7.4: Mutations and Cancer - Biology

7.4: Mutations and Cancer - Biology

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What would happen if this cycle proceeds at will?

Your cells may grow and divide without performing their necessary functions, or without fully replicating their DNA, or without copying their organelles. So the cell cycle needs to be highly regulated and tightly controlled. And it is.

Control of the Cell Cycle

How does the cell know when to divide? How does the cell know when to replicate its DNA? How does the cell know when to proceed into mitosis or cytokinesis? The answers to these questions have to do with the control of the cell cycle. But how is the cell cycle controlled or regulated? Regulation of the cell cycle involves processes crucial to the survival of a cell. These include the detection and repair of damage to DNA, as well as the prevention of uncontrolled cell division. Uncontrolled cell division can be deadly to an organism; its prevention is critical for survival.

Cyclins and Kinases

The cell cycle is controlled by a number of protein-controlled feedback processes. Two types of proteins involved in the control of the cell cycle are kinases and cyclins. Cyclins activate kinases by binding to them, specifically they activate cyclin-dependent kinases (CDK). Kinases are enzymes that catalyze the transfer of a phosphate group from ATP to another molecule in a cell. They function as a control switch in many cellular functions, turning a function on or off, and regulating other cellular processes. Many times they are involved in activating a cascade of reactions. Cyclins comprise a group of proteins that are rapidly produced at key stages in the cell cycle. Once activated by a cyclin, CDK enzymes activate or inactivate other target molecules through phosphorylation. It is this precise regulation of proteins that triggers advancement through the cell cycle. Leland H. Hartwell, R. Timothy Hunt, and Paul M. Nurse won the 2001 Nobel Prize in Physiology or Medicine for their discovery of these critical proteins.

What makes a Cell Cancerous?

Cancer is a disease characterized by a population of cells that grow and divide without respect to normal limits. These cancerous cells invade and destroy adjacent tissues, and they may spread throughout the body. The process by which normal cells are transformed into cancer cells is known as carcinogenesis. This process is also known as oncogenesis or tumorigenesis.

Nearly all cancers are caused by mutations in the DNA of abnormal cells. These mutations may be due to the effects of carcinogens, cancer-causing agents such as tobacco smoke, radiation, chemicals, or infectious agents. These carcinogens may act as an environmental “trigger,” stimulating the onset of cancer in certain individuals and not others. Do all people who smoke get cancer? No. Can secondhand smoke increase a nonsmoking person's chance of developing lung cancer? Yes. It also increases a nonsmoking person's chance of developing heart disease.

Complex interactions between carcinogens and an individual’s genome may explain why only some people develop cancer after exposure to an environmental trigger and others do not. Do all cancers need an environmental trigger to develop? No. Cancer-causing mutations may also result from errors incorporated into the DNA during replication, or they may be inherited. Inherited mutations are present in all cells of the organism.

Oncogenes and Tumor Suppressor Genes

Some types of cancer occur because of mutations in genes that control the cell cycle. Cancer-causing mutations most often occur in two types of regulatory genes, called proto-oncogenes and tumor-suppressor genes.

  • Proto-oncogenes are genes that normally help cells divide. When a proto-oncogene mutates to become an oncogene, it is continuously active, even when it is not supposed to be. This is like a car's accelerator pedal being stuck at full throttle. The car keeps racing at top speed. In the case of a cell, the cell keeps dividing out of control, which can lead to cancer.

  • Tumor suppressor genes are genes that normally slow down or stop cell division. When a mutation occurs in a tumor suppressor gene, it can no longer control cell division. This is like a car without brakes. The car can't be slowed or stopped. In the case of a cell, the cell keeps dividing out of control, which can lead to cancer.

Several Mutations to Cause Cancer
Oncogenes may be growth factors, protein kinases, GTPases or transcription factors. Growth factors are naturally occurring substances, usually a protein or steroid hormone, capable of stimulating cellular growth, proliferation, and differentiation. They are important for regulating a variety of cellular processes. Usually, they must bind to an extracellular or intracellular receptor to initiate a cellular reaction.

Typically, a series of several mutations that constitutively activate oncogenes and inactivate tumor suppressor genes is required to transform a normal cell into a cancer cell (Figure (PageIndex{2})). Cells have developed a number of control mechanisms to overcome mutations in proto-oncogenes. Therefore, a cell needs multiple mutations to transform into a cancerous cell. A mutation in one proto-oncogene would not cause cancer, as the effects of the mutation would be masked by the normal control of the cell cycle and the actions of tumor suppressor genes. Similarly, a mutation in one tumor suppressor gene would not cause cancer either, due to the presence of many "backup" genes that duplicate its functions. It is only when enough proto-oncogenes have mutated into oncogenes and enough tumor suppressor genes have been deactivated that the cancerous transformation can begin. Signals for cell growth overwhelm the signals for growth regulation, and the cell quickly spirals out of control. Often, because many of these genes regulate the processes that prevent most damage to the genes themselves, DNA damage accumulates as one ages.

Usually, oncogenes are dominant alleles, as they contain gain-of-function mutations. The actions of the mutant allele gene product, many times resulting in a constitutively activated protein, are dominant to the gene product produced by the "normal" allele. Meanwhile, mutated tumor suppressors are generally recessive alleles, as they contain loss-of-function mutations. A proto-oncogene needs only a mutation in one copy of the gene to generate an oncogene; a tumor suppressor gene needs a mutation in both copies of the gene to render both products defective. There are instances when, however, one mutated allele of a tumor suppressor gene can render the other copy non-functional. These instances result in what is known as a dominant negative effect.


  1. Define cancer.
  2. What are cyclin-dependent kinases? What is their role?
  3. Discuss the role of oncogenes and tumor suppressor genes in carcinogenesis.
  4. Why are multiple mutations required for transformation into a cancerous cell?
  5. Identify all the categories of oncogenes and describe two categories.


Cancer is a result of the breakdown of the controls that regulate cells. The causes of the breakdown always include changes in important genes. These changes are often the result of mutations, changes in the DNA sequence of chromosomes. Mutations can be very small changes, affecting only a few nucleotides or they can be very large, leading to major changes in the structure of chromosomes.

Both small and large mutations can affect the behavior of cells. Combinations of mutations in important genes can lead to the development of cancer. The material covered on this page describes the relationship between mutation and cancer, the different kinds of mutations and what causes them. Further information on the topics on this page can also be found in most introductory Biology textbooks, we recommend Campbell Biology, 11th edition.1


Cytotoxic drugs have been in use for cancer therapy since the 1950s, and remain the first line treatment for most cancers today. These drugs inhibit cell proliferation through a range of different mechanisms, including directly damaging DNA, interfering with DNA metabolism and interfering with the mitotic machinery. Successful treatments kill tumour cells, but also exert side effects attributable to a number of factors including the inhibition of cell proliferation in healthy tissues. Treatments may also have long-term negative consequences through inducing genomic changes. In normal somatic cells, mutations induced by chemotherapy may accelerate tumorigenic processes. The development of secondary malignancies is an especially significant issue following childhood cancers and epidemiological studies have associated treatment with alkylating agents and topoisomerase inhibitors with the later development of acute myoblastic leukaemia (AML) and other tumour types [1]. Moreover, treatment-induced mutations in surviving cancer cells increase the genetic heterogeneity of the tumour and may contribute to the development of resistance to further treatment.

Chemotherapeutics are tested for genotoxicity, the ability of the drug to cause DNA damage. The most important currently approved tests are the comet assay for detecting DNA breaks, the chromosome aberration assay and the micronucleus formation test [2]. These assays give indirect and imprecise predictions of carcinogenic potential [3], as a finding of genotoxicity only reveals that a compound has potential to cause genomic mutations, without measuring the outcome in a surviving cell. Mutagenicity itself has primarily been assayed using reporter genes, including the Ames reverse mutation assay in bacteria [4] and HPRT mutagenesis in mammalian cell lines [5]. However, the comprehensive detection of all genomic changes of all types only became available with affordable whole genome sequencing.

Mutagenic effects have been attributed to a large proportion of cancer chemotherapeutic agents. Alkylating agents induce direct DNA adducts and nitrogen mustards such as cyclophosphamide have been shown to induce base substitution mutations in mutation reporters as well as chromosome rearrangements [6]. Platinum-containing crosslinking agents work by a similar mechanism to alkylating agents. Cisplatin adducts have been shown to cause base substitutions in vitro and in reporter genes [7], which were also detected in cisplatin-treated C. elegans worm genomes [8]. Topoisomerase II inhibitors such as etoposide and doxorubicin cause DNA breaks, which are the likely causes of chromosomal translocations in secondary cancers induced by these drugs [9, 10]. Drugs of the diverse antimetabolite family interfere with DNA replication, leading to double strand breaks and chromosome aberrations [11–13]. The microtubule-targeted class of cancer chemotherapeutics are not expected to have a direct impact on mutagenesis, though paclitaxel has been described to affect DNA repair through disrupting the trafficking of DNA repair proteins [14].

In summary, while genotoxic effects have been measured indirectly for most cytotoxic drugs, sequence-based data for mutagenicity are only available for cisplatin, from an invertebrate model [8]. To acquire reliable data on genomic mutagenicity, we performed whole genome sequencing on cultured cells treated with representatives of each major category of cancer chemotherapeutics. Each of the chosen cytotoxic agents (Table 1) has been reported to give a positive result in the Ames test or the related bacterial umu-test [15–19]. HPRT mutagenesis was reported for cisplatin, cyclophosphamide, doxorubicin and etoposide [20–23], but absent for hydroxyurea [24]. We set out to determine how relevant these findings are to genomic mutagenesis in vertebrate cells. Such studies have not been performed previously, but a proof-of-concept is provided by a recent report on the genomic effect of three environmental mutagens in single sequenced mouse embryonic fibroblast clones [25] as well as earlier studies that used whole exome sequencing [26–28]. The main benefit of the obtained mutagenic spectrum data will be the ability to use cancer genome sequences to determine whether the mutagenic drugs have contributed to the development of the tumour, and we provide an important example for this in the reversion of oncogenic gene mutations. The chicken DT40 lymphoblastoma cell line was chosen for treatments for the following reasons: (1) the genome size is about one-third compared to the human genome (2) this cell line has been used very extensively for DNA repair studies and it models mammalian DNA repair well [29] and (3) the availability of a wide range of isogenic DNA repair mutant cell lines will allow future comparisons on the influence of individual repair factors on mutagenesis. This detailed genomic analysis of multiple post-treatment cell clones provides the most comprehensive survey of the mutagenic potential of commonly used cytotoxics in cancer medicine.


ER, PR, and HER2 mRNA Expression Levels Predict Breast Cancer Subtypes

We characterized genetic alterations in 51 breast tumors and 46 breast cancer cell lines. We set out to study the three major subtypes defined by current clinical practice: Samples with amplified or overexpressed HER2 were assigned to the HER2 + subtype HER2 negative samples expressing ER or PR were assigned to the ER + /PR + subtype and other samples were assigned to the triple-negative subtype. These subtypes are determined using a simple algorithm with a clear biological basis, and their relevance to prognosis and treatment is well established.

ER, PR, and HER2 status is generally determined in clinical practice by immunohistochemistry or fluorescence in situ hybridization, but results from these approaches were available for only a fraction of the tumor samples in this study (see Materials and Methods and Supplementary Table S1). We therefore developed logistic regression classifiers for ER and PR status using microarray data for the ESR1 and PGR genes. The microarray data for these genes predicted pathologist-determined marker status very well (Supplementary Fig. S1) with error rates in the training data set of 12 of 143 for ER and 32 of 142 for PR. To infer HER2 status, we identified samples with copy gains at the ERBB2 locus or mRNA overexpression (see Materials and Methods and Supplementary Fig. S1). These procedures were used to assign 51 breast tumors to three subtypes (8 HER2 + , 26 ER + /PR + , 17 triple negative). Assigning the tumors to the five main subtypes defined by gene expression data (26) gave similar results (Supplementary Table S1). None of our samples were assigned to the normal-like gene expression subtype and only one was assigned to the luminal B subtype. Our sample set is therefore insufficient to investigate these additional subtypes.

Application of the mRNA- and copy number–based classifiers to breast cancer cell lines assigned 15 lines as HER2 + , 12 as ER + /PR + , and 19 as triple negative. Our assignments are in good agreement with the recent study of Neve et al. (27), which classified all of our triple-negative cell lines as basal-like and all but one of our ER + /PR + cell lines as luminal. The results from both breast tumors and cell lines indicated that the expression levels of ESR1 and PGR are good surrogates for ER and PR status.

HER2 + and Triple-Negative Breast Tumors Exhibit Higher Copy Number Instability

To understand whether overall genomic instability differs between breast cancer subtypes, we first examined the overall frequencies of copy number alterations in tumors (Fig. 1A). The fractions of the genome exhibiting copy gains differ significantly between the subtypes (P < 0.05, when any threshold for copy number gain ≥2.8 copies is considered). Triple-negative tumors have the highest frequencies of modest gains. However, HER2 + tumors have the highest frequencies of high-level gains, which include focal amplifications. This trend is not explained simply by the ERBB2 amplicon on chromosome 17 because the same differences between the subtypes are observed when chromosome 17 is excluded from consideration (Fig. 1B). The same subtype differences were observed in cell lines (data not shown). We did not find a statistically significant difference in frequencies of copy number loss between the subtypes.

Associations between subtypes and genome-wide frequencies of genetic alterations. A and B. The mean fraction of the genome exhibiting gains, including and excluding chromosome 17, respectively, as different thresholds for copy gain are considered in triple-negative (red), HER2 + (green), and ER + /PR + (blue) samples. C. Box plot of the chromosomal instability signature (CIN25) by subtype. Carter et al. (28) estimated the aneuploidy of individual cancer samples by quantifying the extent to which genes in the same chromosomal region have coordinated mRNA expression levels. The CIN25 signature is made up of genes whose expression is correlated with this measure of aneuploidy. High CIN25 values are associated with poor outcome in several tumor types.

To further characterize the differences in genomic instability among the tumor subtypes, we used a gene expression signature that reflects chromosomal instability and is associated with poor outcome in several cancer types (28). We tested whether the signature is the same in all three subtypes. We found that triple-negative and HER2 + tumors have higher expression of the instability signature than ER + /PR + tumors (P = 0.005 Fig. 1C). This is consistent with the subtype differences in frequencies of copy number gains and also with the worse prognosis associated with HER2 + and triple-negative cancers (8-10).

Copy Number Changes Associated with Breast Cancer Subtypes

In addition to global patterns of copy number alteration, we set out to identify specific regions of copy number alteration associated with subtypes and the functionally important genes within those regions. Figure 2 shows the frequencies of gains and losses for the different subtypes as measured in tumors with Affymetrix 500k single-nucleotide polymorphism (SNP) arrays. We observed some differences in frequencies between subtypes similar to those described in recent studies (e.g., losses on 5q in triple negative, gains on 10p in triple negative and HER2 + , gains on 11q13 in ER + /PR + refs. 22-24). We also observed some additional differences (e.g., losses on 6q in ER + /PR + , gains on 17q21 and 17q23 in ER + /PR + and HER2 + , losses on 15p in triple negative).

Genome-wide gains and losses of breast tumors by subtype. Graphs show gains above the x axis (frequency times magnitude) and losses below the x axis (frequency) in triple negative (A), HER2 + (B), and ER + /PR + (C) subtypes. Arrows, regions of subtype-associated gains asterisks, regions of subtype-associated losses. These regions were selected based on the tumor data only, without regard to replication in cell lines.

Different frequencies of copy number alteration within a region may occur by chance and may be characteristic of one sample set but not observed in others. We sought to identify regions that were both statistically significantly associated with subtypes and observed in two independent sample sets. We first identified chromosomal regions with recurrent gains or losses (>2.5 copies or <1.6 copies in at least 10% of tumor samples) with differences in gain or loss frequency between subtypes in tumors. We then validated these regions in data from an independent set of 47 breast cancer cell lines. A total of seven chromosomal regions showed consistent association in both sample sets (Table 1 Fig. 3).

Regions of Copy Number Alteration Associated with Subtype in Two Independent Sample Sets

Copy number in regions associated with subtype. Blue, losses red, gains. The genomic intervals consistently associated with subtype in both tumors and cell lines were identified. The physically adjacent intervals with the same pattern of subtype association were merged together to form the seven regions shown. The average copy number of the merged intervals is indicated by color: blue, losses red, gains.

Candidate Driver Genes for Subtype-Associated Regions

A recurrent region of copy number alteration typically extends over multiple genes. Identifying the driver genes (genes functionally involved in tumorigenesis) with confidence generally requires extensive experimentation. We therefore developed a computational strategy to identify candidate driver genes for experimental follow-up based on three hypotheses: first, that driver genes are found in the part of a region of recurrent copy number alteration that is most frequently and most significantly gained or lost second, that driver genes undergo marked alterations in gene expression as a consequence of copy number change and third, that changes in expression of driver genes may be associated with poor clinical outcome. Not all of these three hypotheses may hold for every driver gene, but combining the evidence from the three can identify candidates.

In the HER2 amplicon, for example, ERBB2 is found at the most amplified point, is overexpressed on amplification, and is associated with increased occurrence of distant metastasis in the study of van't Veer et al. (ref. 29 Fig. 4A). One other gene in the amplicon, GRB7, also meets these criteria. This may simply be because of its proximity to ERBB2, but RNA interference studies suggest that GRB7 may also contribute to proliferation of breast cancer cells (30).

Identification of candidate driver genes. A. Chromosome 17 35-Mbp region. B. Chromosome 17 45-Mb region. C. Chromosome 17 55 Mb region. D. Chromosome 11 70 Mb region. Top, summarized copy gain (red) and the locations of genes associated with distant metastasis at P < 0.05 (blue). Dotted lines define the focal region within which copy number was associated with subtype and within which driver genes were sought. Bottom, −log10 transformed P values reflecting increased gene expression on amplification. Genes passing the P value threshold (0.05) are labeled.

Applying the same procedure to other regions of copy gain identifies other candidate oncogenes. In the amplicon around 45 Mbp on chromosome 17q21 (Fig. 4B), which is associated with the ER + /PR + and HER2 + subtypes, there is a single gene that fulfills all three criteria: MYST2, which encodes a histone acetyltransferase (HBO1) that has been implicated in the regulation of DNA synthesis and in steroid hormone receptor signaling (31-34). In the amplicon around 55 Mbp (17q23), PPM1D is the only gene meeting all three criteria, although it is not strongly supported by any individual line of evidence. The genes TMEM49 and RPS6KB1 (encoding p70 ribosomal S6 kinase) are also located under the peak of this amplification, and their expression levels are strongly associated with copy number alteration in the region (Fig. 4C). The peak of amplification around 70 Mbp on chromosome 11q13, also associated with ER + /PR + and HER2 + subtypes, contains no genes associated with recurrence (Fig. 4D). Driver gene status is often attributed to CCND1 (encoding cyclin D1), which has significantly increased expression in amplified samples, along with the neighboring genes ORAOV1, FADD, and PPFIA1. In the region around 35 Mbp on chromosome 14q13, which is associated with the ER + /PR + subtype, only FOXA1 has elevated expression in tumors with increased copy number (Supplementary Fig. S2A).

For regions of deletion/loss, the amplitude or the frequency of the alteration does not provide as much information as in regions of copy gain because there are at most two chromosomes to be lost and deletions are usually broad. The identification of candidates therefore relies primarily on the association of gene expression with copy number loss and clinical outcome (Supplementary Fig. S2B and C). Both these lines of evidence support the candidates ARHGAP18, HDAC2, and NCOA7 in deletions around 116 Mbp on chromosome 6q (associated with the ER + /PR + subtype). The deletion located around 80 Mb on chromosome 5q13-14 (associated with HER2 + and triple-negative subtypes) contains a single gene supported by two lines of evidence: RASA1, which encodes the RAS GTPase activating protein p120GAP.

MYST2 Drives the Growth of Breast Cancer Cells

We selected the candidate driver gene MYST2 (also known as HBO1) for further study because it has particularly strong supporting evidence in our analysis. MYST2 is required for growth in 293T cells (31) but is not an established oncogene. Overexpression of MYST2 (Supplementary Fig. S4) dramatically enhanced the anchorage-independent growth of both MCF7 (Fig. 5A and B) and SKBR3 breast cancer cells (Fig. 5C and D). Previous studies in MCF7 cells, which have a modest copy number gain in this region, have also shown that siRNA-mediated knockdown of MYST2 substantially impairs cell proliferation and blocks progression through the S phase of the cell cycle compared with control siRNA treatment (31). Taken together, the observations that MYST2 knockdown blocks proliferation and that MYST2 overexpression can shift cell lines to a more transformed state support the hypothesis that it is the oncogene driving amplification around 45 Mbp on chromosome 17.

MYST2 overexpression enhances colony formation in soft agar. A. MCF7 colonies. Top row, cells transfected with MYST2 expression vector bottom row, cells transfected with control vector. C. SKBR3 colonies. Left and center columns, cells transfected with MYST2 expression vector right column, cells transfected with control vector. B and D. Box plots representing the numbers of colonies in MCF7 and SKBR3, respectively.

Association of PTEN Loss and Somatic Mutations with Breast Cancer Subtypes

We sequenced a panel of genes with well-characterized cancer-causing mutations in the cell line collection, and we evaluated both the mutations we found and published mutation data for association with breast cancer subtypes (Table 2). We observed the following overall mutation frequencies: 73% (TP53), 34% (PIK3CA), 11%(RB1), 9% (PTEN), 7% (CDKN2A), 5% (BRAF), 2% (KRAS), and 2% (HRAS). These are generally very similar to those reported in the COSMIC database (35). TP53, however, has a lower mutation rate in COSMIC (54%), so our cell line collection may have different characteristics from the 80 breast carcinoma samples that currently have data in COSMIC. We did not see evidence that mutations in TP53, PTEN, or CDKN2A are associated with specific breast cancer subtypes. PIK3CA mutations were enriched in the ER + /PR + and HER2 + subtypes, which is consistent with previous reports (17), although this enrichment was not statistically significant.

Amino Acid Mutations and PTEN Protein Loss in Breast Cancer Cell Lines

All four mutations in BRAF, KRAS, and HRAS were in triple-negative samples. This suggests an association between mutations in the RAS/RAF/mitogen-activated protein kinase/extracellular signal-regulated kinase kinase/extracellular signal–regulated kinase pathway and the triple-negative subtype (P = 0.05). An additional triple-negative cell line (CAL-51) has also been reported to harbor an activating oncogenic mutation in a RAS family member (36, 37).

Loss of PTEN has previously been reported to be associated with ER and PR negative status (15, 17, 38). Mutation and deletion at the PTEN locus are imperfect surrogates for loss of PTEN protein (15, 27), so we evaluated the association between PTEN and subtypes in cell lines by Western blot. We observed PTEN loss in 59% (10 of 17) of triple-negative samples but only in 17% (2 of 12) of ER + /PR + samples and 8% (1 of 13) of HER2 + samples (P = 0.002). The chromosomal instability signature was higher in samples with PTEN loss than in those without, although this difference was not statistically significant (P > 0.05 data not shown).

All four mutations in RB1 were found in triple-negative samples, and we found that loss of retinoblastoma protein (RB) function is associated with the triple-negative subtype (P = 0.006). The inactivation of RB was further examined in the 51 breast tumor samples using a 59-gene expression signature reflecting RB dysregulation (39, 40). This signature was significantly higher in the triple-negative subtype than in HER2 + and ER + /PR + subtypes (P = 0.002, Fig. 6). In addition, the chromosomal instability signature was higher in samples with RB1 mutation than in those without (P = 0.01 data not shown).

RB pathway dysregulation is associated with the triple-negative subtype. The RB signature score, reflecting pathway dysregulation, is summarized in a box plot by subtype.

Routes of Metastasis

There are three primary ways tumors can spread to distant organs:

  1. Through the circulatory (blood) system (hematogenous)
  2. Through the lymphatic system
  3. Through the body wall into the abdominal and chest cavities (transcoelomic).

The circulatory system is the primary route of spread to distant organs, while lymphatic vessels provide a route to local lymph nodes , after which metastases often travel through the blood 4 While the circulatory system appears to be the most common route, the extent of lymphatic versus hematogenous spread appears to depend on the origin and location of the primary tumor.6 For example, bone and soft tissue tumors (sarcomas) spread primarily through the blood, while melanoma, breast, lung and gastrointestinal tumors spread through the lymphatic system.7 Transcoelomic spread is fairly uncommon, and appears to be restricted to mesotheliomas and ovarian carcinomas.8

In order for tumor cells to gain access to lymphatic or blood vessels, tumors need to promote the growth of these vessels into and around the tumor. Growth of blood vessels is called angiogenesis, and growth of lymphatic vessels is lymphangiogenesis.

The Lymphatic System
The lymphatic system plays an important role in controlling the movement of fluid throughout the body. Specifically the lymphatic system controls the flow of lymph, a colorless fluid containing oxygen, proteins, sugar (glucose) and lymphocytes (cyte=cell). There are some similarities and differences between the (more well known) circulatory system and the lymphatic system.

Small lymphatic vessels merge into larger ones and these large vessels eventually empty into lymph nodes. Lymph nodes are kidney bean shaped tissues that are found in grape-like clusters in several locations around the body. Lymph nodes are sites of immune system activation and immune cell proliferation (growth). The fluid in this extensive network flows throughout the body, much like the blood supply. It is the movement of cancer cells into the lymphatic system, specifically the lymph nodes, that is used in the detection of metastatic disease. The staging of cancer is discussed in more detail in the Diagnosis and Detection section.

The Anatomic Model
In the anatomic model of metastsis, secondary tumors occur in the organs which they encounter first during their dissemination from the primary tumor. This scenario appears to occur in regional metastases, where tumor cells gain access to nearby tissue or lymph nodes through the blood or lymphatic circulation. 9 For example, liver metastasis is a major occurrence in patients with colorectal cancer. In this case, the capillary bed of the liver is the first encountered by the tumor cells after leaving the colon, and the liver seems to provide a suitable environment for the growth of these secondary tumors. 3 However, metastasis to distant organs occurs through a different mechanism (see next section).

The Seed and Soil Hypothesis
Early cancer researchers noticed a propensity for certain cancers to metastasize to the same organ. In 1889 Stephen Paget observed that patients with breast cancer often developed secondary tumors in the liver. He considered it unlikely that this occurrence was due primarily to accessibility of the liver by the blood supply, as other organs receiving equivalent blood supply rarely developed metastases. He instead developed the "Seed and Soil" hypothesis, in which certain tumor cells (the seeds) can only successfully colonize selective organs (the soil) that have suitable growth environments 10

The current view of the Seed and Soil Hypothesis consists of three important concepts.

  1. Primary tumors and their metastases consist of genetically diverse tumor and host cells (for more on the role of the host cells in cancer, see the section on Tumor Microenvironment).
  2. Metastasis selects for cells that can succeed in all phases of the metastatic process. In essence, a successful metastatic cell must be a decathalete: good in all the events, and not just one or two.
  3. Metastases generally develop in a site specific way. Because the microenvironments (the soil) of each organ is different, individual cancer cells may be able to colonize one specific organ.9

At the heart of the Seed and Soil hypothesis is the idea that successful metastasis depends on the interaction of the metastasizing tumor cells with the cells of the target organ (the stroma, or tumor microenvironment). Not only must tumor cells must be able to produce factors that alter the stromal cells in such a way as to better serve the survival and growth of the tumor, but the environment in which the cancer cell finds itself must be capable of responding to those signals. If the cancer cell finds itself in an inhospitable soil (i.e. it cannot subvert the stroma to serve its needs), successful metastsis will be impossible. 4

Recent studies examining the profile of genes expressed in tumors that metastasis to specific organs have identified specific genetic signatures of these tumors. For example, genes that mediate the metastasis of breast cancer to bone are different than those that mediate metastasis to the lung. In essence, different sets of genes allow tumor cells to specifically interact with the stromal cells of the target organ. These findings may lead to therapeutic strategies to target the metastatic properties of tumors.11

Targeting receptor tyrosine kinases

Members of the EGF receptor family have proved important potential targets for anti-cancer drugs, and several inhibitors have been approved or are in clinical trials. They include the monclonal antibody Herceptin (trastuzumab), which targets the receptor Her, and small-molecule inhibitors. Gary Pestano (Ventana Medical Systems, Tucson, USA) discussed the evaluation of tissue-derived diagnostic phosphorylated biomarkers in the EGF receptor pathway and presented the company's experiences with biomarker validation in the context of colorectal cancer progression. Upward and downward trends of various markers were documented as colorectal tumors underwent local and then distal metastasis. He presented a case study in which biomarker levels measured by the company's proprietary technology revealed a poor prognosis phenotype in a colorectal tumor of a Ventana employee, enabling the patient to elect to have adjuvant combination chemotherapy following radiation and colostomy.

Janet Dancey (National Cancer Institute, NIH, Bethesda, USA) described outcomes in clinical trials of EGF receptor inhibitors. Her receptors, which bind the EGF-like growth factor heregulin, were inhibited using either antibodies that target the extracellular portion of the receptor, or small molecules that target one or more kinase domains, or antisense oligonucleotides intended to suppress expression of a specific Her-family gene. Differences were observed in the toxicity and efficacy of antibodies versus small molecules that can be explained, at least in part, in terms of differences in mechanism of action, off-target activity, and pharmacokinetic behavior in vivo. Single-agent treatment of EGF receptor inhibitors (antibodies or small-molecule inhibitors) gave modest objective responses in lung, brain, head and neck, ovarian, esophageal, liver, and colon cancers. Studies of antibodies or small-molecule inhibitors in combination with cytotoxic chemotherapy or radiation have demonstrated survival benefits in some cases. Many additional randomized controlled trials are under way, and a clearer view of the utility of numerous single-agent and combination approaches to EGF-receptor-targeted therapy should emerge within the next two to three years.

Many cancer patients have mutations of EGF receptor genes, and Daniel Haber (Massachusetts General Hospital and Harvard Medical School, Boston, USA) presented analyses of the impact of EGF receptor mutations on individual sensitivity and resistance to tyrosine-kinase inhibitors. Opening with an anecdotal report of a Boston woman apparently cured of lung cancer with gefitinib, a small-molecule tyrosine-kinase inhibitor that has proved of very limited efficacy in most people, his presentation focused on studies of patients and model systems aimed at understanding why some patients respond to therapy with gefitinib and others do not. Most of the patients (40-80%) responding to small-molecule inhibitors of EGF receptor kinase domains exhibit kinase-activating mutations or gene amplification. In contrast, patients lacking mutations or amplification respond only rarely (10-15%). When compared with signaling by wild-type EGF receptors, signaling by mutant EGF receptors increases phosphorylation of the kinase Akt and the transcription factor Stat5 and downregulates Erk. Exposure of cells bearing mutant receptors to clinically achievable concentrations of small-molecule inhibitors of EGF receptors leads to apoptosis. Significantly higher drug concentrations are required to produce apoptosis in the context of the wild-type receptor. Regrettably, approximately half of the patients responding well to the small-molecule inhibitors do so for only a short time (3-6 months), after which relapse occurs, because the kinase domain has developed a drug-resistance mutation - Thr790Met, which is analogous to the imatinib-resistant Thr315Ile mutation in Bcr-Abl.

Mark Sliwkowski (Genentech, South San Francisco, USA) is examining the EGF receptor family with a view to designing new antibodies that target the receptor Her2, the target Herceptin. High-resolution X-ray crystallographic structures have provided detailed insights into Her2 heterodimerization and Her2-antibody interactions. These structures suggest alternative epitopes for targeting with novel antibodies. The Her2 sheddase (Mmp15, a membrane-linked metalloprotease) is responsible for cleavage of the extracellular component of the receptor, and Sliwkowski also described how resistance to Herceptin may be correlated with Mmp15 cleavage activity, which yields an activated, truncated form of the receptor lacking the epitope recognized by Herceptin. This hypothesis is currently being evaluated. Enzyme-kinetic analyses of Her2 with mutations in the kinase domain have explained the increased sensitivity of tumors bearing these mutant receptors to small-molecule inhibitors compared with tumors bearing a non-mutant receptor. These results suggest that high doses of small-molecule inhibitors will be crucial for treating patients with tumors driven to proliferate by non-mutant Her2.

Flt3 is a receptor tyrosine kinase that is important in leukocyte development and is being targeted as a possible treatment for AML. Donald Small (Johns Hopkins University School of Medicine, Baltimore, USA) described studies on Flt3 in AML patients. Individuals carrying the internal tandem duplication mutation in FLT3 have considerably poorer responses to conventional induction chemotherapy with cytarabine and daunarubicin (the so-called 7+3 therapy) and correspondingly poor prognoses. Recent advances in preclinical characterization and clinical studies of Flt3 inhibitors were described in detail. The responses of patients with the internal tandem duplication mutation to therapy with a single Flt3 inhibitor are critically dependent on the blood levels of the drug. Current clinical studies on Flt3 inhibitors focus on relapsed AML patients who are receiving either high-dose cytarabine or a combination of mitoxantrone, etoposide and cytarabine. Ultimately, the best prospects for such inhibitors are likely to be in the setting of 7+3 induction chemotherapy for newly diagnosed AML patients with the Flt3 internal tandem duplication mutation.

Conclusions and perspectives

The advent of CRISPR technology has revolutionized the biological sciences and provided cancer biologists with a powerful gene-editing method that can alter the genetic make-up of cells in unprecedented ways. Indeed, promising results have been achieved in diverse disciplines, from basic research to the development of potential therapies against cancer, congenital defects, and other chronic diseases. Many challenges associated with CRISPR technology still exist, mainly in clinical use associated with delivery and safety. 69 Improved strategies will be required to increase the targeting efficiency and to minimize off-target effects. Ensuring that CRISPR/Cas9 has precise genome-editing ability is also important. Following CRISPR/Cas9 mediated DSBs, DNA repair can be achieved by either the “error-prone NHEJ” or “precise repair through HDR” in the genome. In an effort to promote HDR over NHEJ, a study by Maruyama et al. used the NHEJ inhibitor Scr7 and observed that Scr7 treatment did indeed increase the levels of HDR-mediated genome editing over NHEJ. 70 In addition to concerns regarding off-target effects, considerations regarding the immune response to CRISPR-mediated gene-editing must also be considered. The Cas9 protein is bacterial in origin and thus might elicit an immune response, which could in turn affect its gene-editing efficiency. Also, the exogenous sgRNAs may be cleared by immune cells like monocytes and macrophages. 71 The delivery approach for CRISPR/Cas9 thus depends upon the objective as well as the target. Transient expression of the sgRNA and the Cas9 protein through microinjection might be safer than other non-viral or viral delivery methods. Viral delivery has the highest efficiency for the expression of the Cas9 protein and sgRNA, but comes with risks. During one in vivo study, the lentivirus particles elicited a strong immune response in treated animals, which affected the efficacy of the lentiviral vector. 72 Another issue with lentiviral delivery is also the possibility that the lentiviral vector could randomly integrate into the cellular genome. To overcome this issue, integrase-deficient lentiviral vectors, nanoparticle carriers, or an inducible system could be used for delivery of CRISPR/Cas9.

The prohibitively high costs for CAR T-cell therapy have made this treatment unavailable to a large section of society. CAR T-cell therapy offered by Novartis, the first approved by the FDA, costs $475,000 per treatment however, further advances in CRISPR technology might help reduce costs and make CAR T-cell therapy available to more patients. So far, the majority of CRISPR clinical trials are being conducted in China. A recent article in the Wall Street Journal suggested that this may be due to the fact that fewer rules and restrictions exist in China. Indeed, Dr. Shixiu Wu from the Hangzhou Cancer Hospital, who was featured in the article, 73 has been able to use CRISPR technology-based treatments on his cancer patients, without the need for national regulatory approval and has few reporting requirements. Many scientists worry that the technology has the potential to harm patients and that unintended consequences from using CRISPR on patients without sufficient oversight could hinder progress in the whole field. Dr. Wu recognized that he was undertaking risks in using CRISPR-based treatments for his cancer patients, but was considering their limited available survival time. The thought is that being able to live for additional time is better than imminent death. Persistent post-treatment monitoring of patients could help to eliminate or treat any unwanted consequences. Indeed, treatment-related observations could potentially save many lives of those who are on the brink of death.

We need consider the limitation as seriously as the potential benefits of this technology. A new human trial, in which the team took a crash course in bioethics and created CRISPR babies, brought the ethical issues and controversies into the public. One hundred and twenty-two Chinese scientists and many other scientists worldwide have already condemned the trial. How to use this powerful without overstepping the ethical bottom line is a serious question that needs careful consideration. We might expect more positive reports from clinical trials as well as the development of improved approaches that will bring new hope for personalized therapy.


Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The author(s) received the following financial support for the research, authorship, and/or publication of this article: Patrick C. Ma received research support from Daiichi Sankyo Inc. and ArQule Inc. in sponsored clinical trial funding and has received consultant honoraria as an advisory board member.


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