This document discusses the history and development of personalized medicine from early genetics concepts to current applications in cancer treatment. It covers Mendel's principles of heredity, Darwin's theory of natural selection, discoveries in molecular biology, and completion of the human genome project. It describes how genome sequencing provides a "parts list" of genes and how different cell types express different gene sets. It outlines how knowledge of a patient's genetic and somatic mutations can guide targeted cancer treatments. Finally, it discusses considerations for building personalized medicine programs, including access to samples, developing technology platforms, integrating information, conducting research, engaging stakeholders, and communicating with communities.

1. Building a Program in
Personalized Medicine
John Quackenbush

2. Mendel’s Contributions:
3. Traits get passed from one generation
to the next with a defined mathematical
relationship
5. Traits from a parent combine to
produce the traits in one’s offspring

3. Darwin’s Contributions:
3. Genetic changes arise spontaneously
5. These changes can get passed from
one generation to the next
7. Natural Selection favors some
variations over others

5. Molecular Biology in 7 Words
Gene Protein
Regulation RNA
Folding
Folding
Function Structure

6. Completion of the Human Genome
Announced June 26, 2000

8. February 2001: Completion of the Draft Human Genome
Public HGP Celera Genomics
May 2006: The “complete” human genome
sequence is announced

9. The Genome Project has provided a
“parts list” for a human cell

10. Different cell types express different sets of genes
Neuron
Thyroid Cell
Lung Cell
Cardiac Muscle
Pancreatic Cell
Kidney Cell
Skeletal Muscle
Skin Cell

11. Disease Progression and
Birth
Personalized Care Treatment Death
Quality
Natural History of Disease Clinical Care Of Life
Environment
Outcomes
+ Lifestyle
Treatment
Options
Disease
Staging
Patient
Stratification
Early
Detection
Genetic
Risk
Biomarkers

12. Applications to Cancer:
A Case Study

13. A Microarray Overview

14. A First Application
Identified genes that
distinguish ALL from AML
Developed a weighted voting
classifier to predict type based
on expression
Science 1999;286:531-7

15. Application to Breast Cancer (I)
Identified an “intrinsic gene signature”
and molecular subclasses of cancer
based on expression and cell of origin.
Nature 2000;406:747-52;
see also Perou et al., PNAS 1999;96:9212-7

16. Application to Breast Cancer (II)
Identified a “70 gene
signature” that correlates
with metastasis and
overall survival.
Nature 2002;415:530-6.

17. Cancer Patients Have Two Genomes
Somatic
In the cancer; may have mutations not in
the germline
Germline X
In all cells;
Passed on to
children; Active Inactive
Genes may impart
cancer risk

18. BRAF Inhibitor Shrinks Metastatic Melanoma
McDermott U et al. N Engl J Med 2011;364:340-350.
BRAF Inhibitor Prolongs Survival in Patients with Metastatic Melanoma
But ONLY in patients whose tumors have the BRAF mutation

19. Cancer Patients Have Two Genomes
Targeted Treatments Require Knowledge of the Mutation
Patient A Mutation A
Drug A
X
A
Malignant Cell Growth
Patient B Mutation B
Drug B
X
B
Malignant Cell Growth
Patient C Mutation C
Drug C
X
C
Malignant Cell Growth

20. Disease Progression and Personalized Care
Birth Treatment Death
Quality
Natural History of Disease Clinical Care Of Life
Environment
Outcomes
+ Lifestyle
Treatment
Options
Disease
Staging
Patient
Stratification
Early
Detection
Genetic
Risk
Biomarkers

21. Turning the vision into a reality
Assure access to samples and rational consent
Develop a technology platform
Make information integration as a central mission
Conduct research as a vital component
Present data and information to the local community
Enable research beyond your own
Engage corporate partners
Communicating the mission to the community.

22. Assure Access to Samples

23. Access, Research, Security
Patients want to be part of the process of curing disease
Informed consent needs to be structured to allow patients
to be partners in the research process
HIPPA requires both informed consent and that we assure
patient confidentiality
But “identifiability” is a moving target in a genomic age
With the <$1000 genome, in the age of Facebook, what
this means remains unclear
The new Genomics is a disruptive technology.

24. Develop a
Technology Platform

25. The cost decreases exponentially with time
Illumina GAII
ABI SOLiD
Continuing the Regression:
Genomes for $100 in February 2014
The $1000 Genome:
October 2012
25

26. 2010: Enabling a New Era in Genome
Analysis
Illumina HiSeq
100Gb (~30X genome
coverage)
150bp reads
Two samples/week
<$10,000 per genome

27. Just Announced: The Life Technologies
Ion Torrent Proton
The Promise from LTI
A Genome in ~24 hours
for $1000
Promised in Q3 2012

28. Let the games begin!
The Oxford Nanopore MiniON
The USB sequencer

29. The Challenge
New technologies inspired by the Human Genome
Project are transforming biomedical research from
a laboratory science to an information science
We need new approaches to making sense of the
data we generate
The winners in the race to understand disease are
going to be those best able to collect, manage,
analyze, and interpret the data.

31. Make information integration
as a central mission

32. Beating Information Overload
Clinical Cytogenomics
Genomics
Data Metabolomics
Transcriptomics Proteomics
Epigenomics
Improved Diagnostics
Central
Individualized Therapies
Warehouse
More Effective Agents
Chemical
Published
Biology PubMed
The Datasets
Genome
Clinical
Trials The Drug
Disease
Etc. HapMap Databases Bank
(OMIM)

34. Conduct research as a vital
component

35. Data Generation
Illumina partnered with us to generate comprehensive mRNA,
microRNA, and methylation, and copy number variation (CNV)
profiles on these FFPE ovarian cancer samples
Renee Rubio and Kristina Holton developed protocols for
efficient extraction of mRNA/microRNA and genomic DNA
from FFPE cores
Quality was validated using BioAnalyzer and hybridizations to
Illumina DASL arrays
mRNA/microRNA and DNA were extracted from 132 samples
and profiled in collaboration with Illumina on a prototype
12k DASL array
Data were normalized and analyzed using the ISIS class
discovery algorithm.

36. Identifying modules using ISIS*
Module:
Set of genes
supporting a
bi-partition
ISIS searches for stratifications of samples into two groups that
maximize a DLD score.
*ISIS: Identifying splits of clear separation (von
Heydebreck et al., Bioinformatics 2001)

37. Module 2 (gene expression)

38. Survival and Validation
1090 high grade, 1606 published
late stage ovarian tumors
serous tumors

39. Present data and information
to the local community

40. LGRC Research Portal

41. LGRC Data Download
Data download
• Browse by basic metadata
• Browse by clinical /
phenotype attributes
• Download ‘raw’ data
• Secure transfer via single
use ‘tickets’ . Enables
authorized users access to
the specified result basket for
a single session.

42. LGRC Research Portal

43. PAGE DETAILS
Search
-Facets
-Search within results
-Keyword prompts
-Search history
Table:
-Paged results
-Sortable columns
Actions:
-Go to Gene detail page
-Add genes to ‘gene set’

44. PAGE DETAILS
Annotation summary & summary
view for each assay/data type:
Accordion style sections
Annotation -GEXP – expression profile across
major Dx categories
Summary -RNASeq – Exon structure of the
gene
-SNPs – Table of SNPs in region of
gene, highlighting association
with major Dx group
- Methylation – Methylation
profile in region around gene
-Genomic alterations – table of
CNVs & alterations observed w/
Gene Expression Summary freq in region around gene
Actions:
- Click through to assay detail
page
-Add gene to set
RNASeq

45. LGRC Research Portal

47. LGRC Research Portal

48. PAGE DETAILS
- View aggregate statistics
- View cohort details
- Build cohort sets
- Build composite phenotypes
Actions:
-Go to data download for selected
cohort
-Go to assay detail for selected
cohort
-Go to cohort manager

49. LGRC Research Portal

51. Engage corporate partners

52. We need to find the best tools
We received an $1M Oracle Commitment grant to
create our integrated clinical/research data warehouse
We’ve partnered with IDBS to create data portals
We are working with Illumina on a variety of projects
We are forging relationships with Thomson-Reuters to
link genomic profiling data to drug, trial, and patent
information
We are building partnerships with Roche, Genomatix,
NEB, and others interested in entering the personal
genomics space.

53. Enable research beyond
your own

54. John Quackenbush, Director
Mick Correll, Associate Director

55. The Mission
The mission of the CCCB is to provide broad-based support for the
analysis and interpretation of ‘omic data and in doing so to further basic,
clinical and translational research. CCCB also will conduct research that
opens new ways of understanding cancer.

56. CCCB
Collaborative Consulting Model
1. Initial meeting to understand project scope and objectives
Consulting
3. Development of an analysis plan and time/cost estimate
IT Infrastructure
Sequencing
5. During project execution, data and results are exchanged
through a secure, password-protected collaboration portal
7. Available as ad-hoc service, or larger scale support agreements

57. Communicate the mission to
the community.

58. The LGRC

59. What can we learn from the Genome
Predicting risk will always be difficult – genetic variants
are not deterministic, they simply “weight the dice”
toward certain outcomes and must be considered in the
context of environmental factors and chance.
In disease, we can learn a great deal from analyzing
genomic data and searching for relevant, actionable
mutations
Patient involvement is critical as patients are our partners
in doing research.

61. Genomics is here to stay

62. Acknowledgments
The Gene Index Team Center for Cancer Gene Expression Team
Corina Antonescu Computational Biology Fieda Abderazzaq
Valentin Antonescu Mick Correll Stefan Bentink
Fenglong Liu Victor Chistyakov Aedin Culhane
Geo Pertea Howie Goodell Kathleen Fleming
Razvan Sultana Lan Hui Benjamin Haibe-Kains
John Quackenbush Lev Kuznetsov Jessica Mar
Array Software Hit Team Niall O'Connor Melissa Merritt
Katie Franklin Jerry Papenhausen Megha Padi
Eleanor Howe Yaoyu Wang Renee Rubio
John Quackenbush John Quackenbush (Former) Stellar Students
Dan Schlauch http://cccb.dfci.harvard.edu Martin Aryee
Raktim Sinha Kaveh Maghsoudi
Joseph White Jess Mar
Eskitis Institute Systems Support
Christine Wells Stas Alekseev, Sys Admin
Alan Mackay-Sim Administrative Support
Joan Coraccio
<johnq@jimmy.harvard.edu> Julianna Coraccio
http://compbio.dfci.harvard.edu