Systems Biology of Tumor Physiology: Rethinking the Past, Defining the Future
(Springer Science+Business Media, 2016)
The e-book version is free if your university library has a subscription to Springer. The Springer page for the book is here.
Chapter 1 - Macrophages, Extracellular Matrix, Estrogens and Breast Cancer Risk (See Excerpt Below)
Chapter 2 - Cellular Plasticity, Cancer Stem Cells, and Cells-of-Origin (See Excerpt Below)
Chapter 3 - Using Mouse Models and Making Sense of Them (See Excerpt Below)
Macrophages, Extracellular Matrix, Estrogens and Breast Cancer Risk
Citation: David H. Nguyen. “Macrophages, Extracellular Matrix, Estrogens and Breast Cancer Risk.” Systems Biology of Tumor Physiology, Springer Science+Business Media (New York). Estimated Publication Date 2015. [Draft 12/21/14]
Outline of Chapter 1
Section 1. The Estrus Cycle Regulates Macrophages in the Mammary Gland
Section 2. The Estrus Cycle Regulates Macrophages in the Ovaries
Section 3. The Effects of Estrogens on Macrophages
Section 4. Macrophages as Targets of Endocrine Disrupting Agents
Section 5. Estrogen Effects on Macrophage ROS Production
Section 6. Complex Interplay Between Macrophages and the Extracellular Matrix
Section 7. Testing the Estrus-Regulated Macrophage-Dependent Hypothesis of Breast Cancer Risk
The primary purpose of this chapter is to succinctly present evidence that macrophages are one of the important components within the complex mechanism underlying why lifelong exposure to estrogen is a risk factor for breast cancer. In line with the systems biology theme of this book, this chapter underscores the integrated way in which multiple organs are regulated by macrophages and systemic hormones. This systems biology view reveals that breast cancer risk due to life-long estrogen exposure needs to be understood as having multiple biological mechanisms that are distinct, yet deeply intertwined. This chapter develops what will be referred to as the estrus-regulated, macrophage-dependent theory of breast cancer risk due to life-long exposure to estrogen.
The secondary purpose of this chapter is to present an example of how a systems biology perspective provides an integrated understanding of the ways in which mammalian physiology is intricately tied to tumor development. The advantage of an integrated perspective is that it reveals more mechanisms that can become candidates for therapeutic intervention, since rarely, if ever, is there just one biological mechanism that is responsible for a complex disease phenotype. The systems biology perspective also guides the scope and strategy of how the research literature can be approached. This will be helpful to investigators who are just starting to study physiological mechanisms that pervade the entire organism. It is also helpful to those who study minute molecular/subcellular mechanisms but who seek to have a global understanding of how physiological mechanisms interact to affect their research question.
Epidemiological studies show a correlation between the following: early onset of puberty and breast cancer risk; late onset of menopause and breast cancer risk; exposure to endocrine disrupting chemicals and the early onset of puberty (Collaborative Group on Hormonal Factors in Breast, 2012). The question of interest now becomes about how we can mitigate breast cancer risk by modulating the biological mechanisms behind these risk factors. This chapter attempts to provide the reader with an overview of why macrophages may be a good candidate as targets of prevention and therapeutic modulation. The last section of the chapter has several goals: (1) propose ways of directly testing the theory that is outlined in this chapter via experiments; (2) propose ways of indirectly testing the theory by presenting verifiable predictions of breast biology in different populations of people; (3) discuss the epidemiology studies relating age, breast density, and the theory outlined in this chapter.
Section 7. Testing the Estrus-Regulated Macrophage-Dependent Hypothesis of Breast Cancer Risk
Directly and Indirectly Testing the Hypothesis
Up to this point, the content of this chapter was meant to lay the foundation for why macrophages should be considered crucial preventive and therapeutic targets in the biological mechanisms underlying the risk of breast cancer from life-long exposure to estrogen. This last section will briefly outline methods for and challenges to testing the hypothesis that estrus and estrogen-mimicking compounds increase breast cancer risk by inducing macrophages to stiffen the extracellular matrix, which then feeds back to alter macrophage behavior. There are two ways of testing this hypothesis. First is the direct way of experimentally mimicking the effect of estrus-regulated, macrophage-dependent stiffening of the mammary gland microenvironment. The second and indirect way is to make verifiable predictions of altered breast biology in human populations that is consistent with the theoretical framework outlined by the hypothesis.
Experimental Approaches to Directly Testing the Hypothesis
A direct way of testing this hypothesis is to implant a gel substrate or actual human breast fragment into a mouse for an extended period of time, allowing many estrus cycles to occur, and then measuring the physical properties of the implant, the structural arrangement of the implant’s matrix, and the degree of ROS production of macrophages associated with the implant. A number of materials are commonly used as gels that carry cells or molecules for implanting into mice (reviewed in (Frantz et al., 2010)), but since macrophages are known to promote the formation of and to digest collagen I, a major component of the mammary gland ECM, a collagen I gel would a good candidate substrate. It should be considered, however, that the type of collagen I used should be those that are extracted in a way that preserves their terminal ends, which contain the moieties required for crosslinking individual molecules into a fibril (Sabeh et al., 2009). Since reconstituted collagen gels do not fully replicate the in vivo ECM (Sabeh et al., 2009), intact explanted fragments from human reduction mammoplasties would be closer to the ideal model system. It is important to keep in mind that the human mammary gland has major structural differences compared to the mouse mammary gland. In humans, the ductal epithelia is surrounded by two thick layers of fibrous stroma, whereas the epithelia of mice only have a thin layer of fibroblasts that separate the ductal compartment from the adipose compartment. The scarring that results from the transplantation procedure may confound measures of stiffness in a xenografted tissue, so considerations should be taken to minimize this effect. But regardless of impeded physical measures, histological and immunohistological features can still be measured via the physical properties of fibrillar collagen and the use of antibodies that are specific to human collagen. Immunohistochemistry would be particularly useful in studying the arrangement of human collagen in human explants that were placed into the mouse mammary gland, since it is known that the prevalence of fibrillar collagen is associated with increased tissue stiffness. Xenografted human explants should be placed into the intact mammary glands of adult female mice, as opposed to mouse mammary glands in which the epithelial compartment has been previously removed. This is because the circuitry that recruits macrophages to the mammary gland during each estrus cycle involves complex feedback between multiple compartments (epithelial cells, fibroblasts, adipocytes, etc.). Human mammary fragments would have to be implanted into the mammary glands of immunocompromised mice (Sheffield and Welsch, 1988), which have intact mammary glands (Militzer and Schwalenstocker, 1996). The length of time and window of time for implantation should also be considered. As female mice mature and then age, their menstrual cycles begin to slow down (Nelson et al., 1982). The varied aspects of the estrus cycle are likely dependent on the genetic background of the strain.
Another method of testing this hypothesis is to measure physical forces, ECM structure, and macrophage phenotype in intact mammary glands of groups of mice that have different estrus rates. Mice that have a shorter cycle length would naturally have more cycles within their lifetime than those that have longer cycles. A genetically engineered BRCA1 mutant mouse model has been reported to exhibit shortened estrus cycles (Hong et al., 2010). Other BRCA1 mutant models may exhibit the same ovarian effect. It is possible to remove the endogenous ovaries of a mouse and then replace them with the over-active ovaries of a syngeneic donor. This approach would allow the endogenous mammary glands to be controlled by a highly active donor ovary.
Indirectly Testing the Hypothesis by Making Verifiable Predictions
The Effect of Aging
The hypothesis and theory – depending on how one uses the idea – developed in this chapter can be indirectly tested by making predictions in human populations. Regarding this estrus-regulated, macrophage-dependent ECM stiffening theory of risk from lifelong estrogen exposure, one of the most straightforward questions to ask is whether the breasts of older women are denser with ECM compared to those in younger women. Women with denser breasts have four to six times the risk of breast cancer than those with less dense breasts (Bertrand et al., 2013; Boyd et al., 2007; Byng et al., 1998; Pinsky and Helvie, 2010). A study by Checka and colleagues (Checka et al., 2012) analyzed the mammograms of 7,007 women and showed that in general breast density decreases as women age, and that density was highest in women who were pre-menopausal (the average age of menopause is generally considered to be 50). 81% of women less than 40 years-old had dense breasts, while 74% of women between 40 and 49 years had dense breasts. The percentage drops to 57% for those between 50 and 59, then to 44% for those between 60 and 69, then to 36% for those between 70 and 79, and finally rising to 41% for those above 80 years. These data are consistent with the hypothesis that the more menstrual cycles a women undergoes, the more dense her breast tissue will be.
It should be noted that the Checka study examined the percentage of women characterized as having dense breasts across continuous age groups, but does not examine the increase in breast density within individual women across their lifetimes. This study did not address whether certain groups of women develop dense breasts early in life and then maintain that level of density up to and beyond menopause, or gradually develop density up through menopause after which the density level is maintained or lost. As with many aspects of physiology, there are likely subgroups of women with regard to this phenotype. Radiographic breast density, as measured by the Checka study and others, is defined as the ratio of the area represented by epithelial and stromal cells, which appears white, compared to fatty tissue, which appears radiolucent (varying shades of gray). The Checka study involved patients that were mostly Caucasian and who had medical insurance. It is well known that racial disparities significantly affect breast density, along with the age of onset of breast cancer and the type of breast cancer that develops. African-American women have higher rates of triple-negative breast cancers (Kurian et al., 2010), the most aggressive type of breast cancer. Thus, race is likely another important factor in the rate of density gain and loss.
Breast tissue from reduction mammoplasty or other surgical procedures from women of varying ages would be great for examining the structural arrangement of collagen in the extracellular matrix. An increase of fibrillar collagen and stiffness with increasing age would support the theory outlined in this chapter.
The Effect of Premature Ovarian Failure
A second question for indirectly testing the theory is to ask whether women with premature ovarian failure have less dense breast tissue than normal women, since those who live beyond the ovarian failure would have had fewer menstrual cycles than normal women of the same age. Benetti-Pinto and colleagues (Benetti-Pinto et al., 2014) examined the mammographic density of 56 women who experienced premature ovarian failure (POF) at an average age of 32.35 years. Each subject had two mammograms that were taken an average of 5.25 years apart. Consistent with the theory outlined in this chapter, the authors found that the breast density of women with POF decreased across five years, regardless of whether they used estrogen-progestin therapy. The same research group conducted a similar study six years prior to this one in which they did a one-time-point comparison of mammographic density between POF patients who were on hormone therapy and normal women (Benetti-Pinto et al., 2008). In that study they did not find a difference in breast density between the POF group and the normal controls, suggesting that POF does not decrease breast density. However, the findings from the 2014 study nullify the results of the 2008 study, because the 2014 study revealed that parity significantly reduces the degree of breast density in POF patients. In the later 2014 study 32% (18/56) of POF patients were nulliparous (never having had a full-term pregnancy) while those who were multiparous (having had more than one full-term pregnancy) were shown to have a lesser degree of breast density. In other words, having had children decreases women’s degrees of breast density. This is significant for the comparison of the two studies because the earlier 2008 study reported 54.8% of the POF patients as “breastfeeding,” meaning that they had at least one full-term pregnancy. The fact that half of the POF group in the 2008 study was parous means that the POF cohorts between the two studies are not comparable. Further evidence for this can be found in the two papers. In the 2008 study, the POF group had an average age of 36.9 years and an average digitized breast density of 25.1%. In the 2014 study, the nulliparous POF group at the second mammography screening had an average age of 40.33 years and an average digitized breast density of 25.4%. It is known that mammographic density decreases with age (Checka et al., 2012), so even though the POF group in the 2008 study was several years younger, it had a comparable degree of breast density as the POF group in the 2014 study; likely due to the fact that half of the 2008-POF group was parous, which correlates with decreased breast density. The effect of parous POF patients also applies to the third study from this research group, which was published in 2010 (Soares et al., 2010). Soares and colleagues compared the mammographic density of POF patients who were on hormone therapy to normal post-menopausal women who were on hormone therapy, finding no difference. However, as in their 2008 study, half of the POF cohort had breastfed and were thus parous. Additionally, in the Soares study the patients in the POF group had already been diagnosed for an average of 85.9 months (7.16 years) at the time of the study. 7.16 years may have been a time during which significant reduction in breast density in the POF cohort had already occurred, since the 2014 study showed this effect. In summary, further studies with larger sample sizes and comparable experimental design are warranted to clarify the effect of POF on mammographic density.
As with the data linking aging to breast density, mammography does not reveal the structural characteristics of the extracellular matrix nor the physical properties of the tissue. Measuring these factors in breast tissue from women several years after they underwent POF compared to age-matched normal women would be a way of confirming the predictions of the theory outlined in this chapter.
The Effect of Hormone Replacement Therapy in Post-Menopausal Women
A third question for indirectly testing the theory outlined in this chapter is to ask if hormone replacement therapy in post-menopausal women increases breast density. Exogenously treating a patient with estrogens and progestins is akin to an “over-expression” experiment in which the hypothesized effect is induced by artificially adding or promoting a factor. In this case, the theory outlined in this chapter would predict that treatment with exogenous hormones would increase breast density in post-menopausal women. Indeed, multiple studies have shown this to be true, have reported that varying doses of hormone treatment correlate with varying degrees of increased density, and that not all women who receive treatment exhibit the same degree of increased density (Christodoulakos et al., 2006; Laya et al., 1995; Marugg et al., 1997). Though the post-menopausal patients in these studies exhibit increased mammographic density due to hormone therapy, it is unknown if macrophages are recruited to their mammary tissue as part of why the density increased. An increase in the amount of epithelial cells, stromal cells, and extracellular matrix would all result in increased mammographic density. However, it is unknown whether the new extracellular matrix in these post-menopausal women is arranged in ways that are similar to the effect of macrophages on the matrix during normal menstruation.
The Effect of Wiskott-Aldrich Syndrome on Breast Density
A fourth question for indirectly testing the theory is to ask if women with mutated macrophages have less dense breasts than women with normal macrophages. The Wiskott-Aldrich Syndrome (WAS) is an X-linked disorder that results in people who have a mutated form of the Wiskott-Aldrich Syndrome protein (WASP). The WASP protein is involved in actin polymerization, which is critical for cell movement. Since the WASP gene is primarily expressed in hematopoietic cells, WAS patients exhibit recurring infections, easy bruising, prolonged bleeding, and a low number of small platelets (reviewed in (Thrasher and Burns, 2010)). Since macrophages are derived from the hematopoietic system, WAS patients should exhibit deficits or impairments regarding physiological features that are governed my macrophages. Macrophages from patients with WASP mutations exhibit impaired filopodia, lamellapodia, and cell migration (reviewed in (Thrasher and Burns, 2010)). Given the role of macrophage in remodeling the ECM of the mammary gland, the theory outlined in this chapter would predict that WAS patients would have breast tissue that is less dense and that has altered extracellular matrix, compared to normal women, because of impaired macrophage function. The advent of modern medical treatments such as bone marrow transplants and antibiotics has allowed WAS patients to survive for multiple decades, which is enough time for their breast tissue to have undergone many menstrual cycles (Blaese et al., 2013). Since WAS patients may have little concern for developing breast cancer, which is a disease significantly influenced by age, there would be shortage of mammography data with which to examine breast density. However, due to the types of cancers that WAS patients experience and die from (Salavoura et al., 2008), they may commonly undergo thoracic computed tomography (CT) scans for diagnostic purposes. The CT data would allow investigators to determine the density of their breast tissue as a function of age. CT scans are relatively comparable to mammography for determining breast density (Salvatore et al., 2014). Retrospective analysis of breast density via CT data should consider and extrapolate the menstrual phase of the patient at the time of the scan. WAS patients who undergo bone marrow transplantations may have most of their myeloid-derived cells, which includes macrophages, replaced, which may be a confounding factor in determining whether the WAS affects breast density based on dysfunctional macrophages. However, differing WAS patients will have received bone marrow transplants at different times and thus may still have had significant years of adult life with dysfunctional macrophages affecting the mammary glands. Furthermore, not all bone marrow graft procedures yield a completely recovered immune system after the same amount of time, which may still leave a graft recipient with a number of years with dysfunctional mammary macrophages.
Cellular Plasticity, Cancer Stem Cells, and Cells-of-Origin
Citation: David H. Nguyen. “Cellular Plasticity, Cancer Stem Cells, and Cells-of-Origin.” Systems Biology of Tumor Physiology, Springer Science+Business Media (New York). Estimated Publication Date 2015. [Draft 04/23/15]
Outline of Chapter 2
Section 1 – Defining the Terminology Necessary for Understanding This Field
Section 2 – Confusion Among Stem Cell, Cancer Stem Cell, and Cell-of-Origin Concepts
Section 3 – Evidence for the Cell-of-Origin of Tumors Theory
Section 4 – Evidence for the Cellular Plasticity of Tumor Development Theory
Section 5 – Confounding Factors in the Plasticity Versus Cell-of-Origin Debate
It is now recognized that tumor cells share similarities to stem cells in that they do not have a strong sense of functional identity. In the case of the skin, a mature cell that becomes stem-like no longer has the identity of a skin cell, meaning it changes shape and behaves in ways that make it not fit in with its normal skin cell neighbors. Accompanying these changes in behavior are changes in which genes are transcribed or repressed. This is why the transcriptome of cancer cells or cancer stem cells often share many similarities with the transcriptome of embryonic or adult stem cells.
What is the point of understanding whether a tumor arises from one cell or a number of different cells? These are the cells that allow the tumor to regrow after treatment has shrunk it. The regrown tumor is often meaner than the original tumor. These are also the cells the leave the tumor and colonize other organs in the form of metastasis, which is often what kills a patient. Thus, if medical research wants to develop more specific therapies that have fewer side effects and a lower risk of relapse, knowledge of cells-of-origin or cancer stem cells is essential. Furthermore, as nanotech research and in situ live-imaging technologies seek to treat cancer, they cannot be guided to target specific cells and spare normal cells if we do not know the properties of cells-of-origin or cancer stem cells.
This chapter will discuss common concepts that are similar but not identical, which cause confusion within the field about how different studies relate to each other. For example, the cell-of-origin of a tumor may not be the same cell as the “cancer stem cell” (CSC) of the tumor. Furthermore, even if a tumor were derived from one transformed stem cell, there are processes within a tumor that would alter the identity of cells that arose from that initial stem cell, or that would produce hybrid cancer stem cells that may or may not resemble the initial stem cell.
Section 1 – Defining the Terminology Necessary for Understanding This Field
To understand the concepts of cell-of-origin, cancer stem cell, dedifferentiation, and plasticity in cancer biology, it is important to clarify terms that are commonly used in the field and its various subfields.
Stem cell – A stem cell is a cell that has two defining properties: (1) it can self-renew, meaning it can divide to produce two daughter cells, at least one of which remains a stem cell; and (2) it has the potential to mature (see the definition of “differentiation”) into multiple different types of cells.
Progenitor cell – A progenitor cell is similar to a stem cell, except that it has less potential with regard to what cell types it can mature to become. Depending on the type of tissue, progenitors can self-renew like stem cells do, producing more cells that maintain an identity of being a progenitor. Progenitors might also undergo what is called transit amplification, meaning they divide rapidly to form my copies of themselves. These types of progenitors are referred to as transit amplifying cells. The purpose of transit amplification is to produce many progenitors from a few starting progenitors, such that they turn into many mature cells. This effect spares stem cells from having to divide many times. For example, one stem cell can divide once to produce a stem cell and a progenitor cell. The resulting progenitor cell can then divide many times to produce many progenitor cells, so that the original stem cell doesn’t need to divide unnecessarily. Depending on the organ, there can be early progenitors and late progenitors. The further down the line a progenitor becomes as it matures, the less potential it has to become different types of cells, meaning the more committed it is to a certain “fate,” or identity.
Differentiation – Differentiation is the process in which a stem or progenitor cell matures into a cell with a specific function and identity. In cell biology and tissue biology, cellular function and identity go hand-in-hand. To speak generally, a stem cell that differentiates into one type of skin cell will take on the physical properties of skin and will not be able to do what a liver cell does for the body. Differentiation is how cells specialize in performing a certain duty, which requires them to turn on the genes and make the proteins necessary for that duty. The change in programing of what genes are turned on and which are turned off is an important reason why cells that differentiate into one type cannot differentiate into another type under normal circumstances. Differentiation is accompanied by a change in chromatin structure, epigenetic marks, transcriptomic profile, and cytoskeletal structure. A differentiated cell is a cell that has matured into a specific cell type that has a specific function.
Potency – This concept describes the potential that a stem or progenitor has for becoming different types of differentiated cells. The term totipotent means that a stem cell can become any differentiated cell type in the body. During mammalian embryonic development, a process called gastrulation occurs after which the three main germ layers are formed: endoderm, mesoderm, and ectoderm. Cells in these germ layer lineages are called pluripotent, meaning they can become multiple differentiated cell types that are derived from the same germ layer. For example, nerve cells, skins cells, and breast cells are all derived from the ectoderm. A bipotent progenitor cell is one that can only become one of two differentiated cell types within the same germ layer lineage. A monopotent progenitor is one that can divide to produce of itself, but when their descendants differentiate, they can only become one cell type.
Lineage Commitment – This concept refers to the point of no return as a stem cell matures into an early progenitor and then to a late progenitor. The further along the progenitor goes along this continuum, the more it becomes committed to a certain fate, meaning the less potential it has to differentiate into different cell types.
Transdifferentiation – This describes the situation when a differentiated cell from one germ lineage (endoderm, mesoderm, or ectoderm) becomes a differentiated cell from another germ lineage. In mammals, this does not happen in a normal animal unless genetic engineering or tissue engineering is involved. Transdifferentiation is one of the main goals of adult stem cell research. For example, turning a person’s skin cells (ectoderm origin), which are very abundant, into stem cells that can then be injected into that person’s damaged heart (mesoderm origin) for the purposes of tissue regeneration.
Dedifferentiation – Dedifferentiation is the process in which a differentiated cell undergoes changes to lose its matured cell identity to become progenitor-like or stem-like. A dedifferentiated morphology is one of the hallmarks of cancerous cells. Dedifferentiation is accompanied by changes in cell-to-cell interactions, which allows the dedifferentiated cell to take on an odd shape or to divide “out of line” such that its daughter cells are no longer neatly packed like the other cells around them.
Plasticity – The concept of plasticity describes the potential of a differentiated cell to dedifferentiate back into a progenitor-like or stem-like state, and then to differentiate into a new differentiated state. The underlying concept is that a cell’s identity, and thus its function, is not permanently fixed after a cell has differentiated. Within a tumor that contains many different regions of distinct morphology, or an early stage invasive tumor that is next to normal tissue, there are areas of transition showing groups of neighboring cells changing from one shape to another in ways that normal, differentiated cells do not do. Such drastic changes in cell morphology change the transcriptomic and proteomic identity of the cell. Thus, within the permissible environment of a tumor there is ample evidence of cellular plasticity.
Niche – A niche is defined as the location in which a stem cell or progenitor cell likes to reside and perform its function of making progenitors or more of itself. The niche is a very important concept in stem cell biology and cancer biology, because those who study tissue development, organ regeneration, wound healing, or metastasis all study at least one facet of how the niche regulates stem cell function. Why are stem cells in tissue located where they are? What activates a dormant stem cell to produce progenitors that heal a wound? Do stem cell-like circulating tumor cells prefer certain locations when they seed into and metastasize in distant organs? Are these locations natural stem cell niches or does the roaming stem-like cell induce its own niche? Is there such a thing as an inducible niche? If so, how should we understand and predict sites of metastasis? These questions are highly relevant for understanding the fundamentals of organ development, wound healing, and tumor development. An organism’s niche within its habitat is also an important concept in ecology. If defined in detail, the concept of a niche draws together many of the topics that are studied in ecology (i.e. types of species interactions, physical or biological factors that affect population density, ways of using available resources, waste management, mating habits, etc.). The niche is one of the concepts that unifies ecology. The niche is also a concept that unifies the topic of this chapter: plasticity, cancer stem cells, and cells-of-origin. A niche isn’t just a basket of extracellular matrix proteins in which a stem cell sits. In a niche, the stem cell is surrounded by neighboring cells that communicate with it. These cells often play very important roles in regulating the activity of the stem cell. A stem cell in a niche can be affected by physical contact with its neighbors, paracrine signals released by its neighbors, signals from cellular projections that reach over from a distance, among other mechanisms of cell-to-cell communication. The reality, as with other aspects of biology, is likely multiple simultaneous signals.
Microenvironment – The microenvironment refers to physical attributes surrounding a cell, which includes the extracellular matrix molecules, temperature, pH, salinity, hormonal milieu, electro-magnetic signals, connective tissue cells (fibroblasts, blood vessels, lymph vessels), and immune cells that surround a cell. The term microenvironment is often used to describe a general condition of, within, or surrounding a cell/tumor; often a condition that promotes a specific behavior of the tumor.
Clonality – This concept is important in discussions of cancer stem cells and cells-of-origin because it underlies part of the reason why certain therapies shrink a tumor, only to have the tumor return. A clone in this context is defined as a single cell that divides to give rise to a large population of identical cells. Polyclonal describes a tumor or population of cells that is derived from multiple distinct clones. Monoclonal describes a population of cells that is derived from one cell.
Cancer Stem Cell – A cancer stem cell (CSC) is a cell that divides to replenish a population of cancer cells. CSCs can exists in cell lines that are grown in two-dimensional and three-dimensional culture systems or in tumors that are grown in living organisms. CSCs are the cells that survive a therapeutic treatment that destroys the vast majority of the tumor. CSCs divide to produce cells that make up the returned tumor. CSCs have special properties that allow them to be distinguished from non-CSCs. CSCs are better able to repair damage that occurs inside of them, such as DNA and protein damage. They are also better able to quench reactive oxygen species inside of them, reducing the amount of damage that they incur when under stressful conditions. CSCs are often the cells that leave the primary tumor, survive in the blood stream, and metastasize distant organs. Section 2 of this chapter describes common confusions between CSCs and cells-of-origin.
Cell-of-Origin – A cell-of-origin is a normal stem or progenitor cell that becomes abnormal and gives rise to a cancer, liquid or solid. Due to its origin from a normal cell, the cell-of-origin is not necessarily the same thing as a cancer stem cell, though it may be. Cancer stem cells were derived from studying tumors, while cells-of-origin are identified by lineage tracing experiments that can track normal cells that become part of tumors. Section 2 of this chapter describes common confusions between CSCs and cells-of-origin.
Section 2 – Confusion Among Stem Cell, Cancer Stem Cell, and Cell-of-Origin Concepts
The purpose of understanding cancers stem cells and cells-of-origin is to allow for the development of more specific therapies and therapies that result in less relapse. However, discussions of these two topics often results in confusion, since their definitions can overlap. Clarity on this matter is important because cancer prevention strives to keep normal cells from becoming cancerous, while cancer treatment seeks to eliminate cells that are already cancerous. The point of cancer research at the molecular and cellular level is so that these details allow us to produce better therapies that have fewer side effects. Clear definitions of function and identity are necessary for nanotechnology and targeted drug delivery methodologies to specifically target CSCs or cells-of-origin, while sparing normal cells.
The microenvironment of the tumor is drastically different than a normal tissue. Thus, even if a tumor arose from one cancerous cell among normal neighbors, by the time that one cell has divided enough times to form a tumor within that tissue, the microenvironment of that tumor may have turned non-CSCs into CSCs. Therefore, a tumor that started from a transformed normal stem cell, the cell-of-origin, may harbor CSCs that did not come from that stem cell. A tumor may harbor any combination of cells-of-origin and CSCs. This is because as a tumor grows, it enlarges and engulfs neighboring areas that also have normal stem cells. Those engulfed stem cells may then become additional cells-of-origin. Furthermore, the engulfed normal differentiated cells may become additional CSCs. Thus, a tumor may contain multiple cells-of-origin and multiple CSCs, each of which joined the tumor at different times. Given the complexity of the combinatorial possibilities of cells-of-origin and CSCs within a tumor, individual research papers need to assume simplifications in their conclusions about cells-of-origin and CSCs. However, for both those who are advancing this area of knowledge and those who are new to it, being aware of this complexity will help reveal new mechanistic insights, organize existing literature and future knowledge, and guide hypotheses.
Using Mouse Models and Making Sense of Them
Citation: David H. Nguyen. “Using Mouse Models and Making Sense of Them.” Systems Biology of Tumor Physiology, Springer Science+Business Media (New York). Estimated Publication Date 2015. [Draft 07/27/14]
The purpose of this chapter is to provide some insights into the use and misuse of mouse models of cancer. The goal is that this chapter will help investigators better plan their mouse experiments. It also will be helpful in making sense of data that is conflicting or incongruous across independent experiments or research groups. This chapter is structured into three sections to give coherence among the individual topics that are discussed. The sections also represent the three general phases of a study involving rodent models and the challenges that are commonly encountered in each. The guidelines are certainly not exhaustive, since there are many factors that were not discussed due to space limitations.
Section 1 – Selecting the Right Model
Section 2 – Correctly Using the Right Model
Section 3 – Making Sense of the Data
In vivo experiments have an unpredictable nature to them, which underscores the complexity of mouse physiology that is yet to be completely understood. While there are no conventions that every research group follows when performing mouse experiments, appreciating the complexities highlighted in this chapter will make it easier to compare independent data sets in a meaningful way. Several institutions have attempted to define conventions for reporting the experimental details and results of rodent models: CAMARADES (www.camarades.info) (Sena et al., 2014), SYRCLE (www.syrcle.nl) (Hooijmans and Ritskes-Hoitinga, 2013), ARRIVE (http://www.nc3rs.org.uk/page.asp?id=1357) (Kilkenny et al., 2010), and SABRE (http://www.sabre.org.uk/) (Muhlhausler et al., 2013). These conventions are an important move in the right direction, since their goal is to improve transparency, inter-study comparability, and reproducibility. The conventions should be increasingly enforced by funding agencies and journals for the following reasons. (1) Meta-analysis of pre-clinical rodent models can identify adverse effects of novel treatments before those treatments go into clinical trials on humans (reviewed in (Pound et al., 2004)). (2) More reliable studies prevent biomedical research involving animal models from losing credibility as a worthwhile scientific pursuit. (3) More reliable studies keep tax-payer-funded researchers accountable to the public, which will eventually get fed-up with bad subfields of science. (4) More reliable studies reduce the waste of tax-payer funds and curtail the risk of further reduced funding for certain subfields of research.
Many of the topics discussed in this chapter are factors that increase the “noise” within data from mouse experiments. While there is no way to completely control every single factor, a few simple precautions can dramatically increase the yield of “clean” data. Studies involving mouse models often become tissue banks of information that can be probed to find new insights or directions for research. Taking the guidelines outlined in this chapter into consideration while planning and doing mouse experiments may help the investigators get more mileage out of their data.
There is an increasing distrust of research involving animal models, especially in areas that seek to find new treatments that will immediately translate into treatments for human diseases (Check Hayden, 2014; Lutz, 2011; SABRE-Research-UK, 2014). There are several reasons behind this frustration. (1) Studies using inbred mouse strains do not represent the genetic diversity that exists in human populations, so the effectiveness of mice as a model of a human disease may need to be tested across multiple different strains, depending on the question of interest. (2) Many animal studies are often not done very well, as is commonly stated in the publications that systematically review animal model data prior to, or in light of, human clinical trials that fail (Hooijmans and Ritskes-Hoitinga, 2013; Kilkenny et al., 2010; Muhlhausler et al., 2013; Sena et al., 2014). (3) A mouse is physiologically not the same as a human – it’s just a model, not a replica. (4) The physiology of a mouse is very complicated and we have yet to understand enough that we can control the experiment to the degree that we want. In light of these challenges, this chapter attempts to help make things clearer along the lines of the book’s subtitle: “Rethinking the Past, Defining the Future.” It does so by suggesting guidelines that may shed light on previous data while guiding the structure of future experiments such that more useful information can be extracted within and across independent studies. The guidelines in this chapter also follow the theme of the book’s main title: “Systems Biology of Tumor Physiology.” The guidelines treat the tumor as if it were an endocrine organ that communicates with the brain and other endocrine organs. The guidelines suggest that more information can be gained from a mouse model of cancer if we pay attention to the global physiology of the mouse while considering the cancer as an organ that affects, and is affected by, that physiology. The guidelines in this chapter come from personal experience, having wrestled with the literature, peer-reviewing literature, conversations, and commiserations with scientists from across the globe.
Outline of Chapter 3
Section 1 – Selecting the Right Model
-The Age of the Mice
-Appreciate the Estrus Cycle to Reduce Variation in Data
-Developmental Deformities Give Insight into Future Phenotypes
-The Composition of Mouse Chow
Section 2 – Correctly Using the Right Model
-Mock Procedures for Control Groups Yield Cleaner and More Consistent Data
-Time of Day for Treatments and Exposures
-The Confounding Effects of Anesthesia
-Completely Thaw Frozen Plasma and Re-Suspend for Cleaner Data
Section 3 – Making Sense of the Data
-Collect the Endocrine and Other Organs for Retrospective Analysis
-The Bimodal Distribution
-Circulating Tumor Cells Don’t Stay on One Side of the Mouse
-The Non-ubiquitous Activation of a Ubiquitous Artificial Promoter
-Varied Mechanisms Can Yield the Same Phenotype: A Shrinking Tumor
-The Gut Microbiome Affects Cancer
Collect the Endocrine and Other Organs for Retrospective Analysis
Endocrine systems are integrated and communicate with each other via positive and negative feedback loops. Harvesting tissues in a small population of mice (5 to 10 mice per group) from each experimental condition will provide useful information about the global physiological status of the mice. The information from organs outside of the tumor will provide a contextualized picture of why a tumor is behaving the way it does in one treatment but not another. A tumor can be considered an organ not just because it shares morphological similarities with the tissue from which it arose, but also because, like organs do, it communicates with other organs via endocrine pathways.
The following items are examples of ways in which a cancer’s effect and behavior can be better understood by quantifying the characteristics of organs that it affects. These surrogate metrics are not substitutes for direct measurements on a tumor, but together with the direct measurements on a tumor they provide a holistic understanding of the complex, integrated mechanisms that yield a tumor’s phenotype. These surrogate metrics are also great for (1) generating mechanistic hypotheses, and (2) comparing incongruent mouse data sets that should otherwise be similar. The following examples were selected because they can be measured with a ruler or weight scale, but which organs to pick and how to measure them (i.e. non-invasive imaging) depends on the details of the experiment and the available technologies. Organs can be fixed in formalin or other appropriate fixatives for long-term storage and later analysis. Or, the organs can be measured and then immediately processed for extracting live cells. Preserving an organ by formalin-fixation is advantageous in that it allows paraffin-embedding, slicing, and histological examination. Histology can provide much more information than size and weight alone.
The thymus is the organ where T lymphocytes mature. It grows rapidly after birth, but begins to shrink at the start of puberty. However, when a mouse is stressed, the thymus shrinks faster than usual (Dominguez-Gerpe and Rey-Mendez, 2003; Pearse, 2006). Thus, thymus weight and size may serve as a surrogate measure of inadvertent stressors that affected one treatment group but not another. Normal, age-related shrinkage of the thymus is considered involution, while stress-induced shrinkage of the thymus in young-adult mice is considered shrinkage. It should be noted that in old mice, involution and atrophy may appear histologically similar (Pearse, 2006).
An enlarged spleen is indicative of an activated immune system (Bronte and Pittet, 2013). A difference of weight and size of the spleen between two experimental conditions suggest that one condition is activating the immune system. Without further dissection of cell surface markers and functional in vitro assays on primary splenic lymphocytes, it is difficult to pinpoint a more detailed mechanism. However, an enlarged spleen is indicative of an immune defense against factors that the mouse considers as harmful. Measuring the spleen at the time of euthanasia is a simple surrogate measure of differential immune activity across experimental groups of mice.
The liver is responsible for a number of homeostatic functions, including detoxification and response to acute infection. Part of the liver’s response is to increase in size, which it does in response to the aforementioned insults. However, it also increases in size in response to normal physiological hormones such as those governing pregnancy and lactation; and it grows in response to an increased dietary intake of fat, carbohydrates, and protein (Maronpot et al., 2010). Thus, the size and weight of the liver is a surrogate measure of many potentially overlooked physiological mechanisms underlying a cancer phenotype. It may be useful in reconciling incongruent data between mouse experiments that should have exhibited similar results.
One effect of a treatment or mutation on female mice may be to alter the rate of the estrus cycle throughout the lifetime or experimental time course, as was the cause for BRCA1-/- mice (Hong et al., 2010). It may be unfeasible, or experimentally undesirable, to measure the exact length of multiple, consecutive estrus cycles. Furthermore, investigators may be interested in indications that the treatment, condition, or mutation that they applied onto their mice altered circulating estrogen levels beyond the effect of normal estrus. This question is relevant when studying endocrine disrupting agents or steroid hormones, which often interact in physiological feedback loops. In the cases in which estrogen levels or endocrine disrupting agent levels were not directly measured in blood plasma, the weight and fibrotic state of the heart may give clues to altered lifelong levels of circulating estrogen. Abundant evidence supports the role of circulating estrogen in preventing cardiac hypertrophy, cardiac thinning, and cardiac fibrosis. In particular, the estrogen receptor-beta (ER-b) is the protein that mediates this protective effect of estrogen. In female mice, Angiotensin II (AngII) causes cardiac hypertrophy and collagen deposition, which is inhibited by activated ER-b (Pedram et al., 2008). AngII was also shown stimulate cardiac fibroblasts to become cardiac myofibroblasts by inducing the expression of TGFB1, which in turn induced expression of vimentin, fibronectin, and collagens I and II; all of which contribute to fibrosis. Treatment with estrogen or an ER-b agonist (dipropylnitrile) blocked all of these events (Pedram et al., 2010). The ER-b agonist β-LGND2 is also effective against AngII-induced cardiac pathology (Pedram et al., 2013).
The prediction of whether a treatment, condition, or mutation – if it affects lifelong levels of circulating estrogen – should increase or decrease hypertrophy and fibrosis depends on the specifics each experiment. Nonetheless, cardiac weight can be measured with a scale and cardiac fibrosis can be measured by immuno-staining for collagen I, collagen II, vimentin, or fibronectin. These pieces of information will give clues about abnormal lifelong estrogenic activity. It is worth noting that as rodents and humans age, the heart naturally undergoes hypertrophy and fibrosis (Anversa et al., 1990; Cornwell et al., 1991; Olivetti et al., 1991; Swynghedauw et al., 1995), so age effects should be considered and adjusted for via appropriate control specimens. Lastly, the weight of the heart can be normalized by the length of the tibia, to adjust for the influence of differential bodily growth rate between genders or treatment groups (Stauffer et al., 2006).
Mammary Glands of Male Mice
Some strains of male mice maintain a small ductal structure in their mammary fat pads throughout adulthood. These male ductal structures do not expand like the female glands during puberty. Vandenberg and colleagues demonstrated that male mammary ducts can be induced to grow via exposure to bisphenol A (BPA), an endocrine disrupting chemical and environmental pollutant (Vandenberg et al., 2013). The expansion of the male mammary ductal system is an excellent surrogate measure to determine if male mice within a treatment group were inadvertently exposed to estrogenic compounds or if a treatment of interest had a feminizing effect on males.
Also see the topic entitled “Genetic Background” (section 1) for why collecting certain endocrine organs will be helpful for reconciling data or phenotypes from different mouse strains that underwent the same experimental procedures.