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European Biopharmaceutical Review
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Next Generation Sequencing is revealing new insights into the genetic
nature of different cancers, leading to better cell culture models for
disease research
Over the last few decades, modern genomic tools such as microarrays and
Next Generation Sequencing (NGS) have revolutionised biological and
clinical research by facilitating the highresolution, genome-wide
analysis of genomic structure, gene expression and epigenetic modifi
cation. More recently, the cost of using these technologies has
plummeted, making them affordable to almost any researcher interested in
understanding the molecular basis of biology and pathology. For example,
data released at the end of 2012 by the National Human Genome Research
Institute (see Figure 1 on page 47) illustrated that the cost per genome
had dropped below $8,000 as of January 2012, with no slowdown in sight
(1).
One area where NGS provides considerable value is in enhancing our
understanding of the cancer genome and improving cancer prognosis, as
the technique allows an entire genome to be sequenced at the level of
the individual nucleotide. This means that single
nucleotide polymorphisms, insertions, deletions, copy number variations
and larger rearrangements can all be identified and correlated with
tumour incidence, progression and response to treatment. Tumour genomes
can also be compared with ‘normal’ somatic genomes and parental data to
tease out the relative importance of different somatic, tumour-specific
and germ-line mutations.
Genetic Complexity of Cancer
Due to the genetic and phenotypic variability between cancers,
successfully identifying important mutations that drive the initiation
and development of the disease is not an easy task (2). In some recent
large-scale studies, such as those by The Cancer Genome Atlas (TCGA) Network and the development of the Catalogue of Somatic Mutations
in Cancer (COSMIC) by the Sanger Institute, researchers explored the
genetic changes present in thousands of human normal/tumour paired
tissue samples looking for such mutations (3,4).
This research has revealed a startling level of genetic complexity and
tumour heterogeneity, even within a tumour from a single patient.
Variation Driven by Selection
Two main theories have been suggested to explain this high level of
variation. The first was proposed by Peter Nowell in 1976 and implies
that tumour formation is driven by competitive selection, whereby
different cells within a tumour accrue mutations over time and the
‘fittest’ out-proliferate their neighbours (5). In this way, each tumour
is thought to be in a continual state of flux, as clonal populations of
rapidly proliferating cells fight to reproduce the fastest and avoid
destruction by the host immune system or drug treatments. This
hypothesis is strengthened by the fact that cancer development and
progression in humans would appear to require a combination
of multiple driver mutations, with the genes affected tending to differ between cell types (6).
The local tumour microenvironment can strongly influence the phenotypic
and genetic heterogeneity of a tumour, particularly during metastasis.
Factors such as blood supply and relative exposure to the host immune
system impart their own selective pressures on
the tumour.This is especially true of solid tumours, where cells in
outer layers exist in different microenvironments to those deep within
the tumour (7).
Variation Due to Differentiation
A recent hypothesis suggests tumours develop from cancer stem cells that
mature to enter a more differentiated state. Individual cells within a
tumour might move along this continuum of differentiation at different
rates, accounting for the genetic, epigenetic
and gene expression variation seen within and between tumours. Lending
credibility to this hypothesis, stem cell-like gene expression patterns
have been detected in early tumour cells (8, 9). This model could be
seen either as complementary or antagonistic to
the selection model, and more data are required to ascertain the exact
contribution of each process to the heterogeneity seen in tumour
samples.
The Power of NGS
Although the NGS data generated to date might suggest that tumours
exhibit a level of uniqueness, such studies are also revealing
previously unknown similarities between tumours, thereby facilitating
accurate molecular classification into informative groups. In one
particularly broad study, the TCGA Network, a joint effort of the
National Cancer Institute (NCI), National Human Genome Research
Institute (NHGRI), National Institutes of Health (NIH) and the US
Department of Health and Human Services, used several techniques
including NGS to comprehensively characterise the molecular portfolio of
breast tumours (3).
The consortium analysed primary breast cancer biopsies and germ-line
samples from 825 patients to produce an informative catalogue of genomic
driver mutations. Importantly, the molecular data illustrated that
common breast cancers could be classified into four clinically relevant
subtypes (see Figure 2):
● Luminal A – ER+ low grade
● Luminal B – ER+ high grade
● HER2-enriched
● Basal-like (Triple Negative Breast Cancer, TNBC)
The segregation of breast tumours into such classes allows clinicians to
choose the best therapeutic options, implying that these new
classifications may have an important impact on future research efforts
and clinical therapeutic decisions.
Improving Treatments
A detailed analysis of the cancer genome, and in particular the
relationship between specific cancer mutations and clinical drug
responses, would improve treatment effectiveness by making it easier to
accurately identify those patient subpopulations most likely to respond
to therapy. In addition, the new therapeutic targets and predictive
biomarkers discovered through NGS studies would facilitate the
development of new anticancer drugs.
The in-depth molecular characterisation of cancer cells also has the
potential to significantly increase the effectiveness of pre-clinical
drug screening, since testing drug candidates via animal and cell models
before proceeding to human clinical trials is a cost-effective option during drug development. Such studies allow researchers to validate the
mode of action and potential efficacy of drug candidates in a variety of
biological settings, providing detailed information on key aspects such
as drug absorption, distribution, metabolism, elimination and toxicity (ADMET).
Although animal models have proven to be powerful for gaining insight
into tumour initiation and the multistage processes of tumourigenesis,
they are low-throughput and require a big investment. For these reasons,
human tumour-derived cell lines have been used for many years as drug
discovery tools. In the genomic era, researchers are using large-scale
NGS studies to investigate whether cancer cell lines accurately represent the genetic alterations and
molecular characteristics of patients’ tumours. These research
programmes are essential, as the more data we have about different cell
culture models and the disease states they most closely
mimic, the better our models will be for predicting ADMET responses,
assessing drug efficacy and identifying the optimal candidates to take
forward to clinical trials.
The Molecular Nature of Cancer
The development of the Cancer Cell Line Encyclopaedia (CCLE) is one such
study, and is part of a wider effort to understand cancer cell culture
models at the molecular level (10,11). The CCLE combines gene
expression, chromosomal copy number and NGS data for 947 human cancer
cell lines, and is expected to aid cell line selection for the testing
of drug sensitivity.
The accurate identification of such disease- and model-specific
biomarkers will be particularly important in the era of personalised
medicine, where matching specific treatment strategies with well-defined
disease subsets will be important drivers of therapeutic success.
Biomarkers that have been used in this way include mutant EGFR and BRAF,
which have already identified drugsensitive genotypes in cells that
strongly correlate with patient responses in the clinic (12,13).
Cell lines are an essential tool for progressing our understanding of
the cancer genome, as they allow the functional and biological
validation of the genetic alterations proposed by NGS data. This is a
critical step to effectively tease out those key genetic abnormalities
driving disease onset, and to identify new therapeutic targets.
Forward-thinking cell line centres that house, develop and optimise
cellular models for drug discovery, screening and validation are already
using this new wealth of molecular data to provide researchers with the
best cell lines for their particular project. A full and accurate
knowledge of the cell line in use is absolutely essential for success
because old or poorly characterised cell lines can be prone to
misidentification, redundancy, contamination or genomic variability
(14). As the depth and breadth of cell line characterisation increases,
particularly from a molecular viewpoint, so will the accuracy of
downstream research studies.
Challenges and Opportunities
NGS is revolutionising the way we research, classify and treat diseases
such as cancer. However, the majority of the deep, integrated ‘omics’
studies investigating cancer sample sets were only published over the
last few years, implying much of the work is still to be done. The
rapidly decreasing costs of carrying out NGS will certainly make it
easier to perform such genomewide studies across large sample and cell
collections. However, the current bottleneck around NGS experiments is
concerned with the complexity and time-consuming nature of data
analysis, both of which are likely to hinder rapid progress, at least in
the near future.
Furthermore, using NGS to investigate cancer biopsies can be
challenging, as tumours are genetically heterogeneous in nature and
often contain normal somatic cells. Developments in NGS protocols that
enable single cell sequencing might provide greater resolution and more
accurate data.
Conclusion
Given the rapid maturation of NGS as a technology, and the intense
competition between vendors in the marketplace, it would not be
surprising to see these technical challenges overcome quickly. Using
well-characterised tumour cell lines from collections offering a broad
range of molecular and cellular diversity with comprehensive genomic
characterisation will bring a tremendous effectiveness for translating
genomic knowledge into therapeutic opportunities. As we come to
understand more diseases and cell lines from an in-depth molecular
standpoint using NGS and other highthroughput, genome-wide technologies,
such cell culture models will continue to grow in stature as a powerful
way for developing and testing new treatments before they reach
clinical trials.
References
1. Wetterstrand KA, DNA sequencing costs: data from the NHGRI genome
sequencing program (GSP), 2013. Visit: www.genome.gov/sequencingcosts
2. Berger MF, Lawrence MS, Demichelis F, Drier Y, Cibulskis K et al, The
genomic complexity of primary human prostate cancer, Nature 470(7333):
pp214-20, 2011
3. The Cancer Genome Atlas Network, Comprehensive molecular portraits of human breast tumours, Nature 490(61): pp61-70, 2012
4. Forbes SA, Bindal N, Bamford S, Cole C, Yin Kok C et al, COSMIC:
mining complete cancer genomes in the Catalogue of Somatic Mutations in
Cancer, Nucleic Acids Research 39: ppD945-D950, 2011
5. Nowell PC, The clonal evolution of tumour cell populations, Science 195: pp23-28, 1976
6. Rangarajan A, Hong SJ, Gifford A and Weinberg RA, Speciesand cell
type-specific requirements for cellular transformation, Cancer Cell
6(2): pp171-183, 2004
7. Chaffer CL and Weinberg RA, A perspective on cancer cell metastasis, Science 331: pp1,559-1,564, 2011
8. Ben-Porath I, Thomson MW, Carey VJ, Ge R, Bell GW et al, An embryonic
stem cell-like gene expression signature in poorly differentiated
aggressive human tumors, Nature Genetics 40: pp499-507, 2008
9. Schoenhals M, Kassambara A, De Vos J, Hose D, Moreaux J and Klein B,
Embryonic stem cell markers expression in cancers, Biochemical and
Biophysical Research Communications 383(2): pp157-62, 2009
10. Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, et
al, The Cancer Cell Line Encyclopedia enables predictive modelling of
anticancer drug sensitivity, Nature 483(7391): pp603-607, 2012
11. Garnett MJ, Edelman EJ, Heidorn SJ, Greenman CD, Dastur A et al,
Systematic identification of genomic markers of drug sensitivity in
cancer cells, Nature 483(7391): pp570-575, 2012
12. McDermott U, Sharma SV, Dowell L, Greninger P, Montagut C et al,
Identification of genotype-correlated sensitivity to selective kinase
inhibitors by using high-throughput tumor cell line profiling, PNAS
104(50): pp19,936-19,941, 2007
13. Chapman PB, Hauschild A, Robert C, Haanen JB, Ascierto P et al,
Improved survival with vemurafenib in melanoma with BRAF V600E mutation,
New England Journal of Medicine 364:pp2,507-2,516, 2011
14. Korch C, Spillman MA, Jackson TA, Jacobsen BM, Murphy SK et al, DNA
profiling analysis of endometrial and ovarian cell lines reveals
misidentification, redundancy and contamination, Gynecologic Oncology
127(1): pp241-248, 2012
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