Molecular classification of breast cancer: implications for selection of adjuvant chemotherapy

Comparative profiling of plasma proteome from breast cancer patients reveals thrombospondin-1 and BRWD3 as serological biomarkers

Breast cancer is the most common cancer in women worldwide. It is necessary to identify biomarkers for early detection, to make accurate prognoses, and to monitor for any recurrence of the cancer. In order to identify potential breast cancer biomarkers, we analyzed the plasma samples of women diagnosed with breast cancer and age-matched normal healthy women by mTRAQ-based stable isotope-labeling mass spectrometry. We identified and quantified 204 proteins including thrombospondin-1 (THBS1) and bromodomain and WD repeat-containing protein 3 (BRWD3) which were increased by more than 5-fold in breast cancer plasma. The plasma levels of the two proteins were evaluated by Western blot assay to confirm for their diagnostic value as serum markers. A 1.8-fold increase in BRWD3 was observed while comparing the plasma levels of breast cancer patients (n = 54) with age-matched normal healthy controls (n = 30), and the area under the receiver operating characteristic curve (AUC) was 0.917. THBS1 was detected in pooled breast cancer plasma at the ratio similar to mTRAQ ratio (> 5-fold). The AUC value for THBS1 was 0.875. The increase of THBS1 was more prominent in estrogen receptor negative and progesterone receptor negative patients than receptor-positive patients. Our results are evidence of the diagnostic value of THBS1 in detecting breast cancer. Based on our findings, we suggest a proteomic method for protein identification and quantification lead to effective biomarker discovery.

The challenge of gene expression profiling in heterogeneous clinical samples

Almost all samples used in tumor biology, such as tissues and bodily fluids, are heterogeneous, i.e., consist of different cell types. Evaluating the degree of heterogeneity in samples can increase our knowledge on processes such as clonal selection and metastasis. In addition, generating expression profiles from specific sub populations of cells can reveal their distinct functions. Tissue heterogeneity also poses a challenge, as it can confound the interpretation of gene expression data. This chapter will (1) give a brief overview on how heterogeneity may influence gene expression profiling data and (2) describe the methods that are currently available to assess transcriptional biomarkers in a heterogeneous cell population.

Molecular classification of breast carcinomas with particular emphasis on "basal-like" carcinoma: a critical review

During the last 11 years, 5 molecular subtypes of breast carcinoma (luminal A, luminal B, Her2-positive, basal-like, and normal breast-like) have been characterized and intensively studied. As genomic research evolves, further subtypes of breast cancers into new "molecular entities" are expected to occur. For example, a new and rare breast cancer subtype, known as claudin-low, has been recently found in human carcinomas and in breast cancer cell lines. There is no doubt that global gene expression analyses using high-throughput biotechnologies have drastically improved our understanding of breast cancer as a heterogeneous disease. The main question is, however, whether new molecular techniques such as gene expression profiling (or signature) should be regarded as the gold standard for identifying breast cancer subtypes. A critical review of the literature clearly shows major problems with current molecular techniques and classification including poor definitions, lack of reproducibility, and lack of quality control. Therefore, the current molecular approaches cannot be incorporated into routine clinical practice and treatment decision making as they are immature or even can be misleading. This review particularly focuses on the "basal-like" breast cancer subtype that represents one of the most popular breast cancer "entities". It critically shows major problems and misconceptions with and about this subtype and challenges the common claim that it represents a "distinct entity". It concludes that the term "basal-like" is misleading and states that there is no evidence that expression of basal-type cytokeratins in a given breast cancer, regardless of other established prognostic factors, does have any impact on clinical outcome.

A three-gene model to robustly identify breast cancer molecular subtypes

BACKGROUND

Single sample predictors (SSPs) and Subtype classification models (SCMs) are gene expression-based classifiers used to identify the four primary molecular subtypes of breast cancer (basal-like, HER2-enriched, luminal A, and luminal B). SSPs use hierarchical clustering, followed by nearest centroid classification, based on large sets of tumor-intrinsic genes. SCMs use a mixture of Gaussian distributions based on sets of genes with expression specifically correlated with three key breast cancer genes (estrogen receptor [ER], HER2, and aurora kinase A [AURKA]). The aim of this study was to compare the robustness, classification concordance, and prognostic value of these classifiers with those of a simplified three-gene SCM in a large compendium of microarray datasets.

METHODS

Thirty-six publicly available breast cancer datasets (n = 5715) were subjected to molecular subtyping using five published classifiers (three SSPs and two SCMs) and SCMGENE, the new three-gene (ER, HER2, and AURKA) SCM. We used the prediction strength statistic to estimate robustness of the classification models, defined as the capacity of a classifier to assign the same tumors to the same subtypes independently of the dataset used to fit it. We used Cohen κ and Cramer V coefficients to assess concordance between the subtype classifiers and association with clinical variables, respectively. We used Kaplan-Meier survival curves and cross-validated partial likelihood to compare prognostic value of the resulting classifications. All statistical tests were two-sided.

RESULTS

SCMs were statistically significantly more robust than SSPs, with SCMGENE being the most robust because of its simplicity. SCMGENE was statistically significantly concordant with published SCMs (κ = 0.65-0.70) and SSPs (κ = 0.34-0.59), statistically significantly associated with ER (V = 0.64), HER2 (V = 0.52) status, and histological grade (V = 0.55), and yielded similar strong prognostic value.

CONCLUSION

Our results suggest that adequate classification of the major and clinically relevant molecular subtypes of breast cancer can be robustly achieved with quantitative measurements of three key genes.

Predictive and prognostic molecular markers for cancer medicine

Over the last 10 years there has been an explosion of information about the molecular biology of cancer. A challenge in oncology is to translate this information into advances in patient care. While there are well-formed routes for translating new molecular information into drug therapy, the routes for translating new information into sensitive and specific diagnostic, prognostic and predictive tests are still being developed. Similarly, the science of using tumor molecular profiles to select clinical trial participants or to optimize therapy for individual patients is still in its infancy. This review will summarize the current technologies for predicting treatment response and prognosis in cancer medicine, and outline what the future may hold. It will also highlight the potential importance of methods that can integrate molecular, histopathological and clinical information into a synergistic understanding of tumor progression. While these possibilities are without doubt exciting, significant challenges remain if we are to implement them with a strong evidence base in a widely available and cost-effective manner.

Predictors of timing of adjuvant chemotherapy in older women with hormone receptor-negative, stages II-III breast cancer

Adherence to consensus guidelines for cancer care may vary widely across health care settings and contribute to differences in cancer outcomes. For some women with breast cancer, omission of adjuvant chemotherapy or delays in its initiation may contribute to differences in cancer recurrence and mortality. We studied adjuvant chemotherapy use among women with stage II or stage III, hormone receptor-negative breast cancer to understand health system and socio-demographic correlates of underuse and delayed adjuvant chemotherapy. We used Surveillance Epidemiology and End Results (SEER)-Medicare linked data to examine the patterns of care for 6,678 women aged 65 and older diagnosed with stage II or stage III hormone receptor-negative breast cancer in 1994-2002, with claims data through 2007. Age-stratified logistic regression was employed to examine the potential role of socio-demographic and structural/organizational health services characteristics in explaining differences in adjuvant chemotherapy initiation. Overall utilization of guideline-recommended adjuvant chemotherapy peaked at 43% in this population. Increasing age, higher co-morbidity burden, and low-income status were associated with lower odds of chemotherapy initiation within 4 months, whereas having positive lymph nodes, more advanced disease, and being married were associated with higher odds (P < 0.05). Health system-related structural/organizational characteristics and race/ethnicity offered little explanatory insight. Timely initiation of guideline-recommended adjuvant chemotherapy was low, with significant variation by age, income, and co-morbidity status. Based on these findings, future studies should seek to explore the more nuanced reasons why older women do not receive chemotherapy and why delays in care occur.

Matters of the heart: cardiac toxicity of adjuvant systemic therapy for early-stage breast cancer

Breast cancer remains the most common malignancy in women. Since the late 1980s, significant advances have been made in the treatment of this cancer. Those advances, particularly the ones in the adjuvant setting, have led to declines in the mortality associated with breast cancer. But another result has been treatments that are more complex and that potentially carry more toxicity. One key toxicity related to the adjuvant therapy of breast cancer is cardiac toxicity. Some of the agents commonly used for the treatment of breast cancer, including anthracyclines, trastuzumab, and possibly even aromatase inhibitors, have been associated with cardiac toxicity. The present article reviews the current understanding of cardiac toxicity risk and strategies to minimize cardiac morbidity associated with cytotoxic chemotherapy, trastuzumab therapy, and hormonal therapy with aromatase inhibitors for early-stage breast cancer.

CMF revisited in the 21st century

Over the last 35 years, classical CMF (combination chemotherapy with cyclophosphamide, methotrexate and fluorouracil) has been a milestone in the adjuvant treatment of women with breast cancer. However, after an early burst of success lasted just over 10 years, classical CMF has been supplanted by 'third-generation' regimens containing taxanes and anthracyclines. Questions have been raised in the past years concerning the true effectiveness of adjuvant CMF for specific subgroups of patients and particularly, recent retrospective data support the fact that the CMF might have a role in the treatment of patients with triple-negative breast cancer. One possible justification for supporting this role of CMF may be sought in the mechanism of action of drugs used in the regimen, as triple-negative cells may be sensitive to alkylating agents that cause double-strand breaks in DNA. The lesson learned from the CMF could lead us to identify new combinations of drugs that could include the optimal chemotherapy backbone for triple-negative breast cancer such as platinum compounds or alkylating agents or Poly (ADP-ribose) polymerase inhibitors. In conclusion, although we have learned a lot from the use of CMF, many questions are still open and hopefully stimulate our thinking, as clinicians, leading us to find new and more effective ways to treat breast cancer.

Statistical consideration for clinical biomarker research in bladder cancer

OBJECTIVE

To critically review and illustrate current methodological and statistical considerations for bladder cancer biomarker discovery and evaluation.

METHODS

Original, review, and methodological articles, and editorials were reviewed and summarized.

RESULTS

Biomarkers may be useful at multiple stages of bladder cancer management: early detection, diagnosis, staging, prognosis, and treatment; however, few novel biomarkers are currently used in clinical practice. The reasons for this disjunction are many and reflect the long and difficult pathway from candidate biomarker discovery to clinical assay, and the lack of coherent and comprehensive processes (pipelines) for biomarker development. Conceptually, the development of new biomarkers should be a process that is similar to therapeutic drug evaluation-a highly regulated process with carefully regulated phases from discovery to human applications. In a further effort to address the pervasive problem of inadequacies in the design, analysis, and reporting of biomarker prognostic studies, a set of reporting recommendations are discussed. For example, biomarkers should provide unique information that adds to known clinical and pathologic information. Conventional multivariable analyses are not sufficient to demonstrate improved prediction of outcomes. Predictive models, including or excluding any new putative biomarker, need to show clinically significant improvement of performance in order to claim any real benefit. Towards this end, proper model building, avoidance of overfitting, and external validation are crucial. In addition, it is important to choose appropriate performance measures dependent on outcome and prediction type and to avoid the use of cutpoints. Biomarkers providing a continuous score provide potentially more useful information than cutpoints since risk fits a continuum model. Combination of complementary and independent biomarkers is likely to better capture the biological potential of a tumor than any single biomarker. Finally, methods that incorporate clinical consequences such as decision curve analysis are crucial to the evaluation of biomarkers.

CONCLUSIONS

Attention to sound design and statistical practice should be delivered as early as possible and will help maximize the promise of biomarkers for patient care. Studies should include a measure of predictive accuracy and clinical decision-analysis. External validation using data from an independent cohort provides the strongest evidence that a model is valid. There is a need for adequately assessed clinical biomarkers in bladder cancer.

Estrogen and progesterone receptor status affect genome-wide DNA methylation profile in breast cancer

DNA methylation is the main epigenetic modification that occurs at the early stages of carcinogenesis. We performed a genome-wide DNA methylation profiling to evaluate whether the DNA methylation state is different in the estrogen receptor (ER) and progesterone receptor (PR) status of breast cancer. Twelve ER+/PR+ and 12 ER-/PR- breast cancer tissues were selected from the biorepository of the Seoul Breast Cancer Study for Infinium Methylation Assay. The difference of the DNA methylation state of 27 578 methylation sites in 14 000 genes between two groups was evaluated by Student's t-test. False discovery rate (FDR) was estimated to evaluate the probability of false positive associations. Of the 27 578 sites, 148 sites (0.54%) were significantly different between ER+/PR+ and ER-/PR- breast cancers (P < 0.001); 93 hypermethylated and 55 hypomethylated. Five genes, FAM124B (P = 7.26 × 10(-7)), MANEAL (P = 3.38 × 10(-7)), ST6GALNAC1 (P = 2.85 × 10(-6)), NAV1 (P = 5.94 × 10(-6)) and PER1 (P = 6.45 × 10(-6)) remained significant after correction for multiple tests (FDR < 0.05). In a subsequent replication study for five genes, four of the five genes were validated; FAM124B and ST6GALNAC1 were significantly hypermethylated, and NAV1 and PER1 were significantly hypomethylated in ER+/PR+ breast cancers (P < 0.05). In the first genome-wide DNA methylation profiling according to the receptor status of breast cancer, we found that ER/PR status affects the DNA methylation state of FAM124B, ST6GALNAC1, NAV1 and PER1 in breast cancer.

Age/race differences in HER2 testing and in incidence rates for breast cancer triple subtypes: a population-based study and first report

BACKGROUND

Although US year 2000 guidelines recommended characterizing breast cancers by human epidermal growth factor receptor 2 (HER2), national cancer registries do not collect HER2, rendering a population-based understanding of HER2 and clinical "triple subtypes" (estrogen receptor [ER] / progesterone receptor [PR] / HER2) largely unknown. We document the population-based prevalence of HER2 testing / status, triple subtypes and present the first report of subtype incidence rates.

METHODS

Medical records were searched for HER2 on 1842 metropolitan Atlanta females diagnosed with breast cancer during 2003-2004. HER2 testing/status and triple subtypes were analyzed by age, race/ethnicity, tumor factors, socioeconomic status, and treatment. Age-adjusted incidence rates were calculated.

RESULTS

Over 90% of cases received HER2 testing: 12.6% were positive, 71.7% negative, and 15.7% unknown. HER2 testing compliance was significantly better for women who were younger, of Caucasian or African-American descent, or diagnosed with early stage disease. Incidence rates (per 100,000) were 21.1 for HER2+ tumors and 27.8 for triple-negative tumors, the latter differing by race (36.3 and 19.4 for black and white women, respectively).

CONCLUSIONS

HER2 recommendations are not uniformly adhered to. Incidence rates for breast cancer triple subtypes differ by age/race. As biologic knowledge is translated into the clinical setting eg, HER2 as a biomarker, it will be incumbent upon national cancer registries to report this information. Incidence rates cautiously extrapolate to an annual burden of 3000 and 17,000 HER2+ tumors for black and white women, respectively, and triple-negative tumors among 5000 and 16,000 respectively. Testing, rate, and burden variations warrant population-based in-depth exploration and clinical translation.

Cyclophosphamide dose intensification may circumvent anthracycline resistance of p53 mutant breast cancers

The predictive value of p53 for the efficacy of front-line anthracycline-based chemotherapy regimens has been a matter of significant controversy. Anthracyclines are usually combined with widely different doses of alkylating agents, which may significantly modulate tumor response to these combinations. We analyzed three series of de novo stage II-III breast cancer patients treated front line with anthracycline-based regimens of various cyclophosphamide dose intensities: 65 patients with estrogen receptor (ER)(-) tumors treated with anthracyclines alone (Institut Jules Bordet, Brussels), 51 unselected breast cancer patients treated with intermediate doses of cyclophosphamide (MD Anderson Cancer Center, Houston, TX), and 128 others treated with a dose-dense anthracycline-cyclophosphamide combination (St. Louis, Paris). After chemotherapy and surgery, pathologic complete response (pCR) was evaluated. p53 status was determined by a yeast functional assay on the pretreatment tumor sample. In a multivariate analysis of the pooled results, a lack of ER expression and high-dose cyclophosphamide administration were associated with a higher likelihood of pCR. A sharp statistical interaction was detected between p53 status and cyclophosphamide dose intensity. Indeed, when restricting our analysis to patients with ER(-) tumors, we confirmed that a mutant p53 status was associated with anthracycline resistance, but found that p53 inactivation was required for response to the dose-intense alkylating regimen. The latter allowed very high levels of pCR in triple-negative tumors. Thus, our data strongly suggest that cyclophosphamide dose intensification in ER(-) p53-mutated breast cancer patients could significantly improve their response.

Side-population cells in luminal-type breast cancer have tumour-initiating cell properties, and are regulated by HER2 expression and signalling

BACKGROUND

The expression of side-population (SP) cells and their relation to tumour-initiating cells (T-ICs) have been insufficiently studied in breast cancer (BC). We therefore evaluated primary cell cultures derived from patients and a panel of human BC cell lines with luminal- or basal-molecular signatures for the presence of SP and BC stem cell markers.

METHODS

The SPs from luminal-type BC were analysed for BC T-IC characteristics, including human epidermal growth factor receptor 2 (HER2), ERalpha, IGFBP7 expression and their ability to initiate tumours in non-obese diabetic severe combined immunodeficiency (NOD/SCID) mice. Pharmacological modulators were used to assess the effects of HER2 signalling and breast cancer-resistance protein (BCRP) expression on SPs.

RESULTS

The SP was more prevalent in the luminal subtype of BC compared with the basal subtype. HER2 expression was significantly correlated with the occurrence of an SP (r(2)=0.75, P=0.0003). Disappearance of SP in the presence of Ko143, a specific inhibitor of the ATP-binding cassette transporter BCRP, suggests that BCRP is the predominant transporter expressed in this population. The SP also decreased in the presence of HER2 signalling inhibitors AG825 or trastuzumab, strengthening the notion that HER2 contributed to the SP phenotype, likely through downstream AKT signalling. The SP cells from luminal-type MCF-7 cells with enforced expression of HER2, and primary cells with luminal-like properties from a BC patient, displayed enrichment in cells capable of repopulating tumours in NOD/SCID mice. Engraftment of SP cells was inhibited by pretreatment with AG825 or by in vivo treatment with trastuzumab.

INTERPRETATION

Our findings indicate an important role of HER2 in regulating SP and hence T-ICs in BC, which may account for the poor responsiveness of HER2-positive BCs to chemotherapy, as well as their aggressiveness.

Molecular risk assessment of BIG 1-98 participants by expression profiling using RNA from archival tissue

Background

The purpose of the work reported here is to test reliable molecular profiles using routinely processed formalin-fixed paraffin-embedded (FFPE) tissues from participants of the clinical trial BIG 1-98 with a median follow-up of 60 months.

Methods

RNA from fresh frozen (FF) and FFPE tumor samples of 82 patients were used for quality control, and independent FFPE tissues of 342 postmenopausal participants of BIG 1-98 with ER-positive cancer were analyzed by measuring prospectively selected genes and computing scores representing the functions of the estrogen receptor (eight genes, ER_8), the progesterone receptor (five genes, PGR_5), Her2 (two genes, HER2_2), and proliferation (ten genes, PRO_10) by quantitative reverse transcription PCR (qRT-PCR) on TaqMan Low Density Arrays. Molecular scores were computed for each category and ER_8, PGR_5, HER2_2, and PRO_10 scores were combined into a RISK_25 score.

Results

Pearson correlation coefficients between FF- and FFPE-derived scores were at least 0.94 and high concordance was observed between molecular scores and immunohistochemical data. The HER2_2, PGR_5, PRO_10 and RISK_25 scores were significant predictors of disease free-survival (DFS) in univariate Cox proportional hazard regression. PRO_10 and RISK_25 scores predicted DFS in patients with histological grade II breast cancer and in lymph node positive disease. The PRO_10 and PGR_5 scores were independent predictors of DFS in multivariate Cox regression models incorporating clinical risk indicators; PRO_10 outperformed Ki-67 labeling index in multivariate Cox proportional hazard analyses.

Conclusions

Scores representing the endocrine responsiveness and proliferation status of breast cancers were developed from gene expression analyses based on RNA derived from FFPE tissues. The validation of the molecular scores with tumor samples of participants of the BIG 1-98 trial demonstrates that such scores can serve as independent prognostic factors to estimate disease free survival (DFS) in postmenopausal patients with estrogen receptor positive breast cancer.

Trial Registration

Current Controlled Trials: NCT00004205

Genetic characterization of breast cancer and implications for clinical management

Breast cancer is a genetic disease caused by the accumulation of mutations in neoplastic cells. In the last few years, high-throughput microarray-based molecular analysis has provided increasingly more coherent information about the genetic aberrations in breast cancer. New biomarkers and molecular techniques are slowly becoming part of the diagnostic and prognostic armamentarium available for pathologists and oncologists to tailor the therapy for breast cancer patients. In this review, we will focus on the contribution of breast cancer somatic genetics to our understanding of breast cancer biology and its impact on breast cancer patient management.

MULTI-K: accurate classification of microarray subtypes using ensemble k-means clustering

BACKGROUND

Uncovering subtypes of disease from microarray samples has important clinical implications such as survival time and sensitivity of individual patients to specific therapies. Unsupervised clustering methods have been used to classify this type of data. However, most existing methods focus on clusters with compact shapes and do not reflect the geometric complexity of the high dimensional microarray clusters, which limits their performance.

RESULTS

We present a cluster-number-based ensemble clustering algorithm, called MULTI-K, for microarray sample classification, which demonstrates remarkable accuracy. The method amalgamates multiple k-means runs by varying the number of clusters and identifies clusters that manifest the most robust co-memberships of elements. In addition to the original algorithm, we newly devised the entropy-plot to control the separation of singletons or small clusters. MULTI-K, unlike the simple k-means or other widely used methods, was able to capture clusters with complex and high-dimensional structures accurately. MULTI-K outperformed other methods including a recently developed ensemble clustering algorithm in tests with five simulated and eight real gene-expression data sets.

CONCLUSION

The geometric complexity of clusters should be taken into account for accurate classification of microarray data, and ensemble clustering applied to the number of clusters tackles the problem very well. The C++ code and the data sets tested are available from the authors.

Ki67 Expression and Docetaxel Efficacy in Patients With Estrogen Receptor–Positive Breast Cancer

Purpose

The indications of adjuvant chemotherapy for patients with estrogen receptor (ER) –positive breast cancer are controversial. We analyzed the predictive value of Ki67, HER2, and progesterone receptor (PR) expression for the efficacy of docetaxel in patients with ER-positive, node-positive breast cancer.

Patients and Methods

Expression of Ki67, HER2, and PR was measured by immunohistochemistry in tumor samples from 798 patients with ER-positive breast cancer who participated in PACS01, a randomized trial that evaluated the efficacy of docetaxel. Risk reduction was evaluated using a Cox model adjusted for age, tumor size, nodal involvement, treatment arm, and biomarkers. The predictive value of biomarkers was assessed by an interaction test. Disease-free survival (DFS) was the primary end point.

Results

Ki67, HER2, and PR were expressed in 21%, 9%, and 62% of samples, respectively. Hazard ratios for relapse associated with docetaxel were 0.51 (95% CI, 0.26 to 1.01) in ER-positive/Ki67-positive tumors and 1.03 (95% CI, 0.69 to 1.55) in ER-positive/Ki67-negative tumors (ratio for interaction: 0.53; 95% CI, 0.24 to 1.16; P = .11). Five-year DFS rates were 81% (95% CI, 76% to 86%) and 84% (95% CI, 75% to 93%) in patients with ER-positive/Ki67-negative and ER-positive/Ki67-positive tumors treated with docetaxel and 81% (95% CI, 76% to 86%) and 62% (95% CI, 52% to 72%) in patients with ER-positive/Ki67-negative and ER-positive/Ki67-positive tumors treated with fluorouracil, epirubicin, and cisplatin. No trend for interaction was observed between docetaxel and HER2 (ratio for interaction: 0.83; 95% CI, 0.35 to 1.94; P = .66), nor between docetaxel and PR (ratio for interaction: 0.89; 95% CI, 0.47 to 1.66; P = .71).

Conclusion

Ki67 expression identifies a subset of patients with ER-positive breast cancer who could be sensitive to docetaxel treatment in the adjuvant setting.

[Genome-wide expression profiling as a clinical tool: are we there yet?]

Breast cancer is a heterogeneous disease, encompassing a plethora of histological types and clinical courses. Current histopathological classification systems for breast cancer are based on descriptive entities that are of prognostic significance. Few prognostic markers beyond those offered by histopathological analysis are available. Furthermore, a very limited armamentarium of predictive biomarkers has been introduced in clinical practice. High throughput molecular technologies are reshaping our understanding of breast cancer, of which microarray-based gene expression has received the most attention. This method has been successfully used to derive a molecular taxonomy for breast cancer, which has provided interesting insights into the biology of the disease. Microarray-based class prediction studies have generated a multitude of prognostic/predictive signatures. Although these signatures have not been fully translated to clinical practice as yet, they herald the promise of an improvement in breast cancer treatment decision-making. It should be noted, however, that most of the signatures developed to date seem to have discriminatory power almost restricted to oestrogen receptor-positive disease. This review addresses the contribution of gene expression profiling to our understanding of breast cancer and its clinical management and what has yet to be done for these classifiers to be incorporated in clinical practice.

Will children with cancer benefit from the new European Paediatric Medicines Regulation?

In December 2000, the European Parliament voted a resolution addressing the need for better medicines for children in Europe and asking the Commission to prepare a new regulation. Members of the European Parliament considered that there was indeed a health issue to be addressed and resolved at the EU level. Six years later, the EU regulation was published and entered into force on January the 26th, 2007. This European law is going to impact significantly access to new drugs for children with cancer. By considerably changing the landscape of drug development for children, the law will provide an opportunity to make further progress in the cure and quality of cure of children with cancer, at a time when truly innovative and effective anticancer drugs are becoming available. However, there are some risks and pitfalls that need to be anticipated and controlled in order to ensure that children will eventually benefit from the European initiative.

p53 as a specific prognostic factor in triple-negative breast cancer

OBJECTIVE

A recent suggestion is that the predictive value of a single biomarker may rely on the genetic background on the tumor and that different breast cancer subgroups may have different predictive markers of response to chemotherapy. The prognostic value of p53 in the outcome of adjuvant anthracycline-containing chemotherapy was evaluated according to molecular subclasses defined using the expression of estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2.

METHODS

Subjects were patients (n = 135) with invasive ductal carcinoma treated with adjuvant anthracycline-based chemotherapy between 1994 and 2000 in our hospital. Clinico-pathological features were reviewed by retrospective examination of medical records.

RESULTS

Overall survival rate was not independently predictive by p53 status (P = 0.182). However, in triple-negative cases, there was statistically significant survival difference (P = 0.034) and no statistically significant difference (P = 0.783) in non-triple-negative cases by p53 status. In the Cox proportional hazard analysis, p53 was also strongly predictive for relapse-free survival (P = 0.013) and overall survival (P = 0.049) in triple-negative patients.

CONCLUSIONS

p53 status could be a specific prognostic factor in triple-negative breast cancer patients treated by adjuvant anthracycline-based regimen. When p53 is positive in triple-negative breast cancer, we could expect poor survival, prompting aggressive or alternative treatment.

FOXA1 in breast cancer

Breast cancer is a heterogeneous disease and classification is important for clinical management. At least five subtypes can be identified based on unique gene expression patterns; this subtype classification is distinct from the histopathological classification. The transcription factor network(s) required for the specific gene expression signature in each of these subtypes is currently being elucidated. The transcription factor network composed of the oestrogen (estrogen) receptor alpha (ERalpha), FOXA1 and GATA3 may control the gene expression pattern in luminal subtype A breast cancers. Breast cancers that are dependent on this network correspond to well-differentiated and hormone-therapy-responsive tumours with good prognosis. In this review, we discuss the interplay between these transcription factors with a particular emphasis on FOXA1 structure and function, and its ability to control ERalpha function. Additionally, we discuss modulators of FOXA1 function, ERalpha-FOXA1-GATA3 downstream targets, and potential therapeutic agents that may increase differentiation through FOXA1.

Microarray-based Gene Expression Profiling as a Clinical Tool for Breast Cancer Management: Are We There Yet?

Breast cancer is a heterogeneous disease, encompassing several histological types and clinical behaviors. Current histopathological classification systems are based on descriptive entities with prognostic significance. Few prognostic and predictive markers beyond those offered by histopathological analysis are available. High-throughput molecular technologies are reshaping our understanding of breast cancer, of which microarray-based gene expression has received most attention. This method has been used to derive a molecular taxonomy for breast cancer, which has provided interesting insights into the biology of the disease. Class prediction studies have generated a multitude of prognostic/predictive signatures, which herald the promise for an improvement in treatment decision making. However, most of the signatures developed to date seem to have discriminatory power almost restricted to estrogen receptor—positive disease. This review addresses the contribution of gene expression profiling to our understanding of breast cancer and its clinical management.

Gene expression predictors in breast cancer: current status, limitations and perspectives

Breast cancer is characterised by a wide heterogeneity regarding outcome and drug sensitivity. A better prediction of these two parameters at the individual level should improve patient management and therefore also improve both the quality of life and the overall survival of the patient. Several molecular predictors for prognosis (MammaPrint or Oncotype DX) and drug prediction (DLD30, SET index) have been generated using DNA-based arrays or RT-PCR, some of these being tested in phase III trials. Although they exhibit good metric performance and should improve the quality of care in the next decade, these predictors are considered suboptimal regarding the potential of the technology. New study design and arrays should generate more powerful second generation gene signatures.

Molecular profiling including epidermal growth factor receptor and p21 expression in high-risk breast cancer patients as indicators of outcome

BACKGROUND

Patients with high-risk primary breast cancer remain at high risk for relapse. More precise prognostic and predictive tools are needed to improve treatment of such patients.

PATIENTS AND METHODS

Formalin-fixed, paraffin-embedded tumors from 239 high-risk breast cancer patients were examined for expression of human epidermal growth factor receptor 2 (HER2), epidermal growth factor receptor (EGFR), estrogen receptor, progesterone receptor, Ki-67, p16, p21, p27, and p53 by immunohistochemistry. Gene expression of EGFR, HER2, glutathione S-transferase-Pi (GSTP1), excision repair cross complementation1 (ERCC1), p21, beta-tubulin-3, multidurg resistance (MDR1), cyclooxygenase2 (COX2), and cyclin-E was measured by RT-PCR.

RESULTS

Eighty percent of patients presented with locally advanced, or > or =10 axillary nodal metastasis, and 20% with inflammatory breast cancer. The median age was 46 years (26-62 years) and the median number of involved axillary lymph nodes was 12 (0-42). At a median follow-up of 86 months, relapse-free survival (RFS) and overall survival for the entire group were 50% (95% CI 43% to 57%) and 62% (95% CI 56% to 69%). Multivariate Cox stepwise analysis resulted in a simple model for RFS consisting only of p21 expression, EGFR expression assessed by RT-PCR, and number of axillary nodal metastases.

CONCLUSION

A prognostic model on the basis of the expression of a limited number of proteins and genes may help to guide target-specific therapies in patients with high-risk breast cancer.

Critical review of microarray-based prognostic tests and trials in breast cancer

PURPOSE OF REVIEW

We critically review the methods used in microarray profiling, the reliability of published genomic signatures and the design of ongoing trials based on two of these signatures.

RECENT FINDINGS

The main limitations of microarray prognostic signatures are known: instability of gene lists, overoptimistic performance indicators and inadequate validation. Two commercially available gene expression-based prognostic tests (MammaPrint and OncotypeDX) are currently being assessed in two large randomized clinical trials. The overall concordance between patient classification resulting from these tests is only slightly better than the concordance between MammaPrint and the Saint Gallen risk. In North America, TAILORx is specifically evaluating the usefulness of chemotherapy in intermediate-risk patients. In the case of MINDACT, ongoing in the European Union, clinical validation of the prognostic test will be considered successful if the proportion of failures among patients classified as having a good prognosis by the prognostic test is below a predefined level. Randomization will not address the validation of the gene expression-based prognostic test in either of these two trials.

SUMMARY

Microarray-based prognostic tests are irremediably moving to the clinics, but their clinical utility might never be formally established.

Genome-wide association studies of cancer

Genome-wide association studies provide a new and powerful approach to investigate the effect of inherited genetic variation on the risk of human disease. These studies rely on high throughput DNA microarray technology to genotype hundreds of thousands of genetic variants across the human genome. The first genome-wide association studies have identified previously unknown genetic risk factors that influence a range of diseases, including prostate cancer, breast cancer, myocardial infarction, age-related macular degeneration, diabetes, Crohn's disease and obesity. Many more studies are currently underway, including a number that will focus on other cancers (e.g., colorectal). Here we discuss the major issues involved in conducting genome-wide association studies and how these studies can be used to examine cancer phenotypes.

Osteopontin-c is a selective marker of breast cancer

While the acquisition of invasiveness is a critical step in early stage breast carcinomas (DCIS), no established molecular markers reliably identify tumor progression. The metastasis gene osteopontin is subject to alternative splicing, which yields 3 messages, osteopontin-a, osteopontin-b and osteopontin-c. Osteopontin-c is selectively expressed in invasive, but not in noninvasive, breast tumor cell lines, and it effectively supports anchorage independence. We evaluated osteopontin-c as a biomarker. The RNA message for osteopontin-c was present in 16 of 20 breast cancers (80%), but was undetectable in 22 normal specimens obtained from reduction mammoplasty. In contrast, osteopontin-a RNA was expressed at various levels in all 20 breast cancers, 11 tumor-surrounding tissues and 21 normal samples. The splice variant osteopontin-b was present at barely detectable levels in 18 of 20 cancers and in 6 of 22 normal breasts. By immunohistochemistry, 66 of 69 normal breasts were negative, while 3 showed low level staining. Among the breast cancers, 43 of 56 cores (77%) stained positive for osteopontin-c. When correlated with tumor grade, the staining for osteopontin-c increased from grade 1 to grade 3. In a total of 178 breast specimens analyzed, osteopontin-c was present in 78% of cancers, 36% of surrounding tissues and 0% of normal tissues. Furthermore, osteopontin-c detects a higher fraction of breast cancers than estrogen receptor (ER), progesterone receptor or HER2. In conjunction, osteopontin-c, ER and HER2 reliably predict grade 2-3 breast cancer. Hence, osteopontin-c is a diagnostic and prognostic marker that may have value in a diagnostic panel together with conventional breast cancer markers.

Clinical significance of Akt and HER2/neu overexpression in African-American and Latina women with breast cancer

Introduction

Breast cancer patients with HER2/neu overexpression have poor outcomes with a decrease in disease-free survival (DFS) and overall survival. The biology of HER2/neu overexpression in breast tumors in African-American and Latina women is poorly understood. The purpose of this study is to understand the clinical significance of activated Akt (phospho-Akt or pAkt) expression in breast tumors from African-American and Latina patients with corresponding tissue HER2/neu overexpression. Cellular and molecular studies have shown that activation of the cell signaling phosphatidylinositol-3-kinase/Akt cascade via the HER2/neu and other receptor tyrosine kinases induces cell proliferation.

Methods

A total of 234 African-American and Latina patients were selected retrospectively. From this group, 141 tumor tissue samples were analyzed for tissue pAkt by immunohistochemistry (IHC). This cohort consisted of 46 HER2/neu-positive (3+ by IHC) and 95 HER2/neu-negative tumors. The prognostic value of activated tissue Akt in relation to HER2/neu overexpression for DFS was determined.

Results

Patients with low pAkt and HER2-negative tumors had the best DFS. As expected, HER2/neu-overexpressing tumors with low pAkt had a decrease in DFS. Similarly, those with high pAkt and HER2-negative tumors also had poor DFS. However, those with an increase in both HER2 and pAkt had the worst DFS. An increase in pAkt was significantly associated with HER2/neu-positive and lymph node-positive breast tumors. Tumors with high HER2 and high pAkt were metastatic. Multivariate analysis demonstrated that, in addition to the common risk factors such as larger tumor size, lymph node involvement, estrogen receptor/progesterone receptor-negative tumors, and HER2/neu-positive tumors, overexpression of pAkt significantly was associated with a decrease in 5-year DFS. A decrease in DFS with an increase in pAkt was observed in both HER2/neu-positive and -negative groups. However, the DFS was similar between HER2/neu-positive/pAkt-negative and HER2/neu-negative/pAkt-positive groups.

Conclusion

Our data suggest that there may be differences in tumor phenotypes within the HER2/neu-overexpressing breast cancer patients. The overexpression of pAkt may be a powerful prognostic marker for predicting DFS and overall survival of breast cancer patients.

Predicting risk of breast cancer recurrence using gene-expression profiling

The molecular profiling of breast tumors using the powerful microarray technology has uncovered the molecular heterogeneity of breast tumors and has offered novel insight into breast tumorigenesis. The estrogen receptor (ER) has been shown to be the most important discriminator dichotomizing breast cancer into two main subsets. At the same time, proliferation, as captured by the recently developed Genomic Grade Index (GGI) has been found to be the most important prognostic factor in breast cancer, far beyond ER status. Interestingly, this index encompasses a significant portion of the predictive power of many published prognostic signatures. The challenge now is to integrate all the prognostic gene signatures available to date towards a comprehensive genomic fingerprint of the primary tumor. In the future, we should be able to offer individualized treatment to our patients based on a clinical decision-making algorithm that takes into account the clinicopathological parameters, the genomic profile of the primary tumor, the presence of micrometastatic cells and pharmacogenetic data for drug response.

Challenges of cancer biomarker profiling

OBJECTIVES

New biomarkers are being developed to identify individuals at risk for cancer, detect disease earlier, determine prognosis, detect recurrence, predict response to particular agents, and monitor response to treatment. This article attempts to address some of the challenges facing the research and medical communities in the delivery of new biomarkers for individualized medicine.

METHODS

A variety of issues and barriers can affect the transfer of clinical tests from research to clinical practice. Differences in sample collection, handling or storage, and profiling techniques may influence the protein profile obtained by any method.

RESULTS

Standard procedures and quality check schemes are necessary because there is a lack of definition to guarantee reproducibility of new procedures. From technical and economic viewpoints, the assay has to be sufficiently robust to be completed in community-based hospitals. Although traditionally cancer patients were treated with drugs of low toxicity or of high tolerance regardless of their efficacy in a given patient if the benefits of that drug are proven in both experimental and clinical conditions, recent advances have provided opportunities to adapt "tailored" treatment modalities. The evolving trend is the usage of patterns of markers instead of a single marker. Further challenges in biomarker development are in finding the relevant markers that have the right degree of specificity and sensitivity and a reliable test to measure the outcome.

CONCLUSIONS

Discovery, testing, and validation of clinically appropriate and commercially useful tumor markers should permit individualization of therapy.

Efficacy of adjuvant chemotherapy according to Prion protein expression in patients with estrogen receptor-negative breast cancer

BACKGROUND

Prion protein (PrPc) has been previously reported to be associated with resistance to proapoptotic stimuli. We evaluated whether the expression of PrPc was associated with the resistance to adjuvant chemotherapy in patients with estrogen receptor (ER) -negative breast cancer.

PATIENTS AND METHODS

The expression of PrPc by primary tumors was assessed by immunohistochemistry in a series of 756 patients included in two randomized trials that compared anthracycline-based chemotherapy to no chemotherapy. The PrPc expression was correlated with ER expression and the benefit of adjuvant chemotherapy was assessed according to PrPc expression in patients with ER-negative tumors.

RESULTS

Immunostaining analysis showed that PrPc was mainly expressed by myoepithelial cells in normal breast tissue. Tissue microarray analysis from 756 breast tumors showed that PrPc was associated with ER-negative breast cancer subsets (P < 0.001). Adjuvant chemotherapy was not associated with a significant risk reduction for death in patients with ER-negative/PrPc-positive disease [adjusted hazard ratio (HR) for death = 0.98, 95% confidence interval (CI) 0.45-2.1, P = 0.95], while it decreased the risk for death (HR = 0.39, 95% CI 0.2-0.74, P = 0.004) in patients with ER-negative/PrPc-negative tumors.

CONCLUSION

These data indicate that ER-negative/PrPc-negative phenotype is associated with a high sensitivity to adjuvant chemotherapy.

Rational Design, Synthesis, and Biological Evaluation of Progesterone-Modified MRI Contrast Agents

A series of contrast agents for magnetic resonance imaging (MRI) aimed at noninvasively determining the hormone receptor status of cancer in vitro was developed. These MRI contrast agents were prepared by conjugating progesterone to clinically used Gd(III) chelates. These agents exhibited higher progesterone receptor binding affinities in the nanomolar range and intracellular accumulation. High logP values of the modified compounds suggested that the lipophilicity of the steroid conjugates may have contributed to membrane permeability. Synchrotron radiation X-ray fluorescence microscopy and magnetic resonance images revealed that the synthesized conjugates showed the greatest cellular accumulation and significant increase in relaxivity in vitro compared to the previously developed steroid-modified agent. Transcriptional assays using the progesterone response element linked to luciferase indicated that the contrast agents entered the cell, interacted with the biological target, and drove specific progesterone-mediated transcription.

The relationship between FDG uptake in PET scans and biological behavior in breast cancer

BACKGROUND

Positron emission tomography (PET) is a non-invasive imaging modality used in the diagnosis and staging of breast cancer. However, several factors can affect fluoro-deoxyglucose (FDG) uptake by a tumor. To clarify the parameters that most affect FDG accumulation in tumors, the relationship between standardized uptake values (SUVs) and clinicopathological factors and immunohistopathological analysis was investigated in breast cancer.

MATERIAL AND METHODS

PET studies were performed preoperatively on 37 patients with breast carcinoma. SUVs were counted at one hour (early phase) and at two hours (delayed phase) after FDG injection. The relationships between SUVs and 13 clinical, pathological and immunohistchemical factors were studied.

RESULTS

A significant association was found between FDG accumulation and early and delayed phase mitotic counts (p=0.0018 and 0.0010, respectively), Ki67 positive cell percentage (p=0.0098 and 0.0062, respectively), and nuclear grade (p=0.0232 and 0.0195, respectively). On the other hand, nodal status weakly correlated with the delayed phase (p=0.0907). However, other clinicopathological parameters and immunohistopathological status, which included tumor size, age, histology, estrogen receptor, progesterone receptor and Her2/neu overexpression, did not correlate significantly with FDG uptake.

CONCLUSION

Mitotic count and Ki67 reflect cellular aggressiveness. These parameters were strongly correlated with tracer uptake. Thus our data suggested that the biological behavior of breast cancer is reflected in the variation of FDG uptake by the tumor. However, whether FDG uptake is a true prognostic and predictive factor remains to be confirmed in larger studies over an extended period of time.

Molecular staging of prostate cancer in the year 2007

Numerous attempts towards improving patient management by molecular staging have been fruitless so far. No single molecular parameter is routinely analyzed in prostate cancer tissue. This may be partly due to genuine properties of prostate cancer that may make this tumor a difficult target. Furthermore, inherent logistical problems result in a shortage of prostate cancer tissue for research purposes. For the future, it can be hoped that the availability of more powerful molecular techniques in combination with better tissue archives will allow more rapid progress. Powerful DNA array and proteomics methods allow the systematic analysis of virtually all genes of a cancer on the DNA, RNA, and protein level. Although such approaches are sometimes labeled as "fishing expeditions," it cannot be totally disregarded that the simultaneous analysis of all genes has a high likelihood of identifying significant new information. In future, one of the major scientific challenges will be the validation of several potential biomarkers in large enough and clinically well-characterized patient cohorts. In particular, studies on needle core biopsies and hormone refractory cancers are imperatively needed for investigating the natural history of the disease or to discover potential predictive markers for radiation therapy and new therapeutic target genes to answer the clinically most important questions for optimal clinical decision making in prostate cancer patients: which patients will not require local therapy? If local therapy is needed, what is the treatment of choice? What medications should be given if metastases are present?

Genetic polymorphisms in the cyclooxygenase-2 gene, use of nonsteroidal anti-inflammatory drugs, and breast cancer risk

Introduction

The association between use of nonsteroidal anti-inflammatory drugs (NSAIDs) and breast cancer risk remains unclear. Inconsistencies in previously reported findings may be partly due to differences in expression of cyclooxygenase (COX)-2. We hypothesized that genetic polymorphisms (COX-2 .926, COX-2 .5209, and COX-2 .8473) may reduce overall breast cancer risk or risk for subtypes of breast cancer by modulating the inflammatory response and may interact with aspirin or any NSAID use.

Methods

We conducted a population-based, case-control study in which we genotyped 1,067 breast cancer cases and 1,110 control individuals included in the Long Island Breast Cancer Study Project.

Results

No major effects of the three COX-2 variant alleles on breast cancer risk were found. A total of eight distinct haplotypes and 18 diplotypes were observed in the population. Overall, no significant associations between COX-2 haplotypes/diplotypes and breast cancer risk were observed. Among women who used aspirin or any NSAID there was little evidence for an interaction with the at-risk COX-2 genotypes, with one exception. Among women with hormone receptor positive breast cancer, the reduced risk for any NSAID use was only evident among those who had at least one variant C allele of COX-2 .8473 (odds ratio = 0.7, 95% confidence interval = 0.5 to 1.0; P for the interaction = 0.02). There was no corresponding interaction for aspirin use, possibly because of limited power.

Conclusion

These data provide modest evidence that the C allele of COX-2 .8473 may interact with NSAIDs to reduce risk for hormone receptor positive breast cancer.