Brain Tumor Models to Predict ClinicalOutcome: Like the Phoenix?

Lois A. Lampson

Abstract

Small animal models have given great insight into tumor biology, but have been less successful at predictingclinical outcome. It is expected that newer models will be more predictive because they are better tumormimics. Unfortunately, other impediments to clinical prediction remain, as reviewed in this chapter. Giventhese limits, plus advances in other approaches, how can small animal models best be exploited fordeveloping new tumor therapies? Suggestions include focus on targets that are shared by many differenttumors, and use of small animal models to reveal broad principles or answer specific questions, rather thanaiming for rodent cures.

Key words: Clinical trial, Predict, Disappoint, Glioma, Brain metastases

Come l’arabia fenice. . .Non e questa, non e quella/Non fu mai, non vi sara. . .

Like the phoenix of Arabia. . .Is not this one, is not that one/Never was, never will be. . .

Cosi’ Fan Tutte, Act 1, scene 1

1. Introduction

Small animal models have been invaluable for probing all aspects oftumor biology, from tumor initiation to the mechanisms underly-ing the response—or resistance—to therapy. This chapter exploresone area where the place and best use of small animal models is stillevolving: predicting therapeutic outcome in human patients.For brain tumors, just as for many other tumors and disorders,clinical trials have often been disappointing, despite promisingresults in small animal models (1–5). In seeking the reasons,much of the discussion centers on the models themselves, especiallytheir failings as biological mimics of the relevant tumor or otherpathology.

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Neuromethods (2013) 77: 3–20DOI 10.1007/7657_2011_24© Springer Science+Business Media New York 2012Published online: 13 March 2012

Recent work has made real advances in the extent to whichsmall animal models mimic human disease, for brain tumors as wellas for other types of cancer (2, 6) and disorders. Complementaryadvances have occurred in parallel. Enhanced ability to analyzehuman tissue and new strategies for clinical trials aid translation intheir own right (7) and contribute to the iterative process of refin-ing the animal models.

Appreciation of the challenges to successful tumor therapy hasalso advanced. Deeper understanding of tumor biology has, as washoped, led to identification of rational targets and developmentof new drugs and strategies against them. Unfortunately, not allseemingly eligible patients respond and, among those who do, theresponse is often transient (8). There is good reason to expect thatnew combination therapies will give further improvements, but thevery wealth of potential targets, agents, combinations, doses, andschedules increases the challenge of efficiently identifying successfulstrategies.

In developing the next round of therapies, it is useful to reviewthe different kinds of reasons that have made it hard to predictclinical outcome from small animal models. As will be brought out,the need for good tumor mimics is but one among many concerns.

2. ModelChallenges: A LongChain of Events

A long-held ideal has been that one would select a small animalmodel for a particular tumor (or other disorder), use the model todemonstrate efficacy of a novel therapy, and, ultimately, find thatthe therapy was indeed beneficial to human patients. Disappoint-ments in many fields show how far we are from this ideal (1, 3–5).

One general problem is that, in the ideal scenario, the chainof events that must take place in the model is so long (Fig. 1).Any discrepancy between the rodent tumor and human tumor, orthe rodent response and human response, can mean that the out-come will not be predictive for human patients. As tumor mimics,as well as in other ways, traditional brain tumor models are weak inmany links of this long chain.

3. ModelChallenges:Weaknesses asTumor Mimics Two kinds of examples illustrate the challenges to developing

good brain tumor mimics. Among primary brain tumors, glioma,especially glioblastoma multiforme (GBM), the most commonand aggressive primary brain tumor of adults, has been a major

4 L.A. Lampson

focus for work in small animal models. Brain metastases, fromtumors that originate in other organs, present an equally difficultclinical problem, and somewhat different challenges for tumormodels.

3.1. Primary Brain

Tumors

In traditional models for primary brain tumor, typically a tumor-derived cell line is implanted in the brain or even under the skin,and the test therapy is administered within a few days or evenbefore the tumor. Among many mismatches, taking glioma as anexample: A homogeneous, long-term cell line does not modelthe complex composition of an endogenous tumor; a rapidlyexpanding cell mass does not model glioma patterns of growthand spread; a subcutaneous site does not model the brain microen-vironment; the short time frame curtails reciprocal interactionsbetween tumor and host; the use of immunodeficient hosts, as isnecessary for study of human cell lines, prevents an appropriatecontribution by the host immune response; the criteria for efficacy,typically slowed tumor growth, do not match clinical endpoints(2, 9, 10).

Some of these problems have been addressed by improvementsto the traditional models (10–13), and some are avoided altogetherin genetically engineered mouse (GEM) models. It seems onlylogical that, as better mimics of tumor biology, the improvedmodels will be more predictive of clinical outcome, as many authorssuggest. In practice, it is not yet known how much more predictive

PRIMARY BRAIN TUMOR initiation Primary tumor initiation

PRIMARY BRAIN TUMOR growth Primary tumor growth

BRAIN METASTASES

Spontaneous host response to tumor

TEST THERAPY

Initial response to therapy or initial resistance

Initial host response to therapy and to tumor damage (side-effects, toxicity)

Longer-term response to therapy or acquired resistance

Longer-term host response to therapy and to tumor damage

(side-effects, toxicity)

Fig. 1. A long chain of events. This depicts the chain of events from tumor initiation through the final effects of tumortherapy, for both primary brain tumors and brain metastases. Mismatches at any point between a small animal model andhuman patient can mean that results in the model will not be predictive for the patient.

Predicting Outcome 5

the newest GEM models will be, or how they will compare toalternatives. Currently, some positive examples are cases where theGEM model mirrors the known efficacy—or lack of efficacy—of atreatment that has already been tested in the clinic, while oftenGEMmodels are simply not used (2, 9, 14, 15). Some of the factorsthat may still limit successful prediction of clinical outcome, even inthe newest GEM models, are listed in Table 1.

3.2. Brain Metastases The major effort for GEM brain tumor models has focused onprimary brain tumors. Even though brain metastases are many-fold more common, corresponding GEM models are not yetwell-developed. The natural starting point for mimics of brainmetastases would be models of the solid tumor of origin. ForGEM models of solid tumors in general, however, metastasis toappropriate sites has been more difficult to achieve than otheraspects of tumor growth (6, 15, 16).

The typical model for parenchymal brain metastases is still toinject tumor-derived cell lines into the blood (17–20) or evendirectly into the brain (21). Similarly for meningeal tumor, whetherprimary or metastatic, the most common method is to inject cellsinto the cerebrospinal fluid (CSF) (21–23). It is possible to addressmany specific questions using these methods, and individual cell

Table 1Why results from small animal models have not predicted clinical outcome

Topic Challenge

Long chain Full sequence of steps, from tumor growth to therapy, allows many chances formismatch between model and human

Poor mimic Basic features of tumor biology may differ from humanCriteria for success in the model may differ from clinical endpoints

Heterogeneity Tumor relatedVariations among tumors of the same name (No one model mimics all)Variations among sites of the same tumorVariations among cells at the same tumor siteHost relatedVariations in host response, and in effect of therapy, among species, etc.a

Biochemical differences, among species, etc.a

Species differences in scale

Promiscuity A given target molecule or pathway may affect many functions and many cell types.Net effect may vary among species, etc.a

Unknowns Key effector mechanisms. May vary among species, etc.a

Drug concentration delivered to tumor cell. Impediments may vary among species, etc.a

aMay vary among species, among inbred rodent strains, among human individuals, among tumor sites, andamong individual tumor cells

6 L.A. Lampson

lines may show appropriate distributions after delivery to the blood(24). Many other aspects of the multistep, interactive process thatleads to the establishment, growth, and possible response totherapy of brain metastases are lost. For example, injecting cellsinto the blood obscures not only the timing of the genetic changesthat predispose to metastasis (25, 26) but also the normal timing oftumor cells’ entry to the brain.

The timing of tumor dissemination is of particular interest inthe brain, for primary tumor as well as metastases. Although brainmetastases are often detected late in the clinical course, it is notknown when the cells first entered the brain. Similarly, althoughdistantly infiltrating tumor is characteristic of glioma, it is notknown when the cells first became so widely distributed. Thus, itis not known how to weigh the importance of preventing newmetastases or new infiltration, as opposed to attacking disseminatedtumor that is already in place (27).

Although the need for better tumor mimics has been a majorchallenge, it is not the only one. Even as GEM models for primaryor metastatic tumor improve, one major source of complexity lies inthe many facets of tumor heterogeneity, some being harder thanothers to incorporate into small animal models.

4. ModelChallenges:A MultifacetedHeterogeneity Brain tumors, like other tumors, display a multifaceted heteroge-

neity: among tumors of the same type, among—and within—sitesof the same tumor, and among different small animal hosts. Thisheterogeneity is reflected in tumor growth, in the spontaneousresponse to the tumor, and in the response to therapy. Althoughsome aspects of this heterogeneity are well handled, or even used toadvantage, by current models, others are more challenging.

4.1. Heterogeneity

Within a Tumor Type

Increasingly, tumors and other disorders are being subdivided, inresponse to new analysis (26, 28, 29). Brain tumors are no exception(30–32). This affects the path from bench to bedside in more thanone way. It means that, even for tumors of the same name, no singlesmall animalmodel will be appropriate for all patients. Further alongthe path, itmeans that, when a clinical trial is ultimately conducted, apositive response in a small subset of patients may go undetected,while other patients may be unaffected or even harmed.

At one level, GEMmodels are well-adapted to take this kind ofheterogeneity into account. As human tumor variants are identi-fied, corresponding changes can be introduced into the models.As information accumulates, about the tumor itself and about theresponse to therapy among different patients, the model can befurther refined.

Predicting Outcome 7

One factor that complicates this iterative process is that, evenwithin the same rodent species, the same genetic manipulation canhave very different effects in different inbred strains. GEM modelsof glioma and of metastatic tumor both provide examples of suchstrain-specific differences (15, 26).

4.2. Heterogeneity

Among Inbred Strains

Although initially a source of complexity, differences among inbredstrains can be an advantage, to the extent that they help reveal orexplain variation among individual humans. Comparisons amonginbred rodent strains have led to important discoveries in variousfields, and there are ongoing efforts to improve their usefulness(15, 33, 34).

Although it can give key insights, comparison among inbredstrains is not a sharply focused approach, nor is it all-inclusive.There is no way to know a priori that any particular point that isrevealed by the inbred strains will be important in humans, or thatthe most important points will emerge. Rather, just as for otherfindings, the early and frequent analysis of human tissue is anessential part of an iterative process for confirming human relevance(35–37) as well as refining the animal models (15–26).

4.3. Heterogeneity

Among Species

As fruitful as the iterative process of refining animal models canbe, it is ultimately limited by heterogeneity among species. Bio-chemical differences and differences in scale pose different kinds ofchallenges.

Biochemical differences. For any given property, the potential forspecies differences between humans and mice or rats is well appre-ciated. Species differences can also mislead in a more subtle way.Brain tumors do grow in mice and rats; novel therapies do affectthem. The rodent biology will be internally consistent, informative,and fascinating in its own right—and yet may not mimic the biol-ogy of human patients. Examples are seen at all levels of tumorstudy.

At the level of the tumor itself, not all tumor behaviors seenin GEM models have their counterparts in human tumors.Conversely, not all aspects of human tumor biology have been easilymodeled. The difficulty of obtaining metastases in GEM models ofappropriate primary tumors is a pertinent example (6, 15, 16).At the level of the host contribution to therapy, many examples ofspecies differences are seen in the immune response (38–41).

These examples draw attention to the many possibilities forspecies differences in a complex, unfolding pathway (Fig. 1). Theypoint to the potential value of focusing on discrete points, ratherthan the final effect on tumor growth, in the small animal host.Equally, the examples point up the importance of frequent cross-checking of preclinical findings against human tissue.

8 L.A. Lampson

Responding to biochemical differences: humanized small animalhosts. Turning to nonhuman primates is not realistic as a generalresponse to the problem of species differences (42). Biochemicaldifferences are still present, problems of ethics and cost are exacer-bated, and the advantages of smaller hosts are lost.

A more common response to biochemical species differences isto cause the rodent to express relevant human genes, cells, ortissues (2, 43, 44). Tumor xenografts are an example, but theapplications are much broader. For example, mice can be made toexpress human tumor antigens or human immunocompetent cells.

Use of such humanized mice does greatly expand the range ofhuman properties that can be studied in vivo. The limitation is thatbiological processes are interwoven, not compartmentalized (45).This means that host/human boundaries can be pushed back, butnot avoided entirely (43, 46).

For example, human lymphocytes will not interact appropri-ately with murine homing receptors. If the mouse’s homing recep-tors are humanized, then human lymphocytes can show moreappropriate traffic patterns. The complication is that other kindsof functions, unrelated to lymphocyte traffic, are also served by thehoming receptors, and these may also be affected.

The integrin family of cell surface proteins illustrates thesepoints. The integrins, like so many protein families (45), serve mul-tiple functions, in a variety of cell types. Integrins are importantnot only for lymphocyte homing but also for tumor growth andmetastasis. They also serve functions that are unrelated to tumorsor host defense, including normal brain functions. These differentfunctions are served by multiple possible interactions between thedifferent integrins and their counter-receptors (47–52). Humanizingmice with respect to one particular integrin receptor or functionmay thus affect other functions, not directly related to tumor ther-apy. Side effects that are not relevant to humans may becreated. Conversely, effects that would be clinically relevant maynot be revealed.

Differences in scale. One kind of species difference that cannot behumanized is a difference in scale. The life span of a mouse or rat issimply not long enough for the full chain of events that includestumor initiation, interplay between the tumor and the environ-ment, initial response and ultimate resistance to therapy, and devel-opment of long-term side effects in human patients (Fig. 1).Compressing the first steps may still allow development of tumors,but may also increase the divergence from human tumor biology(2, 15). The different time frames thus give a second reason, inaddition to biochemical differences, to focus on specific points,rather than attempting to follow a longer chain of events, in a singlerodent model or single experiment.

Predicting Outcome 9

A complementary difference in scale between species is thedifference in size. As compared to humans, the absolute limit oftumor size is much smaller in the rodent brain, while an intracerebralinjection of test agents may affect a relatively larger proportion ofthe brain. Either factor may contribute to results in the rodent modelthat are not predictive for human patients. While interpreting resultsfrom small animal models, simply keeping in mind possible conse-quences of the differences in size may be helpful.

4.4. Heterogeneity

Among—and

Within—Tumor Sites

Brain tumors provide many examples of heterogeneity amongtumor sites within a single patient. This is a defining feature ofGBM, and there can also be great variation among individual brainmetastases (53). Both the tumor itself and the local environmentcontribute to this heterogeneity. A tumor is plastic, changing ateach site as it grows. The local regulatory environment is site-specific, even at baseline (54–56), and is also plastic. It changes aspart of the spontaneous response to the tumor and in response totherapy, as well as to unrelated events (27, 43, 46, 57).

The interaction of a tumor with its environment, and therelevance of specific features of the local microenvironment, isincreasingly appreciated. The vasculature and angiogenesis, com-ponents of the innate or adaptive immune response, and physicaland metabolic impediments to drug delivery (as discussed inSect. 6.2) are features of the environment that have been stressedby different authors. For the brain as a whole, characteristic featuresknown to affect tumor growth or response to therapy includethe blood–brain barrier (BBB) (as reviewed in (27)) and differencesamong major anatomical regions, such as parenchyma vs. meningesor gray matter vs. white matter.

A still finer level of organization is relevant for the smallesttumor foci. Microenvironments within the brain are well-mappedin terms of their neurobiological functions, such as the distributionsof particular neuropeptides and neurotransmitters. Despite theirnames, these neurochemicals, like most other biologically activemolecules, affect many different functions, in many cell types(45, 54, 55). They can affect the growth of a tumor, the spontaneousresponse to the tumor, and the response to therapy. For example, thewidespread neuropeptide, substance P (SP), can affect both tumorgrowth (58, 59) and cytokine-mediated therapy (54, 55).

The different rodent species and inbred strains each have theirown variations at the level of the local environment. For example,the maps of neuropeptides and neurotransmitters vary amongspecies and among strains (60). Local regulatory differences thuscontribute to heterogeneity among tumor sites and also amongsmall animal hosts. The smaller the tumor focus, the more relevantthese local differences would be.

Even within a single tumor focus, individual cells vary intheir properties, including their response to any given therapy.

10 L.A. Lampson

One consequence is that acquired resistance to a given therapy canreflect outgrowth of tumor variants that were already present; itneed not reflect new mutations (61).

4.5. Tumor Size

and Other Variables

The larger a tumor mass, the greater the extent to which it will havecreated its own regulatory environment. The smaller the focus, themore relevant is the regulatory environment of the surroundingtissue. Important examples of microscopic tumor in the braininclude residual, disseminated, or infiltrative glioma and the earliestblood-borne micrometastases from other organs. Despite theimportance of microscopic tumor, few studies of brain tumortherapy have directly evaluated microtumor targets (8, 17–19, 27,62, 63).

Age and gender are two other variables that are known to beimportant for human brain tumors, but are not usually studieddirectly, when therapy is tested in small animal models. Lessoften mentioned is that the needle wound and anesthesia that arenecessary for implanting tumor or injecting test drugs canalso affect tumor growth or the antitumor response; while inter-preting preclinical tests of therapy, these factors are seldom takeninto account.

Physiological changes that may be unrelated to the tumor canaffect the outcome of a given therapy (43, 46, 57). For example,there may be inflammatory changes or, for a variety of reasons,the levels of drug efflux proteins or drug-metabolizing enzymesmay change (64–68). The effect of co-morbidities is of specialconcern for older patients (43). If new therapies are tested inyoung adult hosts that display the tumor of interest but are other-wise healthy, these factors are obscured (43–46).

5. ModelChallenges:Promiscuityof Function Yet another aspect of heterogeneity is seen in the functional diversity

that is characteristic of so many biological molecules, with an indi-vidual molecule able to affect many functions and many cell types(45). Specific examples discussed in this chapter include the manyfunctions of the integrins (47–52), the neurotransmitter, substanceP (54, 55, 58–60); the complement cascade (69–73); and the P450family of drug-metabolizing enzymes (64, 74). In each case, tumorgrowth, the response to tumor, and normal neurobiological func-tions are all affected. In each case, species and strain differencesfurther complicate the task of predicting the net effect, for humanpatients, of manipulations that are first tested in animal models.

Predicting Outcome 11

6. ModelChallenges:Two Unknowns

6.1. Unknown Effector

Mechanisms

Even for the most successful tumor therapies, it can be difficult toidentify the key effector mechanisms (27). For example, althoughthe monoclonal antibody rituximab has now been used successfullyfor several years, it is not yet known which of the many possibleeffector mechanisms are most important in different patients (75).

Evolving experience with farnesyltransferase inhibitors providesa complementary example; in this case, the target itself has beenhard to define (76–78). As another kind of example, it is often notknown whether apparent efficacy of new treatments against meta-static tumor reflects attack of metastases per se or simply attack ofthe primary tumor mass (27, 79).

The many aspects of tumor heterogeneity complicate the chal-lenge of identifying—or modifying—underlying effector mechan-isms. The most important mechanism can vary among species,strains, or individuals, or among different tumor sites. To take anexample from immunotherapy, an important set of immune effec-tor functions is mediated by the complement cascade, which differsamong organs (69) and also displays polymorphisms among indi-viduals. Moreover, complement has nonimmunological functions,including those in the brain (70–73), which may be affected ifcomplement is manipulated.

Thus, the most relevant mechanisms—or toxicities—may notbe constant from patient to patient or site to site. More subtly, thesmall animal model that is the best mimic for a given tumor will notnecessarily be the bestmodel for the effectormechanism that ismostrelevant to that particular patient or to human patients in general. Asinformation from human tissue accumulates, relevant mechanismsfor individual patients and sites should become clarified. Smallanimal models can then be evaluated and modified accordingly.

6.2. Unknown Doses Tumor resistance to therapy is often discussed in biochemical terms(such as use of an alternative metabolic pathway), but failure todeliver an appropriate dose to the tumor can underlie resistance aswell (3, 10, 11, 80–85). The ability of the BBB to block drug accessis well-known (27), but more general impediments are just asrelevant. Drug efflux proteins, drug-metabolizing enzymes, theextracellular matrix, and interstitial pressure are examples of factorsthat can impede delivery of therapeutics within solid tumors at anysite (3, 10, 11, 80, 81, 84, 85).

Information about drug doses from rodent models can misleadin different ways. The dose used to control tumor in the model maybe too high for an equivalent dose to be safely used in humans or,conversely, too low to reveal toxicity (11, 12). The different types ofheterogeneity are relevant as well. Both the optimal dose and theefficiency of delivery may vary with the tumor size or site (54–56), as

12 L.A. Lampson

well as among individuals (86), rodent species, or inbred strains.For example, effective doses and also toxicities may vary betweensick and healthy individuals (9, 43, 46), and drug-metabolizingenzymes have different distributions in different hosts (2). Thus,results in a rodent model will not necessarily predict whether, inhumans, the intended drug dose reaches the tumor site (87) ortumor cell (82, 84).

7. ModelChallenges:A Wealth ofAlternatives Targeted therapies have only begun to fulfill their promise: They

can control tumor, but not for all patients, and often not indefi-nitely (8). Goals for confronting tumor resistance, whether initialor acquired, include identification of responsive patients, definitionof underlying mechanisms of resistance and response, and strategiesfor preventing or responding to the resistance. The first two goalsbenefit from ongoing analysis of human tissue, complemented bygrowing insight into the kinds of factors that are relevant, as morecases are solved (37, 75, 88–94). As important factors are defined,small animal models can be adapted to display them, as part of theiterative process described in earlier sections.

Concerning the third goal, preventing or responding to theresistance, it is logical that combination therapies could preventoutgrowth of resistant tumor, and in vivo testing should giveimportant insights, especially with good tumor mimics. One chal-lenge is to limit the number of possibilities to be tested in vivo.Among many variables, established properties of single drugs maybe altered in combination (43, 95); the sequence with which thecomponents are given can alter the outcome (95); and conclusionsmay change as the field evolves, for example, as new trials areconducted or other components of the therapy improve (96, 97).Overlaid on all of this are the many aspects of heterogeneityreviewed above.

Balanced against the abundance of variables are developmentsthat aid choice and analysis. As mechanisms of tumorigenesis, andof resistance to therapy, become better understood, and as moreinformation is obtained directly from human tissue, rational choicescan be made more easily. Focus on targets that are shared by manytumors can further control the number of possibilities to be stud-ied. Fortunately, many of the mechanisms that support tumorgrowth are indeed widely shared, often cutting across conventionaltumor types. Even if a particular molecular change is not shared,the same regulatory pathway may be altered (98).

As a well-known example, of epidermal growth factor receptor(EGFR) is an important target for many different cancer types, and

Predicting Outcome 13

many different targeted therapeutics, with different patterns of reac-tivity, have been developed against EGFR family members (12, 95,97, 99–104). Of course, any particular tumor may be resistant, orbecome resistant, to any particular therapy. Thus, a given therapeuticmay be effective against non-small cell lung cancer (NSCLC), breastcancer, and glioma, but not every NSCLC, breast cancer, or glioma,and not indefinitely. As the bases for resistance become better under-stood, the explanations and responses are also likely to apply to manytumors and to cut across tumor types.

Another factor that may help to limit the variables to be testedis that resistance can be cyclical, with a given agent showingrenewed benefit, beyond progression. One possible mechanism,applicable to many tumors, is that different preexisting subpopula-tions may wax or wane, as the therapy is continued or halted (61).For brain tumors in particular, variations in the BBB, as the tumorgrows or responds to therapy, can be an additional factor (27).

Emphasis on common tumor targets can help control the num-ber of possibilities to be tested, but there are still many variables.Even for shared targets, the context is important. The net effect ofthe abnormalities in a given tumor, the normal regulatory pathwaysin that cell type, and the microenvironment can all influence tumorgrowth and response to therapy (105, 106). This complexity doesnot necessarily mandate a unique targeted therapeutic—or a uniqueanimal model—for each tumor. As understanding and experienceevolve, it should be increasingly possible to develop a basic set oftherapeutics that are each appropriate for many different tumors,including different tumor types. Individual patients would receivepersonalized combinations, rather than unique agents.

7.1. A Specific Example The cytochrome P450 family of drug-metabolizing enzyme pro-vides specific examples of current challenges to tumor therapy andthe evolving role of small animal models. Efflux pumps and meta-bolizing enzymes can each contribute to tumor resistance,by preventing therapeutics from achieving their intended dose attumor sites (82, 84). The P450 enzymes themselves are hetero-geneous at many levels. Different family members interact withdifferent drugs and have characteristic distributions among cells,tissues, individuals, strains, and species (43, 64, 66, 107).

A wealth of background information is available about thestructure, function, and distribution of the P450 enzymes andenzyme/drug interactions (43, 64–66, 107). For a promisingnew drug or new combination, this body of information can delaythe need for small animal models, and allow more efficient usewhen they are required. From the drug structure, one may beable to predict which P450 enzyme will be most important forhuman patients (43, 65). The enzyme/drug interaction can beconfirmed in cell lines that express the correct P450 variant, andthe distribution of the appropriate enzyme in humans can be

14 L.A. Lampson

confirmed in archived tissue. Strategies to modulate enzymeexpression or function can first be tested in vitro.

To take into account the full environment, animal models areneeded. For example, enzyme levels may be altered in the contextof growing tumor, tumor therapy, or unrelated processes (64, 68).Modulating drug metabolism can have complex effects withinsolid tumors, and small animal models can reveal effects ondrug distribution that apply beyond the specific drug/enzymeinteraction being studied (82, 84).

When in vivo testing is needed, one value of the earlier stepsis that they can prevent a misleading mismatch between theP450 variant and distribution in the model as compared tohuman patients. Rather, a rodent model can be selected, huma-nized, or otherwise modified to express the correct P450 variantin the appropriate cell types (107). Insight can be gained fromfocus on specific questions, such as the distribution of a particulardrug within the tumor (82, 84); it is not necessary to follow afull sequence of steps leading to rodent tumor therapy (Fig. 1).

8. Summary:Challenges andResponses

A long chain of steps, imperfect mimicry, multifaceted heterogeneity,and an abundance of choices all complicate the use of small animalmodels to develop new therapies. Fortunately, these challenges arebalanced by improvements that can help to simplify the task.

The chain of steps is shortened because, as mechanisms of tumorgrowth and resistance are better understood, analysis can befocused on specific steps, rather than the endpoint of a longerchain (Fig. 1). Often, these more pointed questions can beanswered without resort to rodent models: from human tissue; inless complex animals or in vitro; in silico; or even in biblio, given thegreater ease of accessing already-published work.

Improved ease and power of analysis allow one to get moreinformation directly from human tissue. In parallel, improvementsin medical imaging aid study of ongoing responses in brain tumorpatients, while new clinical trial designs allow more efficient com-parisons among alternatives (7). As the search for biomarkers bearsfruit, direct information will be even more accessible. The sameimprovements allow for more frequent cross-checking of preclinicalfindings against human material.

When animal models are needed, a variety of technical improve-ments provide better tumor mimics. More sophisticated geneticmanipulations are complemented by developments in other areas.For example, an original reason to study subcutaneous tumor wasthat tumor growth could easily be followed within a single animal,

Predicting Outcome 15

by measuring the expanding mass with calipers. Improvements inimaging techniques allow tumor growth to be followed noninva-sively even in the brain (10), although microtumor still presentschallenges (17–19, 62, 63).

Heterogeneity is better accounted for as its underlying sources areidentified and incorporated into experimental work. The abundanceof choices should be reduced as growing understanding allows focuson targets and mechanisms that are shared among many tumors andpatients, often cutting across conventional tumor types.

9. Looking Ahead

Even as small animal models become more sophisticated, their rolein predicting clinical outcome can be reduced and refined, as otherapproaches also evolve. When small animals are needed, theirincreased validity as tumor mimics will be complemented by grow-ing ability to focus on specific questions and to cross-check againsthuman tissue.

The promise of new tumor therapies is just starting to berealized. Just because so many of the challenges are so widespread,shared by many tumor types, it is likely that the solutions, as theyevolve, will also be shared. The multifaceted tumor heterogeneitydoes not necessarily require a unique therapy—or a unique animalmodel—for each case. Rather, a given insight or therapeutic is likelyto be relevant for many tumors, including tumors of different types.

In terms of the opening quotation, there may be no perfectanimal model, no phoenix, for predicting clinical outcome forbrain tumor therapy. No single model can fully correspond to agiven human tumor, nor can it show the full course of events.Instead, both specific points and general principles, learned froma variety of models, will complement other sources of insight, andmay apply to many different tumors of different types. One will beable to say for appropriate sets of tumors: these tumors maybe diverse, but with respect to this abnormality or this therapeutic,Cosi’ Fan Tutti (they all act the same).

Acknowledgment

I thank Cara TrippMcClallen for helping to prepare the manuscript.

16 L.A. Lampson

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