Laura Papi
email: l.papi@dfc.unifi.it

Anna Laura Putignano

Caterina Congregati

Ines Zanna

Francesco Sera

Doralba Morrone

Mario Falchetti

Marco Rosselli

Del Turco

Laura Ottini

Domenico Palli

Laura Papi Ae

Anna Laura

Putignano Ae

A E Ines

Zanna Ae

A E Doralba

Morrone Ae

A E Marco

Rosselli Del

Turco Ae

A E Domenico

Palli Ae

Maurizio Genuardi

Á C Congregati

Á M Genuardi

Á F Sera

Á D Palli

Á L Ottini

M R D Turco

Founder mutations account for the

Keywords: Hereditary breast/ovarian cancer, Male breast cancer, BRCA1, BRCA2, Founder mutation

Introduction

Germline mutations in the BRCA1 (MIM ?113705) and BRCA2 (MIM ?600185) tumour-suppressor genes predispose to early-onset breast and ovarian cancer. Although many different pathogenic mutations have been identified throughout the whole coding sequence of both genes, a number of alterations recur in some ethnic groups. Well known examples of BRCA1 and BRCA2 founder mutations have been observed in Ashkenazi Jews, Icelanders, Norwegians, Swedes, Finns, French Canadians and Dutch (reviewed in [START_REF] Ferla | Founder mutations in BRCA1 and BRCA2 genes[END_REF]).

Compared to the above mentioned populations, the spectrum of molecular defects associated with genetic diseases in countries with a long-standing history of ethnic admixture and migration, like Italy, is usually highly heterogeneous. Nonetheless, a few founder recurrent mutations in BRCA1 [START_REF] Baudi | Evidence of a founder mutation of BRCA1 in a highly homogeneous population from Southern Italy with breast/ovarian cancer[END_REF][START_REF] Marroni | Reconstructing the genealogy of a BRCA1 founder mutation by phylogenetic analysis[END_REF][START_REF] Malacrida | BRCA1 p.Val1688del is a deleterious mutation that recurs in breast and ovarian cancer families from Northeast Italy[END_REF] and BRCA2 [START_REF] Pisano | Identification of a founder BRCA2 mutation in Sardinia[END_REF][START_REF] Monne | Identification of a founder BRCA2 mutation in Sardinian breast cancer families[END_REF] originated from different regions of Italy have been observed.

In our analysis of breast/ovarian cancer families from the Central-Eastern part of Tuscany, we found four recurrent BRCA1 mutations, one of which was associated with a history of male breast cancer. Haplotype analysis of nine polymorphic markers flanking the BRCA1 gene was performed for the two most frequent mutations identified. We found that both sequence variants were derived from common ancestors, and then estimated the number of generations elapsed since the origin of the most frequent mutation, c.3228_3229delAG. Overall, we demonstrate that the vast majority (72.7%) of BRCA1-attributable hereditary breast/ovarian cancer families that are originary from this part of Italy harbour a limited number of ''regional'' mutations.

Materials and methods

Subjects

Families were ascertained at the Medical Genetics Unit of the University of Florence and at the ISPO, Scientific Institute for Cancer Prevention of Tuscany, in the period 1999-2005. Pedigrees were reconstructed for at least three generations and demographic data were obtained to determine the geographic origin of both the paternal and the maternal family branches. Where possible, cancers were verified from cancer registries, hospital notes or death certificates. Probabilities of carrying a mutation in the BRCA1 or BRCA2 genes were estimated using the CaGene Software, derived from the BRCAPro model (URL: http://astor.som.jhmi.edu/BayesMendel/brcapro.html) [START_REF] Berry | BRC-APRO validation, sensitivity of genetic testing of BRCA1/ BRCA2, and prevalence of other breast cancer susceptibility genes[END_REF]. Genetic testing was offered to affected probands with a calculated BRCA mutation probability [5%. A total of 104 unrelated probands from Florence and the surrounding provinces of Prato, Pistoia and Arezzo in Central/Eastern Tuscany, underwent BRCA1/2 genetic testing. Two additional male breast cancer families from Tuscany, ascertained in a population-based study [START_REF] Ottini | BRCA1/BRCA2 mutation status and clinical-pathologic features of 108 male breast cancer cases from Tuscany: a population-based study in Central Italy[END_REF], were also included for haplotype analysis.

Blood samples from 18 nuclear cancer-free families, originary from Central-Eastern Tuscany, and from 15 noncarrier relatives belonging to Tuscan families segregating BRCA1 recurrent mutations were used to estimate allele frequencies and control haplotypes. Informed consent was obtained from all study participants.

Mutation analysis

DNA was extracted by standard phenol-chloroform procedures. All coding exons and flanking intron sequences of BRCA1 and BRCA2 were analysed by denaturing high performance liquid chromatography (dHPLC) or direct sequencing. dHPLC was carried out on an automated instrument (Transgenomic Inc., San Jose, CA); optimal temperatures for heteroduplex detection were determined using WAVEMAKER software (Transgenomic). BRCA1 exons 9, 11, 13, 16 and BRCA2 exon 22 were directly investigated by sequencing because of their high content of polymorphic variants. DNA sequencing was carried out on an ABI 310 DNA analyser (Applied Biosystems) using the ABI PRISM BigDye terminators cycle sequencing kit (Applied Biosystems).

Mutation nomenclature follows the Human Genome Variation Society (URL: http://www.hgvs.org/mutnomen/) recommendations. The DNA mutation numbering is based on the BRCA1 and BRCA2 cDNA sequences (reference sequences: NM_007296.2 and NM_000059.3, respectively) with the A of the ATG translation-initiation codon numbered as ?1. Amino acid numbering starts with the translation initiator methionine as ?1.

Microsatellite analysis

Thirty-eight individuals belonging to 14 families segregating two recurrent BRCA1 mutations, c.3228_3229delAG (n = 11) and c.3285delA (n = 3) and 54 controls, were genotyped at nine short tandem repeat markers located within or adjacent to BRCA1. The following microsatellites, listed in order from centromere to telomere, were analyzed: D17S1872, D17S250, D17S800, D17S1328, D17S1323, D17S1325, D17S902, D17S806 and D17S588 (Fig. 1). PCR primer sequences were obtained from the Genome Database (URL: http://www.gdb.org). Forward primers were labelled with 6-FAM or TET fluorescent dyes and PCR products were analysed on a model 310 automated sequencer (Applied Biosystems). Product size determination was assessed with Genotyper (version 2.1) software (Applied Biosystems).

Haplotyping and estimate of mutation age

Haplotypes were constructed manually, assuming the least number of possible recombinations. Mutation age estimate was carried out with the DMLE?2.2 software program (URL: http://www.dmle.org) [START_REF] Reeve | DMLE?: Bayesian linkage disequilibrium gene mapping[END_REF].

The method implemented in the DMLE?2.2 software is an extension of the Bayesian LD mapping method of Rannala and Reeve [START_REF] Rannala | High-resolution multipoint linkagedisequilibrium mapping in the context of a human genome sequence[END_REF] that was initially designed for high-resolution mapping of a disease mutation based on the observed linkage disequilibrium between the mutation and linked markers in DNA samples of disease chromosomes. It uses the Markov chain Monte Carlo methods to generate the marginal posterior probability density of mutation age, based on the following parameters: observed haplotypes or genotypes in normal and affected chromosomes, map distances between markers and mutation site, fraction of mutated chromosomes sampled, and estimated population growth rate.

We used haplotypes for the nine 17q21 microsatellite markers in affected and unaffected chromosomes, with the following parameters: (a) chromosome map distances derived from the Marshfield and/or Gene ´thon sex-average genetic maps (URL: http://www.ncbi.nlm.nih.gov/map view/) and by Bergman et al. [START_REF] Bergman | The western Swedish BRCA1 founder mutation 3171ins5; a 3.7cM conserved haplotype of today is a reminiscence of a 1500-year-old mutation[END_REF]; (b) population growth rate: 0.054; (c) proportion of mutation-carrying chromosomes sampled: 0.00698.

The population growth rate (r) was estimated from the equation: N = N 0 e gr , in which N is the estimated present population size, N 0 is the estimated size of the population at reference time and g is the number of generations between these two time points. The population of the city of Florence currently comprises about 330,000 people. Historical and demographic data indicate that it included about 15,000 people in year 1200 [START_REF] Beloch | Storia della popolazione d'Italia[END_REF] and 59,500 in year 1550; accordingly, the average growth rate of this population was estimated to be 0.085 between years 1200 and present time and 0.119 from 1550 to the present time, assuming 25 years/generation. There are no data regarding the population of the entire region of Tuscany before year 1550; in that year it was estimated to include about 912,000 individuals [START_REF] Beloch | Storia della popolazione d'Italia[END_REF] and now comprises about 3,619,000 inhabitants; the approximate growth rate per generation is therefore 0.076. We then calculated the ratio between the growth rates of the city of Florence (0.119) and of the entire region, inclusive of rural areas (0.076), for the period 1550 to present. The value thus obtained, 1.57, was used to estimate the growth rate since 1200 of the whole region, based on the urban data only (0.085) for this period. We thus calculated a value of r = 0.054 = (0.085 9 1/1.57), which was subsequently used for mutation age estimates.

The proportion of mutation-carrying chromosomes sampled was estimated from a population of 1,175,000 inhabitants in Florence and the surrounding provinces of Prato and Arezzo in Central-Eastern Tuscany assuming a prevalence of BRCA1 mutation carriers of 0.014 (1:714). The latter represents the median value calculated from a meta-analysis of prevalence data available for populations of European ancestry [START_REF] Ford | Estimates of the gene frequency of BRCA1 and its contribution to breast and ovarian cancer incidence[END_REF][START_REF] Whittemore | Prevalence and contribution of BRCA1 mutations in breast cancer and ovarian cancer: results from three US population-based case-control studies of ovarian cancer[END_REF][START_REF] Risch | Population BRCA1 and BRCA2 mutation frequencies and cancer penetrances: a kin-cohort study in Ontario, Canada[END_REF][START_REF] Whittemore | Prevalence of BRCA1 mutation carriers among US non-Hispanic whites[END_REF][START_REF] Antoniou | A comprehensive model for familial breast cancer incorporating BRCA1, BRCA2 and other genes[END_REF], since estimates of BRCA1 mutation prevalence are not available for the Italian population.

Results

Mutation analysis and phenotypic characteristics

Germline deleterious BRCA1 and BRCA2 mutations were identified in 22 and 13 out of 104 probands from Central-Eastern Tuscany, respectively. We found ten different BRCA1 and twelve different BRCA2 germline mutations (Table 1). Four of the ten BRCA1 mutations and one of the twelve BRCA2 mutations were observed in more than one family. The BRCA1 c.3228_3229delAG and c.3285delA mutations were observed in nine and three families, respectively. Two additional BRCA1 mutations, c.1377_1378insA and c.5062_5064delTGT and one BRCA2 variant, c.9117G[A, were observed in two families each.

Haplotype analysis

Three families with the c.3285delA mutation and 11 families carrying the c.3228_3229delAG, as well as 18 nuclear families used as controls, were investigated.

Haplotype analysis using polymorphic markers spanning the BRCA1 locus was performed on probands and, when possible, on additional family members. Two to seven family members were genotyped in 8 of the 11 families segregating the c.3228_3229delAG and in 1 of the 3 families with the c.3285delA. In the remaining families only the index case was available for genotyping.

Two distinct haplotypes were associated with each of the two recurrent BRCA1 mutations, c.3228_3229delAG and c.3285delA (Fig. 1). These haplotypes were absent in non-carriers from the families segregating the two mutations and were not found in control chromosomes. In addition, in all families with only one individual available for analysis, the observed genotypes were compatible with the shared haplotypes identified in families with the same mutation.

Age estimate of the BRCA1 c.3228_3229delAG mutation

The DMLE?2 program was used to estimate the age of the c.3228_3229delAG mutation based on haplotype data from patients and controls. Using the median prevalence of 1:714 to estimate the number of mutation-carrying chromosomes sampled and considering a growth rate of 0.054, c This variant is considered to be deleterious based on RNA studies [START_REF] Bonatti | RNA-based analysis of BRCA1 and BRCA2 gene alterations[END_REF] the mutation age was estimated to be *129 generations (95% credible set: 88-184) (Fig. 2).

Clinical characteristics associated with the BRCA1 c.3228_3229delAG mutation Ovarian cancer was recorded in 8 of the 11 families exhibiting the BRCA1 c.3228_3229delAG mutation (Table 2). The age at first BC diagnosis ranged between 30 and 82 years (median 43.5 years). The age at first OC diagnosis ranged between 30 and 68 years (median 47.0 years). Interestingly, male BC cases were present in 3/11 families with the BRCA1 c.3228_3229delAG mutation.

Discussion

In the present study, we have investigated high risk breastovarian cancer families referred to a single breast cancer family clinic of Central Italy in the period 1999-2005. The majority of the families investigated (104/185; 56.2%) originate from Florence and the surrounding provinces of Prato, Arezzo and Pistoia (Fig. 3). Of the 104 probands of Tuscan origin, 35 (35.65%) were heterozygotes for a BRCA1/BRCA2 deleterious mutation. Although the BRCA1/BRCA2 mutation spectrum was highly variable, two mutations, c.3228_3229delAG and c.3285delA, were present in 57% of all BRCA1-attributable families. The BRCA1 c.3285delA has never been reported in the Breast Cancer Information Core (BIC) (URL: http://research. nhgri.nih.gov/bic/) and, to the best of our knowledge, in Italy. Two additional BRCA1 mutations, c.1377_1378insA and c.5062_5064delTGT, were detected in two unrelated families each in our series. Both of these have been shown to be Italian founder mutations, originating from Western Tuscany [START_REF] Marroni | Reconstructing the genealogy of a BRCA1 founder mutation by phylogenetic analysis[END_REF] and North-Eastern Italy [START_REF] Malacrida | BRCA1 p.Val1688del is a deleterious mutation that recurs in breast and ovarian cancer families from Northeast Italy[END_REF], respectively. Therefore, overall recurrent founder mutations accounted for 72.7% of BRCA1 mutations in our series. The BRCA2 mutation spectrum was more variable. We found two unrelated families carrying the same BRCA2 c.9345G[A mutation causing exon 23 skipping, already described in another patient from Pisa, Tuscany [START_REF] Bonatti | RNA-based analysis of BRCA1 and BRCA2 gene alterations[END_REF]. In

F41 Female BC34 S-OC41, M-OC56, MA-OC68, MGM-OC43 Gastric, leukaemia F61 Female BC57 M-BC82, MA-BC45, MA-BC60 Colon, brain, pancreas F74 Female OC52, BC54 M-OC56 Cerebellar F114 Female OC42 MA-OC66 Gyn? F187 Female BC43 S-OC44, F-BC40, PGM-BC71, PGGM-BC42 Gastric, colon, lung F188 Female BC42 S-BC47, S-BC52, S-OC45, N-BC37, N-BC37 None F223 Female OC44 S-BC41, M-BC30, MA-OC50, MGM-OC49 None F263 Female OC53 M-OC43 Gyn?
F5 a Male BC60 S-BC45, S-BC41?OC41, PA-OC30, PC-OC57, PC-BC64 Leukaemia We then used DMLE?2.2 software to estimate the age of the BRCA1 c.3228_3229delAG; the c.3285delA mutation was not investigated because the number of families in which it was identified was considered inadequate to estimate its age.

The estimate of the number of generations since the origin of the BRCA1 c.3228_3229delAG mutation was 129 (95% credible set: 88-184) generations, corresponding to *3,225 years (95% confidence intervals: 2200-4600). Therefore its origin in Tuscany should date back to the Villanovian and Etruscan periods, indicating that it is one of the oldest founder mutations so far described in breastovarian cancer predisposing genes (Table 3). However, it is worth nothing that the DMLE?2.2 method estimates the time of origin of the mutation, which may be older than that of the most recent common ancestor estimated by the other methods used in the studies reported in Table 3 [START_REF] Rannala | High-resolution multipoint linkagedisequilibrium mapping in the context of a human genome sequence[END_REF][START_REF] Slatkin | Estimating allele age[END_REF]. In addition, DMLE?2.2 estimates are very sensitive to the population growth rate; i.e. using a growth rate of 0.085, calculated only on the Florentine population, the estimated number of generations since the origin of the mutation was 92 (95% credible set: 65-127).

Interestingly, male breast cancer was present in three out of the eleven families segregating the BRCA1 c.3228_ 3229delAG mutation. It is well known that male breast carcinoma is associated with deleterious germline mutations in BRCA2, and the estimated cumulative risk of breast carcinoma by age 70 years is 6.3% [START_REF] Easton | Cancer risks in two large breast cancer families linked to BRCA2 on chromosome 13q12-13[END_REF] to 6.8% [START_REF] Tai | Breast cancer risk among male BRCA1 and BRCA2 mutation carriers[END_REF] among BRCA2 mutation male heterozygotes. However, the relationship between male breast carcinoma and deleterious germline mutations in BRCA1 is not as well established. BRCA1 mutations have been detected at very low frequency in series of unselected male breast cancer patients [START_REF] Ottini | BRCA1 and BRCA2 mutation status and tumor characteristics in male breast cancer: a population-based study in Italy[END_REF][START_REF] Friedman | Mutation analysis of BRCA1 and BRCA2 in a male breast cancer population[END_REF][START_REF] Csokay | High frequency of germ-line BRCA2 mutations among Hungarian male breast cancer patients without family history[END_REF][START_REF] Evans | BRCA1/2 mutation analysis in male breast cancer families from North West England[END_REF]. On the other hand, a lifetime risk of male breast cancer of 5.8% in BRCA1 mutation carriers has been calculated, but this estimate was based on data from high-risk families and was not adjusted for ascertainment [START_REF] Brose | Cancer risk estimates for BRCA1 mutation carriers identified in a risk evaluation program[END_REF]. More recently, the cumulative risk of breast cancer by age 70 years in BRCA1 male carriers was estimated at 1.2% in a very large US cohort of BRCA1/BRCA2 families [START_REF] Tai | Breast cancer risk among male BRCA1 and BRCA2 mutation carriers[END_REF]. The high number of BRCA1 c.3228_3229delAG families with male breast cancer cases might be a specific phenotypic effect of the mutation itself; however, there are 24 previous reports of BRCA1 c.3228_3229delAG in BIC and, to our knowledge, no association with male breast cancer has been pointed out. Therefore, it is possible that the genetic background of Tuscan male carriers influences phenotypic expression of this BRCA1 variant. The presence of mutations in other breast cancer predisposing genes or in modifier genes might increase the risk of breast cancer among male heterozygotes for this specific mutation. The observation that a high proportion of families with BRCA1 alterations from central-eastern Tuscany harbours a limited number of founder mutations has significant impact on clinical management of at risk subjects. Direct screening for these mutations could be undertaken as a first step for molecular analysis, at least in individuals of ascertained Tuscan ancestry. The identification of a large set of families carrying an identical mutation that predisposes to breast and ovarian cancer will provide unique opportunities to study the effect of other genetic and environmental factors on penetrance and disease phenotype. Should an increased risk of male breast cancer associated with the c.3228_3229delAG sequence variant be confirmed, it would be appropriate to consider offering appropriate surveillance to male carriers of this mutation.

Fig. 1

 1 Fig. 1 Haplotype branching trees derived from haplotype analysis. The most common haplotype is indicated in bold numbers. The markers used are shown together with their position in the Marshfield genetic map. Family haplotypes are indicated with the corresponding

Fig. 2

 2 Fig. 2 Plot of the posterior probability densities of mutation age expressed in number of generations estimated with DMLE software. Broken lines define the boundaries of the 95% confidence interval

a

  Families ascertained in a population-based study of male breast cancer (Ottini personal communication) b BC: breast cancer; OC: ovarian cancer c M: mother; F: father; S: sister; N: niece; PGM: paternal grandmother; PGGM: paternal grand-grandmother; MA: maternal aunt; MC: maternal cousin; MGM: maternal grandmother; PA: paternal aunt; PC: paternal cousin d Gyn?: undefined gynaecological cancer addition, the BRCA2 c.6468_6469delTC frameshift mutation, identified in a single family in this study, has previously been described in an unrelated male breast cancer patient from Florence[START_REF] Ottini | BRCA1 and BRCA2 mutation status and tumor characteristics in male breast cancer: a population-based study in Italy[END_REF].The most frequent BRCA1 germline mutations identified in our sample are c.3228_3229delAG and c.3285delA. Haplotype analysis of nine microsatellite markers, covering a region of *9.14 Mb surrounding BRCA1 on 17q21, was conducted on 14 families and the results obtained clearly indicate that both mutations can be traced back to common ancestors.

Fig. 3

 3 Fig. 3 Geographic distribution (province of residence) of the families segregating founder BRCA1 mutations in Tuscany. Each family is represented by the symbol corresponding to the observed mutation

Table 1

 1 BRCA1 and BRCA2 deleterious mutations in high risk families from Tuscany

	Gene	Description of mutation Effect of mutation	Number of families	BIC nomenclature a	Number of times
				with the same mutation		recorded in BIC
	BRCA1 c.3228_3229delAG	p.Arg1076fs	9	3347_3348delAG	24
		c.3285delA	p.Lys1095fs	3	3404delA	0
		c.1377_1378insA	p.Ile460fs	2	1499insA	12
		c.5062_5064delTGT	p.Val1688del b	2	5181_5183delTGT	6
		c.182_183delTG	p.Cys61fs	1	301_302delGT	0
		c.2155_2168del14	p.Lys719fs	1	2274_2287del14	0
		c.2296_2297delAG	p.Ser766fs	1	2415_2416delAG	3
		c.2405_2406delTG	p.Val802fs	1	2524_2525delTG	5
		c.4327C[T	p.Arg1443Stop	1	4446C[T	126
		c.5251C[T	p.Arg1751Stop	1	5370C[T	2 9
	BRCA2 c.9117G[A	r.8954_9117del (Exon 23 skipping) c 2	9345G[A	1 4
		c.289G[T	p.Glu97Stop	1	517G[T	0
		c.632-2A[G	r.517_631del (Exon 7 skipping)	1	IVS7-2A[G	0
		c.2950G[T	p.Glu984Stop	1	3178G[T	0
		c.3860delA	p.Asn1287fs	1	4088delA	2
		c.5086_5087insA	p.Gly1696fs	1	5314insA	0
		c.5851_5854delAGTT	p.Ser1951fs	1	6079_6082delAGTT	11
		6468_6469delTC	p.Ser2156fs	1	6696_6697delTC	22
		c.7707delT	p.Gly2569fs	1	7935delT	0
		c.7963C[T	p.Gln2655Stop	1	8191C[T	0
		c.8878C[T	p.Gln2960Stop	1	9106C[T	1 0
		c.9501?3A[T	r.9257_9501del (Exon 25 skipping) d 1	IVS25?3A[T	1 5

a BIC: Breast Cancer Information Core; note that sequence variants are designated differently in this study (for which the nomenclature recommendations of the Human Genome Variation Society were used) and in BIC (http://research.nhgri.nih.gov/bic/) b This variant is considered to be deleterious based on functional studies

[START_REF] Malacrida | BRCA1 p.Val1688del is a deleterious mutation that recurs in breast and ovarian cancer families from Northeast Italy[END_REF]

Table 2

 2 Families with the BRCA1 c.3228_3229delAG mutation

	Family ID Sex of proband Phenotype and	Family history of breast and	Family history
			age at diagnosis b	ovarian cancers and age of diagnosis c	of other cancers d
	F11	Female	BC42?44	M-BC39, MA-BC?	Colon

Table 3

 3 Age estimates for BRCA1 and BRCA2 founder mutations

	Gene	Mutation according	Mutation age	Country or	Reference
		to BIC nomenclature	in generations	population
		(number of families)	(confidence interval)
	BRCA1	3347_3348delAG (11)	129 (88-183)	Tuscany, Italy	Present study
		1499insA (14)	30 (22-56)	Tuscany, Italy	[3]
		G1738R	11 (4-24)	Greece	[27]
		3171ins5 (18)	50	South Sweden	[11]
		3744delT (8)	23-36	Finland	[28]
		4216-2A[G (9)	\10	Finland	[28]
		2804delAA (19)	32 (15-49)	Netherlands	[29]
		185delAG (17)	46 (23-80)	Ashkenazi Jews	[30]
		4184del4 (6)	170 (70-350)		[30]
		5382insC (19)	38 (18-68)	European, mixed	[30]
		Intron 5 splice (5)	75 (19-165)		[30]
		1294del40 (6)	9 (1-45)		[30]
	BRCA2	156-157insAlu (14)	120-130	Portugal	[31]
		9346-2A[G (9)	7-11	Finland	[28]
		7708C[T (8)	10-20	Finland	[28]
		8555T[G (5)	7-9	Finland	[28]

Breast Cancer Res Treat (2009) 117:497-504

d This variant is considered to be deleterious based on RNA studies (this study; data not shown)

Acknowledgements

The study was supported by Regione Toscana in the frame of the High-Risk Cancer Family Project and from a grant of Ente Cassa di Risparmio, Firenze, Italy.


J M Bueno-De-Mesquita

S C Linn
email: s.linn@nki.nl

R Keijzer
email: richardkeijzer@gmail.com

J Wesseling
email: j.wesseling@nki.nl

D S A Nuyten
email: n.nuyten@nki.nl

C Krimpen
email: ckrimpen@kg.nl

C Meijers
email: c.meijers@vumc.nl

P W Graaf
email: pwgraaf@rdgg.nl

M M E M Bos
email: bos@rdgg.nl

A A M Hart
email: g.hart@nki.nl

Ae S C Linn

Ae R Keijzer

Ae J Wesseling

Ae D S A Nuyten

Ae C Van Krimpen

Ae C Meijers

Ae P W De Graaf

Ae M M E M Bos

Ae A A M Hart

Ae E J T Rutgers
email: e.rutgers@nki.nl

Ae J L Peterse

Ae H Halfwerk

Ae R De Groot

Ae A Pronk
email: pronka@rdgg.nl

Ae A N Floore
email: arno.floore@agendia.com

Ae A M Glas

Ae L J Van't Veer

Ae M J Van De Vijver

Á J Wesseling

Á R De Groot

L J Van't Veer

Á M J Van De Vijver

J M Bueno
email: j.bueno@nki.nl

P W De Graaf

A A M Hart

C Van Krimpen

H Halfwerk
email: h.halfwerk@nki.nl

A N Floore

Á A M Glas

Á L J Van't Veer

A N Floore

M J Van De Vijver
email: m.j.vandevijver@amc.uva.nl

Validation of 70-gene prognosis signature in node-negative breast cancer

Keywords: Gene expression profile, Prognosis signature, Breast cancer, Microarray

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Introduction

Adjuvant systemic treatment substantially improves distant metastasis-free survival and overall survival in lymph nodenegative breast cancer patients [START_REF]Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: an overview of the randomised trials[END_REF]. It is generally agreed upon that patients with poor prognosis benefit most from this treatment [START_REF]Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: an overview of the randomised trials[END_REF][START_REF] Eifel | National Institutes of Health Consensus Development Conference Statement: adjuvant therapy for breast cancer, November 1-3, 2000[END_REF]. However, it has a wide range of acute and long-term toxicities. Using traditional risk indexes it has been estimated that 33 patients are treated to save one life [START_REF] Cleator | Molecular profiling of breast cancer: clinical implications[END_REF]. Therefore, an accurate selection of patients who will benefit from adjuvant systemic treatment is essential.

The main prognostic factors in breast cancer are axillary lymph node involvement, age, tumour diameter and histological grade [START_REF] Harris | Staging of breast cancer[END_REF]. Clinicopathological risk assessment is based on these factors and is used to guide decisions on adjuvant systemic treatment. A large number of potential prognostic factors have been investigated to predict disease outcome. Even the strongest prognostic factors (e.g. lymph node status, tumour diameter and histological grade) are moderately accurate in classifying breast tumours according to their clinical behaviour. The Oxford Overviews of systemic treatments demonstrate that a significant proportion of patients are long-term survivors without adjuvant systemic therapy [START_REF]Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: an overview of the randomised trials[END_REF][START_REF]Early Breast Cancer Trialists' Collaborative Group (1998) Tamoxifen for early breast cancer: an overview of the randomised trials[END_REF][START_REF]Polychemotherapy for early breast cancer: an overview of the randomised trials[END_REF]. Furthermore, there are patients that will develop metastatic disease despite this treatment.

Several research groups have recently used gene expression profiling to define subgroups of tumours associated with good or poor outcome [START_REF] Perou | Molecular portraits of human breast tumours[END_REF][START_REF] West | Predicting the clinical status of human breast cancer by using gene expression profiles[END_REF][START_REF] Sorlie | Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications[END_REF][START_REF] Van't Veer | Gene expression profiling predicts clinical outcome of breast cancer[END_REF][START_REF] Van De Vijver | A gene-expression signature as a predictor of survival in breast cancer[END_REF][START_REF] Sotiriou | Breast cancer classification and prognosis based on gene expression profiles from a population-based study[END_REF][START_REF] Huang | Gene expression predictors of breast cancer outcomes[END_REF][START_REF] Paik | A multigene assay to predict recurrence of tamoxifen-treated, nodenegative breast cancer[END_REF][START_REF] Habel | A population-based study of tumor gene expression and risk of breast cancer death among lymph node-negative patients[END_REF][START_REF] Pawitan | Gene expression profiling spares early breast cancer patients from adjuvant therapy: derived and validated in two population-based cohorts[END_REF][START_REF] Wang | Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer[END_REF][START_REF] Foekens | Multicenter validation of a gene expressionbased prognostic signature in lymph node-negative primary breast cancer[END_REF][START_REF] Buyse | Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer[END_REF][START_REF] Wang | A 76-gene signature to predict distant metastasis in lymph-node negative breast cancer[END_REF][START_REF] Esteva | Prognostic role of a multigene reverse transcriptase-PCR assay in patients with node-negative breast cancer not receiving adjuvant systemic therapy[END_REF]. One of these gene expression profiles predicting breast cancer recurrence is the 70-gene prognosis signature [START_REF] Van't Veer | Gene expression profiling predicts clinical outcome of breast cancer[END_REF]. This prognosis signature potentially is a more powerful prognostic factor for distant metastases than current used clinicalopathological factors in node-negative breast cancer patients. Van de Vijver et al. validated this signature in a series of 295 breast cancer patients [START_REF] Van De Vijver | A gene-expression signature as a predictor of survival in breast cancer[END_REF]. Buyse et al. performed a second international validation in 302 patients [START_REF] Buyse | Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer[END_REF].

To obtain a reliable estimate of the prognostic value of the 70-gene prognosis signature, validation in several series is essential. The three objectives of this study therefore were: (I) to assess the prognostic value of this prognosis signature in a relatively recently diagnosed, representative node-negative breast cancer patient series; (II) to evaluate its prognostic value in the initial validation-series after expanding the follow-up [START_REF] Van De Vijver | A gene-expression signature as a predictor of survival in breast cancer[END_REF]; and (III) to compare its prognostic value with commonly used clinicopathological risk indexes.

Methods

Patients, tumours and histopathology

Fresh frozen tumour samples (stored at minus 70°C) and clinical data were collected from a consecutive patient series treated at two Dutch centres, the Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital (NKI; Amsterdam) and the Reinier de Graaf Hospital (RdGG; Delft) between 1996 and 1999. Patients included (\55 years) had received adequate local therapy for early stage breast cancer, defined as node-negative, tumour diameter pT1-2. Patients with prior malignancies (except basal cell carcinomas and cervical dysplasia) or bilateral breast cancers were excluded.

Hereafter this validation series is referred to as 'the NKI-RdGG validation series'. Institutional approval from the medical-ethical committee of the NKI was obtained for conduct of this study.

Collection of clinical and pathological data, as well as central review of paraffin-embedded tumour samples was performed at the NKI blinded to the prognosis signature. Histological grade according to the Elston & Ellis method [START_REF] Elston | Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up[END_REF], oestrogen receptor and progesterone receptor (assessed by immunohistochemistry) were determined by two experienced breast pathologists (JW and MJvdV). In case of discordance between the original pathological examination and central review with regard to grade, the examination was performed again by a panel of three experienced breast pathologists (JW, MJvdV, JLP) who then agreed on a final grade. The oestrogen and progesterone receptor were considered positive if 10% or more of tumour cells stained positive using an immunohistochemical assay.

RNA extraction & microarray analysis

A patient's (good or poor) 70-gene prognosis signature was determined using frozen samples. Frozen sections were stained with H&E and analyzed by an experienced breast pathologist (MJvdV). Eligible samples had to contain at least 50% tumour cells. Thirty-seven patients (24 NKI and 13 RdGG patients) were excluded because the percentage of tumour cells was insufficient; however, the clinical and pathological characteristics of the excluded patients were not significantly different from those in the study population (data not shown).

Details of RNA isolation, microarray analysis and correlation of microarray data with the prognosis signature have previously been described [START_REF] Van't Veer | Gene expression profiling predicts clinical outcome of breast cancer[END_REF][START_REF] Van De Vijver | A gene-expression signature as a predictor of survival in breast cancer[END_REF][START_REF] Glas | Converting a breast cancer microarray signature into a high-throughput diagnostic test[END_REF]. Microarray analysis for obtaining the 70-gene prognosis signature (MammaPrint Ò ) was performed at Agendia laboratories, a spin-off company of the NKI, blinded to clinical outcome data. Agendia's 'MammaPrint diagnostic service' is cleared by the Food and Drug Administration as a medical device and is ISO-17025 accredited, utilizing a custom designed array chip ''MammaPrint Ò ''. This array chip assesses the mRNA expression of the 70 genes in triplicate using the Agilent oligonucleotide microarray platform.

Established clinical risk classifications indexes

Hereafter clinicopathological risk is referred to as 'clinical risk', which was based on data of centrally reviewed tumour samples. Clinicopathological risk indexes included: St Gallen guidelines [START_REF] Goldhirsch | International Consensus Panel on the treatment of primary breast cancer. V: Update 1998[END_REF][START_REF] Goldhirsch | Meeting highlights: International Consensus Panel on the treatment of primary breast cancer[END_REF][START_REF] Goldhirsch | Meeting highlights: International Consensus Panel on the treatment of primary breast cancer. Seventh International Conference on adjuvant therapy of primary breast cancer[END_REF][START_REF] Colomer | Validation of the 2001 St Gallen risk categories for node-negative breast cancer using a database from the Spanish Breast Cancer Research Group (GEICAM)[END_REF][START_REF] Senn | Comments on the St. Gallen Consensus 2003 on the primary therapy of early breast cancer[END_REF], Nottingham Prognostic Index [29-31], Dutch CBO guidelines 2004-available at www.oncoline.nl- [START_REF]Richtlijn Behandeling van het Mammacarcinoom[END_REF][START_REF]Kwaliteitsinstituut voor de Gezondheidszorg CBO Vereniging voor Integrale Kankercentra[END_REF] and Adjuvant! Online-available at www.adjuvantonline.com [34][START_REF] Olivotto | Population-based validation of the prognostic model ADJUVANT! for early breast cancer[END_REF][START_REF] Ravdin | Computer program to assist in making decisions about adjuvant therapy for women with early breast cancer[END_REF]. In this study, a moderate or high clinical risk was an indication for adjuvant systemic treatment.

According to St Gallen guidelines, a low clinical risk was defined as oestrogen and/or progesterone positive and all of the following features: tumour size smaller or equal to 2 cm, grade 1 (Elston & Ellis), and age equal or above 35 years [START_REF] Goldhirsch | International Consensus Panel on the treatment of primary breast cancer. V: Update 1998[END_REF][START_REF] Goldhirsch | Meeting highlights: International Consensus Panel on the treatment of primary breast cancer[END_REF][START_REF] Goldhirsch | Meeting highlights: International Consensus Panel on the treatment of primary breast cancer. Seventh International Conference on adjuvant therapy of primary breast cancer[END_REF][START_REF] Colomer | Validation of the 2001 St Gallen risk categories for node-negative breast cancer using a database from the Spanish Breast Cancer Research Group (GEICAM)[END_REF][START_REF] Senn | Comments on the St. Gallen Consensus 2003 on the primary therapy of early breast cancer[END_REF]. All others were considered as moderate or high risk.

The Nottingham Prognostic Index (NPI) computes a score using the following algorithm: 0.2 * size (cm) ? grade ? nodal status [29][START_REF] Giardina | Prognostic factors in breast cancer: the predictive value of the Nottingham Prognostic Index in patients with a long-term follow-up that were treated in a single institution[END_REF][START_REF] Kattan | A tool for predicting breast carcinoma mortality in women who do not receive adjuvant therapy[END_REF].

The Adjuvant! Online Software calculates a 10-year survival probability based on the patient's age, tumour size, histological tumour grade, oestrogen receptor status and nodal status [34][START_REF] Olivotto | Population-based validation of the prognostic model ADJUVANT! for early breast cancer[END_REF][START_REF] Ravdin | Computer program to assist in making decisions about adjuvant therapy for women with early breast cancer[END_REF]. A low clinical risk was defined as patients with a 10-years survival probability of at least 90%.

According to the Dutch CBO guidelines 2004, a low clinical risk was defined as being above the age of 35 years and having a tumour of grade 1 with size less than 30 mm, grade 2 and size less than 20 mm, or grade 3 with size 10 mm or less [START_REF]Richtlijn Behandeling van het Mammacarcinoom[END_REF][START_REF]Kwaliteitsinstituut voor de Gezondheidszorg CBO Vereniging voor Integrale Kankercentra[END_REF].

Each of the clinical risk indexes was combined with the prognosis signature, resulting in groups of patients as clinical low risk-good prognosis signature, clinical low risk-poor prognosis signature, clinical high risk-good prognosis signature, and clinical high risk-poor prognosis signature patients.

Statistical analysis

Calculations were performed using the S? (version 6.2) statistical package and SPSS (version 14.0 for Windows). The differences in patients and tumour characteristics between the 70-gene prognosis signature good and poor patients were tested using the Pearson Chi-Square test. In case of ordinal variables (year of diagnosis, age, and histological grade) with more than two groups, we tested for trends (using Cochran-Armitage test). The level of agreement between the guidelines and the prescription of adjuvant systemic treatment was expressed by means of a Cohen's kappa. A kappa of one indicates perfect agreement, where a kappa of zero indicates no agreement. The two main endpoints were: time from surgery to distant metastasis as first event, which was the endpoint used to identify the 70-gene signature [START_REF] Van't Veer | Gene expression profiling predicts clinical outcome of breast cancer[END_REF]; and overall survival, defined as time from surgery to death.

In the analysis of distant metastasis, patients whose first failure was distant metastasis were counted as failures; all other patients were censored at the date of their last followup, death, contralateral breast cancer, other second primary or loco-regional recurrence. P-values have not been corrected for multiple comparisons.

Metastasis as first event

Unadjusted metastasis-free curves were drawn using the method of Kaplan-Meier [START_REF] Kaplan | Nonparametric estimation from incomplete observations[END_REF] and compared using the Wald test based on Cox's Proportional Hazard regression analysis [START_REF] Cox | Regression models and lifetables[END_REF]. Values are expressed as percentages ±SE, calculated according to the method of Tsiatis [START_REF] Klein | Small sample moments of the estimators of the variance of the Kaplan-Meier and Nelson-Aalen estimators[END_REF].

Proportional hazard regression analysis was also used to adjust the association between the prognosis signature and metastasis for other variables. For this adjustment we chose on a-priori grounds the variables used in the four mentioned clinical risk assessment methods for the prediction of recurrence. These are age, tumour diameter, histological grade and oestrogen receptor status. We added adjuvant chemo-and hormonal treatment and clinical risk as well. Categorical variables (centre, year of surgery, tumour diameter, Grade and clinical risk) were used as factors, using the first category as reference.

Logistic regression analysis was performed to estimate the additional value of the 70-gene prognosis signature to traditional prognostic clinicopathological risk factors (age, tumour size, histologic grade, oestrogen receptor, progesterone receptor, and HER2-receptor). The traditional prognostic model, based on the above mentioned prognostic factors, was compared to a new model also including the prognosis signature. Time dependent receiver operator characteristic (ROC-) curves were computed for both distant metastasis as first event and overall survival by using the 70-gene prognosis signature outcome and the above mentioned prognostic factors.

Update follow-up initial validation series

The follow-up of the 151 node-negative patients of the initial validation series was updated until January 2005, blinded to the 70-gene prognosis signature and clinical risks [START_REF] Van De Vijver | A gene-expression signature as a predictor of survival in breast cancer[END_REF]. The same statistical analyses were performed as described above. In the logistic regression analysis only age, tumour size, histologic grade, and oestrogen receptor were used as prognostic parameter in the traditional model while progesterone receptor and HER2 status were not available for this dataset.

Results

NKI-RdGG validation series

For 123 node-negative invasive breast cancer patients (NKI-RdGG validation series) a 70-gene prognosis profile was obtained. Patient characteristics are summarized in Table 1. Median follow-up was 5.8 years (range: 0.1-9.0), mean age was 47 years (range: 27-55) and the mean tumour diameter was 20 mm (range: 5-50). Adjuvant systemic treatment was administered to 37% (45/123) of the patients; 15% (18/123) chemotherapy, 11% (14/123) endocrine treatment and 11% (13/123) both. During follow-up 30 cancer-related first events occurred [14 distant metastases (47%), 5 local recurrences (17%), 3 regional recurrences (10%), 4 contralateral breast cancers (13%), and 4 second primary cancers (13%; other types of cancer)]. Eighteen patients died (17 breast cancer related and 1 due to another cancer).

Fifty-two percent of the patients had a tumour with a good and 48% with a poor prognosis signature. The poor prognosis signature was associated with larger tumours, a high histological grade, and a negative oestrogen and progesterone receptor; these patients received more often adjuvant systemic treatment (Table 1). In the sub-group of patients that did receive any form of adjuvant systemic treatment (45; 37%), 42% (19/45) had a good and 58% (26/45) a poor prognosis signature (data not shown).

Clinical risk and prognosis signature

According to the Adjuvant! Online, the NPI, the St Gallen guidelines and Dutch CBO guidelines 2004, respectively 76, 51, 87 and 55% of the patients were assessed as moderate or high risk (Table 1). In 38% of the patients (47/123; kappa 0.25) the clinical risk according to the Adjuvant! Online was discordant with the prognosis signature. Six patients (5%) were Adjuvant! Online clinical low risk and had a poor prognosis signature; and 41 patients (33%) were clinical high risk and had a good prognosis signature (Suppl. Table 1). If CBO guidelines 2004, St Gallen guidelines or NPI were used, respectively in 30% (37/123; kappa 0.40), 41% (50/123; 0.21) and 26% (32/123; kappa 0.48) of the patients the risk assessment was discordant with prognosis signature. Figure 1a-d show the Kaplan-Meier curves with distant metastasis as first event and overall survival, respectively, for prognosis signature and clinical risk using Adjuvant! Online for the NKI-RdGG series.

At 5 years, the probability of remaining free of distant metastasis (as first event) was 98% (SE 2%) for good and 78% (SE 6%) for poor prognosis signature patients with an estimated hazard ratio (HR) of 5.7 (95% CI: 1.6-20; P = 0.007; Table 2) in the univariate analysis. The 5-year overall survival probability was 97% (SE for good and 82% (SE 5%) for poor prognosis signature patients with an estimated HR of 3.4 (95% CI: 1.2-9.6; P = 0.021). In this series none of the four clinical risk indexes did have prognostic power (Table 2) for distant metastasis as first event or overall survival; and neither did the combined clinical risk-prognosis signatures for each of the clinical risk indexes (data not shown).

Multivariate analysis with clinical risk indexes

The prognostic value of the prognosis signature remained strongly significant in the multivariate analysis for distant metastasis as first event and overall survival when adjusted for the four clinical risk indexes. Its HR with distant metastasis as first event in the univariate analysis was 5.7 and ranged in the multivariate analysis from 4.8 to 5.8 dependent on what index was used (Table 3A). For overall survival the univariate HR was 3.4 and in the multivariate analysis it ranged from 2.7 to 3.3. If the performance of these four clinical risk indexes was adjusted for the prognosis signature, none of the clinical risk indexes were found to have an independent association with distant metastasis as first event or with overall survival (Table 3B).

Logistic regression analysis and ROC curves

In the logistic regression analysis, comparing the traditional prognostic model (based on age, tumour size, Note: percentages in tables and texts may not add up to 100% or add up over 100% due to rounding off grade, oestrogen receptor, progesterone receptor, and HER2 status) in the presence versus the absence of the prognosis signature, the change in log likelihood for distant metastasis was 5.2 (P = 0.023). The area under the ROC curve (AUC; Fig. 2) for the predicted probability in the absence and presence of the prognosis signature was 0.66 (95% CI 0.50-0.82) and 0.75 (95% CI 0.61-0.89), respectively. The AUC for the prognosis signature alone was 0.69 (95% CI 0.56-0.82). Although the confidence intervals overlap there appears to be additional value for the model in which the prognosis signature is combined with the traditional prognostic factors, as shown by the larger AUC. The ROC curve for overall survival showed that the prognosis signature did not add value to the prognostic model as the AUC's did not differ substantially and 95% confidence intervals overlap almost completely for the different models (Fig. 2). The AUC of the predicted probability of the model in the absence and presence of the prognosis signature were, respectively 0.69 (95% CI 0.56-0.82) and 0.69 (95% CI 0.55-0.84); the AUC for the prognosis signature was 0.64 (95% CI 0.51-0.77).

Updated initial validation series

By updating the median follow-up from 7.3 years to 10.2 years (range: 0.7-21.3), for the 151 node-negative patients of the initial validation series [START_REF] Van De Vijver | A gene-expression signature as a predictor of survival in breast cancer[END_REF] 8 additional deaths occurred. During the 10.2 years of follow-up 74 breast cancer-related first events and 51 deaths (48 breast cancer related and 3 deaths due to other causes) occurred. The first events were: 48 distant metastases (65%), 8 local recurrences (11%), 6 regional recurrences (8%), 10 contralateral breast cancers (14%), and 2 second primary breast cancers (3%).

Poor prognosis signature patients in this series were more often high risk patients according to the clinical indexes mentioned above than the good prognosis signature patients (Table 1). 3a-d show the Kaplan-Meier curves of distant metastasis as first event and overall survival for the prognosis signature and clinical risk index (Adjuvant! Online). At 10 years, the distant metastasis (as first event) free survival was 86% (SE 5%) for good and 50% (SE 6%) for poor prognosis signature patients with an estimated HR of 5.5 (95% CI: 2.5-12, P \ 0.001; univariate analysis; Table 4). The estimated 10-year overall survival was 94% (SE 3%) in good and 51% (SE 5%) in poor prognosis signature patients with an estimated HR of 10.7 (95% CI: 3.9-30; P \ 0.001; univariate analysis).

In this updated initial validation series the NPI clinical risk had prognostic power for both distant metastasis as first event and overall survival (Table 4). The clinical risk using Adjuvant! Online (90%) and Dutch CBO guidelines 2004 only had prognostic value for overall survival. The combined clinical risk-prognosis signatures (for each of the clinical risk indexes) had a performance in between the clinical risk indexes and the prognosis signature (Table 4 and data not shown).

The performance of the prognosis signature adjusted for the four clinical risk indexes using distant metastasis as 3C). If the performance of these clinical risk indexes was adjusted for the prognosis signature, none of the clinical risk indexes retained an independent association with distant metastasis as first event or overall survival (with the exception of the CBO guidelines 2004 regarding overall survival; Table 3D).

In the logistic regression analysis the traditional prognostic model in the presence versus the absence of the prognosis signature, the change in log likelihood for distant metastasis was 15.8 (P \ 0.001). The AUC of the ROC curve (Fig. 4) for the predicted probability in the absence and presence of the prognosis signature was, respectively 0.70 (95% CI 0.61-0.79) and 0.76 (95% CI 0.68-0.85). The AUC for the prognosis signature alone was 0.68 (95% CI 0.60-0.77). Although the confidence intervals overlap there appears to be additional value for the model in which the prognosis signature was combined with the traditional prognostic factors as the AUC was larger. The change in log likelihood for overall survival was 19.7 (P \ 0.001) and showed that the prognosis signature adds additional value to the prognostic model. The AUC for overall

ROC Curve for overall survival

Fig. 4 Receiver operating characteristic (ROC) curves of prognosis signature, prognostic factors and both for the updated initial validation series. Prognostic factors used in model were age, tumour size, histologic grade, and oestrogen receptor for distant metastasis as first event (left) and overall survival. In both curves the diagonal segments are produced by ties (right). AUC = area under the curve of the predicted probability of the model in the absence and presence of the prognosis signature were, respectively 0.74 (95% CI 0.66-0.82) and 0.82 (95% CI 0.75-0.89); the AUC for the prognosis signature is 0.74 (95% CI 0.66-0.82).

Discussion

In the NKI-RdGG validation-series of relatively recently diagnosed breast cancer patients as well as in the updated initial validation series, the 70-gene prognosis signature was a prognostic factor for distant metastasis as first event (respectively HR 5.7 and 5.5) and overall survival (respectively HR 3.4 and 10.7) in the univariate analysis. After adjustment for commonly used clinical risk indexes, the prognosis signature remained an independent prognostic factor. In contrast, if the clinical risk indexes were adjusted for the prognosis signature, their prognostic value was lost. The logistic regression analysis suggests that the prognosis signature would have additional value in clinical practice if the traditional prognostic factors would be combined with the prognosis signature. The confidence intervals did overlap, probably because of small sample size in both series and relatively short follow up in the NKI-RdGG validation series.

Buyse et al. had previously confirmed the prognostic value of the 70-gene prognosis signature in the first international and independent validation study in 2006 (TRANSBIG validation; N = 302; median follow-up: 13.6 years; Suppl. Table 3) [START_REF] Buyse | Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer[END_REF]. These patients had been selected for not having received any form of adjuvant systemic treatment, in order to be able to study the true prognostic value not confounded by adjuvant systemic treatment and were diagnosed between 1990 and 1998. The HR of the prognosis signature for distant metastasis free survival was 2.3 (P = 0.002; univariate) and for overall survival it was 2.8 (P \ 0.001). Remarkably, in this series classical clinicopathological factors did not have prognostic value for distant metastases as first event and overall survival (univariate analysis); the only exception was the oestrogen receptor (respectively, HR 2.2 (95% CI 1.4-3.5; P = 0.001) and HR 2.4 (95% CI 1.5-3.7; P \ 0.001). In our updated initial validation series with comparable follow-up these factors did perform as prognostic factors for both distant metastasis and overall survival. The classical clinicopathological factors did not have prognostic value in the NKI-RdGG validation series. This was probably due to the small number of events in this series. In this series 37% of the patients received some form of adjuvant systemic treatment. The proportion of poor signature patients (58%) in this treated group was slightly larger than the good prognosis signature patients (42%; P \ 0.01).

Interestingly, if we assume that the proportional risk reduction of adjuvant systemic treatment is similar for both poor and good prognosis signature patients, then the prognostic value of the prognosis signature would even have been higher with only untreated patients.

In this validation study only 7 and 5% of the patients with oestrogen-receptor-negative tumours in the NKI-RdGG and updated initial validation series, respectively, had a good prognosis signature. The prognosis signature has less discriminative value regarding prognosis in this subgroup. Additional prognostic tests might be needed for these patients.

In breast cancer, two other prognostic gene expression profiles have been reported, the 76-gene signature [START_REF] Wang | Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer[END_REF][START_REF] Foekens | Multicenter validation of a gene expressionbased prognostic signature in lymph node-negative primary breast cancer[END_REF][START_REF] Wang | A 76-gene signature to predict distant metastasis in lymph-node negative breast cancer[END_REF] and the 21-gene recurrence score (Oncotype DX Ò assay) [START_REF] Paik | A multigene assay to predict recurrence of tamoxifen-treated, nodenegative breast cancer[END_REF][START_REF] Habel | A population-based study of tumor gene expression and risk of breast cancer death among lymph node-negative patients[END_REF][START_REF] Esteva | Prognostic role of a multigene reverse transcriptase-PCR assay in patients with node-negative breast cancer not receiving adjuvant systemic therapy[END_REF]. The 76-gene signature identified nodenegative breast cancer patients at high risk of recurrence. The 21-gene recurrence score quantified the likelihood of distant recurrence in tamoxifen-treated patients with nodenegative, oestrogen-receptor positive breast cancer. A recent comparison of the 70-gene signature and an estimation of the 21-recurrence score using microarray data suggests that these two prognostic signatures identify partly overlapping series of patients [START_REF] Fan | Concordance among gene-expression-based predictors for breast cancer[END_REF].

For these promising gene expression profiles, prospective studies will ultimately show that indeed a survival benefit can be achieved while avoiding unnecessary adjuvant systemic treatment. The 70-gene prognosis signature has been used in the Dutch RASTER-study (Netherlands Cancer Institute in collaboration with the Dutch Health Care Insurance Board) [START_REF] Bueno-De-Mesquita | Use of 70-gene signature to predict prognosis of patients with node-negative breast cancer: a prospective community-based feasibility study (RASTER)[END_REF] and is currently used in the international MINDACT-trial (TRANSBIG consortium in collaboration with the European Organisation for Research and Treatment of Cancer) [START_REF] Bogaerts | Gene signature evaluation as a prognostic tool: challenges in the design of the MINDACT trial[END_REF].

Conclusion

In conclusion, the results of this representative independent validation of the 70-gene signature are in agreement with our previous findings [START_REF] Van De Vijver | A gene-expression signature as a predictor of survival in breast cancer[END_REF] and those of the TRANSBIG validation study [START_REF] Buyse | Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer[END_REF] now also including more recently diagnosed patients. It provides evidence that this prognosis signature is an independent prognostic factor in nodenegative breast cancer and outperforms current traditional clinical indexes. Using this prognosis signature in addition to the traditional clinical indexes may improve the selection of patients benefiting from adjuvant systemic treatment.

Fig. 1

 1 Fig. 1 Kaplan-Meier Curves of metastasis as first event (a, b) and overall survival (c, d) of the 70-gene prognosis signature (good prognosis signature versus poor prognosis signature) (a, c) and Adjuvant! Online (low risk versus high risk) (b, d) in the NKI-RdGG validation series

Fig. 2

 2 Fig. 2 Receiver operating characteristic (ROC-) curves of prognosis signature, prognostic factors and both for the NKI-RdGG validation series. Prognostic factors used in model were age, tumour size, histologic grade, oestrogen receptor, progesterone receptor, and HER2 status for distant metastasis as first event (left) and overall survival (right). AUC = area under the curve

Table 1

 1 Relations of clinical and pathological variables with 70-gene prognosis signature in the NKI-RdGG validation series and the updated

	initial validation series
		NKI-RdGG validation series		Updated initial validation series
		Total	Good	Poor	P-value	Total	Good	Poor	P-value
		n (%)	n (%)	n (%)		n (%)	n (%)	n (%)
	Total	123	64 (52%)	59 (48%)	-	151	60 (40%)	91 (60%)
	Hospital				0.15
	RdGG	52 (42%)	31 (60%)	21 (40%)	-	-	-	-	-
	NKI	71 (58%)	33 (46%)	38 (54%)	-	-	-	-	-
	Age				0.19				0.072
	B35 years	12 (10%)	4 (33%)	8 (67%)	-	13 (9%)	2 (15%)	11 (85%)	-
	36-40 years	10 (8%)	7 (70%)	3 (30%)	-	32 (21%)	9 (28%)	23 (72%)	-
	41-45 years	20 (16%)	8 (40%)	12 (60%)	-	41 (27%)	21 (51%)	20 (49%)	-
	46-50 years	41 (33%)	21 (51%)	20 (49%)	-	51 (34%)	22 (43%)	29 (57%)	-
	[50 years	40 (33%)	24 (60%)	16 (40%)	-	14 (9%)	6 (43%)	8 (57%)	-
	Type of surgery				0.020
	Breast conserving	89 (72%)	54 (61%)	35 (39%)	-	-	-	-	-
	Mastectomy	34 (28%)	10 (29%)	24 (71%)	-	-	-	-	-
	Tumour size (pTNM)				0.016				0.032
	pT1 (B20 mm)	76 (62%)	46 (61%)	30 (39%)	-	82 (54%)	39 (48%)	43 (52%)	-
	pT2 (21-50 mm)	47 (38%)	18 (38%)	29 (62%)	-	69 (46%)	21 (30%)	48 (70%)	-
	Histologic grade				P \ 0.001				P \ 0.001
	1 (well)	20 (16%) 18 (90%) 50 (41%) 11 (22%)	39 (78%)	-	71 (47%)	10 (14%)	61 (86%)	-
	Oestrogen receptor				P \ 0.001				P \ 0.001
	Negative	29 (24%)	2 (7%)	27 (93%)	-	42 (28%)	2 (5%)	40 (95%)	-
	Positive	94 (76%)	62 (66%)	32 (34%)	-	109 (72%)	58 (53%)	51 (47%)	-
	Progesterone receptor				P \ 0.001
	Negative	39 (32%)	6 (15%)	33 (85%)	-	-	-	-	-
	Positive	84 (68%)	58 (69%)	26 (31%)	-	-	-	-	-
	HER2-receptor				0.067
	Negative	113 (93%)	61 (54%)	52 (46%)		-	-	-	-
	Positive	9 (7%)	2 (22%)	7 (78%)		-	-	-	-
	Missing: 1 (1%)
	Adjuvant systemic treatment				P \ 0.001				0.11
	None	78 (63%)	45 (58%)	33 (42%)	-	141 (93%)	57 (40%)	84 (60%)	-
	Chemotherapy	18 (15%) -	-
	St. Gallen guidelines				P \ 0.001				P \ 0.001
	Low risk	16 (13%)	15 (94%)	1 (6%)	-	19 (13%)	15 (79%)	4 (21%)	-
	Intermediate/high risk	107 (87%)	49 (46%)	58 (54%)	-	132 (87%)	45 (34%)	87 (66%)	-
	Nottingham Prognostic Index				P \ 0.001				P \ 0.001
	Low risk	60 (49%)	46 (77%)	14 (23%)	-	67 (44%)	44 (66%)	23 (34%)	-
	Moderate/high risk	63 (51%)	18 (29%)	45 (71%)	-	84 (56%)	16 (19%)	68 (81%)	-
	Dutch CBO guidelines 2004				P \ 0.001				P \ 0.001
	Low risk	55 (45%)	41 (75%)	14 (25%)	-	56 (37%)	36 (64%)	20 (36%)	-
	High risk	68 (55%)	23 (34%)

Table 1

 1

	continued
		NKI-RdGG validation series		Updated initial validation series
		Total	Good	Poor	P-value	Total	Good	Poor	P-value
		n (%)	n (%)	n (%)		n (%)	n (%)	n (%)
	Adjuvant! Online				0.001				P \ 0.001
	Low risk	29 (24%)	23 (79%)	6 (21%)	-	37 (25%)	23 (62%)	14 (38%)	-
	High risk	94 (76%)	41 (44%)	53 (56%)	-	114 (75%)	37 (32%)	77 (68%)	-

RdGG, Reinier de Graaf Hospital, Delft, The Netherlands; NKI, Netherlands Cancer Institute, Amsterdam, The Netherlands. NPI, Nottingham Prognostic Index

Table 2

 2 Results of univariate analysis with distant metastasis as first event and overall survival for patients of the NKI-RdGG validation series

	Variable	Unit	Distant metastasis as first event	Overall survival
			HR	95% CI	P-value	HR	95% CI	P-value
	70-gene prognosis signature	Poor vs. good	5.7	1.6-20	0.007	3.4	1.2-9.6 0.021
	Institute	RdGG vs. NKI	0.81	0.30-2.2	0.68	1.2	0.46-3.3 0.69
	Year of surgery	1997 vs. 1996	0.37	0.08-1.8	0.15	0.35 0.07-1.6 0.32
		1998 vs. 1996	0.25	0.05-1.2		0.56 0.17-1.9
		1999 vs. 1996	0.29	0.08-1.1		0.35 0.09-1.3
	Surgery	Ablation vs. BCT	2.3	0.86-6.2	0.097	3.6	1.4-9.2 0.007
	Age	Per year	1.0	0.93-1.1	0.99	0.93 0.94-1.1 0.93
	Diameter	pT2 vs. pT1	1.0	0.37-2.8	0.99	0.89 0.33-2.4 0.83
	Histologic grade	Moderate vs. good	0.86	0.16-4.7	0.18	2.5	0.26-19	0.057
		Poor vs. good	2.4	0.52-11		6.1	0.80-47
	Oestrogen receptor	Pos. vs. neg.	0.46	0.17-1.2	0.14	0.40 0.15-1.0 0.057
	Progesterone receptor	Pos. vs. neg.	0.56	0.21-1.5	0.25	0.52 0.21-1.3 0.18
	Adjuvant systemic treatment None vs. any adjuvant systemic treatment a 0.64	0.21-2.0	0.44	0.80 0.28-2.3 0.67
	St Gallen guidelines	Intermediate/high vs. low risk	2.5	0.34-19	0.37	3.0	0.40-22	0.29
	NPI	Moderate vs. low risk	2.2	0.78-6.5	0.14	2.8	0.99-7.8 0.053
	CBO guidelines 2004	High vs. low risk	1.8	0.64-5.3	0.26	2.3	0.84-6.6 0.11
	Adjuvant! Online	High vs. low risk	4.6	0.61-35	0.14	2.5	0.59-11	0.22
	Combined clinical-prognosis	Low-poor vs. low-good	0.00	0.00-?	0.049	0.00 0.00-?	0.10
	signature risk b	High-good vs. low-good	1.1	0.10-12		0.83 0.14-5.0
		High-poor vs. low-good	6.3	0.83-49		3.3	0.75-15
							NPI, Nottingham
	Prognostic Index. BCT, Breast conserving treatment

a Adjuvant systemic treatment may consist of chemotherapy, endocrine treatment or both b Combined clinical and prognosis signature risk using Adjuvant! Online and 70-gene prognosis signature. Risk stated before dash is clinical risk. After the dash the prognosis signature is stated Note: percentages in tables and texts may not add up to 100% or add up over 100% due to rounding off RdGG, Reinier de Graaf Hospital in Delft, the Netherlands; NKI, Netherlands Cancer Institute in Amsterdam, the Netherlands.

Table 3

 3 Results of multivariate analysis with distant metastasis as first event and overall survival. Performance of prognosis signature adjusted for prognostic clinical risks in the NKI-RdGG validation series (A) and updated initial validation series (C); and performance of other prognostic clinical risk indexes adjusted for prognosis signature in the NKI-RdGG validation series (B) and updated initial validation series (D)

	Adjusted for		Performance of 70-gene prognosis signature (poor vs. good)
			Distant metastasis as first event	Overall survival
			HR	95% CI	P-value	HR	95% CI	P-value
	(A) NKI-RdGG validation series
	Adjuvant! Online		4.8	1.3-17	0.018	3.0	1.0-8.9	0.044
	St. Gallen guidelines		5.8	1.5-22	0.011	3.1	1.0-9.2	0.043
	Nottingham Prognostic Index	5.4	1.4-21	0.015	2.7	0.87-8.1	0.086
	Dutch CBO guidelines 2004	5.6	1.5-21	0.010	2.9	0.98-8.6	0.055
	Predictor		Performance of predictor (high vs. low risk) adjusted for 70-gene prognosis signature
			Distant metastasis as first event		Overall survival
			HR	95% CI	P-value	HR	95% CI	P-value
	(B) NKI-RdGG validation series
	Adjuvant! Online		2.5	0.32-20	0.38	1.65	0.36-7.7	0.52
	St. Gallen guidelines		0.95	0.11-8.4	0.97	1.64	0.20-14	0.65
	Nottingham Prognostic Index	1.1	0.36-3.5	0.83	1.90	0.62-5.8	0.26
	Dutch CBO guidelines 2004	1.0	0.34-3.2	0.94	1.66	0.56-4.9	0.36
	Adjusted for	Performance of 70-gene prognosis signature (poor vs. good)
		Distant metastasis as first event		Overall survival
		HR	95% CI	P-value	HR	95% CI	P-value
	(C) Updated initial validation series
	Adjuvant! Online	5.3	2.4-12	P \ 0.001	9.6	3.4-27	P \ 0.001
	St. Gallen guidelines	5.2	2.3-12	P \ 0.001	9.9	3.5-28	P \ 0.001
	NPI	4.3	1.8-10	0.001	8.5	2.9-25	P \ 0.001
	CBO guidelines 2004	4.9	2.1-11	P \ 0.001	8.3	2.9-24	P \ 0.001
	Predictor	Performance of predictor (high vs. low risk) adjusted for 70-gene prognosis signature
		Distant metastasis as first event		Overall survival
		HR		95% CI	P-value	HR	95% CI	P-value
	(D) Updated initial validation series
	Adjuvant! Online	1.2		0.58-2.5	0.61	1.7	0.72-4.1	0.22
	St. Gallen guidelines	1.4		0.41-4.6	0.61	1.6	0.38-6.8	0.53
	NPI	1.9		0.94-3.7	0.073	1.7	0.86-3.3	0.13
	CBO guidelines 2004	1.5		0.74-2.9	0.27	2.2	1.0-4.8	0.044
	HR: hazard ratio; 95% CI: 95% confidence interval

Table 4

 4 Results of univariate analysis with distant metastasis as first event and overall survival in the updated initial validation series[START_REF] Van De Vijver | A gene-expression signature as a predictor of survival in breast cancer[END_REF] Adjuvant systemic treatment may consist of chemotherapy, endocrine treatment or both b Combined clinical and prognosis signature risk using Adjuvant! Online and 70-gene prognosis signature. Risk stated before dash is clinical risk. After the dash the prognosis signature is stated Note: percentages in tables and texts may not add up to 100% or add up over 100% due to rounding off NPI, Nottingham Prognostic Index

	Variable	Unit	Distant metastasis as first event	Overall survival
			HR	95% CI	P-value	HR 95% CI	P-value
	70-Gene prognosis signature Poor vs. good	5.5	2.5-12	P \ 0.001	10.7	3.9-30	P \ 0.001
	Age	Per year	0.96	0.92-1.0	0.18	0.96 0.91-1.0	0.091
	Diameter	pT2 vs. pT1	1.9	1.1-3.3	0.014	2.0	1.1-3.5	0.015
	Histologic grade	Moderate vs. good	3.1	1.0-9.2	0.040	5.9	1.3-26	0.010
		Poor vs. good	5.4	1.9-15		11	2.7-47
	Oestrogen receptor	Pos. vs. Neg.	0.58	0.33-1.0	0.058	0.37 0.21-0.65 0.010
	Adjuvant systemic treatment None vs. any adjuvant systemic treatment a 1.2	0.72-1.9	0.55	1.2	0.78-2.0	0.36
	St Gallen guidelines	Intermediate/high vs. low risk	2.6	0.80-8.3	0.11	4.1	0.99-17	0.052
	NPI	Moderate vs. low risk	3.1	1.6-5.9	P \ 0.001	3.4	1.8-6.6	P \ 0.001
	CBO guidelines 2004	High vs. low risk	2.4	1.2-4.5	0.011	3.9	1.9-8.4	P \ 0.001
	Adjuvant! Online	High vs. low risk	1.7	0.84-3.6	0.14	2.8	1.2-6.6	0.017
	Combined clinical-prognosis	Low-poor vs. low-good	3.9	0.99-16	P \ 0.010	?	0.00-?	P \ 0.004
	signature risk b	High-good vs. low-good	0.9	0.19-3.9		?	0.00-?
		High-poor vs. low-good	5.3	1.6-17		?	0.00-?

a

Breast Cancer Res Treat (2009) 117:483-495

(10%) -34 (23%) 27 (79%) 7 (21%) -2 (intermediate) 53 (43%

) 35 (66%) 18 (34%) -46 (30%) 23 (50%) 23 (50%) -3 (poor)

(6%) 17 (94%) -4 (3%) -4 (100%) -Endocrine therapy 14 (11%) 9 (64%) 5 (36%) -4 (3%) 1 (25%) 3 (75%) -Both 13 (11%) 9 (69%) 4 (31%) -

(1%) 2 (100%)

(66%) -95 (63%) 24 (25%) 71 (75%) -Breast Cancer Res Treat (2009) 117:483-495

Acknowledgments

We are grateful to P.M. Ravdin (University of Texas, Health Science Centre, San Antonio (Texas), USA) for helpful advice with the use of Adjuvant! Online. In addition, we would like to thank G. Sonke for his help in the statistical analyses.

Competing interest statement Dr. L.J. Van Veer, Dr. M.J. Van de Vijver and A.A.M. Hart are named inventors on a patent application for the 70-gene signature used in this study. Dr. Van't Veer report being shareholder in and (part time) employed by Agendia, the commercial company that markets the 70-gene signature as Mam-maPrint Ò . A.N. Floore and A.M. Glas report being employed by Agendia.