CLINICAL TRIAL

Health economic impact of risk group selection accordingto ASCO-recommended biomarkers uPA/PAI-1 in node-negativeprimary breast cancer

Volker R. Jacobs • Ronald E. Kates • Eva Kantelhardt • Martina Vetter •

Rachel Wuerstlein • Thorsten Fischer • Manfred Schmitt • Fritz Jaenicke •

Michael Untch • Christoph Thomssen • Nadia Harbeck

Received: 6 February 2013 / Accepted: 20 March 2013 / Published online: 9 April 2013

� Springer Science+Business Media New York 2013

Abstract Invasion factors uPA/PAI-1 are guideline-rec-

ommended (ASCO, AGO) biomarkers for decision support

regarding adjuvant chemotherapy (CTX) in women with

primary breast cancer. They define a high-risk group with

strong benefit from adjuvant CTX and a low-risk group

with uncertain benefit and excellent survival without CTX.

In a target population (age [ 35/N0/G2/HR?/HER2-),

administration of adjuvant CTX is not mandatory in Ger-

many and other countries. Based on existing data, this

economic model was developed to determine for the first

time health economic impact of uPA/PAI-1 testing.

Incremental cost-effectiveness ratio (ICER) resulting from

uPA/PAI-1 testing was estimated for the target population

by Markov modeling and sensitivity analysis. Survival

data, CTX–uPA/PAI-1 interactions, and uPA/PAI-1 hazard

ratios were derived from the Chemo N0 trial and other

evidence. Incremental costs were computed from a payer’s

perspective appropriate to the German setting. Incremental

effectiveness in life years (ly) was estimated taking into

account age-adjusted life expectancy, disease-free survival

(with/without CTX), and 2 years post-relapse survival.

Sensitivity analysis was performed by varying residual

adjuvant CTX benefit in the low-risk group, denoted

HR_CTX(LR), in range 0.8–0.99. All patients receive

adjuvant endocrine therapy. Test is restricted to patients

willing to forgo CTX if both markers are below specific

cut-off values and to undergo CTX otherwise. For a typical

55-year-old patient, comparing to an ‘‘all-CTX’’ strategy

without the test, ICER (all-CTX vs. test) [ €50,000 if

HR_CTX(LR) [ 0.85, with savings of €18,500 per low-

risk patient attributable to the test. The cost-effectiveness

of forgoing CTX is very high as HR_CTX(LR) approaches

one. Conversely, comparing to a ‘‘no-CTX’’ strategy (e.g.,

patients who initially refuse CTX) without the test, the test

is very cost-effective at all ages in the target group if high-

risk patients are willing to undergo CTX: ICER (test vs. no-

CTX) \ €6,000 at age 55 and even better at younger ages,

remaining \ €25,000 up to age 75. The main determinantsVolker R. Jacobs and Ronald E. Kates contributed equally to this

work.

V. R. Jacobs (&) � T. Fischer

Frauenklinik (OB/GYN), Paracelsus Medical University (PMU),

Mullner Hauptstr. 48, 5020 Salzburg, Austria

e-mail: volkerjacobs@hotmail.com

R. E. Kates � R. Wuerstlein � N. Harbeck

Breast Center, Frauenklinik Maistrasse, University of Munich

(LMU), Munich, Germany

E. Kantelhardt � M. Vetter � C. Thomssen

Frauenklinik (OB/GYN), University Clinic of Halle/Saale, Halle

(Saale), Germany

R. Wuerstlein � N. Harbeck

Comprehensive Cancer Center (CCC), University of Munich

(LMU), Munich, Germany

M. Schmitt

Frauenklinik (OB/GYN), Technical University Munich (TUM),

Munich, Germany

F. Jaenicke

Frauenklinik (OB/GYN), University Clinic of Hamburg-

Eppendorf, Hamburg, Germany

M. Untch

Frauenklinik (OB/GYN), Helios-Klinikum Berlin-Buch, Berlin,

Germany

N. Harbeck

Breast Center, Frauenklinik Großhadern, University of Munich

(LMU), Munich, Germany

123

Breast Cancer Res Treat (2013) 138:839–850

DOI 10.1007/s10549-013-2496-z

of cost utility are age and residual CTX benefit in low-uPA/

PAI-1 patients. The uPA/PAI-1 test is cost-effective in the

target group compared to either an ‘‘all-CTX’’ or a ‘‘no-

CTX’’ scenario. This model thus lends health economic

support to current guideline recommendations that

uPA/PAI-1 testing is beneficial for BC patients with no

lymph node involvement.

Keywords Economics � Biomarker � Cost model �Markov model � Cost-effectiveness analysis � Prevention of

chemotherapy � Costs

Background and objectives

In primary breast cancer, administration of an adjuvant

chemotherapy regimen is justified for those patients whose

expected survival benefit from the regimen outweighs

expected negative effects of the therapy. This premise

underlies essentially all adjuvant therapy guidelines. The

complexity of therapy decision support derives in large part

from the requirement of determining individual ‘‘expected

survival benefit,’’ which is heterogeneous even within

breast cancer sub-groups defined by conventional factors

such as lymph node involvement (N), tumor stage (pT),

age, and hormone receptor (HR±) status. According to

current guidelines in Germany, for example, patients with

age [ 35/N0/G2/HR?/HER2- are currently considered to

be at ‘‘intermediate’’ relapse risk and have an undetermined

indication for adjuvant chemotherapy based on these

established clinicopathological factors alone.

Without additional information, the optimal adjuvant

therapy decision for a patient within a heterogeneous sub-

group represents a compromise between benefits for some

and harm (or at best wasted resources) for others. In a

typical population of node-negative (N0), primary breast

cancer patients treated by chemotherapy, the mixture of

effects comprises considerable benefit (avoidance of

relapse) for a low percentage of patients, mild to moderate

harm (immediate and possibly long-term but nonlethal

side-effects of chemotherapy) for a much larger group, and

severe harm (e.g., cardiac insufficiency) in a small group.

Demands are increasing to verify health care resource

allotment for cost-effectiveness worldwide since health

care resources are limited even in high income countries

[1]. This is related to the use of adjuvant chemotherapy [2]

as well as CTX-related concomitant medication as G-CSF

[3] and anti-hormonal therapy [4]. Cost-effectiveness

computations for use of prognostic biomarkers for adjuvant

chemotherapy selection in breast cancer have a potentially

strong role to play in decision support, but there are also

substantial methodological challenges [5], particularly

regarding the applicability of prognostic studies.

Also new gene profiling tests support market access with

economic evaluations [6–8]. Requirements for publishing

economic evaluations have been defined to standardize and

assure quality and transparency of potential interests [9].

In most countries, including Germany, which has a cost-

related and disease-specific flat-rate reimbursement system,

therapy policies take not only the patient’s point of view,

but also a societal or payer point of view, i.e., including

costs into account. In order to objectify therapy decisions

from the perspective of these stakeholders, cost-effective-

ness ratios, e.g., Euros per quality adjusted life year,

abbreviated €/QALY, can be compared with a given will-

ingness to pay (WTP) threshold. This method can support

difficult health care policy choices by putting a common

quality metric on different disease contexts.

With the establishment and recommendation of novel

prognostic and especially predictive factors, we can utilize

evidence-based models to reveal some of the previously

hidden heterogeneity in survival and therapy benefits and

thus improve the level of therapy optimization from any

point of view in question. In clinical practice, of course,

there are costs associated with the determination of novel

factors. Therefore, the objective of health economics

modeling in this context is to quantify the cost-effective-

ness of a proposed test, by taking into account therapy

decisions based on the test and their consequences for the

entire subsequent course of events—including stochastic

variations within the population.

Tumor invasion factors uPA/PAI-1 have been guideline-

recommended by AGO Germany [10] since 2002 and

ASCO since 2007 [11]; quality-assured biomarkers for

decision support regarding adjuvant chemotherapy (CTX)

in primary breast cancer patients and considerable experi-

ence on the uPA/PAI-1 testing process in clinical practice

including realistic costs is now available. Despite the

strong evidence basis for uPA/PAI-1 [12–18], no cost-

effectiveness evaluation of the type discussed above has

been performed and published so far [19]. The present

model is intended as a first step to fill this gap by esti-

mating the financial benefit and cost-effectiveness for the

ideal and optimal application of uPA/PAI-1 testing in

defined target groups. The objective is accomplished here

by comparing the expected total net survival benefits and

costs of a therapy strategy utilizing the uPA/PAI-1 test

results to a base case, i.e., a therapy strategy not utilizing

the test. As explained in detail below, the estimation

methodology utilizes a Markov-state model of disease-free

and post-relapse survival with competing survival risks

typical for otherwise healthy women within a range of ages

in the target population. The resulting analysis will quan-

tify the cost-effectiveness of uPA/PAI-1 testing versus two

alternative scenarios (‘‘all-CTX’’ or a ‘‘no-CTX’’) by

computing the consequences of adjuvant therapy decisions

840 Breast Cancer Res Treat (2013) 138:839–850

123

based on the test for the subsequent course of events in

virtual populations.

Methods and health economic model

Formulation of cost-effectiveness as a virtual study

At the top level, cost-effectiveness of testing in a target

group is formulated here as a virtual study comparing the

consequences of two possible decisions trees: one with

uPA/PAI-1 testing and the other without it (testing case

versus base case). Quantitative testing of uPA and PAI-1 in

tumor tissue is performed with a standardized ELISA test.

Results of uPA and PAI-1 testing are defined by specific

cut-off levels with 3.0 ng/mg for uPA and 14.0 ng/mg for

PAI-1 in overall protein. A result of B cut-off level is

defined as low or negative and [ considered high or

positive. In the base case with no uPA/PAI-1 testing, the

decision for or against adjuvant chemotherapy can only be

affected by policy for the target group (and in principle by

other individual factors). In the testing case, the decision

recommendation will depend in addition on uPA/PAI-1

testing results, i.e., it will be the same as the usual policy

for one test outcome and will be different for the other.

Each computation described here can thus be thought of

as a two-armed virtual study performed on a virtual pop-

ulation drawn randomly from the target patient group and

assigned randomly to one of the two arms (testing or base

case).

The main figure of merit used here is denoted ‘‘incre-

mental cost-effectiveness ratio’’ (ICER), defined as the

ratio of incremental cost to incremental effectiveness.

These are computed as follows: For each virtual patient,

survival and costs are followed over time. ‘‘Incremental

effectiveness’’ in the present context involves the differ-

ence in survival distributions in the study arms for those

patients whose chemotherapy decision is changed by the

test. In principle it could also involve corresponding dif-

ferences in quality of life and possible severe harm (e.g.,

cardiac insufficiency) in a small group, but these differ-

ences are not included explicitly here.

Computation of incremental cost differences requires

inclusion of only those costs that could differ between the

virtual study arms; hence, primary surgery and endocrine

therapy costs are excluded from the analysis. Testing costs

for uPA/PAI-1 are obviously absent in the base case.

Chemotherapy costs—both direct and indirect—will differ

between the study arms for those patients whose chemo-

therapy decision is changed by the test. In addition, costs

due to treatment following relapse could also differ

between the study arms.

As mentioned earlier, from some perspectives, e.g., for

payers, it is customary to compare the ICER to a WTP

threshold, e.g., 50,000 Euro/ly. The cost-effectiveness of

the uPA/PAI-1 test in population subgroups can then be

objectified by asking whether the ICER is likely to exceed

this WTP or not in the subgroup. This particular compar-

ison is provided here only as a reference and not as a

recommendation.

Target population characteristics

The target population for the present analysis consists of

primary breast cancer patients staged as metastasis-free

(M0), node-negative (N0), pathological grade G2,

age [ 35 years, estrogen receptor and/or progesterone

receptor positive (HR?), and HER2 negative. In the target

population, it is assumed that all patients due to their HR?

status will receive adjuvant endocrine therapy appropriate

to their menopausal status (tamoxifen or aromatase inhib-

itors) according to current guidelines—i.e., for at least 5

years, unless they suffer relapse. The scenarios considered

here exclude the option of neo-adjuvant (rather than adju-

vant) chemotherapy in the tested (target) population, but

have no effect on this choice for the remaining population

if, e.g., node positive, G3, HR-, or HER2?, or age B35.

Markov states

The health economic computations utilize a Markov sur-

vival model with three possible states abbreviated as fol-

lows: relapse-free survival (RFS), post-relapse survival

(PRS), and death by any cause (D), the ‘‘absorbing state’’.

Markov models can be defined by their transitions and

transition rates. The possible transitions in the present

model are RFS ? PRS, RFS ? D, and PRS ? D. The

transition RFS ? D highlights the competing risks of

‘‘death due to breast cancer relapse’’ and ‘‘death from other

causes’’. This latter competing risk, which of course

depends on the age of the patient, is an important health

economic consideration, because it influences the potential

survival benefits of therapy.

Design of virtual studies

Two virtual study designs (scenarios) are considered, dif-

fering in the assumption of how patients would be treated

without the test. In each design, the health economic

impact of risk group selection by uPA and PAI-1 testing is

calculated by comparing the survival and cost sequences in

two hypothetical cohorts of virtual patients randomly

drawn from this population and eligible in principle for

Breast Cancer Res Treat (2013) 138:839–850 841

123

testing. Figure 1 illustrates how the two cohorts differ in

the decision process for the first virtual design (all patients

would receive CTX if no test were given).

The lower branch describes a so-called ‘‘base model’’

with no test. In this 1st design, patients in the lower branch

receive CTX, regardless of whether they would have been

classified as low or high risk had they been tested. In the

upper branch or ‘‘test model’’, it is assumed that all patients

are tested and classified as either high-risk or low-risk

according to uPA and PAI-1 result in addition to the

mentioned clinical pathological results. For the purposes of

this computation, 100 % test compliance is assumed, i.e.,

all of the high-risk patients of this cohort and none of the

low-risk patients of this cohort will receive chemotherapy

(CTX).

Figure 1 illustrates that, whether or not a test is per-

formed, both cohorts consist of a mixture of low- and high-

risk patients, due to the underlying tumor biology. The

performance of a test has no effect on response to che-

motherapy, but it does influence the decision process and

thus both the cost sequence and the survival sequence in

‘‘low-uPA/PAI-1 patients’’. There is no modeled effect on

either costs or survival in the ‘‘high-uPA/PAI-1’’ group,

aside from the cost of the test itself.

In the base model of the second virtual design (no

chemo), patients receive only endocrine therapy. In the

testing cohort, it is again assumed that all patients are

tested and classified as either high-risk or low-risk

according to the uPA/PAI-1 results and are 100 % com-

pliant, i.e., the high-uPA/PAI-1 group changes to adjuvant

chemotherapy. The cost-effectiveness ratio in this situation

refers to the high-uPA/PAI-1 group, because (aside from

the test itself) costs and survival are unaffected in the low

group (no CTX).

Survival models and sensitivity analysis

For those patients suffering distant breast cancer relapse

within the time horizon of 180 months, post-relapse sur-

vival is taken as 24 months. For the remainder of the target

population, i.e., those surviving free of distant relapse for

180 months, survival characteristics are assumed to cor-

respond to WHO actuarial life tables for Germany [20]

from 2010. For example, according to these tables, median

survival of 55-year-old women in Germany is 29.4 years.

The core assumptions of this computation are based

directly on hazard rates and ratios derived from Level-1

evidence, particularly from the prospective randomized

Chemo N0 trial [21–23] and from a pooled analysis including

over 8,000 patients [24, 25]. The details also utilize survival

curves and interaction hazard ratios [26] based on over 3,000

patients in Germany and Holland with uPA/PAI-1.

We consider a virtual sample of primary breast cancer

patients drawn from the target population and assume with

relapse-free survival characteristics similar to those of the

10-year follow-up data of the N0, G2 segment of the

Chemo N0 trial. As has been reported in several large

studies [27–29] including but not limited to the pooled

analysis [24], the hazard function for relapse in node-

negative breast cancer is generally not strictly constant in

time. We modeled it by a moderate linear ‘‘acceleration’’ of

50 % within the first 18 months, a linear drop back to a

constant value by 36 months, and a constant thereafter up

to the defined time horizon of 180 months. The hazard rate

was initially calibrated to the Chemo N0 trial with a

10-year disease-free survival of 89 %. The virtual sample

was assumed restricted to hormone-receptor-positive

patients receiving endocrine therapy and responding with a

hazard ratio of 0.70 [26].

Fig. 1 Decision tree and

consequences for 1st design (all

receive CTX in base model)

842 Breast Cancer Res Treat (2013) 138:839–850

123

It was further assumed that in the target population 60 %

will test with high uPA/PAI-1 status, the other 40 % low.

The assumed hazard ratio for patients in the target group

with high uPA/PAI-1 status compared to low uPA/PAI-1

status (no adjuvant chemotherapy) is HR_high-low = 2.5,

which represents a compromise between [26] and the

Chemo N0 trial and is consistent with the pooled analysis

[23]. Note that this hazard ratio is unaffected by adjuvant

endocrine therapy, which has no interaction with uPA/PAI-

1. The residual adjuvant CTX benefit in low uPA and PAI-

1 tested patients is uncertain, but is assumed to lie in the

range from HR_CTX(LR) = 0.8–0.99.

Cost and quality adjustment models

Costs for medication and uPA/PAI-1 testing

Costs of medication including febrile neutropenia (FN)

prophylaxis was calculated as provider’s costs in € of 2010

at an academic hospital chemotherapy unit in Germany.

Cost were taken from hospital charts and purchase

department and divided into direct and indirect costs. The

currency conversion rate is approx. €1.00 = US$ 1.30 in

March 2013.

Cost calculations

All cost calculations were performed according to official

prices or service costs charged in 2010 in the German

health care system. The calculations are based on CTX

standards of the Certified Breast Center of Martin-Luther-

University of Halle an der Saale, Germany, which are

Guideline-based and comparable to other German breast

centers and hospital settings.

Cost calculations were performed for (1) direct CTX

medication costs including the individual guideline-based

CTX regimen, (2) CTX-related concomitant medication for

prevention of, e.g., emesis, sickness, and CTX-related

toxicity as well as for (3) prophylaxis of febrile neutrope-

nia, a common side effect of CTX’s hematological toxicity.

Medication costs were taken from the official 2010 German

pharmaceutical price list (Rote Liste Service GmbH,

Frankfurt, Germany) [30].

Standard CTX regimen in the target group of breast

cancer as previously defined was applied with 6 cycles of

FEC 500/100/500 mg/m2 (q21) or 3 cycles of 5-Fluoro-

uracil, Epirubicin, and Cyclophosphamid FEC 500/100/

500 mg/m2 (q21) followed by 3 cycles of Docetaxel DOC

100 mg/m2 (q21). For this model the ratio of CTX was

defined with 70 % FEC and 30 % FEC ? DOC regimens.

The average standard patient was defined with a body

surface area (BSA) of 1.75 m2, and all costs were calcu-

lated accordingly.

CTX-related co-medication was calculated according to

CTX standard protocols and included for each FEC

500/100/500 cycle (aprepitant, ondansetron, dexamatha-

son, mesna, and furosemide) and for each DOC 100 cycle

(dexamethason, dimetindenmaleat, ranitidine) with the

prescription value of the smallest medication package

size.

FN prophylaxis was calculated at a hypothetical optimal

application rate of 100 % according to the EORTC

Guideline [31], which implies for CTX regimen FEC and

FEC-DOC an upfront G-CSF application of 100 % with

pegfilgastrim (Neulasta�) with a dose of one for each CTX

cycle (6 applications).

For CTX application of anthracycline, a port is guide-

line-recommended to avoid common CTX-related periph-

eral vascular toxicity and allow easy access for laboratory

and i.v. application. Therefore revenues for implantation

and explantation on an out-patient base according to Ger-

man ambulatory reimbursement in 2010 were included. To

prevent clotting of blood in this foreign body material,

repeated flushing with heparin is mandatory (n = 7; n = 1

before and n = 6 after each CTX cycle), for which the

service price is included. This price is taken from the

German Medical Fee Schedule (Gebuhrenordnung fur

Arzte (GOA) 2010) [32].

The following CTX-related diagnostics necessary for

each CTX application were estimated: Laboratory param-

eters like small and complete blood count to potentially

identify neutropenia, and other blood parameter and tumor

markers were estimated and defined with a flat rate of €40

per cycle. Cardiac ultrasound to identify potential cardiac

side effects of anthracycline CTX was performed after

every 3rd CTX cycle with anthracycline (29 for FEC

regimen; 19 for FEC-DOC regimen). The internal transfer

price of the hospital (= price between departments) of

€57.60 per cardiac ultrasound was used.

Additional direct and indirect medical and non-medical

costs as well as secondary and tertiary costs effects of CTX

for patients, health care and the entire social system were

not included. These include anything from i.v. fluids,

preparation of CTX, transportation costs, waste manage-

ment, out of pocket payments by patients, CTX-related

comorbidity to comortality, rehospitalization, wigs for

CTX-related alopecia, sickness benefits, earlier return to

work.

uPA/PAI-1 test costs

The costs for uPA/PAI-1 testing were analyzed in detail

and divided into direct and indirect costs. Costs were

estimated according to appropriate resource consumption

[33].

Breast Cancer Res Treat (2013) 138:839–850 843

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Results

Cost calculations

For this model, the direct costs of the two standard CTX

regimen 69 FEC 500/100/500 and 39 FEC 500/100/500

followed by 39 DOC 100 including CTX, concomitant

medication, FN-prophylaxis and diagnostics like cardiac

ultrasound and laboratory parameter were calculated

according to actual provider costs in 2010 at €17,608.68

(Table 1) and €20,098.59 (Table 2), respectively. The

average direct costs of CTX at an estimated application

rate of 70 % for 69 FEC 500/100/500 and 30 % of 39

FEC 500/100/500 ? 39 DOC 100 were calculated as 0.79

€17,608.68 ? 0.39

€20,098.59 = €12,326.08 ? €6,029.58 = €18,356.38.

Estimated costs of i.v. port handling and care were

€1,040.26 (Table 3).

The estimated direct costs of uPA/PAI-1-testing were

€275.97; indirect costs including administration, han-

dling, and in-house or on-site transportation or mailing

were estimated as €110.00 for a total of €385.97

(Table 4). If samples need to be sent to an external

laboratory, additional transportation costs would occur,

depending on distance between hospital and laboratory,

single or consolidated shipment, and type of transporta-

tion used.

Incremental cost-effectiveness

Potential savings in direct, per-patient expected costs of

two leading CTX regimens (69 FEC or 39 FEC–39 DOC)

resulting from uPA/PAI-1 test were calculated from a

German health provider’s perspective. In this scenario,

adjuvant CTX costs are saved in 40 % of patients by

testing; including medications, prophylaxis, diagnostics,

additional supporting therapies and procedures, CTX costs

about €20,000 for both regimens considered (69 FEC

€19,366; 39 FEC ? 39 DOC €22,145; Fig. 2); relapse

medical costs were modeled at €40,000.

ICER of uPA/PAI-1 testing compared to ‘‘all-CTX’’-

scenario

In the first scenario, suppose that all patients of the target

group receive adjuvant CTX in the absence of the test, but

with the test, patients with low uPA and PAI-1-concen-

trations in the tumor tissue are spared adjuvant CTX.

Consider a patient with low uPA and PAI-1-concentra-

tions in the tumor within the target population of

35 \ age \ 75, N0, G2, HR?, HER2-: Survival curves

without CTX were estimated using Chemo N0 10-year dis-

ease-free survival of 89 % assuming endocrine therapy

benefit (hazard ratio HR = 0.7). Expected lost life years (ly)

due to relapse—with versus without test—were estimated

Table 1 Direct costs for 6

cycles of FEC 500/100/500 with

concomitant medication and

upfront FN prophylaxis

Comment Costs (€)

Diagnostics

Cardiac ultrasound After every 3rd CTX cycle 57.60

Laboratory parameter Blood count, tumor marker, etc. 40.00

Concomitant medication

Aprepitant 125 mg To prevent nausea and emesis 63.55

Ondansetron 8 mg To prevent nausea and emesis 160.14

Dexamethason 16 mg To prevent nausea and emesis 16.58

Uromitexan 400 mg To prevent urotoxicity 54.68

Uromitexan 400 mg To prevent urotoxicity 54.68

Lasix 20 mg To induce diuresis 16.95

Dexamethason To prevent nausea and emesis 34.68

CTX

5-Fluorouracil 875 mg Chemo 15.48

Epirubicin 175 mg Chemo 762.67

Cyclophosphamid 875 mg Chemo 132.64

FN prophylaxis

Pegfilgastrim Upfront for 6 cycles at each 1,563.53

Summary

3 CTX cycles with 1 cardiac ultrasound 8,804.34

Costs for 69 FEC with 2 cardiac ultrasounds 17,608.68

844 Breast Cancer Res Treat (2013) 138:839–850

123

from corresponding survival curves using age-adjusted life

expectancy minus 2 years post-relapse survival.

Figure 2 shows the ICER as a function of patient age

assuming CTX benefit (DFS) for low-uPA/PAI-1 patients

of HR_CTX(LR) = 0.90, corresponding to about the

midpoint of the uncertainty range. For example, at age 55,

median DFS benefit of CTX would be only about 0.25 ly (3

months) at an estimated net cost of €18,500 or about

ICER = €75,000/ly. The figure indicates that the test is

cost-effective at a €50,000 threshold above age 45, since

the ICER for CTX would otherwise exceed €50,000. Note

that in this virtual study, the cost-effectiveness does not

depend on the efficacy of chemotherapy in the high-uPA/

PAI-1 group.

Sensitivity analysis on residual adjuvant CTX benefit

in low-uPA/PAI-1 patients

Figure 3 shows the results of sensitivity analysis performed

by varying residual adjuvant CTX benefit in the low-uPA

and/orPAI-1 group in the range HR_CTX(LR) = 0.8–0.99

for age 55 years. If the test is not given, then the resulting

cost per ly of giving adjuvant CTX to all patients

(increasing curve) and the survival impact (decreasing

curve, right axis) depend on the assumed adjuvant CTX

benefit in low-risk patients. If the health care provider were

willing to pay €50,000 for 1 ly, then a HR of at most 0.85

would be required in the low-risk group. Very low CTX

benefit (HR approaching 1) is associated with astronomical

costs/ly.

The figure also shows estimated net savings (about

€18,500, lowest curve) in chemotherapy costs per low-risk

patient who does not receive adjuvant chemotherapy as a

result of her favorable uPA/PAI-1 test.

ICER of uPA/PAI-1 testing compared to ‘‘no-CTX’’-

scenario

We now consider a virtual subset of the target group as

defined above who would opt against the general chemo-

therapy recommendation but whose rejection of CTX

would be reversed by a high-uPA/PAI-1 test result (100 %

test compliance). In this scenario, CTX is unaffected in

low-uPA/PAI-1 patients.

Figure 4 shows the favorable ICER of uPA/PAI-1 test-

ing compared to a ‘‘no-CTX’’ scenario as defined above. In

this scenario, the test is very cost-effective at all ages

within the range considered: The ICER (test vs. no-CTX

strategy) remains below €6,000 at age 55 and is even more

favorable at younger ages, remaining below €25,000 up to

age 75. The median survival benefit per high-uPA/PAI-1

patient is substantial at all ages considered. Unlike the

Table 2 Direct costs of 3

cycles of FEC 500/100/500

followed by 3 cycles of DOC

100 with concomitant

medication and upfront FN

prophylaxis

FEC 500/100/500 Costs (€)

3 CTX cycles FEC with 1 cardiac ultrasound (see Table 1) 8,804.34

DOC 100

Diagnostics Comment Costs (€)

Laboratory parameter Blood count, tumor marker, etc. 40.00

Concomitant medication

Dexamethason i.v. Allergy prophylaxis 16.58

Fenistil Allergy prophylaxis 8.72

Ranitidin 50 mg Allergy prophylaxis 14.54

Dexamethason p.o. To prevent nausea and emesis 34.68

CTX

Docetaxel 175 mg Chemo 2,086.70

FN prophylaxis

Pegfilgastrim Upfront for 6 cycles at each 1,563.53

Summary

Costs for 3 cycles 11,294.25

Costs for 39 FEC ? 39 DOC 20,098.59

Table 3 Cost for venous port implantation and explantation and port

care

Service (including

port)

Comment Costs

(€)

Port implantation As out-patient, before CTX 496.01

Port explantation As out-patient, after CTX 487.97

Port irrigation n = 7 as out-patient, each at

€8.04

56.28

Summary 1,040.26

Breast Cancer Res Treat (2013) 138:839–850 845

123

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

180,000

200,000

0.80 0.82 0.84 0.86 0.88 0.90 0.92 0.94 0.96 0.98 1.00

hazard ratio of chemo in low-risk group

ICE

R

0.0

0.1

0.2

0.3

0.4

0.5

0.6

ly b

enef

itcost savings/patientdelta cost/ lydelta ly

Fig. 3 Sensitivity analysis with respect to uncertain CTX benefit for

low uPA/PAI-1 patients

Table 4 Direct and indirect

cost calculation for uPA/PAI-1-

testing

Direct costs for uPA/PAI-1 test Costs (€)

Preanalytics

Acceptance of specimen, weighing, visual assessment

Morcellation of tissue in dismembrator, lysis over night

Analytics

Centrifugation of lysis substrate

Determination of overall protein, BCA test 10.00

Aliquotation, dilution series, storage

FEMTELLE ELISA test for uPA/PAI-1 160.97

Quality management 20.00

Calculation of concentration in (a) ng/ml and (b) ng/mg

Of overall protein, technical validation of calculation

Biomedical validation of calculation

Postanalytics

Writing, print out and mailing of report, answering of queries, documentation (IT)

Labor time for technical assistant 40.00

Consumable supplies 45.00

Summary of direct costs for uPA/PAI-1 test 275.97

Indirect costs for uPA/PAI-1 test Costs

(€)

Administration

Writing of invoice, controlling of payment, purchase, etc. 10.00

Transportation

Specimen in LN from office/hospital/OR to pathology 15.00

Pathology

Acceptance of specimen, visual assessment, cutting of test specimen, control cut for HE-

section, report

25.00

Documentation, communication with physician and transport, dry ice for transportation 30.00

Transport

Dry ice transport over night to laboratory 30.00

Summary of indirect costs for uPA/PAI-1 test 110.00

Overall costs for uPA/PAI-1 test 385.97

-

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

180,000

200,000

35 40 45 50 55 60 65 70 75

age

ICE

R

0.0

0.1

0.2

0.3

0.4

0.5

0.6

ly b

enef

it

delta cost/ lydelta ly

Fig. 2 ICER for CTX (without the uPA/PAI-1 test) in the low-risk

group. Decrease of ly-benefit results from decreasing life expectancy

with age (dashed curve, right axis)

846 Breast Cancer Res Treat (2013) 138:839–850

123

other scenarios, the computation is independent of residual

adjuvant CTX benefit in the low-uPA/PAI-1 patients,

because they do not receive CTX in either case; the

strongest influence is the well-established strong benefit of

adjuvant CTX in high-uPA/PAI-1 patients.

Discussion

According to current guidelines in Germany and other

countries, ‘‘intermediate-risk’’ primary breast cancer

patients (M0/N0/G2/HR?/HER2-, age [ 35) have an

undetermined indication for adjuvant chemotherapy, based

on these factors alone. By refining risk and therapy

response assessment, the uPA/PAI-1 test can be used to

determine an adjuvant chemotherapy indication (negative

for low and up to cut-off uPA and PAI-1 values, otherwise

positive).

This model has examined the health economic conse-

quences, in particular the ICER, of applying the uPA/PAI-1

test for adjuvant chemotherapy determination in this target

group compared to (1) an ‘‘all-CTX’’ and (2) a ‘‘no-CTX’’

scenario. Cost-effectiveness was quantified taking com-

peting mortality risks into account and assuming 100 %

test compliance. Risks for harmful consequences and

quality of life losses associated with chemotherapy were

not included. Under these assumptions, cost utility depends

on patient age. The computations reveal a strong positive

case for uPA/PAI-1 testing within the collectives consid-

ered here.

In the first scenario (test vs. ‘‘all-CTX’’), assuming CTX

benefit in low-uPA/PAI-1 patients given by

HR_CTX(LR) = 0.90, the test was cost-effective at a

€50,000 threshold above age 45, since the ICER for CTX

would otherwise exceed €50,000. Note that in this virtual

study, the cost-effectiveness does not depend on the effi-

cacy of chemotherapy in the high-uPA/PAI-1 group.

However, the residual benefit of CTX in low-uPA/PAI-1

patients is an important uncertain parameter. For this

reason, sensitivity analysis was performed in the ‘‘all-

CTX’’ vs. test scenario by varying the residual CTX benefit

for low-uPA/PAI-1 patients within an uncertainty range.

For a typical 55-year-old, the ICER (all-CTX vs.

test) [ €50,000 if HR_CTX(LR) [ 0.85, with savings of

€18,500 per low-risk patient attributable to the test.

In clinical breast cancer practice, adjuvant therapy rec-

ommendations by physicians can include concomitant,

patient-specific health factors or subjective opinions that

may or may not be guided by systematic evidence. More-

over, the decision for chemotherapy in an individual breast

cancer patient may reflect her personal physiological,

emotional, or other subjective considerations. The conse-

quence is that in a sub-group (such as node-negative

patients over 35 years with HR-positive, HER2-negative,

G2 tumors) for whom chemotherapy is recommended but

survival benefits are heterogeneous, we will find patients

who opt against the chemotherapy recommendation in the

absence of a test but who would comply with a recom-

mendation to undergo CTX if the uPA/PAI-1 test indicates

high risk. Under these assumptions, the cost-effectiveness

of uPA/PAI-1 testing for these patients was strongly posi-

tive throughout a broad age range.

The cost-effectiveness of the uPA/PAI-1 test as com-

puted here compared to the all-CTX scenario would be

strengthened by inclusion of long-term survival risks

associated with chemotherapy such as cardiac toxicity and

leukemia. Cost-effectiveness in this scenario would be

further strengthened by if QALY rather than ly were used

as the figure of merit, the precise numbers depending of

course on assignment of quality of life differences to

treatment states.

The incremental costs were computed here from a

payer’s perspective appropriate to the German setting. If

our calculations are to be used as a rough guide to cost-

effectiveness in other settings, it is important to remember

that cost-effectiveness of any test regarding adjuvant che-

motherapy in primary breast cancer will generally depend

on specifics of the country and the health care system

involved. Important country-specific factors include the

following:

• Target group for which guidelines allow an option for

no adjuvant chemotherapy (here age [ 35/N0/G2/

HR?/HER2-).

• Accepted quality-of-life adjustment for chemotherapy.

• Competing survival risks (Life tables).

• Direct and indirect costs of chemotherapy.

• Willingness of the health care provider to pay.

Cost estimates for the present model—notably for che-

motherapy, concomitant medication and FN prophylaxis as

well as port use—were comprehensively defined in

‘‘Methods and health economic model’’ section. In

0

5,000

10,000

15,000

20,000

25,000

30,000

35 40 45 50 55 60 65 70 75

age at primary therapy

ICE

R

0.0

1.0

2.0

3.0

4.0

5.0

6.0

ly g

aine

d pe

r hi

gh-r

isk

delta cost/ lydelta ly

patie

nt d

ue to

test

Fig. 4 Favorable ICER of uPA/PAI-1 testing compared to ‘‘no-

CTX’’-scenario as a function of age

Breast Cancer Res Treat (2013) 138:839–850 847

123

practice, costs can vary both by health-care system and

even within a single country in time due to changes in CTX

regimens that reflect medical advances, incorporation of

trial results, changing medication prices, use of generics,

bonus concepts, etc. A strong cost driver is the decision for

upfront FN prophylaxis, whose costs can exceed those of

chemotherapy medication itself.

Costs excluded from this calculation cover multiple

secondary and tertiary effects of CTX. If included, these

often hidden costs could also exceed those of primary CTX

and related medication and if taken into account would

tend to strengthen the case for uPA/PAI-1 testing.

The true benefits and costs in practice will depend on

compliance by patients and physicians. In contrast to the

potential savings estimated here assuming 100 % test

compliance, one observes deviations from 100 % compli-

ance to both conventional guidelines and validated tests,

resulting in cases of undertreatment and overtreatment

despite clear indications.

As an additional limitation, in clinical practice, the

fraction of G2, HR?, HER2- patients for whom CTX

savings could be realized could be affected by

• existence of sufficient tissue for the assay,

• availability of logistics for test performance,

• additional clinico-pathological decision criteria.

Finally, note that the perspective (patient rather than

payer) could affect all computations.

In a recent publication, a Markov model was applied to

estimate and compare cost-effectiveness of adjuvant che-

motherapy for scenarios corresponding to 1st–3rd genera-

tion regimens compared to a no-therapy scenario in a UK-

setting [2]. Prognostic information was considered, but not

factors predictive of therapy response (in the sense of a

survival model interaction). Despite differences in costs,

WTP, inclusion of QoL losses in the model, and the greater

diversity of the target group considered [2], there is a

parallel to the present work in their conclusion that for an

ER-positive patient receiving endocrine therapy, ‘‘no che-

motherapy’’ becomes the optimal treatment strategy in

terms of cost-effectiveness for 10-year recurrence-free

survival exceeding about 82 % (for age [ 60) to about

86 % (age [ 40). This favorable level of RFS applies to a

rather low fraction of patients using only conventional

factors, but to a considerably higher percentage using the

prognostic information of uPA/PAI-1. The consequence is

thus to enlarge the group of patients who can be spared

chemotherapy.

In the case of the 70-gene test (MammaPrintTM), cost

utility compared to Adjuvant onlineTM or St. Gallen

guidelines was studied from a Dutch perspective [6]. There

were a few technical differences in survival simulation to

the method used here, particularly the model assumption

that patients classified as true low or false high risk had a

zero probability to experience a relapse and the use of

different data sources. Nonetheless, the conclusion of cost-

effectiveness of the test is similar and occurs for the same

reasons as in our comparison of uPA/PAI-1 testing with the

‘‘all-CTX’’ strategy. Moreover, the computations support

the hypothesis mentioned above that our conclusions would

be strengthened if QoL losses due to chemotherapy were

included. Similar conclusions were reached when including

the 21-gene assay (Oncotype DXTM) in the comparison [7,

8].

Cost-effectiveness computations for use of biomarkers

in adjuvant chemotherapy selection in breast cancer are

likely to play an increasingly important role with the

advent of new tests and the potentially complex structure of

tumor classification gene expression. It is entirely possible

that a fusion of different tests, such as the 21-gene assay

and uPA/PAI-1, could isolate an even lower-risk group

than either test alone and strengthen the case for omission

of chemotherapy. Cost-effectiveness computations, e.g.,

using Markov models, could also play a key role in

approval of tests that improve prediction of response to

specific chemotherapy regimens, particularly in the case of

an expensive, targeted therapy that is highly effective in a

specific group. Validated multivariate predictive models

(interaction of test result with therapy) have enormous

impact on these calculations. However, health economic

analysis poses rather specific requirements on the design

and quality of studies used to provide transition rates and

other data in Markov state models [5]. These include the

need for models reporting hazard ratios within a multi-

variate model, meta-analysis or pooled analysis, efforts to

correct for publication bias, etc. Small studies claiming a

putative marker to be prognostic in a univariate model are

seldom of use in these computations. Future biomarker

studies could profit from consideration of health economics

considerations during the design phase.

Conflict of interest All authors besides one declared no conflict of

interest.

Disclosures REK received consulting fees from American Diag-

nostica unrelated to this project.

References

1. Sullivan R, Peppercorn J, Sikora K, Zalcberg J, Meropol NJ,

Amir E, Khayat D, Boyle P, Autier P, Tannock IF, Fojo T,

Siderov J, Williamson S, Camporesi S, McVie JG, Purushotham

AD, Naredi P, Eggermont A, Brennan MF, Steinberg ML, De

Ridder M, McCloskey SA, Verellen D, Roberts T, Storme G,

Hicks RJ, Ell PJ, Hirsch BR, Carbone DP, Schulman KA,

Catchpole P, Taylor D, Geissler J, Brinker NG, Meltzer D, Kerr

848 Breast Cancer Res Treat (2013) 138:839–850

123

D, Aapro M (2011) Delivering affordable cancer care in high-

income countries. Lancet Oncol 12(10):933–980

2. Campbell HE, Epstein D, Bloomfield D, Griffin S, Manca A,

Yarnold J, Bliss J, Johnson L, Earl H, Poole C, Hiller L, Dunn J,

Hopwood P, Barrett-Lee P, Ellis P, Cameron D, Harris AL, Gray

AM, Sculpher MJ (2011) The cost-effectiveness of adjuvant

chemotherapy for early breast cancer: a comparison of no che-

motherapy and first, second, and third generation regimens for

patients with differing prognoses. Eur J Cancer 47(17):

2517–2530

3. Hershman DL, Wilde ET, Wright JD, Buono DL, Kalinsky K,

Malin JL, Neugut AI (2012) Uptake and economic impact of first-

cycle colony-stimulating factor use during adjuvant treatment of

breast cancer. J Clin Oncol 30(8):806–812

4. Lux MP, Hartmann M, Jackisch C, Raab G, Schneeweiss A,

Possinger K, Oyee J, Harbeck N (2009) Cost-utility analysis for

advanced breast cancer therapy in Germany: results of the ful-

vestrant sequencing model. Breast Cancer Res Treat 117(2):

305–317

5. Williams C, Brunskill S, Altman D, Briggs A, Campbell H,

Clarke M, Glanville J, Gray A, Harris A, Johnston K, Lodge M

(2006) Cost-effectiveness of using prognostic information to

select women with breast cancer for adjuvant systemic therapy.

Health Technology Assess 10(34):1–204

6. Retel VP, Joore MA, Knauer M, Linn SC, Hauptmann M, Harten

WH (2010) Cost-effectiveness of the 70-gene signature versus St.

Gallen guidelines and Adjuvant Online for early breast cancer.

Eur J Cancer 46(8):1382–1391

7. Retel VP, Joore MA, van Harten WH (2012) Head-to-head

comparison of the 70-gene signature versus the 21-gene assay:

cost-effectiveness and the effect of compliance. Breast Cancer

Res Treat 131(2):627–636

8. Yang M, Rajan S, Issa AM (2012) Cost effectiveness of gene

expression profiling for early stage breast cancer: a decision-

analytic model. Cancer 118(20):5163–5170

9. Lippman ME, Ethier S, Hayes DF (2009) Cost-effective analyses

in breast cancer research and treatment. Breast Cancer Res Treat

115(2):221–222

10. AGO Guidelines. www.ago-online.de/index.php?lang=de&site=

mamma_guide_topical&topic=mamma_guide

11. Harris L, Fritsche H, Mennel R, Norton L, Ravdin P, Taube S,

Somerfield MR, Hayes DF, Bast RC Jr (2007) American Society

of Clinical Oncology 2007 Update of Recommendations for the

Use of Tumor Markers in Breast Cancer. J Clin Oncol

25(33):5287–5312

12. Duffy MJ, Reilley D, O’Sullivan C, O’Higgins N, Fennelly JJ,

Andreasen P (1990) Urokinase-plasminogen activator, a new and

independent prognostic marker in breast cancer. Cancer Res

50(21):6827–6829

13. Janicke F, Schmitt M, Hafter R, Hollrieder A, Babic R, Ulm K,

Gossner W, Graeff H (1990) Urokinase-type plasminogen acti-

vator (uPA) antigen is a predictor of early relapse in breast

cancer. Fibrinolysis 4(2):69–78

14. Janicke F, Schmitt M, Graeff H (1991) Clinical relevance of the

urokinase-type and tissue type plasminogen activators and of

their type 1 inhibitor in breast cancer. Sem Thromb Hemostasis

17:303–312

15. Janicke F, Schmitt M, Pache L, Ulm K, Harbeck N, Hofler H,

Graeff H (1993) Urokinase (uPA) and its inhibitor PAI-1 are

strong, independent prognostic factors in node-negative breast

cancer. Breast Cancer Res Treat 24:195–208

16. Foekens JA, Schmitt M, van Putten WLJ, Peters HA, Kramer

MD, Janicke F, Klijn JG (1994) Plasminogen activator inhibitor-1

and prognosis in primary breast cancer. J Clin Oncol 12(8):

1648–1658

17. Stephens RW, Brunner N, Janicke F, Schmitt M (1998) The

urokinase plasminogen activator system as a target for prognostic

studies in breast cancer. Breast Cancer Res Treat 52(1–3):99–111

18. Harbeck N, Thomssen C (2011) A new look at node-negative

breast cancer. Oncologist 16(Suppl 1):51–60

19. Jacobs VR, Kates R, Kantelhardt EJ, Vetter M, Schmitt M, Jaenicke

F, Untch M, Thomssen C, Harbeck N (2010) Health economic

impact of risk group selection according to ASCO-recommended

biomarkers uPA/PAI-1 in node-negative primary breast cancer. 33rd

Annual San Antonio Breast Cancer Symposium, San Antonio, TX,

USA, December 7–12, 2010. Cancer Res 70(24 Suppl):196–197

20. World Health Organization (WHO). World Health Statistics

2010. WHO, Geneva, Switzerland 2010

21. Harbeck N, Schmitt M, Meisner C, Friedel C, Untch M, Schmid M,

Sweep CGJ, Lisboa BW, Lux MP, Beck T, Hasmuller S, Kiechle

M, Janicke F, Thomssen C, for the Chemo-N0 Study Group (2013)

Ten-year analysis of the prospective multicenter Chemo-N0 trial

validates ASCO-recommended biomarkers uPA and PAI-1 for

therapy decision making in node-negative breast cancer patients.

Eur J Cancer 2013 (in print). 10.1016/j.ejca.2013.01.007

22. Harbeck N, Schmitt M, Vetter M, Krol J, Paepke D, Uhlig M,

Paepke S, Janicke F, Geurts-Moespot A, von Minckwitz G,

Sweep F, Thomssen C (2008) Prospective biomarker trials

Chemo N0 and NNBC-3 Europe validate the clinical utility of

invasion markers uPA and PAI-1 in node-negative breast cancer.

Breast Care (Basel) 3(s2):11–15

23. Schmidt M, Victor A, Bratzel D, Boehm D, Cotarelo C, Lebrecht

A, Siggelkow W, Hengstler JG, Elsasser A, Gehrmann M, Lehr

HA, Koelbl H, von Minckwitz G, Harbeck N, Thomssen C (2009)

Long-term outcome prediction by clinicopathological risk clas-

sification algorithms in node-negative breast cancer—comparison

between Adjuvant!, St Gallen, and a novel risk algorithm used in

the prospective randomized Node-Negative-Breast Cancer-3

(NNBC-3) trial. Ann Oncol 20(2):258–264

24. Look MP, van Putten WL, Duffy MJ, Harbeck N, Christensen IJ,

Thomssen C, Kates R, Spyratos F, Ferno M, Eppenberger-Castori

S, Sweep CG, Ulm K, Peyrat JP, Martin PM, Magdelenat H,

Brunner N, Duggan C, Lisboa BW, Bendahl PO, Quillien V,

Daver A, Ricolleau G, Meijer-van Gelder M, Manders P, Fiets

WE, Blankenstein MA, Broet P, Romain S, Daxenbichler G,

Windbichler G, Cufer T, Borstnar S, Kueng W, Beex LV, Klijn

JG, O’Higgins N, Eppenberger U, Janicke F, Schmitt M, Foekens

JA (2002) Pooled analysis of prognostic impact of urokinase-type

plasminogen activator and its inhibitor PAI-1 in 8377 breast

cancer patients. J Natl Cancer Inst 94(2):116–128

25. Look M, van Putten W, Duffy M, Harbeck N, Christensen IJ,

Thomssen C, Kates R, Spyratos F, Ferno M, Eppenberger-Castori

S, Fred Sweep CG, Ulm K, Peyrat JP, Martin PM, Magdelenat H,

Brunner N, Duggan C, Lisboa BW, Bendahl PO, Quillien V,

Daver A, Ricolleau G, Meijer-van Gelder M, Manders P, Edward

Fiets W, Blankenstein M, Broet P, Romain S, Daxenbichler G,

Windbichler G, Cufer T, Borstnar S, Kueng W, Beex L, Klijn J,

O’Higgins N, Eppenberger U, Janicke F, Schmitt M, Foekens J

(2003) Pooled analysis of prognostic impact of uPA and PAI-1 in

breast cancer patients. Thromb Haemost 90(3):538–548

26. Harbeck N, Kates RE, Look MP, Meijer-Van Gelder ME, Klijn

JG, Kruger A, Kiechle M, Janicke F, Schmitt M, Foekens JA

(2002) Enhanced benefit from adjuvant chemotherapy in breast

cancer patients classified high-risk according to urokinase-type

plasminogen activator (uPA) and plasminogen activator inhibitor

type 1 (n = 3424). Cancer Res 62(16):4617–4622

27. Janicke F, Prechtl A, Thomssen C, Harbeck N, Meisner C, Untch

M, Sweep CG, Selbmann HK, Graeff H, Schmitt M, German N0

Study Group (2001) Randomized adjuvant chemotherapy trial in

high-risk, lymph node-negative breast cancer patients identified

Breast Cancer Res Treat (2013) 138:839–850 849

123

by urokinase-type plasminogen activator and plasminogen acti-

vator inhibitor type 1. J Natl Cancer Inst 93(12):913–920

28. Kim SJ, Shiba E, Kobayashi T, Yayoi E, Furukawa J, Takatsuka

Y, Shin E, Koyama H, Inaji H, Takai S (1998) Prognostic impact

of urokinase-type plasminogen activator (PA), PA inhibitor type-

1 and tissue-type PA antigen levels in node-negative breast

cancer: a prospective study on multicenter basis. Clin Cancer Res

4(1):177–182

29. Schmitt M, Harbeck N, Brunner N, Janicke F, Meisner C,

Muhlenweg B, Jansen H, Dorn J, Nitz U, Kantelhardt EJ,

Thomssen C (2011) Cancer therapy trials employing level-of-

evidence-1 disease forecast cancer biomarkers uPA and its

inhibitor PAI-1. Expert Rev Mol Diagn 11(6):617–634

30. Rote Liste (German pharmaceutical price list). www.rote-liste.de

31. Aapro MS, Bohlius J, Cameron DA, Dal Lago L, Donnelly JP,

Kearney N, Lyman GH, Pettengell R, Tjan-Heijnen VC,

Walewski J, Weber DC, Zielinski C, European Organisation for

Research and Treatment of Cancer (2011) 2010 update of EO-

RTC guidelines for the use of granulocyte-colony stimulating

factor to reduce the incidence of chemotherapy-induced febrile

neutropenia in adult patients with lymphoproliferative disorders

and solid tumours. Eur J Cancer 47(1):8–32

32. German Medical Fee Schedule (Gebuhrenordnung fur Arzte/

GOA). www.gesetze-im-internet.de/go__1982/

33. Hanseatische Krankenkasse (HKK) Erste Krankenkasse zahlt

Brustkrebs-Test [First health care fund reimburses breast cancer

test]. Press Release, November 10th 2010. www.hkk.de/top/

presse/pressearchiv/mitteilung_einzelansicht/?tx_ttnews%5Btt_-

news%5D=147&tx_ttnews%5BbackPid%5D=554%cHash=8963

6c6086

850 Breast Cancer Res Treat (2013) 138:839–850

123