Cracking the Cancer Code | Biowire Spring 2011

Decrypting Its Genesis and Proliferation with Cell-Line Models

Biowire Spring 2011 — Cell Lines — Models of Disease

Subscribe to Biowire and eBiowire
Download Biowire Spring 2011 (7.0 Mb PDF)
Biowire Spring 2011 Home


Few words can be as terrifying. And that apprehension, once confined to conversations in the clinic, has gone on to pervade our collective health consciousness — a fact revealed by our increased use of the term in print5 (Figure 1).

Figure 1. Historical in-print frequency of cancer 

Figure 1. Historical in-print frequency of “cancer” according to Google Books Ngram Viewer5.

What underlies this newfound preoccupation with cancer? Could it simply be a consequence of the decline (sometimes cure) of other dread diseases? Or perhaps it is a fiscal concern, since the cost of cancer care in the U.S. may exceed $200 billion by the year 20206. Possibly, it is a byproduct of the hundreds of billions of dollars in research funding spent during the “War on Cancer” (The National Cancer Act of 1971), which incidentally helped spawn 2.2 million cancer-related journal articles since its enactment. More likely, it is because there are few whose lives are not poignantly touched by the physical devastation and emotional anguish it causes.

For the person contemplating his or her own mortality, there is good reason to be concerned. Cancer may soon surpass heart disease as the primary cause of death in developed countries7,8. In the U.S., one in every two men and one in every three women will be diagnosed with the disease in their lifetime, over a half million people in the U.S. will die from it this year, and — despite massive levels of funding over the past four decades — the percentage of those succumbing to cancer has remained constant7,9. While we may feel cancer is a modern malady, glibly attributable to modern lifestyles or the environment (and for some cancers it certainly is), a paleopathological analysis of archeological remains suggests that cancer has been killing at roughly the same rate since ancient times4.

back to top Back to Top

Cancer is a Complex Disease

Effective cancer prevention and therapy requires knowing the molecular changes in regulatory genes and their disease-associated consequences. In many ways, that understanding may be just beginning with genetic surveys of hundreds or thousands of tumors10–14. However, the value of these genome-wide population genetic studies is a matter of contention9. What is obvious is that cancer is a complex disease that requires multiple components in multiple regulatory systems to fail before the disease manifests. This “complexity of cancer progression can be understood as the result of multiple, sequential mutations, each of which has a relatively small but positive effect on net cell growth”15. The complexity arises because the cancer-causing changes, specific to one type of cancer, can result from the accumulation of any one of a number of combinations of mutations over time; and each different type of cancer can have a diverse array of mutant-gene combinations16. What we see, then, is “phenotypic heterogeneity” coupled with “genetic heterogeneity”17,18; and, with multiple pathways leading to the same clinical outcome, the “rules” we need for understanding both the biological mechanics and the therapeutically critical relationships get discouragingly confounded9,17.

back to top Back to Top

Three Types of Cancer Genes

What we do know is that, at the level of the cancer cell, the changes that end up causing unregulated proliferation (tumorigenesis) and somatic dispersal (metastasis) are the consequence of the altered functions of three types of gene products19 that together control six cell physiologies20.

Oncogenes comprise the first type of cancer gene, and consist of growth factors, growth factor receptors, signaling molecules, transcription factors, cell death (apoptosis) regulators, or molecules involved in chromatin remodeling21. When oncogenes are activated, either through mutation or duplication (amplification), or — most often — through chromosomal rearrangements13,14, they promote cell division (mitosis). These are genetically dominant traits, so the activation of only one of each oncogene’s chromosomal copies (alleles) is all that is usually needed to establish a growth advantage.

The second type of cancer gene encodes tumor suppressors, which, as their name suggests, act to limit cell proliferation, acting in essence like anti-oncogenes. Usually, these proteins contribute to tumor development only when the mutagenic event inactivates both copies (alleles) of the suppressor, i.e., homozygous mutations. There are occasions, however, when the loss of a single tumor suppressor allele creates a pro-cancerous state. This pro-cancerous haploinsufficiency22 arises from five distinct genetic rearrangements. Sometimes, mutations in tumor suppressors destroy their normal activity but bestow upon the suppressors new functions that actually give the molecules a tumor-promoting gain of function23,24. For the tumor suppressor p53, a specific human-tumor-associated inactivating mutation was tumorigenic when the cells were also making normal protein, but not when the cells only made mutant protein25.

A specialized case of this type of paradoxical gain of function was recently described for an oncogene that acts by inactivating tumor suppressors26,27. In this example, they found that a mutant EZH2 protein (unlike the normal EZH2 that inactivates tumor suppressor genes by adding methyl groups) was more effective in inhibiting tumor suppressors when in a hetero-dimeric complex with normal EZH2 — and, therefore, promoting of a pro-cancerous state — than either mutant-mutant or normal-normal homo-dimers. This complex nature of regulatory proteins, sometimes acting like oncogenes and other times acting like suppressors, could be a common occurrence in cancer biology28 and may explain the discordance in the experimental record surrounding many cancer-associated genes.

The third cancer gene category consists of factors charged with keeping the cell’s genetic content intact. One subset of this DNA stability group is composed of those genes whose proteins act to repair problems (mutations) at the molecular (nucleotide) level that occur spontaneously or due to environmental mutagens29. The other subset operates on a mesoscale and ensures the stability of chromosomal structure, regulates homologous recombination, and coordinates chromosomal segregation during mitosis29. Defects in these genes make the genome hyper-mutable or prone to developing chromosomal rearrangements such as translocations, amplifications, duplications, and loss30. The abnormal chromosome number (aneuploidy) usually seen in cancer cells is caused by deficits in these factors29. It is easy to imagine that the disruptions of DNA stability could be the initiating event in cancer, where these initial changes would, in turn, precipitate additional alterations in the tumor suppressors or oncogenes to create the fully tumorigenic condition13.

back to top Back to Top

Six Physiological Changes Required for Cancer Progression

These three classes of tumor-promoting genes cooperatively participate in the control of distinct cell-physiological abilities19. For cancer to develop and progress from a benign tumor cell into a fully malignant phenotype capable of migrating to another part of the body, it must acquire six cellular attributes19:

  1. The first is the ability of cancer cells to independently promote their own proliferation. This happens directly through the deregulation of growth factor signaling by: A) altering growth factor receptor sensitivity, B) producing their own self-stimulatory growth factors, or C) changing the cell-surface molecules that interact with the local cellular environment (e.g., integrins).
  2. The second attribute is the resistance to anti-growth factor inhibition. The mechanics behind this resistance are similar to those for oncogenes, i.e., anti-growth factor receptors are either down-regulated, they are rendered non-functional, or the specific intracellular signaling molecules that carry anti-growth signals to the effectors are themselves inactivated or ablated by mutation.
  3. A third behavior includes mechanisms to evade programmed cell death (apoptosis), a condition that allows aberrant cells to survive. Disruption of a complex network of damage detection systems — through gene loss or mutation — offers tumor cells an escape from the integral suicide pathways.
  4. The fourth quality is immortalization through the disruption of senescence. Unlimited proliferative capacity is acquired by bolstering the processes that normally protect chromosomal termini, which as a consequence allows the tumor cell to avoid the lethal inter-chromosomal rearrangements that occur after the cell-specific limits on the number of cell divisions is reached.
  5. Promoting angiogenesis is the fifth requirement and involves the generation of new vasculature that supports the tumor’s growth and supplies routes for tumor cells to escape to the rest of the body. Under normal circumstances, there is a balance between vascular agonists and antagonists; in cancer, however, there is a tip toward the pro-angiogenic state, resulting in the de novo production of new blood vessels.
  6. The last component is tissue invasion and dispersal, or metastasis. Through the gain of migratory characteristics and the ability to slip through tissues, including vessel walls, tumor cells seed sites far removed from the local tumor. This is the deadliest attribute, since it results in the progressive and lethal disruption of life-essential functions in the newly colonized tissue31. Among the mutation-affected molecules limiting metastatic potential are those required for cell-to-tissue attachment, those that signal contact between cells, and over 20 metastasis suppressors32. In contrast, the illicit production of pro-metastatic factors allows unrestricted movement. These factors include things like cancer-cell-secreted enzymes that digest surrounding connective tissues (proteases).

back to top Back to Top

Cancer Gene Mutations

If “cancer is essentially a genetic disease19, then how many cancer genes are there? This depends how one counts, but it appears that over 400 genes34 are frequently mutated in cancer with a range from 6 to 20 to 80 mutations in these genes being present in any one tumor12,15,10. Futreal et al.13 in an earlier study determined that mutations in 291 tumor genes, i.e., more than 1% of the human genome, contribute to the disease. Of these contributory genes, 90% show up as somatic mutations, with the most common mutation being chromosomal translocations. Sjoblom and colleagues12 assayed 13,000 genes in breast and colon tumors and found that 189 genes (11 per tumor) — which are involved in transcription, adhesion, and invasion — were frequently mutated; however, of the individual tumors they examined, none had more than 6 mutated genes in common. In colon and breast cancer, Wood et al.10 found 280 genes that were frequently mutated in cancer (many of the same as Sjoblom) and could be considered tumor “drivers.” The Cancer Genome Project14 examined 518 protein kinase genes in over 200 diverse tumors and found nearly a thousand mutations. Of these, about 120 “driver” mutations that confer a growth advantage were identified, suggesting that there were many cancer genes and many more mutations that could lead to cancer. In their analyses, different tumors had widely differing mutation rates. For example, they found glioma had 22 mutations per megabase of protein kinase DNA, while renal tumors had 0.7, and tumors deficient in a DNA repair mechanism had 32 per megabase.

back to top Back to Top

Metastasis is the Killer

It is rare that the primary tumor kills its host. Instead, it is the secondary tumors that are to blame for the patient’s demise. The biology of metastasis has been understudied, even though it represents the most relevant aspect of cancer in terms of point of therapy and mortality7,9,32,35,36; yet the cellular and molecular mechanisms controlling metastasis are beginning to emerge32. So far, over 20 metastasis suppressors have been identified that moderate: 1) Invasion and intravasation in conjunction with the primary tumor vasculature, 2) Anti-apoptosis of the migrating cells, 3) Arrest and extravasation from the vasculature at the target tissue, and 4) Metastatic colonization and vascularization in the new tissue location32.

Cancer dogma maintains that genetic alterations, occurring over time, evolutionarily select for cells that acquire more and more metastatic attributes largely due to their heightened genetic instability11,15,20. However, recent studies describe surprising results. Klein35 has shown in a mutational analysis that cells forming metastases disperse early in the tumorigenic process and before a genetically homogeneous population emerges in the primary tumor. Similar results were seen when the genomes of a primary tumor and its metastases were compared38. These investigators found that metastases arise from a small population of cells in the primary tumor and not from an evolved, more metastatic-like cell that eventually dominates the tumor. Stephens’ work suggests that, in a small percentage of cancers, the tumor-causing mutations can arise, not through evolution, but instead emerge after a singular “cellular crisis” where “nearly all [mutations in that cell] occur during a single cellular catastrophe39,40.”

back to top Back to Top

Engineered Cell-Line Models of Disease

Cancer research has relied upon cell lines to explore the biological mechanisms behind the disease. How well they reflect the natural biology of the disease depends upon the questions being asked. Still, whatever the choice, it usually ends up being a compromise. At the moment, the routine cell line choices for in vitro work are primary cells, tumor-derived cells, or “immortalized” lines. Someday, differentiated pluripotent stem cells may be added to the list41.

Primary Cells
Primary cells, isolated directly from human tissue, while ostensibly best in representing normal tissues, usually proliferate slowly (if at all) with a lifespan of about two to three passages42, although others claim being able to attain more than 30 generations before they become senescent. Regardless, this limits their use for many applications. In addition, the variability caused by different genetic and epigenetic backgrounds between donors must be taken into account when comparing results between different primary lines. Still, primary cell lines can be useful models for some investigations43.

Immortalized Cells
Immortalized cells, on the other hand, which began laboratory life as primary cells, can be converted to stable proliferating lines by transduction with growth-promoting factors, although human cells are generally highly resistant to all methods of transformation44. One successful method has been to stably introduce a human telomerase reverse transcriptase (h-tert) gene into the primary cell; the overexpression of this enzyme protects chromosomal termini and bestows immortality. Unfortunately, the enzyme only permits unlimited proliferation after extended passaging, when the production of the c-myc oncogene increases, and the production of the CDKN2A tumor suppressor is repressed45. Similar successful approaches using h-tert required the additional overexpression of two exogenously added oncogenes to make the cells “immortal”44.

Tumor-Derived Cell Lines
Tumor-derived cell lines, immortal because of their transformed nature, have been the mainstay of most research. These lines are often derived from pleural effusions or metastases and therefore may not reflect the early events of tumorigenesis42. In addition, because of their inherent genetic instability, cells of the same line from one source may not be comparable to those from another46. Nevertheless, microarray analysis of established lines has been able to stratify the expression patterns of established cell lines into groups that are characteristic of human primary tumor subtypes47–49. Studies on representative panels of these cells, therefore, may better reflect the physiological and molecular differences seen in the different kinds of cancers47,48.

Gene Knockouts
With the advent of new zinc finger nuclease (ZFN) gene manipulation technologies50,51, making collections of genetically engineered cell lines is much more tractable than in years past34. For knocking out a particular biological activity, the nucleases can be targeted to protein-coding regions, where the endogenous Non-Homologous End-Joining (NHEJ) activity will create small deletions or insertions adjacent to the chromosomal ZFN binding site. If the protein-coding frame is shifted as a result of the engineered mutation, this usually results in either an early termination of protein translation (with the production of a truncated non-functional protein), or the lack of protein due to an unstable messenger RNA transcript (through actions of the non-sense-mediated-decay machinery)52–54. It is not hard to imagine that the small deletions and other inactivating mutations regularly found in clinical samples55 could be systematically recreated with ZFN technology in a panel of genetically identical (isogenic) cell lines.

Making engineered cell lines from the same parental cell stock (isogenic) ensures that the genetic and epigenetic factors are held as constant as possible52,56. In the case of cancer research, this helps address the problems associated with experimentally derivatizing genes in the complex cancer gene deletion/amplification/mutation milieu found in established tumor cell lines. Holding heritable variables constant in this way was critical in being able to successfully perform genome-wide evaluations of cancer-gene epigenetic control57 and to compare DNA-damage pathway activity in cells differing only in their tumor suppressor status58.

Gene Replacements and Knock-Ins
As opposed to gene knockouts, if a point mutation, protein tagging, or gene replacement is the objective, a “donor DNA” fragment, whose sequence spans the ZFN cut site, can be introduced into the system at the same time as the ZFN. In this case, homologous recombination processes will incorporate the donor into the chromosome at the cut site, exactly replacing the endogenous sequences. In this way, explicit genetic mutations (or corrections) can be introduced into a particular cell background.

This method would be the choice for making one, or a series of, explicit cancer-associated single-nucleotide polymorphism(s) (SNPs) in a defined genetic (isogenic) and epigenetic background58,59 . Understanding both gain-of-function mutations in tumor suppressors22 and activating mutations in oncogenes21 could be approached in this way. Indeed, knock-in methods have been used to test the effect of cancer-promoting mutations in signaling pathway components by inserting mutant genes into a chromosomal “safe harbor” location56, although it is conceivable that this could be done at the endogenous loci.

The aberrant chromosomal numbers (aneuploidy) within cancer cell lines can cause problems with most gene targeting methods. Using the ZFN system, we have been able to go into both established cancer cell lines and immortalized “normal” cell lines (e.g., MCF10A)60 and make precise genetic alterations in many of the endogenous genes known to participate in tumor development. Producing the homozygous condition was possible even when the base cell line was triploid or even tetraploid61 in the alleles of interest. During the generation of homozygous mutations with ZFNs, a byproduct of the method includes heterozygote (or hemizygote) production. Again, while a homozygous alteration of the genome may be the desired outcome, heterozygous mutations, as discussed previously, may have the most potent biological effect with respect to setting up the cancer phenotype25–27.

Expression Levels
The functions of many proteins have been determined using small interfering RNA (RNAi) technology to experimentally reduce gene activity62. Sometimes, however, residual gene activity after RNAi treatment can still deliver the full phenotype (Jun et al., unpublished; cited by Hannon62) or very different biology from the nullizygous (homozygous knockout) cells63.

Using ZFN-induced deletions, Hibbs et al.64 have shown that it is possible to completely abrogate cancer gene activity in a tumor cell line (A549), even when the gene target is polyploid. In addition, their results reinforce the point that it sometimes requires all gene copies to be inactive before the protein levels substantially fall. Using Baxspecific ZFNs, they targeted all four allelic copies in the lung cancer line. Clonal cell isolates from the ZFN treatment produced clones with a varied number of gene copies disrupted by frame-shifting deletions — some having one disrupted, others all four. If only one, two, or three allelic copies were inactivated, however, the protein levels surprisingly remained at 75% of normal; it was only when all four alleles were disrupted that the protein levels dropped to zero, showing that compensatory mechanisms exist that can override the gene dosage.

Multiplexed Mutations and Models of Disease
Because of the facility afforded by ZFN technology, multiple genetic alterations may be inserted into a single cell. This ability to “trait stack” deletions, point mutations, or reporter tags (such as fluorescent proteins) represents an unparalleled opportunity to build panels of cells that uniquely interrogate specific biological relationships hypothesized to synergistically promote the oncogenic state. For instance, it is entirely possible to mimic the “mutation accumulation” model of tumorigenesis with a collection of cell lines, each having an additional mutation that creates another “hallmark” property20.

back to top Back to Top

The Future of Cancer Cell Research

For cancer to become a curable disease, the dynamical relationships of the myriad of molecules controlling its critical biological behaviors must be understood as an integrated holistic system65. Key molecules, which systemically participate in tumorigenesis and metastasis, are being identified through statistical analyses of genome-wide surveys49,57,66–69.

And these, in turn, are used to assemble the cancer “interactome”70, which at the moment consists of over 250 disease-gene interactions71. However, the Gordian knot presented by this profound complexity, even when using modern mathematical approaches to model the disease17, has given us little advantage in designing interventions9 . Yet, we must address the heterogeneity18 if we are to have any hopes of effective treatments or cures.

That advantage may come from a dual approach of performing “reductionist” experiments designed around hypothetical “interactomes.” Since we now have the ability to genetically modify multiple points in multiple pathways in an isogenic cell, we can experimentally isolate and test the cascade of molecular events that theoretically lead to fulminant metastatic disease. Information gathered in such a system may be the fastest way to reveal the points where therapeutic intercessions could “reverse multiple levels of dysfunction69”.

back to top Back to Top


  1. Virchow R, Cellular Pathology. 1859, London: John Churchill.
  2. Schultz M, Rudolf Virchow. Emerging Infectious Diseases. 2008;14(9):2.
  3. Brooks M, Young Frankenstein. 1974: USA.
  4. Johnson GR. Unearthing Prehistoric Tumors, and Debate. New York Times. 27 Dec 2010: D1, D7.
  5. Jean-Baptiste Michel, Yuan Kui Shen, Aviva Presser Aiden, Adrian Veres, Matthew K. Gray, William Brockman, The Google Books Team, Joseph P. Pickett, Dale Hoiberg, Dan Clancy, Peter Norvig, Jon Orwant, Steven Pinker, Martin A. Nowak, and Erez Lieberman Aiden. Quantitative Analysis of Culture Using Millions of Digitized Books. Science. 16 December 2010;331(6014):176–82.
  6. Mariotto AB, et al. Projections of the Cost of Cancer Care in the United States. J Natl Cancer Inst. 2011;2010–20.
  7. Leaf C. Why we’re losing the war on cancer [and how to win It]. FORTUNE. 22 Mar 2004;149:76–92.
  8. Garcia M, Jemal A, Ward EM, Center MM, Hao Y, Siegel RL, Thun MJ. Global Cancer Facts & Figures 2007. Atlanta, GA: American Cancer Society, 2007:1.
  9. Miklos GLG. The Human Cancer Genome Project — one more misstep in the war on cancer. Nature Biotechnology. 2005;23(5):535–37.
  10. Wood LD, et al. The genomic landscapes of human breast and colorectal cancers. Science. 2007;318(5853):1108–13.
  11. Stratton MR, Campbell PJ, Futreal PA. The cancer genome. Nature. 2009;458(7239):719–24.
  12. Sjoblom T, et al., The consensus coding sequences of human breast and colorectal cancers. Science. 2006;314(5797):268–74.
  13. Futreal PA, et al. A census of human cancer genes. Nat Rev Cancer. 2004;4(3):177–83.
  14. Greenman C. Patterns of somatic mutation in human cancer genomes. Nature. 2009;446(7132):153–58.
  15. Beerenwinkel N, et al. Genetic progression and the waiting time to cancer. PLoS Comput Biol. 2007;3(11):e225.
  16. Karakosta A, et al. Genetic models of human cancer as a multistep process. Paradigm models of colorectal cancer, breast cancer, and chronic myelogenous and acute lymphoblastic leukaemia. J Exp Clin Cancer Res. 2005;24(4):505–14.
  17. Buchanan AV, Weiss KM, Fullerton SM. Dissecting complex disease: the quest for the Philosopher’s Stone? Int J Epidemiol. 2006;35(3):562–71.
  18. Visvader J. Cells of Origin in Cancer. Nature. 2011;469:314–22.
  19. Vogelstein B, Kinzler KW. Cancer genes and the pathways they control. Nat Med. 2004;10(8):789–99.
  20. Hanahan D, Weinberg RA. The hallmarks of cancer. Cell. 2000;100(1):57–70.
  21. Croce CM, Oncogenes and cancer. N Engl J Med. 2008;358(5):502–11.
  22. Santarosa M, Ashworth A. Haploinsufficiency for tumour suppressor genes: when you don’t need to go all the way. Biochim Biophys Acta. 2004;1654(2):105–22.
  23. Oren M, Rotter V. Mutant p53 gain-of-function in cancer. Cold Spring Harb Perspect Biol. 2010;2(2):a001107.
  24. Li Y, et al. PTEN has tumor-promoting properties in the setting of gain-of-function p53 mutations. Cancer Res. 2008;68(6):1723–31.
  25. Harvey M, et al. A mutant p53 transgene accelerates tumour development in heterozygous but not nullizygous p53-deficient mice. Nat Genet. 1995;9(3):305–11.
  26. Luiggi C. Unusual cancerous allies: A normal enzyme joins forces with its mutant form to result in certain human lymphomas. The Scientist. 16 Nov 2010. 27 Jan 2011 (http://
  27. Sneeringer CJ, et al. Coordinated activities of wild-type plus mutant EZH2 drive tumor-associated hypertrimethylation of lysine 27 on histone H3 (H3K27) in human B-cell lymphomas. PNAS. 2010;107:20980–85.
  28. Akst J. Two-faced proteins? Proteins that both hinder and spur cancer progression may not be as uncommon as previously thought. The Scientist. 11 Jan 2011. 27 Jan 2011 (
  29. Friedberg EC, DNA damage and repair. Nature. 2003;421(6921):436–40.
  30. Ricke RM, van Ree JH, van Deursen JM. Whole chromosome instability and cancer: a complex relationship. Trends Genet. 2008;24(9):457–66.
  31. Lengauer C, Kinzler KW, Vogelstein B. Genetic instabilities in human cancers. Nature. 1998;396(6712):643–49.
  32. Steeg PS. Tumor metastasis: mechanistic insights and clinical challenges. Nat Med. 2006;12(8):895–904.
  33. Horak CE, et al. The role of metastasis suppressor genes in metastatic dormancy. Apmis. 2008;116(7–8):586–601.
  34. Wellcome Trust Sanger Institute. Cancer Gene Census. (regularly updated) 27 Jan 2011. (http://www.sanger.
  35. Chambers AF, Groom AC, MacDonald IC. Dissemination and growth of cancer cells in metastatic sites. Nat Rev Cancer. 2002;2(8):563–72.
  36. Steeg PS, Theodorescu D. Metastasis: a therapeutic target for cancer. Nat Clin Pract Oncol. 2008;5(4):206–19.
  37. Klein CA. The systemic progression of human cancer: a focus on the individual disseminated cancer cell — the unit of selection. Adv Cancer Res. 2003;89:35–67.
  38. Ding L, et al. Genome remodeling in a basal-like breast cancer metastasis and xenograft. Nature. 2010;464(7291):999–1005.
  39. Stephens PJ, et al. Massive Genomic Rearrangement Acquired in Single Catastrophic Event during Cancer Development. Cell. 2011;144:27–40.
  40. Schipani V. Normal today, cancer tomorrow: Researchers discover how cancer can develop overnight. The Scientist. 6 Jan 2011. 27 Jan 2011 ( news/display/57907).
  41. Jiang S, et al. Reconstitution of mammary epithelial morphogenesis by murine embryonic stem cells undergoing hematopoietic stem cell differentiation. PLoS ONE. 2010;5(3):e9707.
  42. Burdall SE, et al. Breast cancer cell lines: friend or foe? Breast Cancer Res. 2003;5(2):89–95.
  43. Stampfer MR. An Overview of Growth, Aging, Senescence, and Immortality in our HMEC Culture System. Human Mammary Epithelial Cell (HMEC) Bank Website. 2010; 27 Jan 2011 (
  44. Hahn WC, et al. Creation of human tumour cells with defined genetic elements. Nature. 1999;400(6743):464–68.
  45. Wang J, Hannon GJ, Beach DH. Risky immortalization by telomerase. Nature. 2000;405(6788):755–56.
  46. Osborne CK, Hobbs K, Trent JM. Biological differences among MCF-7 human breast cancer cell lines from different laboratories. Breast Cancer Res Treat. 1987;9(2):111–21.
  47. Neve RM, et al. A collection of breast cancer cell lines for the study of functionally distinct cancer subtypes. Cancer Cell. 2006;10(6):515–27.
  48. Vargo-Gogola T, Rosen JM. Modelling breast cancer: one size does not fit all. Nat Rev Cancer. 2007;7(9):659–72.
  49. Kao J, et al. Molecular profiling of breast cancer cell lines defines relevant tumor models and provides a resource for cancer gene discovery. PLoS ONE. 2009;4(7):e6146.
  50. Urnov FD, et al. Genome editing with engineered zinc finger nucleases. Nat Rev Genet. 2010;11(9):636–46.
  51. Davis G, Cui X. Zinc Finger Nucleases for Genome Editing. Genetic Engineering and Biotechnology News. 1 Jul 2010;30(13).
  52. Waldman TA, Gene Targeting in Cultured Human Cells. Cell Cycle Checkpoint Control Protocols. Ed. HB Lieberman. Totowa, NJ: Humana Press Inc, 2004:163–74.
  53. Maquat LE. Nonsense-mediated mRNA decay in mammals. J Cell Sci. 2005;118(Pt 9):1773–76.
  54. Lejeune F, Maquat LE. Mechanistic links between nonsense-mediated mRNA decay and pre-mRNA splicing in mammalian cells. Curr Opin Cell Biol. 2005;17(3):309–15.
  55. Komatsu A, et al. Identification of novel deletion polymorphisms in breast cancer. Int J Oncol. 2008;33(2):261–70.
  56. DeKelver RC, et al. Functional genomics, proteomics, and regulatory DNA analysis in isogenic settings using zinc finger nuclease-driven transgenesis into a safe harbor locus in the human genome. Genome Res. 2010;20(8):1133–42.
  57. Andrews J, et al. Multi-platform whole-genome microarray analyses refine the epigenetic signature of breast cancer metastasis with gene expression and copy number. PLoS ONE. 2010;5(1):e8665.
  58. Sur S, et al. A panel of isogenic human cancer cells suggests a therapeutic approach for cancers with inactivated p53. PNAS. 2009;106(10):3964–69.
  59. Breast Cancer Association Consortium. Commonly studied single-nucleotide polymorphisms and breast cancer: Results from the Breast Cancer Association Consortium. J Natl Cancer Inst. 2006;98:1382–96.
  60. Soule HD, et al. Isolation and characterization of a spontaneously immortalized human breast epithelial cell line, MCF-10. Cancer Res. 1990;50(18):6075–86.
  61. Melcher R, et al. Spectral karyotype analysis of colon cancer cell lines of the tumor suppressor and mutator pathway. Cytogenet Genome Res. 2002;98(1):22–28.
  62. RNAi: A Guide to Gene Silencing. Ed. GF Hannon. 1st ed. Cold Spring Harbor: Cold Spring Harbor Laboratory Press, 2003;436.
  63. Karakas B, et al. P21 gene knock down does not identify genetic effectors seen with gene knock out. Cancer Biol Ther. 2007;6(7):1025–30.
  64. Hibbs SM, et al. An Application of Zinc Finger Nuclease Technology to Create Multiple Complete Gene Knockouts in Polyploid Cancer Lines. National Cancer Research Institute Meeting. 2010: Liverpool, England.
  65. Kitano H. Looking beyond the details: a rise in systemoriented approaches in genetics and molecular biology. Curr Genet. 2002;41(1):1–10.
  66. Nikolsky Y, et al. Genome-wide functional synergy between amplified and mutated genes in human breast cancer. Cancer Res. 2008;68(22):9532–40.
  67. Glinsky GV. Genomic models of metastatic cancer: functional analysis of death-from-cancer signature genes reveals aneuploid, anoikis-resistant, metastasis-enabling phenotype with altered cell cycle control and activated Polycomb Group (PcG) protein chromatin silencing pathway. Cell Cycle. 2006;5(11):1208–16.
  68. O’Day E, Lal A. MicroRNAs and their target gene networks in breast cancer. Breast Cancer Res. 2010;12(2):201.
  69. Knox SS. From ‘omics’ to complex disease: a systems biology approach to gene-environment interactions in cancer. Cancer Cell Int. 2010;10:11.
  70. Lage K, et al. A human phenome-interactome network of protein complexes implicated in genetic disorders. Nat Biotechnol. 2007;25(3):309–16.
  71. Goh KI, et al. The human disease network. PNAS. 2007. 104(21):8685–90.

back to Biowire main page

back to top Back to Top

Related Links