**Executive Summary of the NCI Think Tank on Variation in Susceptibility
to Cancers**

At present, we assume that cancer develops in a particular person
because of her/his unique genetic composition and exposures, and the
complex interplay among them. Intensive studies of strongly cancer-prone
families inheriting the same mutant allele of a cancer gene illustrate
that more than just pre-disposing genes confer and modify risk of
developing cancer. Many parameters of the malignancy -- severity,
timing, therapeutic response, and even manifestation of the cancer
itself -- may differ markedly among cancer-prone siblings and between
generations. Technological advances enable us to identify many of the
familial and non-familial genes and aberrant processes that confer some
of the susceptibility to cancer, and to assess the probability that
certain environmental exposures contribute to the likelihood of disease.
However, we require a fuller exposition of the genetic architecture of
cancers as complex traits if we are to define population and individual
cancer risk more accurately. Genetic architecture is the fusion of the
effects of interactions among genes and environmental perturbants; it is
thus more than the sum of those individual factors. To achieve
significant advances in insight about cancer risk, we must propose and
test new theoretical models of genetic architecture and use experimental
model systems to test the resulting hypotheses about individual
susceptibility and resistance factors, their interactions, and their
manifestations. This will require that we redirect many of our on-going
epidemiologic and cancer modeling efforts toward coordinate activities
that play to the strengths of each research community and its unique
contribution to the collective effort.

In addition to epidemiologists and cancer modelers, the scientific
redirection includes augmenting the intellectual inputs to include
computational biologists, mathematical modelers, and evolutionary
biologists, among others. To encourage and sustain the new interactions
requires access to human specimens and data, development of new
analytical strategies to tease apart the contributors to genetic
architecture and new algorithms to model genetic and non-genetic
interactions, interdisciplinary training in computational analysis and
modeling, and computer systems capable of rapid high-level data
analyses.

**Vision** and **Overall Goal:** To understand the complex interactions
of genetic and environmental influences; to predict the risks of cancers
for individuals and populations; to enhance prevention, perfect
diagnosis, target treatment, improve prognosis prediction, and greatly
reduce the burden of cancers.

To achieve these goals, the scientific community must:

-   Integrate studies of experimental model systems, human populations,
    and computational models of molecular signatures in normal and
    dysregulated states relevant to particular features of cancer
    susceptibility.

-   Improve cancer-related phenotyping (i.e., the analysis of molecular
    and systemic changes in cancer progression) by applying emerging
    sensor, imaging, nano-and molecular technologies to individual
    humans and animals, biological specimens, cells, extracellular
    matrix components, and cell-cell interactions.

-   Identify sources of individual variation in cancer-related
    biological processes, both genetic and non-genetic, and model the
    genetic-environmental and stochastic interactions.

-   Develop and apply technologies to investigate genetic variation
    broadly, including alleles, SNPs, haplotypes, and epigenetic
    alterations of nuclear and mitochondrial genomes, miRNAs, highly
    repetitive sequences, transposons, gene copy number polymorphisms
    and other evolutionary molecular features.

**Roadblocks and Challenges** that currently hamper progress in
understanding cancer susceptibility were identified. ***Roadblocks
include*** the cost and limited scope of current high-throughput
technologies for the molecular characterization of tissues and tumors
and the genotyping of individuals; the inadequate scope of systemic
clinical measures of cancer initiation and progression; the difficulty
of assessing environmental exposures relevant to cancer development; the
lack of an adequately large human cohort for prospective studies of
susceptibility, along with the necessary infrastructure and biological
repositories; the need for long-standing informed consent valid for
multiple studies; and the serious lack of infrastructure and
requirements for data sharing and standardization. ***The major
challenges are*** the development of new statistical and computational
approaches adequate to deal with the level of complexity believed to
exist in cancer susceptibility, involving multiple genes and
environmental factors, interacting in complex, non-linear ways; the
integration of human studies with those in experimental systems to
optimize understanding of carcinogenic mechanisms; and developing
interdisciplinary training and research environments that incorporate
the perspectives of biologists and computational scientists.

**Introduction**

The overall goal of defining cancer susceptibility in terms of the
interaction of genetic variation and environmental factors is an
ambitious one that cannot be achieved with a single line of
investigation. It will require integrating studies in experimental model
systems, human populations, and computational models. Studies are
required not only on frank malignancies, but on normal physiology and
dysregulated states along the continuum from the earliest phase of
initiation through metastasis. Several specific requirements were
identified:

**1. Improve cancer-related phenotyping (i.e., the analysis of molecular
and systemic changes in cancer progression) by applying emerging sensor,
imaging, nano-and molecular technologies to individual humans and
animals, biological specimens, cells, extracellular matrix components,
and cell-cell interactions.**

Cancers are a very heterogeneous collection of diseases; correlating
phenotype with genotype depends on a detailed knowledge of the clinical
manifestations of each cancer type. Susceptibility studies are designed
to find and test associations between genotype and a specific cancer
phenotype. Early studies of tumor classification through the use of DNA
microarrays, as in NCI's Director's Challenge program, suggest that
molecular phenotyping is a powerful approach to stratify cancers in ways
not possible using histopathology and the few previously available
molecular markers of cancer behavior. Such disease stratification will
sharpen the ability to study susceptibility.

Although microarray studies are useful, and allow researchers to
ascertain the expression level of thousands of genes simultaneously,
expression analysis alone does not meet the need for a complete
phenotyping program. Techniques to expose epigenetic changes and to
define the serum and cellular proteome are the next frontiers. Studies
of epigenetic change are important because DNA sequence is not the only
heritable change in cells and individuals. Proteomics is important
because mRNA levels correlate imperfectly with protein levels and
proteins are subject to many post-translational modifications that
strongly influence their functions. Metabolomics will become
increasingly important as the field of cancer phenotyping matures.
Moreover, there is a need to evaluate not only absolute levels of genes
and gene products, but their rates of change.

Studies of normal tissues and the tumor microenvironment further
demonstrate that it is insufficient to determine the phenotype of the
cancer cell; the properties of tissues and alterations of those
properties as tumors emerge are what is most relevant. The behavior of a
cancer cell is influenced by the cells and molecules with which it has
contact; fibroblasts, infiltrating lymphoid and myeloid cells,
endothelial cells, the extracellular matrix, and soluble molecules, such
as growth factors, cytokines, chemokines, nutrients, and oxygen, all
influence tumor phenotypes.

Characterizing tumor phenotypes at this level will benefit many areas of
cancer research, and such studies are proceeding. However, it is
worthwhile emphasizing how critical these studies are for susceptibility
research.

**2. Examine all cancer-related biological processes for sources of
individual variation, both genetic and non-genetic, and model the
genetic-environmental and stochastic interactions of genetic networks
and cancer phenotypes.**

Susceptibility to cancer results from both genetic and non-genetic
individual variation within a human population. Genetic variation is
commonly equated with polymorphisms in germline DNA sequence, but this
is an over-simplification. Among other potentially relevant genetic
complexities are: Imprinting; X-chromosome inactivation; mitochondrial
DNA; segmental inversions; deletion or alteration of regulatory
elements; jumping elements; tri-nucleotide repeats; and others. Although
susceptibility to many cancers is influenced by multiple genes, a recent
study suggests that genetic variation is a minor component in cancer
susceptibility, contributing perhaps 25% to the risk. The rest is
attributable to a wide range of environmental factors, including
smoking, diet, hormonal status, chemical exposures, microbial flora, and
history of inflammation.

Environmental factors add unsolved methodological complexities to the
evaluation of cancer susceptibility. Genotypes can be determined with
high precision, but current ways of quantitating environmental exposures
generally do not capture all the information needed for a large study.
Dietary histories, for example, are notoriously inaccurate. Biomarkers
of most chemical exposures are lacking. Microbial flora and inflammation
are increasingly recognized as factors in cancer development, but no
consensus exists on which factors to measure or how to evaluate their
impact.

With so many challenging variables to deal with in susceptibility
studies, it is tempting to make the simplifying assumption that the
contribution of each susceptibility factor is independent. However, the
available evidence indicates that this would be a mistake. In *Epistasis
and the Evolutionary Process* (2000), Templeton states that, using
proper computational methods and algorithms, one finds complex
interactions (epistasis) more often than additive, single-factor
effects. In studying susceptibility, it is necessary to ask complex
questions and develop complex models. A shift away from single gene or
single environmental factor analysis to multi-gene, multi-factor
analysis is required, but current analytical and statistical tools are
inadequate to support such a shift.

**3. Develop and apply technologies to investigate genetic variation
broadly, including alleles, SNPs, haplotypes, epigenetic alterations of
nuclear and mitochondrial genomes, miRNA, and other factors as they are
identified.**

A few relatively high-throughput technologies exist to evaluate some
types of genetic variation, but improvements in existing technologies
and the development of new ones are required. Genome-scale sequencing
efforts mean that complete genome sequences for quite a number of people
will soon be available. One spin-off of this sequencing work has been
the development of methods for whole-genome scanning using
microsatellites, and now single-nucleotide polymorphisms (SNPs). SNP
typing is rapidly becoming the technology of choice for genotyping as
the cost is reduced, and the value of the technique will rise rapidly
with further cost reductions and the development of analysis techniques
that can handle the masses of data generated. The HapMap project leads
the way in identifying and validating human SNPs, and in organizing them
into haplotypes to maximize the information content of SNP mapping.
Similar efforts are underway for model organisms.

However, more progress is needed in other areas of genetic analysis.
High-throughput assessment of epigenetic variation is at least a decade
behind that of DNA sequence variation, and new techniques are required
to interrogate the genomic architecture for regulatory features. With
the discovery of potential new sources of genetic variation, such as
regulatory micro RNAs, the need for technology development continues to
increase.

**Specific Recommendations:**

**1. Given the scale of data sets and complex biological systems, create
new computational, mathematical, and statistical models and analytical
tools that** **embrace, rather than simplify or ignore, the complexity
of variation in** **susceptibility and resistance to cancers. Support
large-scale coordination of studies with calibration, interoperability,
data federation, and standardization of data formats, natural language
processing tools, and dataset query tools.**

Current analytical tools and approaches are inadequate to deal with the
complexities inherent in assessing susceptibility (see Requirement 2,
above). Orders of magnitude more data and types of data will need to be
acquired, assembled, and analyzed into information, and knowledge
extracted. This will require development of new approaches to
statistical analysis, integration of modeling efforts into the field,
and changes in how data are collected, annotated, and stored.

***Analytical Approaches-*** The currently accepted picture of cancer
susceptibility, involving multiple genetic and environmental factors
acting together in a non-additive fashion, cannot be adequately tested
experimentally because of limitations in statistical methods and
computational resources. These limitations necessitate the introduction
of simplifying assumptions that are likely to be invalid. As a result,
potentially critical variables are ignored or oversimplified. The types
of analytical methods that need to be developed involve combinatorial
problems. Epidemiological analysis is generally restricted to use of a
linear modeling paradigm, but such statistical methods are unsuitable
for the amount and complexity of data that will be needed to test
realistic hypotheses. It is necessary to shift from parametric methods
to those that deal more successfully with complex interactions.
Significant computational resources are required to deal with
high-dimensional data sets; although computing power is increasing, new
algorithms and strategies are needed to solve the problems and model
complex systems. The methods depend on non-linearity and non-additivity
and involve applied biostatistics, a field that moves forward slowly. To
anticipate the requirements of research five to ten years in the future,
research is needed now in the way statistical methods and design
algorithms are applied.

***Modeling-*** While simple problems can be analyzed directly, problems
as complex as cancer susceptibility must be computationally modeled to
understand what data needs to be collected and what analytical
approaches are likely to be successful. Modeling a biological process at
early stages of a project can provide information about the amount of
data needed to represent the process accurately, and ensure that enough
data is collected to make analyses feasible. Modeling can also help to
determine what types of interactions among variables are most likely to
affect the outcome, and focus computing power on the most productive
problems.

Models and experiments are refined in an iterative process that requires
regular interaction among model developers, model simulators, and
biologists. The proper integration of modeling into laboratory studies
or epidemiologic projects requires building a mutually beneficial
working relationship with experts in the quantitative discipline in
which modelers are typically trained. Because the empirical approach
relies heavily on statistics, empirical scientists and statisticians
have learned to communicate well. Similarly productive communication
between biologists and computational scientists is less common, limiting
the development of modeling.

***Data Issues: Standardization, Calibration, Test Validation, and
Integration***

Standardized nomenclature, controlled vocabularies, and natural language
processing are needed. Many data sets cannot be pooled because they are
not standardized. Data collection must be standardized -- especially in
the large-scale cohort (see Recommendation 3, below) -- and data sets
integrated so that comparisons can be made. One approach to
standardization and facile data sharing is to aggregate the necessary
technologies in one place. This might involve a consortium of many
academic institutions using the same standards, so that collection could
be correlated with sample analysis. Even modeling should be standardized
to enable investigators access to several well-defined pathways to work
with.

Calibration is critical for pooling of some data types, but it is
currently difficult because groups do not use the same controls, even
where controls are possible. In addition, in some fields such as
structural and functional MRI, no calibration exists to permit
cross-comparisons among instruments, so no valid comparisons are
possible.

Test validation is needed, especially for the most fundamental data
types. If thousands of SNPs will be typed on DNA samples, the procedure
must be validated. The Think Tank participants recommend obtaining a
uniform set of data using lymphoblastoid cells from a control population
for this process.

Finally, mechanisms are needed to integrate new data into what already
exists. Scientific communities need to pool information in ways that
maximize the use of the resources. If a large-scale cohort were to be
established, it will pose particular challenges of data integration
because the database is likely to be a federated system, assembled by
linking datasets at many separate locations. The community's computer
development effort needs to build integrated models that can maintain
security.

**2. Create a common and widely available repository with existing
resources of well-selected, maintained, and annotated human biological
specimens (immortalized lymphoblastoid cell lines, serum, plasma, urine,
etc.) for genetic, epigenetic, proteomic, and metabolomic analyses of
early-onset cancer cases, including those with positive family
histories. Create common databases. Develop a corresponding resource
from a special elderly subpopulation to study resistance to cancer
development in individuals with high lifetime exposure to carcinogens.
Require data deposition in freely accessible databases, with resource
sharing of repository materials.**

Developments in analytical techniques, modeling, and data collection and
storage, all described above, will become especially useful when applied
to large, highly informative population studies. The most ambitious such
proposal is described in the next section, but there was general
agreement at the Think Tank that at least two studies hold special
promise in the short term. The first is a study of early onset cancer
patients and their families, to look for particularly influential
susceptibility genes.

The second study would focus on cancer resistance, the opposite side of
susceptibility. The carcinogenic potential of smoking is very high, but
some heavy smokers live to be 100 years of age, and eighty-five percent
of smokers do *not* develop lung cancer. Family studies should be
developed based on unusual resistance to cancer. For example, Peter
Shields and others have cohorts of 90-year old smokers from Veterans'
Hospitals; these cohorts would be a valuable addition to a large cohort
study. Some individuals develop multiple cancers, but live a long life
because their tumors do not metastasize. The interactive genes that
confer apparent resistance to metastasis should be identified.
Susceptibility or resistance to progression or metastasis should receive
the same attention as susceptibility or resistance to initiation.

These two studies are important on their scientific merits, but they
also provide an opportunity to test developments that need to be applied
to all studies in cancer susceptibility. In addition, they can serve to
pilot high-quality repositories for biological samples, strategies for
dealing with privacy issues, and ways to mandate data and resource
sharing.

***Biological Resources/Repositories***

For these studies and the large cohort study described below, a
repository for human specimens is a necessity. A common resource of
biological specimens should be developed. This should include a standard
set for iterative studies, with enough samples for test training sets of
data. Repositories containing material useful in the study of cancer
susceptibility exist throughout the world, but their optimal use by the
scientific and clinical communities is hampered by impediments. Beyond
the existing repositories of samples from "common" types of cancer and
populations, susceptibility studies require cell lines from families to
investigate familial gene variation. The success of ongoing efforts by
the NCI to optimize the large-scale and efficient acquisition, storage,
annotation, and distribution of biological samples is critical to
progress in cancer susceptibility.

***Resource Sharing***

It is important to capitalize on existing resources, including large
datasets and large specimen stores, many of which have not yet been
analyzed. Investigators could contribute these samples for analysis, and
retain access to the data for a reasonable period of time (e.g., six
months) before the data enters the public domain. NCI could facilitate
such resource sharing by paying for processing the material.

***Privacy, Sample identification, and Patient Concerns***

New mandates for patient privacy provide challenges for the study of
cancer susceptibility. It is critical to retain some identification with
genetic information and to keep family data together, yet current
requirements to de-identify data impede progress in assessing
gene-environmental interactions. Informed consent is also a potential
problem. Durable informed consent must be obtained so that samples are
approved for varied, long-term uses. These issues generated considerable
discussion at the Think Tank, but the Think Tank participants were
optimistic that, given sufficient attention, these problems could be
dealt with.

***Data Sharing***

Little, if any, epidemiological data relevant to cancer susceptibility
is freely available for computer downloading. In addition, there are
hundreds of linkage studies in human genetics from which conclusions
have been published, but the underlying raw data remain unavailable. As
a result, experimentalists have no way to compare their data to that of
other labs, and computer scientists developing algorithms useful for
cancer research have no way to test them, short of entering into formal
collaborations. The NIH or NCI could, and should, exert pressure for
release into the public domain of raw data underlying published
analyses. In a number of large-scale research projects funded by NCI or
elsewhere at NIH, data release is required, and sometimes even
facilitated. This practice needs to be much more widespread, and
reasonable schedules for data release need to be negotiated with input
from the scientific community. In coordination with funding agencies and
professional societies, journals can facilitate data release by
requiring release of underlying data as a condition of publishing the
conclusions.

Datasets are unavailable because they are difficult and expensive to
produce. They can be reused by the original investigator in new studies,
and no incentives exist for sharing them. No credit is given in
university promotion and tenure decisions for releasing data helpful to
others in the field. In fact, even collaborating with others to publish
analyses of aggregated datasets results in multi-author publications for
which most participants receive little credit. The physics research
community had no choice but to work together when experiments became
exorbitantly expensive, and they have succeeded in changing the culture
so that collaborations have intrinsic value. This must happen in the
cancer susceptibility research community if important problems are to be
addressed.

**3. Develop a trans-NIH multiple-disease-oriented framework for
initiation of a very large-scale human population study, with multiple
ethnic groups, environmental exposures, and family histories. Ensure
widespread access to specimens, cell lines, and data.**

A central discussion point of the Cancer Susceptibility Think Tank was
the need to establish a million-person cohort from which standardized
samples and data could be obtained and shared among research groups
interested in complex diseases like cancer. The rationale and logistics
underlying such a large project, and its relationship to existing
cohorts, elicited extensive discussion. Among the considerations:

***Why a million-person cohort?***

Statisticians working on the proposal for the American Gene and
Environment Study concluded that a cohort of this size should be
adequate for analysis of gene-gene interactions of the type anticipated
to play a role in cancer susceptibility. An article by Francis Collins
in S*cience* mentioned the need for a large-scale longitudinal human
cohort, taking tissue samples pre- and post-disease, to make best use of
the technology -- not just after the disease has spread. Case control
studies typically yield biased information because they are based on the
surviving subset of people with that disease. Few studies have collected
long-term human tissue samples.

To design such a large population study is obviously challenging --
deciding what to analyze for and how to obtain a large enough dataset
that the signal-to-noise ratio is robust in the face of real human
variation. The difficulties are compounded by the high cost of the
undertaking, which necessitates that the cohort be applicable to
research on a broad spectrum of diseases. The studies must be very
long-term; thus the information and biological samples obtained and the
way in which they are stored must be carefully chosen to preclude their
obsolescence. Population characteristics can change dramatically in a
short time, as illustrated by variations in colorectal cancer rates with
environment and time.

***Building the cohort: Recruitment and Time-frame***

A trans-NIH multi-disease--oriented cohort should build on existing
cohort studies, and pilot novel strategies for database construction and
recruitment. It is critical to define the minimum data set needed, and
to make a *centralized data base* attractive to the cohort consortium.
Specimen resources should also be centralized, or at least federated, to
ensure access to them. It is also critical to put in place a mechanism
for obtaining available tissue when a participant develops cancer and
has a biopsy or surgery.

A million-person cohort will reach maximal utility within 5-10 years,
but recruitment should start as soon as possible. It is reasonable to
anticipate that technology will advance, and sequencing and genotyping
will become much easier to do. The 5-year goal should be based on
optimizing currently available technologies, while the 10-year goal
should assume the availability of high-throughput techniques applicable
to large populations.

***Specimen Collection, Standardization, Data Analysis, and
Accessibility***

When investing in such a large study, one important step is to determine
whether the current methods of defining diet and other environmental
exposures are adequate to capture all the information that may be of
value. Exposure measurements must be refined. It will also be critical
to know the genetics of cohort participants, from the germline to
somatic genetics and disease.

Specimen collection should include fresh tissue, benign as well as
malignant, associated with a minimum data set containing residency
history and ethnicity. A federated (virtual) facility would store and
distribute tissue and data. The "million cohort" database would be
accessible to all. Collection of tissue would be enabled, as would
international interactions. A grid system should be set up at NIH in
which all data from the "million cohort" would be included, with enough
computer power to do the modeling and analysis. The level of quality
control must give assurance of standardization and comparability of the
data.

**4. Exploit animal models for identification and validation of cancer
phenotypes**

**and susceptibility and resistance genes. Foster comparative genomic
and phenotypic studies of human and rodent (mouse and rat) cancer
susceptibility and resistance.**

Many laboratories use inbred and outbred mouse and rat strains to
disclose the genomic loci that are associated with complex traits, such
as tobacco carcinogenesis, addiction to alcohol, tobacco and other
drugs, diet-induced obesity, and hypertension, among others. There are
usually several dozen or more implicated loci, none of which has a major
effect by itself. Comparative genomics reveals that the majority of the
implicated loci are conserved across three species -- human, rat, and
mouse -- inspiring confidence that murine species can be informative for
the genetic and environmental determinants of human disease
susceptibility. The genomes for other species have also recently been
sequenced. In particular, championship bloodlines of dogs display
inherent susceptibility to a variety of cancers, and the exceptional
documentation of these bloodlines will enable cross-comparisons between
human populations and animals that share the same environment. An
expanding list of publications on the bases of susceptibility and
resistance to cancer and other diseases attests to the fact that
cross-species comparisons are not only useful, but also more facile,
approaches to defining complex traits.

Researchers are more optimistic about understanding genotype to
phenotype mapping using animal models rather than human models. It is
possible to generate animals in which the activity of several loci are
diminished or increased at the same time, and have a strain with a very
acute phenotype within a few months. An unbiased set of phenotyping
strategies can be applied to the strains to expose novel disease
manifestations that may be overlooked in human population studies. In
addition to unaltered inbred or outbred mice, mice that are genetically
altered to be sensitive or resistant to carcinogens are available. These
strains can be bred to others which differ in sensitivity to identify
which of the many contributing genes is dominant. Most wild mouse
strains have a low incidence of cancer, and studies of them indicate
that they have a number of dominant resistance genes. These are only a
few examples of existing mouse resources and approaches that can be
applied to the problem of understanding cancer susceptibility.

Several years ago, mouse models were designed to map simple,
single-gene--based traits; new mouse models are designed specifically
for analysis of complex traits and the contributions of environmental
effects. An integrative approach using newer mouse models to illuminate
results obtained with the million person cohort and to experimentally
model the resulting hypotheses would be particularly powerful. Even with
the best methods to model gene-gene or genetic-environment interactions,
statistical patterns that emerge from the mathematical models require
experimental validation in animals. This may require knockout mice for
evaluating multi-genotype variations. Not all the experimental
techniques are in place yet, nor have mice been engineered to examine
all the genotype interactions, but increased efforts to employ
integrative human/animal/computational strategies are likely to pay
substantial dividends in the study of human cancer susceptibility.

**5. Establish a trans-NIH initiative for the development of common
mouse resources for the analysis of genetic and environmental effects on
development of cancer and other complex trait diseases. Facilitate
multi-level, multi-lab studies of specimens after environmental
exposures for gene-environment interactions for the development of
computational models representing the complex genetic and non-genetic
networks associated with disease susceptibility and progression.**

Using wild-derived strains and out-bred populations, it is possible to
expand genetic variation far beyond that available in standard inbred
strains. Recombinant inbred (RI) lines are a powerful tool in the search
for susceptibility genes, and, even more importantly, for the conduct of
a systems biology approach to the correlation of genotype and phenotype.
A number of Think Tank participants expressed considerable enthusiasm
for the creation of a large, trans-NIH resource of RI strains to
facilitate study of cancer susceptibility and other similarly complex
genetic traits, such as diet-induced obesity. Several trans-NIH groups
are evaluating common mouse resources to understand complex human
diseases, and there are international efforts underway to coordinate the
development and deployment of these resources. The precise nature of
common mouse resources for the analysis of complex gene-environment
interactions in human diseases still requires consensus-building within
the relevant research community. These efforts are the subjects of a
series of workshops currently in development.

**6. Radically modify training programs to provide immersive
interdisciplinary learning** **environments for biologists,
mathematicians, computer scientists,** **statisticians, epidemiologists,
and clinical investigators.**

The field of cancer susceptibility needs investigators who can
understand and apply high-level computational and modeling techniques in
analyzing multiple genetic and environmental interactions. Think Tank
participants identified several approaches for overcoming this problem.

***Develop cross-training, interdisciplinary programs***

Cross-training in biology and computational science is challenging. Most
of the participants expressed a preference for educating scientists with
programming or statistical genetics or mathematical modeling backgrounds
about biology, rather than teaching biologists the math and
computational skills. Thus, it is important to identify students with
good quantitative skills at the undergraduate level who are comfortable
with statistical concepts so that mathematics is a fundamental component
of their broad education. It is also important to educate biologists and
epidemiologists how to generate and apply models. Too often, data that
are analyzed are not translated into a model, or the models that are
created only verify the original assumptions and add nothing new.

A major change is needed in graduate training to prepare scientists in
quantitative and biological areas, but successful prototypes exist. For
example, an innovative Ph.D. program exists for mathematicians,
physicists and computer scientists to learn biology as well as use their
technical skills, and for biologists and chemists to spend most of their
time learning how to model. The field of Neuroscience is unusual in that
it allows researchers to return at any stage of their career for
cross-disciplinary training. Similar opportunities would be of value in
cancer research.

***Provide grant support for interdisciplinary education***

Most NIH training grant programs limit the opportunities for students to
pursue interdisciplinary opportunities or learn computational methods,
yet students should be rewarded for seeking training in other areas.
Competitive grants and awards designated for that purpose would
encourage pursuit of interdisciplinary education. A few NIH grants,
awarded to an institution or an individual, do require interdisciplinary
training, with two mentors and work in two labs in different areas.
Expansion of these programs is strongly encouraged, could be done
immediately, and would have an impact within ten years. Anything that
increases cross-disciplinary training is an opportunity with a good
pay-off. Such training could be encouraged by NCI/NIH and implemented
without additional funds -- just reconfiguration of the present
priorities.

***Support summer programs and/or workshops***

Although interdisciplinary training is hard to find within an
institution, some institutions convene special summer programs. NCI or
NIH could support a program of intensive lectures to introduce young
people to concepts such as model building. A program might involve 50-75
students, staff support, and facilities that can deal with animals,
cells, human material, and computational materials. Students could be
familiarized with enough mathematics to feel comfortable, and high-end
lecturers could be brought in, as is the practice at courses at Woods
Hole and Cold Spring Harbor. Courses of this type have not been
implemented to provide interdisciplinary training for mathematicians and
cancer biologists. Another possibility for productive interaction would
be a genetic analysis workshop, in which the participants could work
with a shared dataset and apply their own methods to analyze the data.

***Increase access to hardware and large-scale resources***

Students occasionally have problems with access to computers, but their
main problem is accessing higher-level analyses. Students who are unable
to parallelize their data may take months to learn the algorithms, and
are unable to perform analyses on the higher power machines. In another
three years, there will be more computers with unusual architectures;
there is an immediate need to train biomedical scientists to use these
new computing technologies and to provide more parallelization training
so the next generation of scientists will be able to move into the new
systems. Grants to purchase instrumentation to be used for training
would encourage institutions to provide training.

***Encourage collaborative career development***

At many universities, junior faculty members who publish with other
people receive less credit than for a solo publication; this strongly
discourages collaborations and interactions across disciplines. Yet
within academia, there are a few models like Biostatistics, in which
people build careers with collaborative studies and still advance the
core of their own discipline. The work-style of NIH-supported individual
investigator communities is generally counter to the kind of
collaborations needed to make real progress. NCI should investigate and
implement incentives of recognition as well as financial rewards to
encourage necessary culture change.

Biomedical scientists tend to utilize computer scientists as
technicians, so computational scientists hesitate to get involved in
collaborations in this area. When they are recognized as a critical
intellectual partner, computer scientists contribute novel ideas to the
design and execution of the project, and analysis of the outcome. It
also helps to have a critical mass of computational people involved in
biological research, so that they have a community to interact with on
theoretical and development issues.

**Specific Recommendations for the NCI:**

Realizing that the goal for cancer susceptibility research set out in
the Executive Summary will be possible only with advances in basic
knowledge about cancer and genetic variation, and the introduction of
new tools and approaches, the following are recommended.

***Requirements to achieve the goal:***

-   Integrate studies of experimental model systems, human populations,
    and computational models of molecular signatures in normal and
    dysregulated states relevant to particular features of cancer
    susceptibility.

-   Improve cancer-related phenotyping (i.e., the analysis of molecular
    and systemic changes in cancer progression) by applying emerging
    sensor, imaging, nano-and molecular technologies to individual
    humans and animals, biological specimens, cells, extracellular
    matrix components, and cell-cell interactions

-   Identify sources of individual variation in cancer-related
    biological processes, both genetic and non-genetic, and model the
    genetic-environmental and stochastic interactions.

-   Develop and apply technologies to investigate genetic variation
    broadly, including alleles, SNPs, haplotypes, and epigenetic
    alterations of nuclear and mitochondrial genomes, miRNA, highly
    repetitive sequences, transposons, gene copy number polymorphisms
    and other evolutionary molecular features.

    ***Specific recommendations:***

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-   Given the scale of datasets and complex biological systems, foster
    the creation of new computational, mathematical, and statistical
    models and analytical tools that embrace, rather than simplify or
    ignore the complexity of variation in susceptibility and resistance
    to cancers. Support large-scale coordination of studies with
    calibration, interoperability, data federation, and standardization
    of data formats, natural language processing tools, and dataset
    query tools.

-   Create a common and widely available repository with existing
    resources of well-selected, maintained, and annotated human
    biological specimens (immortalized lymphoblastoid cell lines, serum,
    plasma) for genetic, epigenetic, proteomic, and metabolomic analyses
    of early-onset cancer cases, including those with positive family
    histories. Create common databases. Develop a corresponding resource
    from a special elderly subpopulation to study resistance to cancer
    development in individuals with high lifetime exposure to
    carcinogens. Require data deposition in freely accessible databases,
    and resource sharing of repository materials.

-   Develop a trans-NIH multiple-disease--oriented framework for
    initiation of a very large-scale human population study, with
    multiple ethnic groups, environmental exposures, and family
    histories. Ensure widespread access to specimens, cell lines, and
    data.

-   Exploit existing animal models for identification and validation of
    cancer phenotypes and susceptibility and resistance genes. Foster
    comparative genomic and phenotypic studies of human and rodent
    (mouse and rat) and other mammal cancer susceptibility and
    resistance.

-   Establish a trans-NIH initiative for the development of common mouse
    resources for the analysis of genetic and environmental effects on
    development of cancer and other complex trait diseases. Facilitate
    multi-level, multi-laboratory studies of specimens after
    environmental exposures for gene-environment interactions for the
    development of computational models representing the complex genetic
    and non-genetic networks associated with disease susceptibility and
    progression.

-   Radically modify training programs to provide immersive
    interdisciplinary learning environments for biologists,
    mathematicians, computer scientists, statisticians, epidemiologists,
    and clinical investigators