

- 114 tumors

## Independent cohort of 295 tumors

*patients < 53 yrs,  lymph node negative or positive*

- Unselected series, mean follow-up ~ 8yrs

- 295 tumors

- 70 prognosis genes

## Identifying Patterns Associated with a Complex Trait

- Identifying patterns of expression associated with a trait is enhanced if we focus on the phenotypic extremes

**Identify Phenotypic**
**Extremes**

## Sub-classifying the Phenotype Using Microarray Data

- The subtypes of the phenotype are identified using the gene expression data
- For each subtype there are patterns (signatures) of expression that serve to enhance identification of these more homogenous groups (homogenous with respect to the processes underlying the phenotype)

**Identify Expression**
**Patterns Associated **
**With Extremes**

**Identify Expression**
**Patterns Associated **
**With Extremes**

## TS<=4,TP<=18, and 

- 0

- 6

- 12

- 18

- 24

- 30

- 36

- Months since start of chemotherapy

- 0.00

- 0.10

- 0.20

- 0.30

- 0.40

- 0.50

- 0.60

- 0.70

- 0.80

- 0.90

- 1.00

- Estimated Probability of Surviving

- p<0.0001 (Logrank)

- TS<=4,TP<=18, and

- DPD<=2.5 (n=12)

- TS>4, or TP>18, or

- DPD>2.5 (n=21)

## 40 Gene Cluster Discriminates between Good and Poor Outcomes in Patients with Renal Cell Carcinoma

- Takahashi et al. PNAS 98: 9754, 2001

**40 Gene Cluster Discriminates between Good and Poor Outcomes in Patients with Renal Cell Carcinoma**

## Slide 21

## SPS Allows Accurate Clustering of Compounds & Allows Identification of Sub-parts of a Compound’s Effect 

**Statin**

**Fibrate**

**NSAID**

**Hepatotoxicty**

**DNA crosslinker**

**Estrogen**

_**Signature type**_

**SPS Allows Accurate Clustering of Compounds & Allows Identification of Sub-parts of a Compound’s Effect **

**Compounds******

**Signatures******

**2-D Hierarchical Clustering of 29 signatures vs 28 compounds**

**Signatures relate expression changes to pharma-cology, toxicology, pathology, chemistry and biology**

## Microarray Design Comparisons

-        cDNA

-  Oligo

- GeneChip

**Notes:**
Substrate = what probes are spotted on
Probes per array an estimate based on market data for cDNA and non-GeneChip oligo.
Probes per transcript an estimate based on market data.
There are additional commercial providers for Membrane and Glass Oligo arrays. These are those w/ the largest market share.

## Knowledge of Biological Truth!  

**Knowledge of Biological Truth!  **

**Microarray Expression Data**

**Information**

***Sample integrity, enzymatic processing,***
***amplification, labeling, array hybridization,***
***washing, array scanning***

***Data transformation, normalization,***
***scaling, statistical selection ***

***Bioinformatics, Cellular processes/ biochemical pathway analyses, ***
***GO ontologies, referencing previous experiences, ***
***database comparisons, “homework” ***

## The Effect of Simvastatin on Expression Levels of Genes

**The Effect of Simvastatin on Expression Levels of Genes**
**in the Cholesterol Biosynthesis Pathway**

**Oral administration of Simvastatin results in significant upregulation of genes **
**involved in cholesterol biosynthesis in the liver.**

## Concerns and issues raised at the drug development, regulatory oversight, patient care interface

***Technical:***
**With so many variables/options in data capture (probes, platforms, RNA sample processing, hyb processing), data analysis (true signal, normalization, statistics, clustering) are we measuring true biology reproducibly and accurately or is there too much systematic experimental error to reasonably contend with? ...are the data misleading  ...or can we get the same true answer from datasets off different platforms and different laboratories? ...can universal reference standards help us?**
**What is a reasonably detailed and practically useful relational data set to constitute a regulatory submission that defeats healthy skepticism concerning data integrity? ...will these data be appropriate for an FDA database?**

## Concerns and issues raised at the drug development, regulatory oversight, patient care interface

***Procedural***
**Would all (or any) of these tests:   ...have to be done under GLP conditions?  ...be interpreted by FDA as relevant to human safety?   Or is a “research information package” approach feasible? ****If such an approach becomes reality, what data are appropriate to submit as such?   ...what data are inappropriate?**
**How will the FDA prepare itself to work with these huge data sets in a timely manner?  ....and ensure that individual reviewers do not prematurely interpret, generate hypotheses, or over interpret those expression alterations whose biological significance have not been scientifically established?**
**How will the FDA communicate which expression alterations have reached a scientifically mature level of understanding and can rationally be considered relevant safety biomarkers?**

## Concerns and issues raised at the drug development, regulatory oversight, patient care interface

***Biological***
**How would the Agency react if an oncogene was activated? ...would the sponsor have to notify the FDA, their clinical investigators, and IRB’s?**
**Will “?more sensitive?” gene expression changes drive drastically lower clinical trial starting doses and prolong Phase I clinical trials? **
**Which expression alterations are reliable and biologically relevant “classifiers,” or “biomarkers” of 1) desirable actions, 2) undesirable but tolerable drug actions, 3) healthy and fully compensatory responses to exposure, 4) intolerable drug actions leading to irreversible outcomes?   ...how are they biologically relevant?**

## Modifying Traditional Drug Development 

**Related **
**Target Hit**

**Lead **
**Compounds**

**Animal**
**Trials**

**Clinical**
**Trials**

**Absorption**

**Metabolism**

**Excretion**

**Toxicity**

**FDA **
**Approval**

## Likely Pattern of Incorporation and Assessment 

**Likely Pattern of Incorporation and Assessment **
**of Microarray Data in Decision Making **
**along the Medical Product Development Pipeline.  **

**General relationship between: 1) the number of microarray endpoints likely to be measured, 2) the degree of precision in each measurement that would likely be expected, and 3) the level of analytical and biological validation achieved by the microarray platform, as a function of the application stage of product development**.

## What CDER is doing to address these issues

- Building the internal capacity infrastructure
- Establish core expertise within the CDER laboratory
- initial approach: arrays, cDNA scanner, the ILSI collaboration
- enabled leveraging: Affymetrix GeneChip system and research agreement; leveraged Rosetta research agreement
- Office of Science Grant from Commissioner: RNA standards development initiative (all medical FDA Centers + NCTR).
- Expand core expertise for CDER review enhancement
- CDER Nonclinical Pharmacogenomics Subcommittee established - focus on regulatory decision making practices, procedures, and policies
- leveraged Iconix DrugMatrix database - expanded and focused reviewer/researcher training

##  = 0.936 (union)

*** *= 0.936 (union)

*** *= 0.984 (intersection)

##  = 0.972 (intersection)

*** *= 0.972 (intersection)

*** *= 0.873 (union)

## 72 “-oncogenes”

**72 “-oncogenes”**

**White:      9 Human and Rodent Genotoxic Carcinogens**
**Blue:         5 Human and Rodent Nongenotoxic Carcinogens**
**Yellow:   14 Rodent Noncarcinogens**

**Diphenhydramine**

**Mannitol**

**Aspirin**

**What if....a reviewer sees increased expression of an oncogene**
** ****in a product submission package?**

## No manufacturing defects
Insignificant platform lot-to-lot variability
Assess integrity of feature location
Unambiguous consensus sequence annotation
Lack of cross-contamination in tiled probe features

**Insignificant platform lot-to-lot variability**
**Assess integrity of feature location**
**Unambiguous consensus sequence annotation**
**Lack of cross-contamination in tiled probe features**

**Efforts have been intiated by CDER, NCTR, CDRH, CBER with NIST, NIEHS, and Microarray Stakeholders (Affymetrix, Rosetta/Agilent, Iconix/Amersham) to Develop Standards useful for evaluating **_***platform features***_***.....***

## Quality (integrity /purity) of starting sample
Quality of processed (labeled/amplified) sample
Hybridization performance (probe sensitivity, specificity)
Image scanning limitations (backgrd/slope/saturation)
Transformation process into “rough” measured data (bckgrd/slope/saturation)
Normalization/scaling to an analytical value worthy of comparison
Data selection and analysis procedures to focus biological thinking (false positive/false negative minimization)
Biological conclusions that are independent of platform and represent biological truth

**Quality of processed (labeled/amplified) sample**
**Hybridization performance (probe sensitivity, specificity)**
**Image scanning limitations (backgrd/slope/saturation)**
**Transformation process into “rough” measured data (bckgrd/slope/saturation)**
**Normalization/scaling to an analytical value worthy of comparison**
**Data selection and analysis procedures to focus biological thinking (false positive/false negative minimization)**
**Biological conclusions that are independent of platform and represent biological truth**

**...****.and for evaluating **_***experimental performance***_

## Goals of CDER PTCC Nonclinical PG Subcommittee

**Develop standards and procedures for submission, review, integration of PG data.**
**Develop internal consensus regarding added value, best interpretations, and regulatory review implications.**
**Develop Center expertise ****to effect an appropriate infrastructure for PG data review and integration****.**
**Develop initiatives to keep abreast of latest developments in PG.**
**Interface with other CDER review discliplines (including CDER IT groups).**
**Provide forum for communication with the regulated industry and other stakeholders.**

## What CDER is doing to address these issues

- Establishing a network to assimilate reasonable consensus
- Develop mechanisms to communicate and deliver needs
- NIST RNA Standards workshop and initiative
- NCTR collaborations ongoing (several biological, database, statistical, ref standards)
- FDA Intercenter Communications group (consumer white paper)
- National Academy of Sciences Committee - Government Liaison
- NIH/NIEHS MIAME-Tox Interchange
- Engage external expertise (non-collaborative)
- Pharm Tox Subcommittee to CDER’s Advisory Committee for Pharmaceutical Science- focused expert group - 6/10/03 Meeting
- Practice mock data submission scheduled

## CDER shares the vision that applications of pharmacogenomic technologies will improve....

** ****(1) recognition (easier, quicker, or        ****	****	****more accurate identification of ****	****	****efficacy, efficacy potential, ****	****	****	****toxicities, and toxicity potential) **
**(2) understanding (modes or ****	****	****	****	****mechanisms of drug actions), **
**(3) assessment (significance and ****	****	****	****relevance of findings to humans) **
**                          ...****.of Pharmaceuticals**

## CDER has a responsibility to enable and not force nor impede evolution/vision

**Related **
**Target Hit**

**Lead **
**Compounds**

**Clinical**
**Trials**

**Absorption**

**Metabolism**

**Excretion**

**Toxicity**

**FDA **
**Approval**

**Animal**
**Trials**

**Animal**
**Trials**