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Cancer Therapy Vol 6, page 773

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Cancer Therapy Vol 6, 773-782, 2008

Molecular prognostic factors: clinical implications

in patients with breast cancerReview Article

Giuseppe Tonini*, Maria Elisabetta Fratto, Gaia SchiavonDepartment of Medical Oncology, University Campus Bio-Medico, Rome

__________________________________________________________________________________

*Correspondence:Giuseppe Tonini MD, PhD., Medical Oncology, University Campus Bio-Medico of Rome, Via Alvaro del Portillo,

200, 00128 Rome, Italy; Tel :0039-06-225411201; Fax: 0039-06-934; e-mail: [email protected]

Key words: Prognostic factors, molecular classification, breast cancer, gene expression profiling, Oncotype Dx

Abbreviations: cyclophosphamide, methotrexate, and 5-fluorouracil, (CMF); estrogen receptor !, (ER); Microarray for Node-Negative

Disease may Avoid Chemotherapy, (MINDACT); National Surgical Adjuvant Breast and Bowel Project, (NSABP); plasminogenactivator inhibitor, (PAI-1); Recurrence Score, (RS); urokinase plasminogen activator, (uPA)

Received: 17 April 2008; Revised: 15 July 2008

Accepted: 4 August 2008; electronically published: October 2008

SummaryMany molecular markers have been recently identified as prognostic and predictive factors in patients with breast

cancer. Histological type, grading, tumor size, lymph node involvement, and estrogen receptor !(ER) and HER-2

receptor status all influence prognosis and the probability of response to systemic therapies. Among other

prognostic factors, p53 mutations and Bcl-2 amplification are associated with higher probability of relapse or death.

Recently, proteomic and gene-expression profiling methods are being explored to quantify the expression of

multiple genes and combine the gene expression measurements into prediction scores that may foretell clinicaloutcome. Among new methods that can measure ER mRNA expression, Oncotype DX represents an important

advance in the diagnosis of ER-positive breast cancers. Several reports have confirmed the prognostic value of this

diagnostic test. Among other multigene prediction scores, the most promising is the Amsterdam Signature, in fact

an on-going trial is evaluating this test to stratify patients that need chemotherapy or not (MINDACT trial). Gene

profiling has also been used to predict metastasis to distant organs. Thanks to gene-expression profiling

technologies, different molecular subtypes of breast cancer associated with different natural histories have been

identified. In fact many studies have shown that the prognosis and chemotherapy sensitivity of the different

molecular subgroups are different. Thanks to these new technologies, new perspectives are opening. Trials based on

genetic profiling will serve as an important resource for evaluating new molecular signatures providing a novel,

personalized dimension for the treatment and care of breast cancer patients. Finally further studies are needed to

improve current prognostic and treatment predictive tools.

I. IntroductionBreast cancer is an heterogeneous disease and the

existing classifications are not fully associated with the

varied clinical course of this disease. Histological type,

grading, tumor size, lymph node involvement, estrogen

receptor ! (ER) and HER-2 receptor status all influence

prognosis and the probability of response to systemic

therapies, having also a predictive value. These clinical

variables can be combined into multivariate outcome

prediction models, as The Nottingham Prognostic Index

and Adjuvant! Model for early breast cancer (DEredita et

al, 2001; Olivotto et al, 2005). However, the substantial

variability in disease outcome within each risk category

means that the outcome prediction models used do not

include all the possible variables of patients with breast

cancer. The different clinical course of patients with

histologically identical tumors may be the result of

molecular differences among cancers. So detailed

molecular analysis of the cancer could give information on

prognosis and prediction. Recently, proteomic and gene-

expression profiling methods are being explored as

diagnostic tools. These tests quantify the expression of

multiple genes and combine the gene expression

measurements into prediction scores that may foretell

clinical outcome more accurately than any of the genes

alone (Pusztai et al, 2006).

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II. Traditional prognostic and

predictive factors in breast cancerTraditionally, prognostic factors provide prospective

information about overall patient prognosis, while

predictive factors provide information about the chance of

patient response to treatment. Some markers may be both

prognostic and predictive. Moreover, a positive prognosticfactor may have a negative predictive value and vice

versa, so accurate studies are needed to understand the

right strategy for patients with breast cancer.

A. Molecular markers as prognostic

factorsThe most significant prognostic factor for patients

with breast cancer is the presence or absence of axillary

lymph node involvement. Furthermore, there is a direct

relationship between the number of involved axillary

nodes and the risk for distant recurrence, so patients with

node-negative status (including the sentinel-node-negative

classification) are patients with low-risk disease (Early

Breast Cancer Trialists Collaborative Group (EBCTCG), 2005).

However, the St Gallen (Cody et al, 2004) treatment

guidelines were updated in 2005 to include the

intermediate-risk category, so the presence of positive

axillary nodes in the absence of other high-risk

characteristics no longer defines high-risk disease (Cody et

al, 2004). Other new adverse prognostic factors were also

accepted: HER2/ neu overexpression, grading, tumor size,

peritumoral vascular invasion and age of the patients.

Table 1 shows the risk categories for patients with

operable breast cancer established at the 2005 St Gallen

meeting. Combining these factors it is possible dividepatients in three categories: low, intermediate and high

risk. According to the category it is possible to make a

choice regarding the right treatment strategy. Although

several novel prognostic factors, including proliferation

markers, such as S-phase fraction and Ki-67, lymph node

micrometastases (Truong et al, 2005, urokinase plasminogen

activator (uPA)/plasminogen activator inhibitor (PAI-1)

system expression (Look et al, 2002), cyclin-E

overexpression have been proposed, more studies are

needed to validate these markers. A recent meta-analysis

showed the prognostic value of Ki-67 in early breast

cancer. 68 studies were identified and 46 studies including

12 155 patients were considered for evaluation of

correlation between KI-67/ MIB-1 + and Disease-Free

Survival and Overall Survival. Ki-67/MIB-1 + resulted

associated with higher probability of relapse (P/=4)

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However, conclusions regarding the prognostic value of

cyclin E in the published literature are mixed and

additional properly designed studies are required to

evaluate whether this marker has clinical utility, especially

in the setting of no adjuvant chemotherapy. Finally, Harris

et al. published the American Society of Clinical

Oncology 2007 Update of recommendations for the use of

tumor markers in breast cancer. Among the thirteen

categories considered, CA 15-3, CA 27.29,

carcinoembryonic antigen, estrogen receptor, progesterone

receptor, human epidermal growth factor receptor 2,

urokinase plasminogen activator, plasminogen activator

inhibitor 1 showed evidence of clinical utility and were

recommended. On the contrary DNA/ploidy by flow

cytometry, p53, cathepsin D, cyclin E, proteomics, certain

multiparameter assays, detection of bone marrow

micrometastases and circulating tumor cells demonstrated

insufficient evidence to support routine use in clinical

practice (Harris et al, 2007). Moreover, Mc Shane and

colleagues suggested guidelines to provide information

about the tumor markers studies, in fact the results

presented for each tumor marker often are in contradiction.

The goal of these guidelines is to evaluate the studies,

understand the usefulness of the data, assessing the value

of the conclusions (McShane et al, 2006). Concluding, more

than 100 individual factors have been investigated and

reported in the literature. However, few of these factors

have found their way into clinical application as

prognostic tools, or contributed greatly to understanding of

tumor biology.

B. Molecular markers as predictive

factors of response/resistance tochemotherapy

Chemoresistance is the main obstacle to successful

therapy in cancer patients. When evaluating a new

prognostic factor, its potential predictive role should be

considered. In fact, a negative prognostic factor may have

a positive predictive value. For example p53 mutations

and HER-2 overexpression identify patients with a poor

prognosis (Carreo et al, 2002), both are also associated

with (although not fully predictive for) responsiveness to

specific treatments (Geisler et al, 2001). Moreover, even if

the identification of reliable predictive factors has the

potential to spare patients from ineffective treatments and

unnecessary side effects, it is difficult to find a factor that

may guarantee therapeutic success. For example, while ER

negativity is associated with lack of response to endocrine

therapy, not all patients with ER+ tumors may benefit

from such therapy. However, for the first time at the 2005

St Gallen meeting, endocrine responsiveness was

identified as the primary consideration when making

treatment decision, instead of the risk category (Goldhirsch

et al, 2005). Moreover, the 2007 St Gallen meeting

underlined the importance of the molecular assessment of

tumor biology. For those with a highly endocrine-

responsive tumor, for example, the priority is to select the

best possible hormonal therapy with the possibility ofadding chemotherapy. For those with an endocrine non-

responsive tumor, the priority is the best possible

chemotherapy regimen. Similarly, while the absence of

HER2/neu overexpression has been established as a

predictive factor for non-responsiveness to trastuzumab

therapy, not all HER2/neu-overexpressing tumors are

trastuzumab sensitive (Tokunaga et al, 2006), reflecting the

complexity of breast cancer genetics. Another problem is

to evaluate if a predictive factor is a causal factor or just a

co-variate. In fact whereas early studies suggested that

HER2 overexpression predict for sensitivity to

anthracyclines (Paik et al, 1998), recent studies have shown

that TOPO-IIa, not HER2, overexpression predicts for

anthracycline sensitivity in tumors with coamplification of

the two genes (Scandinavian Breast Group Trial 9401, 2006).

Regarding mutations in P53 gene, it has also been shown

to correlate with chemosensitivity in patients with breast

cancer. Specifically, responsiveness to anthracycline- or

mitomycin-containing chemotherapy is reduced by

defective P53 status. In contrast, the efficacy of paclitaxel

seems to be independent of p53 expression. Geisler and

colleagues showed in 2003 that mutations in the P53 gene,

in particular those affecting or disrupting the loop domains

L2 or L3 of the p53 protein, were associated with lack of

response to chemotherapy (P=0.063 for all mutations and

P=0.008 for mutations affecting L2/L3, respectively).

Similarly, the overexpression of HER-2 (P = 0.041), a

high histological grade (P=0.023) and lack of expression

of bcl-2 (P=0.018) predicted chemoresistance to

doxorubicin (Geisler et al, 2001). These results were

confirmed by Di Leo and colleagues in 2007 who

evaluated in 108 patients with metastatic breast cancer

treated with doxorubicin or docetaxel.P53 gene mutations

were observed in 20% of patients. In patients with a

mutated p53, a lower percentage of responders was

observed in the doxorubicin arm (17% VS 27%),compared with the docetaxel arm (50% VS 36%). So p53

gene mutations compromised the efficacy of doxorubicin,

not interfering with the antitumor activity of docetaxel (Di

Leo et al, 2007). However, available data from trials do not

support the use of tumor P53 status when selecting

patients for a given treatment. Another study evaluated the

prognostic relevance of a novel semiquantitative

classification of Bcl2 immunohistochemical expression in

breast cancer. Bcl-2 expression was evaluated in 442

patients, resulting as independent predictor of clinical

outcome in both node-negative and node-positive patients.

In fact patients with Bcl-2 negative immunostaining had

higher probability of relapse (5 times) or death (7 times)(Trer et al, 2007) Thus, more research is necessary before

BCL-2 status and other new prognostic factors can be

accepted as a predictive tool for breast cancer therapy.

III. Genomic profile for prognosis and

predictionArray-based molecular profiling represents a

technological advance in how tumor samples can be

analyzed. In fact microarray technology allows the

simultaneous analysis of many thousands of individual

genes in cell or tissue samples, so the complex biology of

cancer may be studied much more comprehensively thanpreviously possible (Gruvberger-Saal et al, 2006). In recent

years, microarrays have been used extensively to study

molecular differences among different types of cancer.

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One of the most studied tumor types is breast cancer,

because of its complexity and the different behaviour of

the same histological tumors. Relationships between gene

expression and distant metastasis have been identified and

characteristic patterns have emerged, reflecting molecular

differences between tumors. This has allowed the

identification of different subtypes of breast cancer based

on gene profiling. These molecular differences have been

shown to correlate with clinical features, such as survival,

prognosis and treatment sensitivity, as well as traditional

histopathological parameters.

A. Gene profiling and distant metastasisGene profiling has been used to predict metastasis to

distant organs. In fact genes associated with bone

metastases were identified in experimental models (Kang et

al, 2003). In humans, Smid et al. identified a 69-gene panel

associated with bone metastasis in patients with node

negative breast cancer. TFF1 was the most differentially

expressed gene in an independent patient cohort (p =0.0015). A classifier of 31 genes was identified, which in

an independent validation set predicted all tumors

relapsing to bone with a specificity of 50% (Smid et al,

2006). Moreover, Minn and colleagues identified in 2005 a

set of genes that mediates breast cancer metastasis to the

lungs. In fact they found significant differences in lung-

metastases-free survival between patients with or without

expression of lung metastasis gene signature, especially in

patients with poor prognosis (p= 0,008) or ER-negative

(p=0,004) (Minn et al, 2005). Many studies are on-going to

find other genes related to a specific site of metastasis,

giving a contribution for clinical research and then clinical

application.

B. Role of Oncotype Gene Recurrence

Score (RS) assay, Amsterdam and Rotterdam

signature in prognosis and predictionDetermination of ER status is essential to determine

whether a patient is a candidate for endocrine therapy.

This test is routinely used in the clinic, but the existing

immunohistochemical assays have only modest positive

predictive value (30%-60%) for response to hormonal

therapies (Bonneterre et al, 2000; Mouridsen et al, 2001)

Furthermore, there are intra- and interlaboratory variation

in ER results because of differences among laboratories

regarding fixation, antigen retrieval and staining methods

(Rhodes et al, 2000). So more accurate and more reliable

predictors of benefit from hormonal therapy based on

gene-profiling are developing and are necessary for a

greater accuracy in the treatment strategy for patients withhormone-responsive breast cancer. Oncotype DX

(Genomic Health Inc., Redwood City, CA) represents an

important advance in the diagnosis of ER-positive breast

cancers (Figure 1). This RT-PCR-based assay measures

ER mRNA expression in a highly quantitative and

reproducible manner and the expression of several

downstream ER-regulated genes (PR, BCL-2, SCUBE-2)

that may contain information on ER functionality..

Figure 1. Oncotype DX Gene Recurrence Score Assay: This test evaluates the expression of 16 cancer genes and 5 Reference Genes ( A)

providing a Recurrence Score from 0 to 100 (B). Thanks to this score, population is stratified in 3 risk categories (C), useful to

understand patient outcomes and the potential benefit of chemotherapy.

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The same assay also quantifies HER-2 expression,

proliferation- and invasion-related genes. Combining

information from each of these measurements into a

prediction score can give important information on patient

prognosis. A study examined the correlation between the

Oncotype DX recurrence score and the likelihood of

distant relapse in 668 ER-positive, node-negative,

tamoxifen-treated patients who were enrolled in the

National Surgical Adjuvant Breast and Bowel Project

(NSABP) clinical trial B14. 51%, 22%, and 27% of these

ER-positive patients were categorized as low,

intermediate, and high risk for recurrence after tamoxifen

therapy, respectively. The observed 10-year distant

recurrence rates were 6.8%, 14.3%, and 30.5% in the three

risk categories, respectively (p < 0.001). In a multivariate

analysis, the recurrence score predicted relapse and overall

survival independently of age and tumor size (Paik et al,

2004). A recent report examined the value of the

recurrence score for predicting benefit from adjuvant

cyclophosphamide, methotrexate, and 5-fluorouracil

(CMF) chemotherapy in 651 patients with ER-positive,

node-negative breast cancer included in the NSABP B20

randomized study. Higher recurrence scores were

associated with greater benefit from adjuvant CMF

chemotherapy (p = 0.038). The hazard ratio for distant

recurrence after CMF chemotherapy was 1.31 for patients

with a recurrence score 31. The absolute improvement in 10-

year distant recurrence-free survival was 28% (60% vs.

88%) in patients with a recurrence score >31, while there

was no benefit in patients with a recurrence score

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(Rotterdam signature) in lymph node-negative breast

cancer patients. This prognostic tool was highly

informative in identifying patients with distant metastasis

within 5 years, even when corrected for traditional

prognostic factors in multivariate analysis. The 5- and 10-

year distant metastasis-free survival were 96% and 94%

for the good profile group and 74% and 65% for the poor

profile group (Foekens et al, 2006). Once validated, these

prognostic signatures could be considered as a prognostic

and predictive test with relevant clinical utility.

IV. Prognostic value of the new

classification for breast cancerDifferent molecular subtypes of breast cancer

associated with different natural histories were identified

thanks to the advent of gene-expression profiling

technologies. Moreover, new therapeutic targets and

treatments that are effective in particular molecular subsets

have been tested so that each patient will be treated only

with the drugs really effective. Thanks to microarraytechnology, investigators determined that there were breast

cancer subtypes with distinct gene expression patternsand

different prognoses (Sotiriou et al, 2003) that persisted in

primary breast cancers as well as their metastases (Weigelt

et al, 2003). The first study to examine comprehensively

gene-expression patterns of breast cancer suggested that at

least four major molecular classes of breast cancer exist:

luminal-like, basal-like, normal-like, and HER-2 positive

(Perou et al, 2000). Other studies using gene expression

profiling revealed that within the ER-positive tumors at

least two subtypes, luminal A and luminal B, with

important differences in gene expression and prognosis

could be distinguished. On the contrary, hormonereceptor-negative breast cancer comprised two distinct

subtypes, the HER2 subtype and the basal-like subtype.

These subtypes differ in biology and behaviour and are

associated with a poor outcome (Pusztai et al, 2003).

Although most investigators have identified also other

subtypes, these studies have been performed on relatively

small datasets, so the number of different breast cancer

subtypes is still unknown. In addition to the biological

implications, the identification of different breast cancer

subtypes is also associated with different prognostic

significance. A small overview of the different cancer

subtypes is described in the next paragraphs (Srlie et al,

2003).

A. Luminal subtypes: Expression

patterns, clinical features and prognosisThe luminal subtypes represent the hormone

receptor-positive breast cancers and have expression

patterns similar to the luminal epithelial component of the

breast (Perou et al, 2000). These patterns include expression

of ER, genes associated with ER activation and luminal

cytokeratins 8/18. These tumors are often grade I and

often do not have p53 mutations (less than 20%). At least

two subtypes of the luminal cluster exist, luminal A and

luminal B. Although both are hormone receptor positive,these two luminal subtypes have different characteristics.

In fact luminal A usually has higher expression of ER-

related genes and lower expression of proliferative genes

than luminal B40. Luminal breast cancers are the most

common subtype of breast cancer, in fact they represent at

least 67% of the tumors (Carey et al, 2004). In general, the

luminal subtypes have a good prognosis, even if luminal B

carry a significantly worse prognosis than luminal A in

multiple data sets (Pusztai et al, 2003).Part of this different

outcome may be due to variations in response to treatment.

Luminal breast cancers are treated with hormone therapy.

In fact several studies have demonstrated that ER-positive

tumors respond poorly to conventional chemotherapy

(Rouzier et al, 2005) Data from Oncotype DX Score suggest

that tumors with low Recurrence Scores are luminal A

whereas those with high Recurrence Scores are luminal B.

Thus, it appears that luminal A tumors may be adequately

treated with endocrine therapy alone, whereas the more

proliferative luminal B tumors may be those that benefit

from chemotherapy added to endocrine therapy (Paik et al,

2004). Among targeted therapies active in luminal

subtypes, the anti-vascular endothelial growth factor

antibody bevacizumab has recently shown to improve

survival in metastatic breast cancer when combined with

paclitaxel (Miller et al, 2005). Interestingly, more than 60%

of the patients in this trial were hormone receptor-positive

and almost none was HER2-positive, suggesting that

bevacizumab may be particularly effective in the luminal

subtypes.

B. HER-2 subtype: Expression patterns,

clinical features and prognosisThe HER2 subtype refers to the large group of

hormone receptor-negative tumors identified by gene

expression array. HER2-positive tumors clinically refer tothose identified by immunostaining for HER2

overexpression or FISH for excess gene copy number,

even if there is not a perfect correspondence between the

clinically evaluated HER2-positive and the HER2-array

subtype. However, tumors that are both hormone-receptor

positive and HER2 positive by immunohistochemistry or

FISH often are included in the luminal subtypes rather

than the HER2-array subtype. The HER2-array tumors are

characterized by overexpression of other genes in the

ERBB2 amplification such as GRB7. Like basal-like

tumors, the HER2-array subtype tumors have often p53

mutations (40%-80%) and are significantly more likely to

be grade 3 (P=0.0002) than luminal A tumors (Srlie et al,

2001). There is no association of the HER2-array subtype

with age, race or known risk factors (Perou et al, 2000).

Moreover, HER2-array subtype tumors are often lymph-

nodes positive, more than two-fold than luminal A tumors.

Despite its poor prognosis, the HER2-array subtype has

also demonstrated sensitivity to anthracycline and taxane-

based neoadjuvant chemotherapy, with significantly

higher pathologic complete response than luminal breast

tumors (46% v 7%; P

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breast cancer. In fact the evaluation of the efficacy of

increased anthracycline dose and intensity (CALGB

8541), of taxane added to anthracycline (CALGB 9344)

and of increased dose density (CALGB 9741) were

demonstrated in the ER-negative subset of patients (Berry

et al, 2004). This suggests that the HER2 and basal-like

subtypes (the majority of ER-negative tumors) are the

tumors that derive the most benefit from improvements in

chemotherapy. However, the treatment for the HER2-array

subtype includes targeted agents, including the anti-HER2

monoclonal antibody trastuzumab. The effectiveness of

trastuzumab in metastatic breast cancer and in the adjuvant

setting supports the idea that this strategy is really

effective (Perez et al, 2005; Romond et al, 2005), even if not

all HER2-positive tumors respond to trastuzumab. PTEN

loss or abrogation (Nagata et al, 2004) and CXCR4

upregulation (Tripathy et al, 2005) have been implicated in

trastuzumab resistance and may provide targets for

combination strategies that could give better results in the

future.

C. Basal-like subtype: Expression

patterns, clinical features and prognosisThe basal-like subtype of breast cancer has

expression patterns similar to the basal epithelial cells of

other parts of the body and to the normal breast

myoepithelial cells. This subtype does not express ER and

related genes, has a low expression of HER2, a strong

expression of basal cytokeratins 5, 6 and 17 and

proliferation-related genes (Weigelt et al, 2003). This

subtype is also characterized by low expression of BRCA1

(Abd et al, 2005). Basal-like tumors often have aggressive

features such as p53 mutations and are significantly morelikely to be grade 3(P < 0.0001) than luminal A tumors

(Miller et al, 2005). Basal-like tumors represent 20% of the

tumors and are the most common among premenopausal

African American women (39%) compared with

postmenopausal African American (14%) or non-African

American women of any age (16%) (P= 0.0001) (Perou et

al, 2000). This subtype is also associated with some risk

factors, in fact most women with BRCA1 mutations

generally develop basal-like breast cancer (Turner et al,

2004). So basal-like breast cancer has a poor prognosis

(Carey et al, 2004), but it is not clear if it is due to poor

therapy options and/or the biological aggressiveness. In

fact basal-like breast cancer can not be treated withconventional targeted therapies for breast cancer such as

endocrine therapy or trastuzumab because of their triple-

negative receptor status (ER, PR, and HER2).

Chemotherapy is the only option for this setting of

patients, in fact basal-like breast cancers are sensitive to

conventional chemotherapy. In two studies (Srlie et al,

2001; Rouzier et al, 2004), response to anthracycline-based

or combination anthracycline and taxane-based

neoadjuvant chemotherapy among basal-like tumors was

evaluated, with higher response rates among basal-like

breast cancers than non-basal like. These studies suggest

that the poor prognosis of patients with basal-like tumors

is not due to the initial chemoresistance, but it reflects the

fewer treatment options available for ER-, PR- and HER2-

negative tumors and/or to the intrinsic aggressiveness of

this subtype. The only targeted option evaluated in this

subtype is the treatment with epidermal growth factor

receptor-inhibitors (Srlie et al, 2001). This hypothesis is

being tested in several preclinical and clinical trials.

Finally, Cheang MG et al showed that basal-like breast

cancer defined by five biomarkers has superior prognostic

value than triple-negative phenotype. In fact, EGFR and

cytokeratin 5/6 are markers of basal-like breast cancer

applicable to standard pathology specimens that can be

added to the estrogen receptor, progesterone receptor and

HER-2 markers. Among the 3,744 patients with invasive

breast cancer, 17% were basal using the triple-negative

definition and 9% were basal using the five-marker

method. They demonstrated that the five-marker panel is

significantly more prognostic than the three-marker panel.

In fact among triple-negative patients treated with

adjuvant anthracycline-based chemotherapy, the additional

positive basal markers identified a cohort of patients with

significantly worse outcome (Carey et al, 2007).

V. Future directionsCurrent breast cancer treatment guidelines are based

on the results of randomized clinical trials in defined

patient populations affected by tumors with different

biological characteristics. Anyway, it is clear that better

prognostic and predictive factors are needed for the choice

of treatment strategies individualized for each patient.

Considering molecular prognostic and predictive factors,

gene profiling seems promising, although further

validation is mandatory. In fact, only small studies in

patients with early breast cancer patients have been

performed. Given the high potential of gene expression

profiling to change clinical practice, it is now important tovalidate this new prognostic tool in large, independent and

prospective trials. In fact only large, well-conducted,

biologically-based prospective trials will allow us to reach

the needed conclusions and to shorten the time for the

clinical application of these new markers and techniques.

Nevertheless, encouraging results from studies using

microarrays for identification of genes related to cancer

prognosis and prediction indicate that this technique will

provide soon a novel, personalized dimension for the

treatment and care of breast cancer patients. These studies

have also identified genes whose protein products may

provide therapeutic targets for the progressive

development of novel, more effective and less toxicchemotherapeutic agents. Some studies have also

identified a group of patients with good prognosis, who

may not benefit from adjuvant systemic chemotherapy. So

further studies are needed to understand the effects of

different treatment regimens on disease outcome and/or

molecular subtype to identify those patients who most

likely could benefit from adjuvant or neoadjuvant

treatment regimens. In addition, microarrays provide an

opportunity to potentially identify new targets for therapy,

in fact it will be important to develop targeted therapies,

especially for patients with aggressive tumors.

Concluding, the next challenge for researchers is try to

translate all the results of preclinical studies in clinical

setting to select only the drugs really effective for each

patient.

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Giuseppe Tonini

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