Tissue microarrays for early target evaluation

TECHNOLOGIES

DRUG DISCOVERY

TODAY

Drug Discovery Today: Technologies Vol. 1, No. 1 2004

Editors-in-Chief

Kelvin Lam – Pfizer, Inc., USA

Henk Timmerman – Vrije Universiteit, The Netherlands

Target identification

Tissue microarrays for early targetevaluationRonald Simon, Martina Mirlacher, Guido Sauter*Division of Molecular Pathology, Institute of Pathology, Basel University Hospital, Schoenbeinstrasse 40, CH-4031 Basel, Switzerland

Early assessment of the probable biological importance

of drug targets, the potential market size of a successful

new drug, and possible treatment side effects are

critical for risk management in drug development.

A comprehensive molecular epidemiology analysis

involving thousands of well-characterized human

tissues will thus provide vital information for strategic

decision-making. Tissue microarray (TMA) technology

is ideally suited for such projects. The simultaneous

analysis of thousands of tissues enables highly standar-

dized, fast and affordable translational research studies

of unprecedented scale.

*Corresponding author: (G. Sauter) [email protected]

1740-6749/$ � 2004 Elsevier Ltd. All rights reserved. DOI: 10.1016/j.ddtec.2004.08.003

Section Editors:Wolfgang Fischer, Rob Hooft, Michael Walker

The analysis of human tissue is a key step in the identification of drug

targets. This analysis is usually done with individual tissues, focusing onthe investigation of individual genes. Often, however, this process is

time-consuming and can give controversial results. Tissue microarraysenable the analysis of a large number of small sample sections of tissue

simultaneously; thereby combining increased statistical accuracy andhigh throughput. In this review, the authors describe the benefits, the

proper handling to address-specific questions, and the limitations of thistechnique.

Introduction

A variety of powerful high-throughput technologies are avail-

able for the identification of potential new drug targets. At

the same time, high-throughput compound screening facil-

itates the identification of applicable drug substances. These

tools generally identify more potentially rewarding drug

targets than resources are available for their further develop-

ment. Thus, an efficient target prioritization strategy is cru-

cial for competitive drug development. Whereas target

identification is usually performed in experimental systems

like cell lines or animal models because of the almost unlim-

ited availability of resources, target evaluation and prioritiza-

tion should be optimally based on findings from patient

tissue specimens representing the disease the drug is devel-

oped against. In situ analyses methods are optimally suited for

target gene analyses as they allow a cellular and sub-cellular

localization of the gene product of interest. However, tradi-

tional slide-by-slide analysis cannot catch up with the speed

of modern high-throughput target identification technolo-

gies. Tissue microarrays (TMAs) permit a completely new

early evaluation strategy of drug targets. The simultaneous

and rapid analysis of thousands of human tissues allows

determination of the frequency of target gene expression,

for example, in all human tumor types, associations with

disease progression, and expression in normal tissues.

TMA technology

Hundreds of cylindrical samples measuring up to 4 mm

(usually 0.6 mm) in diameter are removed from formalin

fixed or frozen tissues and are deposited into one recipient

block [1]. Sections from such TMA blocks containing hun-

dreds of different tissues are then placed on a glass slide. All

methods for in situ tissue analysis, like immunohistochem-

istry (IHC), fluorescence in situ hybridization (FISH) and RNA

in situ hybridization, can be performed on TMAs using similar

protocols as for conventional large sections (Fig. 1).

After its initial description in 1998 [1], TMAs have become

widely used in recent years. More than 150 publications

www.drugdiscoverytoday.com 41

Drug Discovery Today: Technologies | Target identification Vol. 1, No. 1 2004

Figure 1. TMA manufacturing and applications. (a) Donor block from which several 0.6 mm tissue cores have been removed. Note that the original tissue

block remains fully interpretable. (b) Completed TMA. Removed tissue cores from several hundreds of different donor blocks have been assembled in the so-

called ‘recipient block’. (c) Hematoxilin and Eosin (H&E) stained tissue section of the TMA. (d) Higher magnification of a tissue spot analyzed by IHC. Brown

staining indicates high expression of membraneous HER2 receptor protein. (e) TMA sector analyzed by RNA in situ hybridization. This TMA was coated with a

photographic emulsion to detect the radioactively labeled antisense-RNA probe. (f) Magnification (630�) of a tissue spot analyzed by fluorescence in situ

hybridization (FISH). Green signals indicate centromere 7. Excess of red signals demonstrates EGFR gene amplification. Cell nuclei are counterstained with

blue fluorescence.

published in 2003 have utilized TMAs (using ‘‘tissue micro-

array’’ and ‘‘2003’’ as search terms in Medline). Some of the

most powerful applications of TMAs for drug discovery and

development are described below.

TMAs for economical evaluation

Data onthe expression ofa given target gene areoften available

from the literature.However, examplesofgenes that have been

extensively analyzed suggest that such data might often have

little practical importance. For example, HER2, the target gene

for trastuzamab (Herceptin) has been analyzed in thousands of

publications. However, the data are highly controversial, with

published expression frequencies ranging from<10% to>90%

for many important tumor types (reviewed in [2]). For exam-

ple, published incidences for HER2 positivity in non-small cell

lung cancers range between 4% and 100%. High expression

rates reported in some studies might have contributed to the

costly decision to undertake large but disappointing clinical

trials with Herceptin in lung cancer patients [3]. One of these

studies was recently closed early because of an unexpected low

42 www.drugdiscoverytoday.com

number of HER2 positive cases and the lack of response

reported in other studies [4]. The paramount impact of experi-

mental parameters like antibody selection, IHC protocol, tis-

sue characteristics, and scoring criteria on the results of IHC

analyses greatly reduce the comparability of IHC data derived

from different groups.

TMAs containing samples from all different tumor types

make it now possible to analyze a gene of interest in a TMA

format under fully standardized conditions in one experi-

enced laboratory. For such epidemiology studies we used a set

of TMAs containing more than 3500 different samples from

more than 120 different tumor types and subtypes [2,5]. In

recent studies this set of TMAs was used to investigate target

genes for established drug targets like KIT/CD117 (Imatinib;

Glivec) [5], epidermal growth factor (EGFR) (Tarceva, Iressa,

Cetuximab, Erbitux) [2], or HER2 (trastuzamab; Herceptin).

These studies confirmed expression of these targets in a wide

variety of tumor entities. The example of successful applica-

tion of Glivec in KIT-positive gastrointestinal stroma tumors

(GIST) shows the potential clinical and economical impor-

Vol. 1, No. 1 2004 Drug Discovery Today: Technologies | Target identification

Table 1. Summary of studies investigating the prognostic role of BCL2 expression in breast cancer

Authors Year Number

of cases

Follow-up

(years)

Association with

overall survival

Silvestrini et al. [20] 1994 283 6 Yes

Joensuu et al. [16] 1994 174 5 Yes

Lipponen et al. [19] 1995 140 10 Yes

Hellemans et al. [15] 1995 124 7 No

Barbareschi et al. [23] 1996 178 5 n/a

Van Slooten et al. [21] 1996 202 4 n/a

Krajewski et al. [18] 1997 53 53 No

Kapranos et al. [17] 1997 90 10 n/a

Charpin et al. [14] 1998 82 10 No

Veronese et al. [22] 1998 98 5 Yes

All previous studies 1424 ?

TMA study (unpublished) 2221 10 Yes (P < 0.0001)

tance of also including less frequent tumor entities in early

epidemiological target gene evaluations [6].

A TMA study investigating a representative number of all

different tumor types results in a reliable ranking list of

positive tumor entities. In combination with the published

incidence rates of these tumor entities the total number of

annual new positive cases can be calculated either across all

tumor types or for a specific tumor type [2]. These numbers

are useful for calculating the potential economical value of a

successful drug and also to select optimal tumor entities for

later clinical trials.

TMAs for biological evaluation

TMAs containing a large number of samples from one tumor

type with attached clinico-pathological information allow an

estimation of the biological importance of a drug target.

Associations with advanced stage, high grade, presence of

metastases, or poor clinical outcome argue for a role in tumor

progression. Potential drug targets with an association to

tumor progression, metastasis or poor prognosis might have

higher priority for further development than targets that are

unrelated to clinical outcome. The HER2 protein is a prime

example of a gene product strongly related to poor patient

prognosis in breast cancer and also constitutes an excellent

therapeutic target [7]. TMAs are highly efficient for identify-

ing associations between molecular features and prognosis.

For example, significant associations were found between

estrogen or progesterone expression [8] or HER-2 alterations

[9] and survival in breast cancer patients, between vimentin

expression and prognosis in kidney cancer [10], and between

Ki67 labeling index and prognosis in urinary bladder cancer

[11], soft tissue sarcoma [12], and in Hurthle cell carcinoma

[13]. The example of Bcl2 in breast cancer demonstrates the

importance of using large enough TMAs for such studies. Ten

studies had previously analyzed the relationship between loss

of Bcl-2 expression and prognosis in breast cancer. The results

were controversial with four studies finding a significant

association with poor prognosis, and six studies failing to

see such an association [14–23]. Using a TMA containing

more than 2000 breast cancers with clinical follow up data

we analyzed 50% more tumors than in all previous studies

together (Table 1). Our result of a strong association between

loss of Bcl-2 expression and poor prognosis provides strong

proof of the prognostic importance of Bcl-2 analysis in breast

cancer (Fig. 2a). A study analyzing ‘‘only’’ 200–300 tumors

would have been yet another study without the potential to

clarify the issue. The evaluation of potential clinico-patho-

logical associations can be facilitated by software solutions

that help to systematically evaluate different cutoff levels

(Fig. 2b). The comparison of the expression of a target gene

with previously collected molecular data can also provide

further information on the biologic role of target genes.

TMAs for toxicity evaluation

High expression of a target protein in vital normal tissues is a

possible drawback during drug development. The early use of

normal tissue TMAs avoids such unpleasant surprises. A cell

type-specific in situ expression analysis enables comparison of

the expression level for each individual cell type with the

expression in cancer cells, allowing the identification of

possible therapeutic windows. Cell type-specific comparisons

would not be possible if non-in situ methods were used. For

example, EpCam, a target for several anticancer therapies, is

expressed in bile ducts of the liver. Because bile ducts con-

stitute a very small (<1%) component of the liver, compar-

isons of the expression level in normal liver and tumors

would suggest a comfortable therapeutic window for anti-

EpCam drugs (Fig. 3). The in situ analysis of tissues does

reveal, however, the high level of expression in a small but

vital normal liver compartment. TMAs composed of normal

tissues can massively facilitate the process of normal

tissue cross reactivity testing. For example, frozen tissue



Figure 2. (a) Prognostic value of BCL2 in breast cancer. Analysis of 2221 breast cancer samples revealed that weak or lost expression of the anti-apoptotic

protein BCL2 is associated with poor patient survival. Numbers do not add up to 2221, because survival was calculated from the subset of ductal breast

cancers. (b) ThresholdFinderTM (TF) software. Following TMA analysis, TF software can be used to select and simultaneously link thresholds for staining

intensity groups to patient’s outcome and other parameters – panels in the right of the image show associations between expression levels and other

pathological parameters like nodal stage (pn) or tumor stage (pt). The example shows the prognostic influence of estrogene receptor (ER) staining in breast

cancer. Patients with strong (red line) or moderate (orange line) ER expression had a significantly better prognosis than patients with weak or no ER positivity.

The thresholds for these four groups can be modified with the sliders at the bottom of the screen.

TMAs composed of 32 tissues from multiple different donors

reflecting the panel of tissues required by the US Food and

Drug Administration (FDA) for cross-reactivity testing (CRT)

of new biological drugs are used for normal tissue analysis in

our laboratory (Fig. 4). This frozen TMA platform allows for

rapidly executing CRT studies on 4–5 TMA sections. The TMA

format especially facilitates the use of multiple different anti-

body dilutions as recommended by authorities.

Technical issues

The value of TMAs for large-scale tissue analysis is undisputed.

Although the technology is simple and easily applicable, some

Figure 3. EpCam expression in normal liver. (a) Overview. (b) Magnification.

intensity, however, can also be seen in cancer cells. This emphasizes the importa

tissue for such an expression comparison (like northern or western blots) wo


aspects of the TMA area are controversially discussed, whereas

others are underestimated. These include the importance of

tissue heterogeneity, the potential for automation, and the

importance ofpathology expertise.Understanding these issues

is critical for successfully using TMAs.

Tissue heterogeneity

Initially, it was feared that minute tissue samples (diameter

0.6 mm) might not be sufficiently representative of whole

tumors. Many studies have therefore addressed the question

as to whether a higher concordance of TMA and large

section data can be obtained if multiple samples of each

EpCam expression is confined to small areas of bile ducts. A similar staining

nce of in situ analyses for normal tissue evaluation. The use of disaggregated

uld suggest a negligible low expression level in normal liver.


Figure 4. Cross reactivity testing (CRT) TMA. (a) Overview of the first of three TMA slides covering the FDA recommended panel of normal tissues for

cross-reactivity testing. (b) Schematic representation of the TMA showing the localization of each tissue type. Samples of every tissue type have been included

from many different patients, according to FDA recommendations.

tumor are arrayed. As expected, these studies show that the

results obtained on TMAs approach large section results as

more samples are being analyzed [10,11,24–37]. However,

after analyzing several million tumor samples on TMAs, we

feel that one tissue sample per tumor is not only the least

expensive but also the scientifically optimal procedure for

research applications of TMAs. The reasons for this are

explained below (Table 2).

Costs

The use of multiple samples increases the costs of a study.

The costs for tissue engineering and the interpretation of

each staining must by multiplied with the number of

samples per tumor. Investing in a higher number of tumors

thus is much more efficient than analyzing more samples

per tumor.

Table 2. Disadvantages of using multiple samples per tumor

Breast cancer study

(p53 IHC analysis)

200 patients

(four samples per tumor)

Tissue analyses 800

Interpretable cases 194

Time for reading (h) 8

Statistical bias issue Yes

Tissue costs ($) 3000

Pathologist costs ($) 2000

P value prognosis P = 0.0545

Rate of positivity (%) 23.7

IHC, immunohistochemistry.

Statistical bias

Not all arrayed tissues contain interpretable tumor

cells. Having multiple samples per tumor on a TMA will thus

always cause a variable number of interpretable tissue

samples per tumor ranging between 1 and the maximal

number of arrayed samples per tumor. The analysis of three

to five times more tissue for some tumors than for other

tumors introduces statistical problems [38]. At least this

issue must be considered and compensated by statistical

means.

Representativity of large sections

Large sections are gold standard for molecular in situ

tests. However, large sections contain only a small fraction

(<1/10,000) of the entire tumor mass [2]. Because the repre-

sentativity of large sections is therefore questionable by itself,

800 patients

(one sample per tumor)

800 patients

(four samples per tumor)

800 3200

606 774

8 32

No Yes

3000 12000

2000 8000

P < 0.0001 P < 0.0001

18.2 20.4



reproducing the findings of large sections is not an optimal

endpoint for validating the TMA method. By contrast, study-

ing associations of TMA results with clinico-pathological

endpoints are more informative. All previously well estab-

lished associations between molecular features and clinical

endpoints, such as the prognostic importance of ER, PR, p53

and HER2 expression in breast cancer [9,24], Ki67 labeling

index in bladder cancer [11], or vimentin expression in

kidney cancer [10] have been confirmed on TMAs using

one spot per tumor. We are unaware of studies where such

associations could be found on large sections but not on

TMAs made from the same tumors and analyzed by the same

laboratory. Remarkably, there is one study finding clinico-

pathological associations on TMAs but not on corresponding

large sections (see below).

Risk of false positive results

The more samples are placed on a TMA per tumor, the higher

the likelihood of obtaining similar results to those obtained

for large sections. Although this is desirable at first sight, one

must not forget that some of the positive results on large

sections can be ‘‘artificial’’. This issue is nicely demonstrated

by Torhorst et al. [24]. This study shows a strong association

between p53 immunostaining and breast cancer prognosis on

four different TMAs containing one arrayed sample from each

of 500 breast cancers but fails to find a similar association in

large sections despite a much higher rate of positivity [2,24].

Overall, the available data suggest that analyzing one

sample each of an as large as possible cohort of tumors is

the most efficient and economical use of TMAs. The high

level of standardization (all tissues on a single slide are

analyzed under absolutely identical conditions) and the

greater objectivity of the staining interpretation on a small

tissue spot are important factors that apparently compensate

for the conceptual disadvantage of the small size of tissues

analyzed (for review see [2]). The greatest advantage of taking

multiple samples per tumor is the higher fraction of tumors

with interpretable data. This has, however, little importance

if a sufficiently large number of individual tumors are avail-

able. Taking multiple samples per tumor might be justified in

cases where there are very small tumor sets. However, raising

the number of informative cases by 10–20% will unfortu-

nately not massively reduce the risk of non-conclusive study

results in such a scenario.

Pathology expertise and automation

Although TMA technology is often discussed in the context

of other array techniques like DNA or protein arrays, for

which array construction, analysis and data recording can

easily be automated; the TMA method is fundamentally

different. TMAs represent the ultimate miniaturization of

molecular pathology and share their inherent strengths

and limitations. The level of pathology expertise is therefore


decisive for the success of TMA studies, whereas classical array

tools like automation are less relevant. Automated TMA

manufacturing lacks importance, because sufficiently large

ready-to-use ‘‘tissue libraries’’ are lacking. Selection and pre-

paration of appropriate tissues make up for 95% of the TMA

manufacturing process and cannot be automated. More than

20,000 different tissues have been arrayed in our laboratory

and manual TMA making was never perceived a bottleneck.

Classical pathology skills including classification of arrayed

tissues, experimental design, and staining interpretation are

critical for using TMAs. More than 100 studies can be based

on one TMA block. As the molecular data will be compared

with pathological information, the quality of this informa-

tion is of highest importance. A systematic review of all

tumors included in a TMA by a specialized pathologist is

recommended and should include a reclassification of all

tissues according to current classifications.

The difficulties related to the development of a reliable IHC

protocol are underestimated. Non-specific positivity is a fre-

quent problem and requires the use of multiple controls. A

significant fraction of antibodies cannot be optimized for use

on tissue sections. IHC staining results are greatly dependent

on antibody selection, antigen retrieval strategy, staining

protocol and on minor variables such as the section age.

For example, the use of three different antibodies for EGFR

resulted – after state of the art protocol development – in an

up to fivefold difference in the rate of positivity [2]. Using 6-

month-old TMA sections we found a significant decrease of

immunoreactivity for multiple antibodies [39].

Manual (visual) analysis of the TMA staining provides the

greatest level of reading precision and will remain the gold

standard for TMA staining interpretation. It is tempting to

consider automated TMA interpretation in the case of high-

throughput operations. The biggest difficulty for automated

IHC analysis is the selection of a representative tissue area.

This has been solved during the TMA manufacturing process.

Automated TMA imaging systems have great importance for

result documentation but – for most antibodies – they cannot

replace the pathologist for analysis. Technically, IHC staining

quantification is more objective than manual reading, espe-

cially if fluorescent labels are used [40]. However, reading

TMAs includes many components of classical pathology

analysis and goes way beyond pure staining quantification.

Pathologists can rapidly comprehend the full information

provided on a 0.6 mm tissue sample. This includes recogni-

tion of the stained cell types and subcellular compartments

and a reliable distinction of cancer cells from normal or

precancerous tissues. The pathologist will automatically

exclude all necrotic, crushed or damaged tissue elements.

Also he is probable to identify any unexpected staining

patterns. The greatest challenge for automated analysis is

suboptimal IHC, which cannot be avoided for most antibo-

dies at least in a fraction of samples.


Table 3. Comparison of different means of tissue analysis

TMA Large section analysis Lysate array Western blot Northern blot Southern blot PCR

Cell type/compartment-specific analysis +++ +++ ��

DNA copy number analysis +++ +++ �� ++ ++

RNA expression analysis (frozen tissue) ++ ++ �� +++ �� +++

RNA expression analysis (Fo-fixed tissue) �� ++

Protein expression analysis +++ +++ +++ +++ ��

Detection of RNA splice variants �� +++ �� ++

Detection of protein isoforms + + ++ +++ ��

Mutation analysis �� +++

Standardization of analysis ++++ �� ++++ + + + +++

Quantitative measurements + + ++ + + + +++

Automation of analysis + �� +++ �� +++

Reagent economy +++ �� +++ + + + ��

Analysis speed +++ �� +++ �� ++

TMA, tissue microarray; PCR, polymerase chain reaction, Fo, formalin; ++++, excellent; +++, very good; ++, good; +, possible; ��, limited; ��, not possible.

Attempts to improve automated TMA analysis are paral-

leled by an increased use of lysate arrays composed of protein

extracts from tumor tissues [41,42]. These are spotted on glass

slides in a DNA array-like manner and can be analyzed with

the same software used DNA arrays. If morphologic informa-

tion is not needed, lysate arrays might represent a better

option than complicated and expensive systems for auto-

mated TMA analysis.

Conclusions

TMAs allow a high-throughput analysis of large numbers of

well characterized tissues by in situ methods. As such, TMAs

allow a new dimension of molecular tissue analysis. Thou-

sands of normal and diseased tissues can be analyzed in one

project. However, TMAs are not a classical array method but

rather reflect the ultimate miniaturization of classical mole-

cular pathology. IHC and tissue expertise are thus critical

components for the successful use of TMAs. As compared to

other tissue analysis methods, TMAs combine the advan-

tage of preserved tissue morphology with the high speed of

true array methods (Table 3). TMA studies are ideally suited

to rapidly identify suitable indications and possible areas of

side effects of potential new drugs and at the same time

allow an early economical evaluation. Allocation of

resources for costly functional evaluations can then be

prioritized according to the results of the initial TMA

analysis.

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Tissue microarrays for early target evaluation

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