“Good Old” clinical markers have similar power in breast cancer prognosis as microarray gene expression profilers

Clinical analysis of pulmonary mucosa-associated lymphoid tissue lymphoma coexisting with lung cancer

BACKGROUND

Primary pulmonary Mucosa-associated lymphoid tissue (MALT) lymphoma is a sporadic disease with a favorable prognosis. Particularly, pulmonary MALT lymphoma coexisting with lung cancer is not only rare but also prone to misdiagnosis. The clinical characteristics and prognostic factors of this co-occurrence, however, remain poorly understood.

METHODS

We retrospectively analyzed the clinical, imaging, genetic mutations and pathological data among adult patients with pulmonary MALT lymphoma coexisting with lung cancer who were confirmed by pathological examinations after operation at Shanghai Pulmonary Hospital between 1st January 2013 and 31st May 2024.

RESULTS

After exclusions, a total of 14 patients were included in the study, of which eleven patients were women and only 3 were men, with a median age of 57 [IQR: 53-67] years. Pulmonary MALT lymphoma presented a median diameter of 14 mm (IQR: 6-23). Nodule was the most frequent CT feature and existing pattern of pulmonary lesions (n = 8). The lung cancer was with a median diameter of 10.7 [IQR:6,20] mm, with nodules as the predominant CT feature (n = 12). Six patients manifested dual primary malignancies within the same lung lobe, termed collision tumors, whereas the remaining eight had lesions in different lobes. Five cases exhibited EGFR mutant, and one case showed no mutation. 13 patients were pathological confirmed with lung adenocarcinoma and one with microcarcinoma. Postoperatively, all-cause mortality rate was low, indicating a positive prognosis. One patient died 41 months after surgery due to a pulmonary infection, while the remaining 13 patients were in good condition with an average follow-up of 37.92 months.

CONCLUSIONS

In patients with pulmonary lesions, particularly multiple lesions, comprehensive preoperative evaluation is crucial to prevent misdiagnosis or missed diagnoses. Besides, surgical resection is desirable when both lung cancer and MALT are at an early stage and can be resected with minimally invasive surgery (minimally lung resection).

Grading of invasive breast carcinoma: the way forward

Histologic grading has been a simple and inexpensive method to assess tumor behavior and prognosis of invasive breast cancer grading, thereby identifying patients at risk for adverse outcomes, who may be eligible for (neo)adjuvant therapies. Histologic grading needs to be performed accurately, on properly fixed specimens, and by adequately trained dedicated pathologists that take the time to diligently follow the protocol methodology. In this paper, we review the history of histologic grading, describe the basics of grading, review prognostic value and reproducibility issues, compare performance of grading to gene expression profiles, and discuss how to move forward to improve reproducibility of grading by training, feedback and artificial intelligence algorithms, and special stains to better recognize mitoses. We conclude that histologic grading, when adequately carried out, remains to be of important prognostic value in breast cancer patients.

Recurrence of breast cancer after regional or general anaesthesia: a randomised controlled trial

BACKGROUND

Three perioperative factors impair host defence against recurrence during cancer surgery: the surgical stress response, use of volatile anaesthetic, and opioids for analgesia. All factors are ameliorated by regional anaesthesia-analgesia. We tested the primary hypothesis that breast cancer recurrence after potentially curative surgery is lower with regional anaesthesia-analgesia using paravertebral blocks and the anaesthetic propofol than with general anaesthesia with the volatile anaesthetic sevoflurane and opioid analgesia. A second hypothesis was that regional anaesthesia-analgesia reduces persistent incisional pain.

METHODS

We did a randomised controlled trial at 13 hospitals in Argentina, Austria, China, Germany, Ireland, New Zealand, Singapore, and the USA. Women (age <85 years) having potentially curative primary breast cancer resections were randomised by computer to either regional anaesthesia-analgesia (paravertebral blocks and propofol) or general anaesthesia (sevoflurane) and opioid analgesia. The primary outcome was local or metastatic breast cancer recurrence. The secondary outcome was incisional pain at 6 months and 12 months. Primary analyses were done under intention-to-treat principles. This trial is registered with ClinicalTrials.gov, NCT00418457. The study was stopped after a preplanned futility boundary was crossed.

FINDINGS

Between Jan 30, 2007, and Jan 18, 2018, 2132 women were enrolled to the study, of whom 24 were excluded before surgery. 1043 were assigned to regional anaesthesia-analgesia and 1065 were allocated to general anaesthesia. Baseline characteristics were well balanced between study groups. Median follow-up was 36 (IQR 24-49) months. Among women assigned regional anaesthesia-analgesia, 102 (10%) recurrences were reported, compared with 111 (10%) recurrences among those allocated general anaesthesia (hazard ratio 0·97, 95% CI 0·74-1·28; p=0·84). Incisional pain was reported by 442 (52%) of 856 patients assigned to regional anaesthesia-analgesia and 456 (52%) of 872 patients allocated to general anaesthesia at 6 months, and by 239 (28%) of 854 patients and 232 (27%) of 852 patients, respectively, at 12 months (overall interim-adjusted odds ratio 1·00, 95% CI 0·85-1·17; p=0·99). Neuropathic breast pain did not differ by anaesthetic technique and was reported by 87 (10%) of 859 patients assigned to regional anaesthesia-analgesia and 89 (10%) of 870 patients allocated to general anaesthesia at 6 months, and by 57 (7%) of 857 patients and 57 (7%) of 854 patients, respectively, at 12 months.

INTERPRETATION

In our study population, regional anaesthesia-analgesia (paravertebral block and propofol) did not reduce breast cancer recurrence after potentially curative surgery compared with volatile anaesthesia (sevoflurane) and opioids. The frequency and severity of persistent incisional breast pain was unaffected by anaesthetic technique. Clinicians can use regional or general anaesthesia with respect to breast cancer recurrence and persistent incisional pain.

FUNDING

Sisk Healthcare Foundation (Ireland), Eccles Breast Cancer Research Fund, British Journal of Anaesthesia International, College of Anaesthetists of Ireland, Peking Union Medical College Hospital, Science Fund for Junior Faculty 2016, Central Bank of Austria, and National Healthcare Group.

Applying new Magee equations for predicting the Oncotype Dx recurrence score

BACKGROUND

Breast cancer is one of the most prevalent cancers in women. Oncotype Dx is a multi-gene assay frequently used to predict the recurrence risk for estrogen receptor-positive early breast cancer, with values < 18 considered low risk; ≥ 18 and ≤ 30, intermediate risk; and > 30, high risk. Patients at a high risk for recurrence are more likely to benefit from chemotherapy treatment.

METHODS

In this study, clinicopathological parameters for 37 cases of early breast cancer with available Oncotype Dx results were used to estimate the recurrence score using the three new Magee equations. Correlation studies with Oncotype Dx results were performed. Applying the same cutoff points as Oncotype Dx, patients were categorized into low-, intermediate- and high-risk groups according to their estimated recurrence scores.

RESULTS

Pearson correlation coefficient (R) values between estimated and actual recurrence score were 0.73, 0.66, and 0.70 for Magee equations 1, 2 and 3, respectively. The concordance values between actual and estimated recurrence scores were 57.6%, 52.9%, and 57.6% for Magee equations 1, 2 and 3, respectively. Using standard pathologic measures and immunohistochemistry scores in these three linear Magee equations, most low and high recurrence risk cases can be predicted with a strong positive correlation coefficient, high concordance and negligible two-step discordance.

CONCLUSIONS

Magee equations are user-friendly and can be used to predict the recurrence score in early breast cancer cases.

Use of adjuvant chemotherapy in hormone receptor-positive breast cancer patients with or without the 21-gene expression assay

PURPOSE

We assessed the use of chemotherapy in breast cancer patients to investigate the factors that changed trends in chemotherapy following the adoption of the 21-gene expression assay in tumor genomic profiling.

METHODS

Our study used 2033 patients from the National Cancer Center in Korea diagnosed with hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-negative breast cancer (tumor size of 0.5 cm or larger and 0-3 node metastases) from 2010 to 2015. We analyzed use of the 21-gene expression assay, changes in frequency of adjuvant chemotherapy use, and clinicopathological factors related to adjuvant chemotherapy to assess the impact of the 21-gene expression assay.

RESULTS

Adjuvant chemotherapy use declined from 33.33% (2011) to 13.59% (2015) [relative risk (RR), 0.71; 95% CI 0.56-0.89; p_trend = 0.004] in patients with 21-gene expression assay data. Among patients without assay data, adjuvant chemotherapy use decreased from 76.79 to 40.17% between 2010 and 2015 (RR 0.87; 95% CI 0.84-0.91; p_trend < 0.001), especially for patients with node-negative/micrometastasis (RR 0.85; 95% CI 0.81-0.89; p_trend < 0.001). The frequency of adjuvant chemotherapy was significantly decreased after introduction of the 21-gene expression assay (p < 0.001). Tumor size (p < 0.001), progesterone receptor (PgR) status (p = 0.001), and proliferation index (Ki-67) levels (p < 0.001) were important factors for chemotherapy decision-making in node-negative/micrometastasis patients who did not undergo the assay.

CONCLUSIONS

For HR-positive, HER2-negative breast cancer patients with 0-1 node metastases, chemotherapy use declined significantly after the adoption of the 21-gene assay. PgR status and Ki-67 were useful for chemotherapy decision-making in cases without the 21-gene assay.

Prognostic contribution of mammographic breast density and HER2 overexpression to the Nottingham Prognostic Index in patients with invasive breast cancer

Background

To investigate whether very low mammographic breast density (VLD), HER2, and hormone receptor status holds any prognostic significance within the different prognostic categories of the widely used Nottingham Prognostic Index (NPI). We also aimed to see whether these factors could be incorporated into the NPI in an effort to enhance its performance.

Methods

This study included 270 patients with newly diagnosed invasive breast cancer. Patients with mammographic breast density of <10 % were considered as VLD. In this study, we compared the performance of NPI with and without VLD, HER2, ER and PR. Cox multivariate analysis, time-dependent receiver operating characteristic curve (tdROC), concordance index (c-index) and prediction error (0.632+ bootstrap estimator) were used to derive an updated version of NPI.

Results

Both mammographic breast density (VLD) (p < 0.001) and HER2 status (p = 0.049) had a clinically significant effect on the disease free survival of patients in the intermediate and high risk groups of the original NPI classification. The incorporation of both factors (VLD and HER2 status) into the NPI provided improved patient outcome stratification by decreasing the percentage of patients in the intermediate prognostic groups, moving a substantial percentage towards the low and high risk prognostic groups.

Conclusions

Very low density (VLD) and HER2 positivity were prognostically significant factors independent of the NPI. Furthermore, the incorporation of VLD and HER2 to the NPI served to enhance its accuracy, thus offering a readily available and more accurate method for the evaluation of patient prognosis.

Cost Effectiveness of Molecular Profiling for Adjuvant Decision Making in Patients With Node-Negative Breast Cancer

Purpose

To conduct an economic evaluation of the 70-gene signature used to guide adjuvant chemotherapy decision making both in patients with node-negative breast cancer (NNBC) and in the subgroup of estrogen receptor (ER) –positive patients.

Patients and Methods

We used a mixed approach combining patient-level data from a multicenter validation study of the 70-gene signature (untreated patients) and secondary sources for chemotherapy efficacy, unit costs, and utility values. Three strategies on which to base the decision to administer adjuvant chemotherapy were compared: the 70-gene signature, Adjuvant! Online, and chemotherapy in all patients. In the base-case analysis, costs from the French National Insurance Scheme, life-years (LYs), and quality-adjusted life-years (QALYs) were computed for the three strategies over a 10-year period. Cost-effectiveness acceptability curves using the net monetary benefit were computed, combining bootstrap and probabilistic sensitivity analyses.

Results

The mean differences in LYs and QALYs were similar between the three strategies. The 70-gene signature strategy was associated with a higher cost, with a mean difference of €2,037 (range, €1,472 to €2,515) compared with Adjuvant! Online and of €657 (95% CI, −€642 to €3,130) compared with systematic chemotherapy. For a €50,000 per QALY willingness-to-pay threshold, the probability of being the most cost-effective strategy was 92% (76% in ER-positive patients) for the Adjuvant! Online strategy, 6% (4% in ER-positive patients) for the systematic chemotherapy strategy, and 2% (20% in ER-positive patients) for the 70-gene strategy.

Conclusion

Optimizing adjuvant chemotherapy decision making based on the 70-gene signature is unlikely to be cost effective in patients with NNBC.

Maximum Likelihood Estimation of GEVD: Applications in Bioinformatics

We propose a method, maximum likelihood estimation of generalized eigenvalue decomposition (MLGEVD) that employs a well known technique relying on the generalization of singular value decomposition (SVD). The main aim of the work is to show the tight equivalence between MLGEVD and generalized ridge regression. This relationship reveals an important mathematical property of GEVD in which the second argument act as prior information in the model. Thus we show that MLGEVD allows the incorporation of external knowledge about the quantities of interest into the estimation problem. We illustrate the importance of prior knowledge in clinical decision making/identifying differentially expressed genes with case studies for which microarray data sets with corresponding clinical/literature information are available. On all of these three case studies, MLGEVD outperformed GEVD on prediction in terms of test area under the ROC curve (test AUC). MLGEVD results in significantly improved diagnosis, prognosis and prediction of therapy response.

Comprehensive gene expression meta-analysis of head and neck squamous cell carcinoma microarray data defines a robust survival predictor

BACKGROUND

Head and neck squamous cell carcinoma refers to a heterogeneous disease frequently aggressive in its biologic behavior. Despite the improvements in the therapeutic modalities, the long-term survival rate remained unchanged over the past decade and patients with this type of cancer are at a high risk of developing recurrence. For this reason, there is a great need to find better ways to foresee outcome, to improve treatment choices, and to enable a more personalized approach.

PATIENTS AND METHODS

Nine microarray gene expression datasets, reporting survival data of a total of 841 samples, were retrieved from publicly repositories. Three datasets, profiled on the same version of microarray chips, were selected and merged following a meta-analysis approach to build a training set. The remaining six studies were used as independent validation sets.

RESULTS

The training set led us to identify a 172-gene signature able to stratify patients in low or high risk of relapse [log-rank, P = 2.44e-05; hazard ratio (HR) = 2.44, 95% confidence interval (CI) 1.58-3.76]. The model based on the 172 genes was validated on the six independent datasets. The performance of the model was challenged against other proposed prognostic signatures (radiosensitivity index, 13-gene oral squamous cell carcinoma signature, hypoxia metagene, 42-gene high-risk signature) and was compared with a human papillomavirus (HPV) signature: our model resulted independent and even better in prediction.

CONCLUSIONS

We have identified and validated a prognostic model based on the expression of 172 genes, independent from HPV status and able to improve assessment of patient's risk of relapse compared with other molecular signatures. In order to transpose our model into a useful clinical grade assay, additional work is needed following the framework established by the Institute of Medicine and REMARK guidelines.

Molecular and clinicopathological markers of prognosis in breast cancer

A vast body of research in breast cancer prognostication has accumulated. Yet despite this, patients within current prognostic categories may have significantly different outcomes. There is a need to more accurately divide those cancer types associated with an excellent prognosis from those requiring more aggressive therapy. Gene expression array studies have revealed the numerous molecular breast cancer subtypes that are associated with differing outcomes. Furthermore, as next generation technologies evolve and further reveal the complexities of breast cancer, it is likely that existing prognostic approaches will become progressively refined. Future prognostication in breast cancer requires a morphomolecular, multifaceted approach involving the assessment of anatomical disease extent and levels of protein, DNA and RNA expression. One of the major challenges in prognostication will be the integration of potential assays into existing clinical systems and identification of appropriate patient subgroups for analysis.

On the validity of time-dependent AUC estimators

Recent developments in molecular biology have led to the massive discovery of new marker candidates for the prediction of patient survival. To evaluate the predictive value of these markers, statistical tools for measuring the performance of survival models are needed. We consider estimators of discrimination measures, which are a popular approach to evaluate survival predictions in biomarker studies. Estimators of discrimination measures are usually based on regularity assumptions such as the proportional hazards assumption. Based on two sets of molecular data and a simulation study, we show that violations of the regularity assumptions may lead to over-optimistic estimates of prediction accuracy and may therefore result in biased conclusions regarding the clinical utility of new biomarkers. In particular, we demonstrate that biased medical decision making is possible even if statistical checks indicate that all regularity assumptions are satisfied.

Pathological and molecular diagnosis of triple-negative breast cancer: a clinical perspective

Triple-negative breast cancer (TNBC) is a heterogeneous disease diagnosed by immunohistochemistry and is characterised by tumours that do not express estrogen receptor (ER) or progesterone receptor (PR) at all, and do not overexpress human epidermal growth factor receptor 2 (HER2). Prototypical TNBC is aggressive in nature and associated with a poor prognosis, making the accurate diagnosis of the disease vitally important for ensuring optimal therapy for patients. Morphological and biological analyses can identify subtypes of TNBC, which can have different prognoses, and (in the case of the latter) may eventually be used to predict response to treatment. This mini-review focuses on clinically relevant issues in the diagnosis of TNBC, including the importance of adherence to international guidelines for the detection of ER/PR/HER2 status, and the relationship between TNBC and the overlapping (yet distinct) intrinsic subtype of 'basal-like' breast cancer. In addition, we review the potential use of emerging biomarkers as surrogates for molecular subtypes and as a means of identifying potential responders to new therapies.

The combination of Ki67, histological grade and estrogen receptor status identifies a low-risk group among 1,854 chemo-naïve women with N0/N1 primary breast cancer

Background

The aim was to confirm a previously defined prognostic index, combining a proliferation marker, histological grade, and estrogen receptor (ER) in different subsets of primary N0/N1 chemo-naïve breast cancer patients.

Methods/design

In the present study, including 1,854 patients, Ki67 was used in the index (KiGE), since it is the generally accepted proliferation marker in clinical routine. The low KiGE-group was defined as histological grade 1 patients and grade 2 patients which were ER-positive and had low Ki67 expression. All other patients made up the high KiGE-group. The KiGE-index separated patients into two groups with different prognosis. In multivariate analysis, KiGE was significantly associated with disease-free survival, when adjusted for age at diagnosis, tumor size and adjuvant endocrine treatment (hazard ratio: 3.5, 95% confidence interval: 2.6–4.7, P<0.0001).

Discussion

We have confirmed a prognostic index based on a proliferation marker (Ki67), histological grade, and ER for identification of a low-risk group of patients with N0/N1 primary breast cancer. For this low-risk group constituting 57% of the patients, with a five-year distant disease-free survival of 92%, adjuvant chemotherapy will have limited effect and may be avoided.

Combined analysis of vascular invasion, grade, HER2 and Ki67 expression identifies early breast cancer patients with questionable benefit of systemic adjuvant therapy

INTRODUCTION

Over-treatment of low-risk early breast cancer patients with adjuvant systemic therapies is an important clinical challenge. Better techniques are required which can be used to distinguish between the large group of patients with no residual disease after surgery and consequently no benefit of adjuvant treatment, from the smaller group with high relapse risk. A better integration of available prognostic factors might contribute to improved prediction of clinical outcome.

MATERIAL AND METHODS

The current study included 346 unselected pT1pN0 patients who did not receive adjuvant systemic treatment. In Norway, no patients with this stage were recommended systemic treatment at the time of the study (1995-1998). Histological type, tumour size, grade, vascular invasion (VI), hormone receptor (HR) status, HER2 and Ki67 (cut-off 10%) were analysed. Median follow-up was 86 months for relapse and 101 months for death.

RESULTS

Thirty-eight patients experienced relapse, 31 with distant metastasis. Twenty-one patients died of breast cancer. In univariate analysis grade, HER2, HR, VI and Ki67 had impact on clinical outcome (p < 0.005, log rank). In multivariate analysis, only grade 1-2 vs. grade 3, HER2, VI, and Ki67 status were significant for disease free survival, distant disease free survival, and/or breast cancer specific survival. These factors were used in combination, to separate patients into groups based on the number of unfavourable factors present [combined prognostic score (CPS) 0-4]. Close to 2/3 of the patients (61.4%) had no unfavourable factor (CPS0), whilst 18.4% had CPS ≥ 2. Only 3.6% of those with CPS0 developed metastasis (p < 0.001). The outcome was clearly worse for patients with CPS ≥ 2 (p < 0.001), systemic relapse was detected in approximately 40%.

CONCLUSIONS

This study indicates that the combined use of grade, VI, HER2 and Ki67 identifies a subgroup of breast cancer patients with a relapse risk that may question the benefit of adjuvant systemic therapy.

Molecular alterations associated with breast cancer mortality

Background

Breast cancer is a heterogeneous disease and patients with similar pathologies and treatments may have different clinical outcomes. Identification of molecular alterations associated with disease outcome may improve risk assessment and treatments for aggressive breast cancer.

Methods

Allelic imbalance (AI) data was generated for 122 invasive breast tumors with known clinical outcome. Levels and patterns of AI were compared between patients who died of disease (DOD) and those with ≥5 years disease-free survival (DFS) using Student t-test and chi-square analysis with a significance value of P<0.05.

Results

Levels of AI were significantly higher in tumors from the 31 DOD patients (28.6%) compared to the 91 DFS patients (20.1%). AI at chromosomes 7q31, 8p22, 13q14, 17p13.3, 17p13.1 and 22q12.3 was associated with DOD while AI at 16q22–q24 was associated with DFS. After multivariate analysis, AI at chromosome 8p22 remained an independent predictor of breast cancer mortality. The frequency of AI at chromosome 13q14 was significantly higher in patients who died ≥5 years compared to those who died <5 years from diagnosis.

Conclusion

Tumors from DOD compared to DFS patients are marked by increased genomic instability and AI at chromosome 8p22 is significantly associated with breast cancer morality, independent of other clinicopathological factors. AI at chromosome 13q14 was associated with late (>5-years post-diagnosis) mortality but not with death from disease within five years, suggesting that patients with short- and long-term mortality may have distinct genetic diseases.

A comparison of estimators to evaluate the discriminatory power of time-to-event models

Discrimination measures for continuous time-to-event outcomes have become an important tool in medical decision making. The idea behind discrimination measures is to evaluate the performance of a prediction model by measuring its ability to distinguish between observations having an event and those having no event. Researchers proposed a variety of approaches to estimate discrimination measures from a set of right-censored data. These approaches rely on different regularity assumptions that are needed to ensure consistency of the respective estimators. Typical examples of regularity assumptions include the proportional hazards assumption in Cox regression and the random censoring assumption. Because regularity assumptions are often violated in practice, conducting a sensitivity analysis of the estimators is of considerable interest. The aim of the paper is to analyze and to compare the most popular estimators of discrimination measures for event time outcomes. On the basis of the results of an extensive simulation study and the analysis of molecular data, we investigate the behavior of the estimators in situations where the underlying regularity assumptions do not hold. We show that violations of the regularity assumptions may induce a nonignorable bias and may therefore result in biased medical decision making.

Stromal genes add prognostic information to proliferation and histoclinical markers: a basis for the next generation of breast cancer gene signatures

BACKGROUND

First-generation gene signatures that identify breast cancer patients at risk of recurrence are confined to estrogen-positive cases and are driven by genes involved in the cell cycle and proliferation. Previously we induced sets of stromal genes that are prognostic for both estrogen-positive and estrogen-negative samples. Creating risk-management tools that incorporate these stromal signatures, along with existing proliferation-based signatures and established clinicopathological measures such as lymph node status and tumor size, should better identify women at greatest risk for metastasis and death.

METHODOLOGY/PRINCIPAL FINDINGS

To investigate the strength and independence of the stromal and proliferation factors in estrogen-positive and estrogen-negative patients we constructed multivariate Cox proportional hazards models along with tree-based partitions of cancer cases for four breast cancer cohorts. Two sets of stromal genes, one consisting of DCN and FBLN1, and the other containing LAMA2, add substantial prognostic value to the proliferation signal and to clinical measures. For estrogen receptor-positive patients, the stromal-decorin set adds prognostic value independent of proliferation for three of the four datasets. For estrogen receptor-negative patients, the stromal-laminin set significantly adds prognostic value in two datasets, and marginally in a third. The stromal sets are most prognostic for the unselected population studies and may depend on the age distribution of the cohorts.

CONCLUSION

The addition of stromal genes would measurably improve the performance of proliferation-based first-generation gene signatures, especially for older women. Incorporating indicators of the state of stromal cell types would mark a conceptual shift from epithelial-centric risk assessment to assessment based on the multiple cell types in the cancer-altered tissue.

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.

CD8+ lymphocyte infiltration is an independent favorable prognostic indicator in basal-like breast cancer

Introduction

Tumor infiltrating lymphocytes may indicate an immune response to cancer development, but their significance remains controversial in breast cancer. We conducted this study to assess CD8+ (cytotoxic T) lymphocyte infiltration in a large cohort of invasive early stage breast cancers, and to evaluate its prognostic effect in different breast cancer intrinsic subtypes.

Methods

Immunohistochemistry for CD8 staining was performed on tissue microarrays from 3992 breast cancer patients. CD8+ tumor infiltrating lymphocytes were counted as intratumoral when in direct contact with tumor cells, and as stromal in adjacent locations. Kaplan-Meier functions and Cox proportional hazards regression models were applied to examine the associations between tumor infiltrating lymphocytes and breast cancer specific survival.

Results

Among 3403 cases for which immunohistochemical results were obtained, CD8+ tumor infiltrating lymphocytes were identified in an intratumoral pattern in 32% and stromal pattern in 61% of the cases. In the whole cohort, the presence of intratumoral tumor-infiltrating lymphocytes was significantly correlated with young age, high grade, estrogen receptor negativity, human epidermal growth factor receptor-2 positivity and core basal intrinsic subtype, and was associated with superior breast cancer specific survival. Multivariate analysis indicated that the favorable prognostic effect of CD8+ tumor infiltrating lymphocytes was significant only in the core basal intrinsic subgroup (Hazard ratio, HR = 0.35, 95% CI = 0.23-0.54). No association with improved survival was present in those triple negative breast cancers that lack expression of basal markers (HR = 0.99, 95% CI = 0.48-2.04) nor in the other intrinsic subtypes.

Conclusions

CD8+ tumor infiltrating lymphocytes are an independent prognostic factor associated with better patient survival in basal-like breast cancer, but not in non-basal triple negative breast cancers nor in other intrinsic molecular subtypes.

Improved modeling of clinical data with kernel methods

OBJECTIVE

Despite the rise of high-throughput technologies, clinical data such as age, gender and medical history guide clinical management for most diseases and examinations. To improve clinical management, available patient information should be fully exploited. This requires appropriate modeling of relevant parameters.

METHODS

When kernel methods are used, traditional kernel functions such as the linear kernel are often applied to the set of clinical parameters. These kernel functions, however, have their disadvantages due to the specific characteristics of clinical data, being a mix of variable types with each variable its own range. We propose a new kernel function specifically adapted to the characteristics of clinical data.

RESULTS

The clinical kernel function provides a better representation of patients' similarity by equalizing the influence of all variables and taking into account the range r of the variables. Moreover, it is robust with respect to changes in r. Incorporated in a least squares support vector machine, the new kernel function results in significantly improved diagnosis, prognosis and prediction of therapy response. This is illustrated on four clinical data sets within gynecology, with an average increase in test area under the ROC curve (AUC) of 0.023, 0.021, 0.122 and 0.019, respectively. Moreover, when combining clinical parameters and expression data in three case studies on breast cancer, results improved overall with use of the new kernel function and when considering both data types in a weighted fashion, with a larger weight assigned to the clinical parameters. The increase in AUC with respect to a standard kernel function and/or unweighted data combination was maximum 0.127, 0.042 and 0.118 for the three case studies.

CONCLUSION

For clinical data consisting of variables of different types, the proposed kernel function--which takes into account the type and range of each variable--has shown to be a better alternative for linear and non-linear classification problems.

Breast medical oncologists' use of standard prognostic factors to predict a 21-gene recurrence score

BACKGROUND

Half of all breast cancers are early stage, lymph node negative, and hormone receptor positive. A 21-gene (Oncotype DX®; Genomic Health, Inc., Redwood City, CA) recurrence score (RS) is prognostic for recurrence and predictive of chemotherapy benefit. We explored the ability of oncologists to predict the RS using standard prognostic criteria.

METHODS

Standard demographic and tumor prognostic criteria were obtained from patients with an available RS. Two academic pathologists provided tumor grade, histologic type, and hormone receptor status. Six academic oncologists predicted the RS category (low, intermediate, or high) and provided a recommendation for therapy. The oncologists were then given the actual RS and provided recommendations for therapy. Analysis for agreement was performed.

RESULTS

Thirty-one cases, including nine additional cases with variant pathology reads, were presented. There was substantial agreement in oncologists' ability to discriminate between true low or true intermediate and true high (κ = 0.75; p < .0001). Predictions between low and intermediate were not consistent. The most common discrepancies were predictions of a low RS risk when cases were true intermediate and predictions of an intermediate RS risk when cases were true low. The actual RS resulted in a change in the treatment recommendations in 19% of cases. Of the 186 scenarios and six oncologists in aggregate, five fewer chemotherapy recommendations resulted with the actual RS.

CONCLUSIONS

Oncologists are able to differentiate between a low or intermediate RS and a high RS using standard prognostic criteria. However, provision of the actual RS changed the treatment recommendations in nearly 20% of cases, suggesting that the RS may reduce chemotherapy use. This effect was observed in particular in intermediate-risk cases. Prospective clinical trials are necessary to determine whether decisions based on the RS change outcomes.

Molecular serum portraits in patients with primary breast cancer predict the development of distant metastases

The risk of distant recurrence in breast cancer patients is difficult to assess with current clinical and histopathological parameters, and no validated serum biomarkers currently exist. Using a recently developed recombinant antibody microarray platform containing 135 antibodies against 65 mainly immunoregulatory proteins, we screened 240 sera from 64 patients with primary breast cancer. This unique longitudinal sample material was collected from each patient between 0 and 36 mo after the primary operation. The velocity for each serum protein was determined by comparing the samples collected at the primary operation and then 3-6 mo later. A 21-protein signature was identified, using leave-one-out cross-validation together with a backward elimination strategy in a training cohort. This signature was tested and evaluated subsequently in an independent test cohort (prevalidation). The risk of developing distant recurrence after primary operation could be assessed for each patient, using her molecular portraits. The results from this prevalidation study showed that patients could be classified into high- versus low-risk groups for developing metastatic breast cancer with a receiver operating characteristic area under the curve of 0.85. This risk assessment was not dependent on the type of adjuvant therapy received by the patients. Even more importantly, we demonstrated that this protein signature provided an added value compared with conventional clinical parameters. Consequently, we present here a candidate serum biomarker signature able to classify patients with primary breast cancer according to their risk of developing distant recurrence, with an accuracy outperforming current procedures.

Do two machine-learning based prognostic signatures for breast cancer capture the same biological processes?

The fact that there is very little if any overlap between the genes of different prognostic signatures for early-discovery breast cancer is well documented. The reasons for this apparent discrepancy have been explained by the limits of simple machine-learning identification and ranking techniques, and the biological relevance and meaning of the prognostic gene lists was questioned. Subsequently, proponents of the prognostic gene lists claimed that different lists do capture similar underlying biological processes and pathways. The present study places under scrutiny the validity of this claim, for two important gene lists that are at the focus of current large-scale validation efforts. We performed careful enrichment analysis, controlling the effects of multiple testing in a manner which takes into account the nested dependent structure of gene ontologies. In contradiction to several previous publications, we find that the only biological process or pathway for which statistically significant concordance can be claimed is cell proliferation, a process whose relevance and prognostic value was well known long before gene expression profiling. We found that the claims reported by others, of wider concordance between the biological processes captured by the two prognostic signatures studied, were found either to be lacking statistical rigor or were in fact based on addressing some other question.

Added predictive value of high-throughput molecular data to clinical data and its validation

Hundreds of 'molecular signatures' have been proposed in the literature to predict patient outcome in clinical settings from high-dimensional data, many of which eventually failed to get validated. Validation of such molecular research findings is thus becoming an increasingly important branch of clinical bioinformatics. Moreover, in practice well-known clinical predictors are often already available. From a statistical and bioinformatics point of view, poor attention has been given to the evaluation of the added predictive value of a molecular signature given that clinical predictors or an established index are available. This article reviews procedures that assess and validate the added predictive value of high-dimensional molecular data. It critically surveys various approaches for the construction of combined prediction models using both clinical and molecular data, for validating added predictive value based on independent data, and for assessing added predictive value using a single data set.

Qualitative Assessment of the Progesterone Receptor and HER2 Improves the Nottingham Prognostic Index Up to 5 Years After Breast Cancer Diagnosis

Purpose

To investigate whether the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) can improve the Nottingham Prognostic Index (NPI) in the classification of patients with primary operable breast cancer for disease-free survival (DFS).

Patients and Methods

The analysis is based on 1,927 patients with breast cancer treated between 2000 and 2005 at the University Hospitals, Leuven. We compared performances of NPI with and without ER, PR and/or HER2. Validation was done on two external data sets containing 862 and 2,805 patients from Oslo (Norway) and Auckland (New Zealand), respectively.

Results

In the Leuven cohort, median follow-up was 66 months, and 13.7% of patients experienced a breast cancer–related event. Positive staining for ER, PR, and HER2 was detected, respectively, in 86.9%, 75.5%, and 11.9% of patients. Based on multivariate Cox regression modeling, the improved NPI (iNPI) was derived as NPI − PR positivity + HER2 positivity. Validation results showed a risk group reclassification of 20% to 30% of patients when using iNPI with its optimal risk boundaries versus NPI, in a majority of patients to more appropriate risk groups. An additional 10% of patients were classified into the extreme risk groups, where clinical actions are less ambiguous. Survival curves of reclassified patients resembled more closely those for patients in the same iNPI group than those for patients in the same NPI group.

Conclusion

The addition of PR and HER2 to NPI increases its 5-year prognostic accuracy. The iNPI can be considered as a clinically useful tool for stratification of patients with breast cancer receiving standard of care.

Molecular basis for therapy resistance

Chemoresistance remains the main reason for therapeutic failure in breast cancer as well as most other solid tumours. While gene expression profiles related to prognosis have been developed, so far use of such signatures as well as single markers has been of limited value predicting drug resistance. Novel technologies, in particular with regard to high through-put sequencing holds great promises for future identification of the key "driver" mechanisms guiding chemosensitivity versus resistance in breast cancer as well as other malignant conditions.

Testing the additional predictive value of high-dimensional molecular data

Background

While high-dimensional molecular data such as microarray gene expression data have been used for disease outcome prediction or diagnosis purposes for about ten years in biomedical research, the question of the additional predictive value of such data given that classical predictors are already available has long been under-considered in the bioinformatics literature.

Results

We suggest an intuitive permutation-based testing procedure for assessing the additional predictive value of high-dimensional molecular data. Our method combines two well-known statistical tools: logistic regression and boosting regression. We give clear advice for the choice of the only method parameter (the number of boosting iterations). In simulations, our novel approach is found to have very good power in different settings, e.g. few strong predictors or many weak predictors. For illustrative purpose, it is applied to the two publicly available cancer data sets.

Conclusions

Our simple and computationally efficient approach can be used to globally assess the additional predictive power of a large number of candidate predictors given that a few clinical covariates or a known prognostic index are already available. It is implemented in the R package "globalboosttest" which is publicly available from R-forge and will be sent to the CRAN as soon as possible.

Gene profiling in breast cancer

Breast cancer is a heterogenous disease which shows a great variation in presentation and response to treatment. Currently, the most commonly used prognostic criteria are patient age, tumor size, lymph node status, tumor grade and hormone receptor status. These are however not very accurate. This is partly explained by the fact that they do not demonstrate the inherent genetic variability of breast cancer, which determines the aggressive nature and metastatic potential of the disease. Recent advances in molecular biology have demonstrated that breast cancer is not a single disease. The new diagnostic and prognostic tests based on molecular biology methods have helped identify molecular subtypes of breast cancer that are sensitive to chemotherapy and others that are resistant. This could provide valuable critical information and predict which patients would really benefit from chemo and/or hormonal therapy. Molecular biology will become increasingly important in clinical decision making and as the understanding of molecular processes within cancer cells grow, new targets for therapy will be discovered.

Mixture classification model based on clinical markers for breast cancer prognosis

OBJECTIVE

Accurate cancer prognosis prediction is critical to cancer treatment. There have been many prognosis models based on clinical markers, but few of them are satisfied in clinical applications. And with the development of microarray technologies, cancer researchers have discovered many genes as new markers from the gene expression data and have further developed powerful prognosis models based on these so-called genetic biomarkers. However, the application of such biomarkers still suffers from some problems. The first one is there are a great number of genes and a few samples in the gene expression data so that it is difficult to select a unified gene set to establish a stable classifier for prognosis. The second one is that, due to the experimental and technical reasons, there are existing noises and redundancies in gene expression data, which may lead to building a prognosis predictor with poor performance. The last but not the least one is the microarray experiments are so expensive currently that it is hard to obtain abundant samples. Therefore, it is practical to develop prognosis methods mainly based on conventional clinical markers in real cancer treatment applications. This paper aims to establish an accurate classification model for cancer prognosis, in order to make full use of the invaluable information in clinical data, especially which is usually ignored by most of the existing methods when they aim for high prediction accuracies.

METHODS

First, this paper gives the formal description of general classification problem, and presents a novel mixture classification model to make full use of the invaluable information in clinical data, which is similar to the traditional ensemble classification models except for putting strict constraints on the construction of mapping functions to avoid voting process. Then, a two-layer instance of the proposed model, named as MRS (Mixture of Rough set and Support vector machine), is constructed by integrating rough set and support vector machine (SVM) classification methods, in which, the rough set classifier acts as the first layer to identify some singular samples in data, and the SVM classifier acts as the second layer to classify the remaining samples. Finally, MRS is used to make prognosis prediction on two open breast cancer datasets. One dataset, denoted as BRC-1 hereafter, is a high quality, publicly available dataset of 97 breast cancer tumors of node-negative patients. The other, denoted as BRC-2 hereafter, uses baseline human primary breast tumor data from LBL breast cancer cell collection containing 174 samples.

RESULTS

We have done two experiments on BRC-1 and BRC-2, respectively. In the first experiment, the BRC-1 dataset is divided into train set with 78 patients (34 ones belonging to poor prognosis group and 44 ones belonging to good prognosis group) and test set with 19 patients (12 ones belonging to poor prognosis group and 7 ones belonging to good prognosis). After trained on the train set, the MRS can correctly classify all the 12 patients with poor prognosis, and 6 of 7 patients with good prognosis in the test set. The results are better than previous researches, even better than the 70-gene based biomarkers. And in the second experiment, we construct the classifiers using BRC-2 dataset, and compare MRS with other representative methods in Weka software by 5-fold cross-validation, and comparison results show that MRS has higher prediction accuracy than those methods.

CONCLUSIONS

The proposed mixture classification model can easily integrate methods with different characteristics. It can overcome the shortcomings of traditional voting-based ensemble models and thus can make full use of the information in clinical data. The experimental results illustrate that our implemented MRS classifier can predict the breast cancer prognosis more accurately than previous prognostic methods.

Bridging the gap between systems biology and medicine

Systems biology has matured considerably as a discipline over the last decade, yet some of the key challenges separating current research efforts in systems biology and clinically useful results are only now becoming apparent. As these gaps are better defined, the new discipline of systems medicine is emerging as a translational extension of systems biology. How is systems medicine defined? What are relevant ontologies for systems medicine? What are the key theoretic and methodologic challenges facing computational disease modeling? How are inaccurate and incomplete data, and uncertain biologic knowledge best synthesized in useful computational models? Does network analysis provide clinically useful insight? We discuss the outstanding difficulties in translating a rapidly growing body of data into knowledge usable at the bedside. Although core-specific challenges are best met by specialized groups, it appears fundamental that such efforts should be guided by a roadmap for systems medicine drafted by a coalition of scientists from the clinical, experimental, computational, and theoretic domains.

DATE analysis: A general theory of biological change applied to microarray data

In contrast to conventional data mining, which searches for specific subsets of genes (extensive variables) to correlate with specific phenotypes, DATE analysis correlates intensive state variables calculated from the same datasets. At the heart of DATE analysis are two biological equations of state not dependent on genetic pathways. This result distinguishes DATE analysis from other bioinformatics approaches. The dimensionless state variable F quantifies the relative overall cellular activity of test cells compared to well-chosen reference cells. The variable pi(i) is the fold-change in the expression of the ith gene of test cells relative to reference. It is the fraction phi of the genome undergoing differential expression-not the magnitude pi-that controls biological change. The state variable phi is equivalent to the control strength of metabolic control analysis. For tractability, DATE analysis assumes a linear system of enzyme-connected networks and exploits the small average contribution of each cellular component. This approach was validated by reproducible values of the state variables F, RNA index, and phi calculated from random subsets of transcript microarray data. Using published microarray data, F, RNA index, and phi were correlated with: (1) the blood-feeding cycle of the malaria parasite, (2) embryonic development of the fruit fly, (3) temperature adaptation of Killifish, (4) exponential growth of cultured S. pneumoniae, and (5) human cancers. DATE analysis was applied to aCGH data from the great apes. A good example of the power of DATE analysis is its application to genomically unstable cancers, which have been refractory to data mining strategies.

Intrinsic bias in breast cancer gene expression data sets

Background

While global breast cancer gene expression data sets have considerable commonality in terms of their data content, the populations that they represent and the data collection methods utilized can be quite disparate. We sought to assess the extent and consequence of these systematic differences with respect to identifying clinically significant prognostic groups.

Methods

We ascertained how effectively unsupervised clustering employing randomly generated sets of genes could segregate tumors into prognostic groups using four well-characterized breast cancer data sets.

Results

Using a common set of 5,000 randomly generated lists (70 genes/list), the percentages of clusters with significant differences in metastasis latencies (HR p-value < 0.01) was 62%, 15%, 21% and 0% in the NKI2 (Netherlands Cancer Institute), Wang, TRANSBIG and KJX64/KJ125 data sets, respectively. Among ER positive tumors, the percentages were 38%, 11%, 4% and 0%, respectively. Few random lists were predictive among ER negative tumors in any data set. Clustering was associated with ER status and, after globally adjusting for the effects of ER-α gene expression, the percentages were 25%, 33%, 1% and 0%, respectively. The impact of adjusting for ER status depended on the extent of confounding between ER-α gene expression and markers of proliferation.

Conclusion

It is highly probable to identify a statistically significant association between a given gene list and prognosis in the NKI2 dataset due to its large sample size and the interrelationship between ER-α expression and markers of proliferation. In most respects, the TRANSBIG data set generated similar outcomes as the NKI2 data set, although its smaller sample size led to fewer statistically significant results.

[Prognostic and predictive factors of invasive breast cancer: update 2009]

Prognostic factors supply information on the course of a disease (recurrence-free and total survival) and are independent of the therapy. The most important prognostic factors are lymph node status, tumor diameter and histological differentiation stage, lymph and blood vessel invasion as well as staging, factors which can all be determined by pathologists. The Nottingham prognostic index (NPI) combines the strongest prognostic factors and according to study results is a suitable model for prognosis of breast cancer. Predictive factors give prior information on the probability of the response of a tumor to a defined therapy and include hormone receptor status, the invasion marker uPA/PAI-1, detection of isolated tumor cells, a residual tumor and the histological resection border.Prognostic or predictive factors are clinically relevant when therapy decisions are made possible by their recognition, which lead to an improvement in the total survival, recurrence-free survival or quality of life. The international consensus recommendation of St. Gallen 2007 requires the following as a basis for risk-adapted therapy decisions: tumor size, stage, age, nodal status, hormone receptor status and Her2 overexpression or amplification status.

[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.

Prediction of breast cancer metastasis by genomic profiling: where do we stand?

Current concepts conceive “breast cancer” as a complex disease that comprises several very different types of neoplasms. Nonetheless, breast cancer treatment has considerably improved through early diagnosis, adjuvant chemotherapy, and endocrine treatments. The limited prognostic power of classical classifiers determines considerable over-treatment of women who either do not benefit from, or do not at all need, chemotherapy. Several gene expression based molecular classifiers (signatures) have been developed for a more reliable prognostication. Gene expression profiling identifies profound differences in breast cancers, most probably as a consequence of different cellular origin and different driving mutations and can therefore distinguish the intrinsic propensity to metastasize. Existing signatures have been shown to be useful for treatment decisions, although they have been developed using relatively small sample numbers. Major improvements are expected from the use of large datasets, subtype specific signatures and from the re-introduction of functional information. We show that molecular signatures encounter clear limitations given by the intrinsic probabilistic nature of breast cancer metastasis. Already today, signatures are, however, useful for clinical decisions in specific cases, in particular if the personal inclination of the patient towards different treatment strategies is taken into account.

Cancer gene discovery in mouse and man

The elucidation of the human and mouse genome sequence and developments in high-throughput genome analysis, and in computational tools, have made it possible to profile entire cancer genomes. In parallel with these advances mouse models of cancer have evolved into a powerful tool for cancer gene discovery. Here we discuss the approaches that may be used for cancer gene identification in both human and mouse and discuss how a cross-species 'oncogenomics' approach to cancer gene discovery represents a powerful strategy for finding genes that drive tumourigenesis.

Biological convergence of cancer signatures

Gene expression profiling has identified cancer prognostic and predictive signatures with superior performance to conventional histopathological or clinical parameters. Consequently, signatures are being incorporated into clinical practice and will soon influence everyday decisions in oncology. However, the slight overlap in the gene identity between signatures for the same cancer type or condition raises questions about their biological and clinical implications. To clarify these issues, better understanding of the molecular properties and possible interactions underlying apparently dissimilar signatures is needed. Here, we evaluated whether the signatures of 24 independent studies are related at the genome, transcriptome or proteome levels. Significant associations were consistently observed across these molecular layers, which suggest the existence of a common cancer cell phenotype. Convergence on cell proliferation and death supports the pivotal involvement of these processes in prognosis, metastasis and treatment response. In addition, functional and molecular associations were identified with the immune response in different cancer types and conditions that complement the contribution of cell proliferation and death. Examination of additional, independent, cancer datasets corroborated our observations. This study proposes a comprehensive strategy for interpreting cancer signatures that reveals common design principles and systems-level properties.

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.

Risk estimation of distant metastasis in node-negative, estrogen receptor-positive breast cancer patients using an RT-PCR based prognostic expression signature

Background

Given the large number of genes purported to be prognostic for breast cancer, it would be optimal if the genes identified are not confounded by the continuously changing systemic therapies. The aim of this study was to discover and validate a breast cancer prognostic expression signature for distant metastasis in untreated, early stage, lymph node-negative (N-) estrogen receptor-positive (ER+) patients with extensive follow-up times.

Methods

197 genes previously associated with metastasis and ER status were profiled from 142 untreated breast cancer subjects. A "metastasis score" (MS) representing fourteen differentially expressed genes was developed and evaluated for its association with distant-metastasis-free survival (DMFS). Categorical risk classification was established from the continuous MS and further evaluated on an independent set of 279 untreated subjects. A third set of 45 subjects was tested to determine the prognostic performance of the MS in tamoxifen-treated women.

Results

A 14-gene signature was found to be significantly associated (p < 0.05) with distant metastasis in a training set and subsequently in an independent validation set. In the validation set, the hazard ratios (HR) of the high risk compared to low risk groups were 4.02 (95% CI 1.91–8.44) for the endpoint of DMFS and 1.97 (95% CI 1.28 to 3.04) for overall survival after adjustment for age, tumor size and grade. The low and high MS risk groups had 10-year estimates (95% CI) of 96% (90–99%) and 72% (64–78%) respectively, for DMFS and 91% (84–95%) and 68% (61–75%), respectively for overall survival. Performance characteristics of the signature in the two sets were similar. Ki-67 labeling index (LI) was predictive for recurrent disease in the training set, but lost significance after adjustment for the expression signature. In a study of tamoxifen-treated patients, the HR for DMFS in high compared to low risk groups was 3.61 (95% CI 0.86–15.14).

Conclusion

The 14-gene signature is significantly associated with risk of distant metastasis. The signature has a predominance of proliferation genes which have prognostic significance above that of Ki-67 LI and may aid in prioritizing future mechanistic studies and therapeutic interventions.

Functional genomic analysis of drug sensitivity pathways to guide adjuvant strategies in breast cancer

The widespread introduction of high throughput RNA interference screening technology has revealed tumour drug sensitivity pathways to common cytotoxics such as paclitaxel, doxorubicin and 5-fluorouracil, targeted agents such as trastuzumab and inhibitors of AKT and Poly(ADP-ribose) polymerase (PARP) as well as endocrine therapies such as tamoxifen. Given the limited power of microarray signatures to predict therapeutic response in associative studies of small clinical trial cohorts, the use of functional genomic data combined with expression or sequence analysis of genes and microRNAs implicated in drug response in human tumours may provide a more robust method to guide adjuvant treatment strategies in breast cancer that are transferable across different expression platforms and patient cohorts.

Microarray methods to identify factors determining breast cancer progression: potentials, limitations, and challenges

65-80% of the patients with breast cancer might not benefit from the adjuvant therapy they receive based on 'classical' markers used for the selection for adjuvant therapy. Therefore it is necessary to develop new markers that are able to tailor treatment for an individual patient. A number of microarray methods have been developed in recent years to accommodate this search for new factors that determine breast cancer progression. We give an overview of the most commonly used microarray methods to identify tumour progression markers (oligo- or cDNA arrays, CGH arrays, PCR arrays, and tissue microarrays). Their applications will be illustrated using the most influential examples from literature. The potentials, limitations and the related statistical analyses of each method are discussed. We conclude that microarray studies have led to an increase in the understanding of the complexity and diversity of breast carcinoma and have provided clinical relevant subgroups of breast cancer that may benefit from patient tailored treatment. Still, more extensive external validation and long-term follow-up will be necessary before such assays can be implemented into routine clinical practice. Most likely, these novel prognostic indicators will be complementary to the already available classical prognostic factors.

Histopathologic variables predict Oncotype DX recurrence score

Oncotype DX is a commercially available reverse transcriptase-polymerase chain reaction based assay that provides a Recurrence Score (RS) and has been shown to provide prognostic and predictive information in estrogen receptor-positive lymph node-negative breast cancers. Independent studies of its utility in routine practice are lacking. Slides and surgical pathology reports from 42 cases of breast carcinomas evaluated by Oncotype DX were retrospectively reviewed to determine patient age, tumor size, histologic grade, estrogen and progesterone receptor (ER and PR) and ERBB2 (HER-2/neu) data, with ER and PR reported as a semi-quantitative score reflecting both intensity of staining and proportion of positive cells. We show here that Recurrence Score is significantly correlated with tubule formation, nuclear grade, mitotic count, ER immunohistochemical score, PR immunohistochemical score, and HER-2/neu status, and that the equation RS=13.424+5.420 (nuclear grade) +5.538 (mitotic count) -0.045 (ER immunohistochemical score) -0.030 (PR immunohistochemical score) +9.486 (HER-2/neu) predicts the Recurrence Score with an R2 of 0.66, indicating that the full model accounts for 66% of the data variability. Although the Oncotype DX Recurrence Score holds potential, further validation of its independent value beyond that of histopathologic analysis is necessary before it can be implemented in clinical decision making.