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European Biopharmaceutical Review

Where NGS Leads

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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Dr Fang Tian is currently a Senior Scientist at ATCC and is responsible for its Cell Biology Collection. She completed her postdoctoral research fellowships at Massachusetts General Hospital and the Hillman Cancer Institute at the University of Pittsburgh Medical Center. She has numerous peer-reviewed publications in high-impact journals and also serves as an ad hoc reviewer for Cancer Research and the Journal of Biological Chemistry.
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