Personalizing Oncology with Genomics

White paper
Connecting
insights
Better
outcomes
Personalizing oncology
with genomics
Next-generation sequencing will transform oncology clinical
trials and treatments for cancer.
Philip Breitfeld, M.D., Vice President and Global Head, Therapeutic Centers of Excellence, Quintiles
Jeff Fitzgerald, Director, Personalized Medicine Integration, Quintiles

2 | www.quintiles.com
Introduction 3
Emerging precision 4
Tools and techniques 5
Exploring genomic features with NGS 7
NGS platform peculiarities 8
NGS in clinical trials 9
Transcriptome analysis – expression profiling 9
Tomorrow’s technology today 10
About the authors 12
Table of contents

By 2025, sophisticated genomics-based tools will have transformed cancer care. Imagine a 60-year-old
patient with non-small cell lung cancer (NSCLC). Care will start with a routine tumor biopsy and assessment,
based on a standard pathologic examination plus molecular diagnostics, including analysis of single
nucleotide variation, translocations, copy-number variations, methylation, transcriptome expression and
pathway analysis. Within five days, the oncologist will receive a report of the findings along with treatment
recommendations, such as a combination of tyrosine kinase inhibitors and monoclonal antibodies, which
will be developed in consideration of publicly available information about treatment outcomes in patients
with similar molecular profiles. The report will also include a list of relevant clinical trials. As a result of this
three-step process – obtaining molecular data, matching it to publicly available patient-level data sources
and translation into a clinical action plan – the patient might live another 35 years. The question is: What will
it take to achieve this transformation in oncology?
Before considering this future, we look back at the relationship between cancer agents and potential
genomic targets (Figure 1). From 1960–1990, therapies consisted of what oncologists call the
sledgehammer approach. The compounds disrupted basic chromosomal mechanisms, such as the
replication of DNA. These all-purpose compounds, such as alkylating agents, attacked many tumor types,
but they also created adverse side effects because of damage to healthy tissue.
The silver-bullet era emerged during the last decade of the 20th century. For the first time, compounds
were aimed at specific molecular targets for particular cancers. For example, trastuzumab targets HER2
receptors in breast cancer, and rituximab targets CD20 proteins in B-cell malignancy. Targeting specific
tumor types limits the range of application of these treatments, but also reduces off-tumor effects, which
decreases the side effects in comparison with sledgehammer medications.
A second stage of the silver-bullet era truly leveraged genomic alterations. Imatinib, for instance, attacks
chronic myelogenous leukemia driven by the BCR-ABL translocation, and gefitinib battles NSCLC sparked
by mutations in the epidermal growth factor receptor (EGFR) gene.
In addition, crizotinib provided impressive outcomes in NSCLC patients with the EML4-ALK fusion, which
created an opportunity to develop this agent for a sub-population of patients. This provided a direct
and fast route to regulatory approval. Despite the initial excitement over the efficacy of these silver-bullet
treatments, in some cases resistance has emerged and patients have relapsed, even those who had quickly
experienced complete responses.
Sophisticated
genomics-based
tools will transform
cancer care in the
coming decade. This
paper investigates
what will drive this
transformation in
oncology.
Figure 1 Evolution of cancer drug development paradigm
> alkylating agents
> anthracyclines
> taxanes
> trastuzumab
> rituximab
> imatinib
> gefitinib
> RAF and MEK inhibition
(melanoma)
> c-MET and EGFR
inhibition (NSCLC)
Sledgehammer
Targeting of
tumor markers
Targeting of
driver genomic
alterations
Mitigating
drug resistance
Silver Bullet Emerging Precision
Cytotoxic
chemotherapy

These agents take out one target, and that will not necessarily cure a population of patients, due
to resistance mechanisms offered by the biologic complexity of cancer (Figure 2). As tumors are
heterogeneous in nature, one silver-bullet agent will not destroy all tumor cells. Moreover, a single cancer
cell makes use of multiple molecular pathways to sustain replication, which can sometimes circumvent the
impact of a single treatment. In addition, one patient might have multiple tumors that vary in the dominant
pathobiology. Beyond those challenges, tumors are inherently genetically unstable, making them prone to
replication errors and mutations. This leads to resistant clones and an evolution of the disease. These issues
of complexity make it difficult to predict the outcome of inhibiting just one part of a cancer ecosystem.
Emerging precision
In 2013, Nature published the Cancer Genome Atlas. This information provides drug developers with clues
for aiming a combination of agents at more than one target or pathway in one tumor. It also helps oncologists
select the patients most likely to respond to a specific treatment. These advances pave the way for an era
of emerging precision. The overall goal is to develop a medication that attacks the right target, or targets,
in the right disease, in the right patient, at the right dose to obtain the desired outcome. That requires an
understanding of cancer’s molecular complexity, which can be gained through genomic techniques.
In a 2011 article in Cell, Doug Hannahan of the Swiss Institute for Experimental Cancer Research and
Bob Weinberg of MIT stated that cancer arises from six processes: “sustaining proliferative signaling,
evading growth suppressors, resisting cell death, enabling replicative immortality, inducing angiogenesis,
and activating invasion and metastasis.” They also pointed out the fundamental cause of cancer: genome
instability. A complex molecular circuit drives each of these processes, and the circuits can interact. This
suggests that targeting one step in this complex ecosystem of cancer might be naive. It also reveals the
difficulty in predicting what will happen when even one step is inhibited.
As previously noted, activating EGFR mutations can drive NSCLC in some patients. In those patients,
inhibiting EGFR leads to good initial clinical outcomes, but the disease often recurs. At least one mechanism
for the acquired resistance comes from amplifying the expression of the c-MET gene. This provides the
rationale to create a cocktail therapy that inhibits EGFR and c-MET.
As a second example, RAF mutations can be observed in many cases of melanoma. In 2013, a team of
scientists from the Royal Marsden National Health Service Foundation Trust and the Institute of Cancer
Research in the UK reported in the Journal of Clinical Oncology that therapies blocking RAF can activate
a downstream portion of the pathway, which involves the MEK gene. Consequently, a combination that
inhibits RAF and MEK might be effective in some patients.
The overall goal in the era
of emerging precision is to
develop a cancer medication
that attacks the right target,
or targets, in the right
disease, in the right patient,
at the right dose to obtain the
desired outcome.
Figure 2 The complexity of circuits, individual tumors and populations
Z
Y
Z-coordinate
X-coordinate
Y-coordinate
X Multiple individuals
Multiple circuits/networks in a single cancer cell
Multiple cancer cells in a single tumor in an individual

These examples show that the molecular complexity of cancer demands an understanding of the underlying
genomics. These challenges in cancer and drug development require new technologies to guide the
efficient and economical development of safe and effective new medicines. Next-generation sequencing
(NGS) technologies are already playing a role in providing that broader understanding and helping medical
researchers and drug developers address the challenges posed by the molecular complexity of cancer.
Tools and techniques
The diverse population of tumor types and the complex molecular mechanisms involved in cancer drive
the need for characterized biomarkers, which can distinguish tumors molecularly. These biomarkers must
be reliable and easily accessible. As an example, every cell contains genomic DNA and transcribed RNA,
making nucleic acids stable, easy-to-obtain sources from available tissue. NGS can be used to identify and
routinely analyze nucleic acid-based biomarkers.
NGS determines, in a highly parallel fashion, the precise order of nucleic acids, DNA, RNA and micro RNA.
The Human Genome Project, completed in 2003, used so-called first-generation Sanger sequencing to
unveil the order of nucleotides in human chromosomes. That project cost $3 billion and took 13 years to
generate a draft genome sequence. The second-generation sequencing approach, NGS, uses massively
parallel techniques to decode the order of nucleic acids in billions of fragments of DNA. This advance has
drastically reduced the price of analysis – currently available techniques advertise the ability to decode one
human genome for $1,000. Experts in the field are already discussing the interrogation of a genome in less
than a day for only $100. In fact, the sequencing cost reductions have outpaced Moore’s Law since 2008;
in 2001, it cost more than $5,000 to sequence a million nucleic-acid bases, and that cost dropped to $0.10
by 2012 (Figure 3). Overall, however, NGS requires large investments in infrastructure, both for acquiring
and analyzing the data.
Today’s market offers a wide variety of commercially available platforms, including various devices from
Illumina, Life Technologies (now Thermo Fisher Scientific) and Pacific Biosciences. These platforms bring
NGS technology to a much wider range of users, and can be applied in the continuum of discovery and
clinical development. NGS can be performed in several ways: sequencing by synthesis, ion-detection
sequencing and single molecule–real time. As discussed below, each technique includes pros and cons.
A complex molecular
circuit drives the
processes that cause
cancer. It is difficult
to predict what will
happen when even one
process is inhibited.
This complexity
demands an
understanding of the
underlying genomics.
Figure 3 Rapid drop in cost of sequencing
Data courtesy of the US National Library of Medicine.

Already, the enhanced speed and reduced cost of NGS is changing science and medicine (Figure 4). Overall,
publications using NGS grew exponentially from a handful of publications in 2005 to more than 3,700 in 2013.
Likewise, the number of genomics-oncology publications has increased steadily during the past decade.
These data show how NGS is having a profound effect on oncology research and drug development.
To apply NGS to medical R&D, however, researchers must understand the benefits and shortcomings
of the various commercial platforms (Figure 5). For instance, sequencing by synthesis provides very high
throughput, as many as 300 million reads/day, but it sequences shorter reads, 100–150 base pairs long,
and takes 27 hours per instrument run. Ion-based sequencing delivers fewer reads/day, 160 million, but
works with somewhat longer stretches of nucleic acids, 250 base pairs long, and only takes four hours
per instrument run. Single molecule–real time sequencing runs the fastest, completed in 0.5–3 hours on
the instrument, and sequences the longest reads, up to 15,000 base pairs, but it only delivers two million
reads/day. Another critical consideration is the amount of sample preparation time and labor, bioinformatics
infrastructure requirements, and instrument footprint considerations when making platform decisions.
The applications at hand must be matched to the appropriate sample preparation and NGS technology
platforms. Due to the rapid evolution of this technology, and expertise required for their utilization, sourcing
these processes to providers is more cost-effective and less risky.
Next-generation sequencing
can be performed in several
ways: sequencing by
synthesis, ion-detection
sequencing and single
molecule–real time.
Figure 4 The impact of NGS on oncology publications
0
500
1000
1500
2000
2500
3000
3500
4000
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Next generation sequencing publications
PubMed 2001-2013
0
50
100
150
200
250
300
350
400
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Genomics oncology publications
PubMed 2001-2013
Data courtesy of the US National Library of Medicine.

Exploring genomic features with NGS
In developing new drugs, NGS supplies several powerful approaches. Researchers can sequence the
entire genome with shotgun whole-genome sequencing (WGS). Another powerful approach, whole-exome
sequencing (WES), focuses on the parts of the genome that are transcribed into RNA, and if translated
result in functionally active proteins. In addition, RNA sequencing, or RNA-seq, provides the order and
quantity of nucleic acids in the expressed RNA.
These techniques can all be used for various approaches to applying biomarkers to drug development. For
example, WGS, WES and RNA-seq can be applied to biomarker identification in preclinical development.
Targeted DNA sequencing can be used to cost effectively validate the relationship between nucleotide
variants in actionable regions of the genome, such as oncogenes or tumor suppressors, and correlate
them with drug response in an expanded population of patients. PCR (polymerase chain reaction) based
approaches – such as RT-PCR or digital PCR utilizing flourometric assays – can also be used in later phases
of development for very targeted expression profiling and genotype assessments of relevant biomarkers. All
of these applications, however, require bioinformatic expertise to discover and validate clinical biomarkers.
To search for biomarkers – which can be used in preclinical and clinical development, as well as in
therapeutic stages – various molecular features should be assessed. This begins with sequencing the
DNA of normal and diseased samples. Then, the data can be analyzed for inherited variation known as
single nucleotide polymorphisms (SNPs), which denote changes in an individual’s nucleotide sequence.
The sequence data can be explored further for somatic variation – changes in the genetic code that
occur during a person’s life that can alter the protein product of a gene that is expressed. These somatic
variants can in turn offer a selective growth advantage to the affected cells that can drive the neoplastic
process. The data can also be analyzed for changes in the number of copies of a gene. So-called copy-
number variations – increases or decreases in the abundance of a gene sequence within a genome –
can lead to over or under expression of a gene, respectively. Depending on the regions of the genome
involved, copy-number variations can also be pathogenic. Finally, the sequence data can be assessed
for fusions, i.e., rearrangements of genomic regions that consequently produce the transcription of
inappropriately combining messages. This can turn on signals that should be off or code for proteins that
drive the oncogenic process.
Many techniques
can be used for
various approaches to
applying biomarkers
to drug development,
although all require
bioinformatic
expertise to discover
and validate clinical
biomarkers.
Figure 5 NGS platform characteristics
Parameter
Sequencing by
Synthesis
Ion Sequencing
Single Molecule
Real Time
Max Number of
Reads/Day
300 million 160 million 2 million
Max Read Length 2x150* or 2x100bp 250bp 15,000bp
Total Output Per Run 600 GB 10 GB 800 MB
Instrument Run Time 27 hr* or 11 days 4 hours 0.5-3 hours
Barcodes Available 96 96 48
*Rapid Run Mode

As mentioned previously, heterogeneity in a tumor can cause resistance to therapy. Often, a single tumor
consists of subpopulations with different sequence variants, which can be determined with NGS. In a so-
called polyclonal tumor, one subclone might include common somatic mutations, meaning that they exist
in high frequency, and another subclone could include those mutations plus subclone-specific ones, which
appear at a low frequency. With NGS and a reference “normal” sequence, the sequence of the common
and subclone-specific mutations that can contribute to therapeutic resistance can be simultaneously
discovered. Importantly, NGS can pick out rare somatic events because of the extraordinary number of
measurements that can be derived from this technology’s output.
NGS platform peculiarities
When dealing with rare somatic events, however, errors must also be considered. All sequencing platforms
and sample preparation procedures involved can lead to measurement errors. This can make it difficult
to distinguish between a rare subclone-specific mutation and a sequence error. Consequently, changes
in sequence data should be validated before moving ahead. For example, an orthogonal sequencing
technique can be used to determine if the subclone-specific mutations still appear in the data. Similarly,
bioinformatic methods have been developed to assess specific base call changes and can be used to
distinguish between an error and a rare mutation. To minimize false positives, researchers should work with
providers that are very familiar with the various NGS platforms and their error profiles, and have tools and
capabilities available to perform validation experiments.
WGS, for example, is capable of supplying the sequence of the entire genome but the so-called depth –
amount of redundant measurements of an individual sample’s nucleotide base composition – is relatively
low compared with targeted methods. That provides fewer reads to detect rare events (or heterogeneity) in
a tumor, thereby potentially leading to a higher risk that rare variation within a sample may go undetected.
In addition, different forms of NGS work better in different stages of drug development. WES isolates
regions of the genome that get transcribed and eventually translated into proteins. Variants found in
these regions are more likely to be pathogenic, which makes this a useful tool for zeroing in on potential
drug targets. Further into the development cycle, researchers might choose PCR enrichment to very
selectively amplify regions of interest, because this technology provides a high level of focused sequencing
depth of coverage. This redundancy in measurement yields the capability to detect more rare events in a
heterogeneous population of cells.
As a project focuses more closely on a specific region of the genome, researchers need equally focused
tools. This can lead to switching from using WGS to WES and, ultimately, to studying targeted genomic loci
with PCR panels. Likewise, increasing sample sizes contribute to the need to progress from one technique
to another. For example, many more samples are likely to be used at the stage of analyzing targeted gene
loci than when looking at the entire genome.
Each of these technologies, however, comes with advantages and disadvantages. On the upside, WGS is
comprehensive and unbiased, provides genome structure, and can be used in discovery without requiring
a panel; on the downside, WGS has limited sensitivity to detect rare variants within a sample, can expose
many variants of unknown significance and can provide an overload of data, resulting in high costs for data
storage and analysis.
WES is also comprehensive, provides some genome structure and panels are available off the shelf, but
it too is limited in its sensitivity to detect rare events and can be biased in coverage (some regions of the
target sequence are well measured while others are underrepresented), which leaves gaps in the data.
When using PCR panels, the results are very specific for particular regions of interest and the results are
very actionable; however, this technology includes shortcomings, such as its limited biological breadth,
limited ability to detect rearrangements and the fact that primers can fail on some samples due to
heterogeneity of sequence in the regions in which PCR is primed.
Next-generation sequencing
can pick out rare somatic
events because of the
extraordinary number
of measurements that
can be derived from this
technology’s output.
As a project focuses
more closely on a
specific region of the
genome, researchers
need equally focused
tools.

NGS in clinical trials
As an added benefit, genes for ADME – absorption, distribution, metabolism and excretion – can be
screened in parallel with predictive biomarkers. In doing so, however, researchers must obtain informed
consent for genetic testing. Such a study should also collect samples from everyone; then, if a safety signal
arises, the data for every patient can be analyzed retrospectively without sampling bias.
To use NGS in a clinical trial, other factors must also be considered. In preparation for a study, researchers
should determine the format of samples. For instance, formalin-fixed, paraffin-embedded (FFPE) samples
pose additional technical challenges (base modifications due to formalin fixation) versus fresh-frozen tissues,
which present logistical sample-handling issues. For FFPE samples, the NGS technology must provide a
high depth of sequencing coverage, keeping in mind that the specificity of the results is proportional to the
error rate of the NGS method. The study preparation should also estimate the turnaround time needed for
prospective and retrospective studies. Prospective trials, for instance, need results quickly to plan the next
steps in a patient’s treatment in the context of a clinical trial. To validate the results, orthogonal methods are
also recommended. A collection of other concerns must be addressed. Will enrichment methods be needed
for regions of interest, such as oncogenes or tumor suppressors? The technology and team for computing
and bioinformatics plus data interpretation and reporting must be assembled. The biomarkers selected
must be assessed for analytical and clinical validity. If one is considering an FDA-approved diagnostic test,
the process must also provide clinical utility. Regulatory standards must be followed, and steps toward
development of an in vitro diagnostic (IVD) need to be considered. Many of these steps require collaboration
with experts in these fields.
Transcriptome analysis – expression profiling
To discern a disease’s mechanism of action, researchers often use transcriptome profiling – sequencing
and quantifying a sample’s messenger RNA (mRNA) (Figure 6). This requires several steps: mRNA
isolation, cDNA preparation, library construction, alignment to a reference and, ultimately, quantifying the
copies of each message.
Figure 6 Sequencing mRNA
AAAAA
AAAAA
AAAAA
Generate cDNA Fragment
Library construction
Alignment to
reference
RNA isolation
Abundance estimation
G1:3
G2:12
G3:5

RNA-seq can also determine the role of alternate splicing in a disease. Humans produce more than 100,000
proteins from only about 21,000 genes, and various splicing arrangements account for the ability to make
far more proteins than there are genes. This accounts for the complexity of biology seen at the protein level,
and it can also drive the complexity of disease. Understanding the role of splicing in a disease, however,
reveals new opportunities to cure or constrain it.
Expression profiling can also be used to divide a disease into subgroups and provide insights into
prognosis. Diffuse large B-cell lymphoma, for example, exists in two forms: germinal center B-cell-like and
activated B-cell-like. As George Wright of the National Cancer Institute and his colleagues reported in the
October 19, 2003 Proceedings of the National Academy of Sciences, expression profiling can distinguish
samples based on subgroups, because the disease types vary in the genes that get over- or under-
expressed. By knowing the disease-driven changes in gene expression, a therapy can be assessed by its
ability to revert expression to the patterns found in healthy tissue.
RNA-seq can also reveal gene fusions. Briefly, if two sequences of nucleotides are typically located far apart
in physical reference genome space but keep turning up closer together in individual RNA sequencing
reads, gene fusion may have taken place. This can also be confirmed with fluorescence in situ hybridization
(FISH), but that requires an expert in cytogenetics to read the results, and gene fusions can only be
examined one by one. By contrast, RNA-seq can simultaneously find multiple gene fusions, but it is more
difficult to set up the data analysis. Once an expert-developed system of analysis is created, however, the
results of RNA-seq for gene fusions are more objective and easier to interpret. Overall, NGS offers a cost-
effective means to screen multiple biomarkers in a single experiment. This requires less tissue than serial
testing, such as the multiple singleplex reactions used in FISH and immunohistochemistry. This does not
imply that NGS replaces confirmatory methods at the protein level, but it provides a very effective first pass
where positive, validated biomarkers exist.
Tomorrow’s technology today
To see if it is feasible to take today’s genomic technology to improve matching of patients to clinical trials,
Quintiles partnered with the US Oncology Network. In the typical approach to matching a NSCLC patient
with a trial, for example, the patient would be tested for an activating EGFR mutation. If that returned
a negative result, the patient could be tested for the EML4-ALK fusion. Alternatively, a patient could be
screened for multiple genomic alterations and the resulting information could be used to select the best
protocol from a large menu of trial options.
The Quintiles team studied patients with metastatic colorectal cancer. Using a 50-gene profile developed
with an Ion Torrent PGM NGS platform, we searched for potential drug-targetable genomic alterations. The
results show that the cycle from tumor-sample acquisition to reporting to the clinician can be completed in
about two weeks (Figure 7). We believe that this general method of genomic screening for multiple genomic
alterations will become frequently used for selecting patients for clinical trials, as well as in oncology
practices to help inform treatment decisions.
By knowing the disease-
driven changes in gene
expression, a therapy can
be assessed by its ability
to revert expression to the
patterns found in healthy
tissue.
Figure 7 Cycle of genomics sequencing to match patients with clinical trials
Patient presents
– IC patient
enrollment
sample collection
Quintiles Labs –
Sample processing
– DNA isolation
EA –
Genomic
testing
EA –
Bioinfomatic
analysis
Clinical
annotation
and reporting

To make such techniques widely applicable for research or clinical practice, scientists and oncologists
need noninvasive biomarkers, especially for longitudinal screening for chronic clinical management. These
biomarkers could come from circulating tumor cells (CTCs) or cell-free/plasma DNA. Using cell-sorting
techniques and proteomics markers, CTCs can be distinguished from healthy cells in the blood. Monitoring
the presence or absence of CTCs might be correlated with the progression of disease. Also, CTCs and
NGS might be combined to analyze the development of resistance to treatment in individual cells or
interacting cell populations. Likewise, cell-free/plasma DNA might be sequenced with NGS to use as a
surrogate to indicate tumor recurrence. With today’s technology, those DNA molecules can be counted
even when they exist as only one molecule in 250,000.
Thinking more generally, genomics will change oncology in at least four ways. First, intervention trials will
test combinations of rationally selected agents targeted for specific niche populations of patients with
a specific array of genomic alterations in a particular tumor context. Second, real-world observational
trials and registries are expected to be able to collect patient-level clinical and genomic data on well-
characterized cohorts of patients. Third, the public domain will include readily available, patient-level data
linked to genomic data. Fourth, data aggregators and analytic tools will efficiently and rapidly sort through
these patient-level data sets to improve the design of new agent development programs and clinical
decision-making.
Using genomics-based techniques like these, our 2025 NSCLC patient of the future, and patients with a
wide range of other cancer types, will receive personalized treatment. More importantly, this treatment will
be more effective, safer and less likely to be overturned by resistance-driven recurrence of the cancer. In
short, genomics-based techniques will change the entire oncology ecosystem from the very beginning of
drug discovery through treatment.
Genomics-based
treatment will be
more effective,
safer and less likely
to be overturned by
resistance-driven
recurrence of the
cancer.

About the authors
Philip Breitfeld, M.D.
Vice President and Global Head, Therapeutic Centers of Excellence,
Quintiles
Philip Breitfeld, M.D., is vice president and Global Head of Quintiles’
Therapeutic Centers of Excellence (COE). In this role, Dr. Breitfeld
has overall responsibility for Quintiles’ Therapeutic COEs – cross-
functional teams of therapeutically aligned experts, focused on
optimizing customers’ product development efforts.
Dr. Breitfeld has more than 25 years of experience in oncology,
including 20 years of experience in academic medical institutions in
the U.S., and seven years of experience in the biopharmaceutical
industry focused on oncology drug development and execution of
clinical programs. Prior to joining Quintiles, he held senior oncology
clinical development positions at BioCryst and Merck Serono. Dr.
Breitfeld has global experience in most all malignant hematology
and oncology indications, with biologics, small molecules, cancer
vaccines and hematopoietic stem cell transplantation.
Dr. Breitfeld received his undergraduate degree from Princeton, his
medical degree from the University of Rochester, and was a clinical and
research fellow (Pediatric Hematology-Oncology) at the Dana-Farber
Cancer Institute at Harvard. He completed a second fellowship in
Medical Informatics sponsored by the National Library of Medicine and
was a visiting scientist at the Whitehead Institute at MIT.
Jeff Fitzgerald
Director, Personalized Medicine Integration, Quintiles
Jeff possesses more than a decade of successful sales and support
experience in the genomics tools sector. In his current position,
he supports the integration of genomic technologies to address
biomarker analysis in sponsors’ clinical trials.
Previously while at RainDance Technologies, he cultivated new
business opportunities in research and clinical markets through the
use of its sequence enrichment solutions in conjunction with various
next generation sequencing platforms. In previous sales and support
roles at Affymetrix, he supported key genome and academic research
centers in the use of expression, resequencing, and genotyping
microarrays. Prior to his commercial experience, Jeff sought to
identify molecular markers responsible for tumor progression while
performing research in the Department of Pathology at Brigham and
Women’s Hospital and Harvard Medical School.
Jeff holds a Bachelor of Science from the University of Vermont in
Microbiology and Molecular Genetics.

Personalizing Oncology with Genomics

More Related Content

What's hot

Similar to Personalizing Oncology with Genomics

Personalizing Oncology with Genomics