Development of the 21-gene assay and its application in clinical practice and clinical trials

4-IHC classification of breast cancer subtypes in a large cohort of a clinical cancer registry: use in clinical routine for therapeutic decisions and its effect on survival

The aim of the present study was to evaluate to what extent the combination of standard histopathological parameters determines the biology of breast cancer and the effect on therapy and prognosis. The Clinical Cancer Registry Regensburg (Bavaria, Germany) included n = 4,480 female patients with primary, non-metastatic (M0) invasive breast cancer diagnosed between 2000 and 2012. Immuno-histochemical analyses, i.e., estrogen receptor (ER), progesterone receptor (PR), HER2, and Ki-67 (4-IHC), defined the tumor biological subtypes Luminal A, Luminal B, HER2-like, and Basal-like. Subtype-related differences in therapies and overall survival (OS) were analyzed using multivariable statistical methods. 4344 patients (97.0 %) could be classified into the four common tumor biological subtypes. The two most frequent entities were Luminal A (48.4 %), Luminal B (24.8 %), HER2-like (17.8 %), and Basal-like subtype (9.0 %). A multivariable Cox regression model showed that the best 7-year OS was seen in Luminal A patients and that OS of Luminal B and HER2-like patients was comparable (HR = 1.59, P < 0.001 versus HR = 1.51, P = 0.03). Lowest OS was seen in patients with Basal-like tumors (HR = 2.18, P < 0.001). In conclusion, the classification of tumor biological subtypes by the ER, PR, HER2, and Ki-67 biomarkers is practical in routine clinical work. Providing that quality assurance of these markers is ensured, this classification is useful for making therapy decisions in the routine clinical management of breast cancer patients.

[Molecular taxonomy of luminal breast cancer in 2015]

Luminal breast cancers (i.e. displaying œstrogen receptor expression) account for 70 to 80% of all breast cancers. It encompasses a heterogeneous population of tumors, differing by their clinical course, histopathological characteristics, phenotypes and molecular features. As a continuum of lesions, luminal breast tumors are critically challenged by the recent evolution in treatment decision making. Indeed, whilst about half of luminal breast cancers are associated with a very good prognosis (so-called luminal A tumors with regard to the intrinsic molecular classification), 20% of luminal tumors display a poor clinical outcome (i.e. luminal B tumors), the remaining tumors corresponding to intermediate lesions that are very difficult to accurately classify. Clearly, therapeutic issues are critical, since according to the vast majority of international consensus guidelines luminal A tumors are best treated by endocrine therapy, whilst an additional adjuvant chemotherapy will be proposed to patients harbouring luminal B breast cancer. By providing precise histopathological, phenotypic and molecular characterization of luminal breast tumors, the pathologist is actually the cornerstone of this therapeutic decision. Herein we aim to review the state-of-the-art knowledge on luminal breast carcinomas, with a perspective of routine clinical practice in 2015.

Biological and Clinical Significance of MAD2L1 and BUB1, Genes Frequently Appearing in Expression Signatures for Breast Cancer Prognosis

To investigate the biologic relevance and clinical implication of genes involved in multiple gene expression signatures for breast cancer prognosis, we identified 16 published gene expression signatures, and selected two genes, MAD2L1 and BUB1. These genes appeared in 5 signatures and were involved in cell-cycle regulation. We analyzed the expression of these genes in relation to tumor features and disease outcomes. In vitro experiments were also performed in two breast cancer cell lines, MDA-MB-231 and MDA-MB-468, to assess cell proliferation, migration and invasion after knocking down the expression of these genes. High expression of these genes was found to be associated with aggressive tumors and poor disease-free survival of 203 breast cancer patients in our study, and the association with survival was confirmed in an online database consisting of 914 patients. In vitro experiments demonstrated that lowering the expression of these genes by siRNAs reduced tumor cell growth and inhibited cell migration and invasion. Our investigation suggests that MAD2L1 and BUB1 may play important roles in breast cancer progression, and measuring the expression of these genes may assist the prediction of breast cancer prognosis.

Progress in adjuvant chemotherapy for breast cancer: an overview

Breast cancer is the most common cause of cancer and cancer death worldwide. Although most patients present with localized breast cancer and may be rendered disease-free with local therapy, distant recurrence is common and is the primary cause of death from the disease. Adjuvant systemic therapies are effective in reducing the risk of distant and local recurrence, including endocrine therapy, anti-HER2 therapy, and chemotherapy, even in patients at low risk of recurrence. The widespread use of adjuvant systemic therapy has contributed to reduced breast cancer mortality rates. Adjuvant cytotoxic chemotherapy regimens have evolved from single alkylating agents to polychemotherapy regimens incorporating anthracyclines and/or taxanes. This review summarizes key milestones in the evolution of adjuvant systemic therapy in general, and adjuvant chemotherapy in particular. Although adjuvant treatments are routinely guided by predictive factors for endocrine therapy (hormone receptor expression) and anti-HER2 therapy (HER2 overexpression), predicting benefit from chemotherapy has been more challenging. Randomized studies are now in progress utilizing multiparameter gene expression assays that may more accurately select patients most likely to benefit from adjuvant chemotherapy.

Is There a Role for Oncotype Dx Testing in Invasive Lobular Carcinoma?

Oncotype Dx Breast Cancer Assay is a 21-gene assay used in estrogen receptor (ER)-positive breast cancer to predict benefit from chemotherapy (CT). Tumors are placed into one of three risk categories based on their recurrence score (RS). This paper explores the impact of tumor histopathologic features and Oncotype Dx RS on the treatment plan for invasive lobular carcinoma (ILC). Invasive lobular carcinoma cases submitted for Oncotype Dx testing were identified from a clinical data base. The histopathologic and immunohistochemical features and RS subcategory of each tumor, and treatment regimen and medical oncologic assessments of each patient were reviewed. A total of 135 cases of ILC had RS testing, which represented 15% of all ILC diagnosed at the institution over the time period. 80% of ILC was of the classical subtype and all tumors were ER positive and human epidermal growth factor receptor 2 (HER-2) negative by immunohistochemistry. Sixty three percent of cases were low risk (LR), 35.5% were intermediate risk (IR) and 1.5% were high risk (HR). Both HR cases were pleomorphic ILC. Sixty eight percent of classical ILC had a LR score, while 70% of pleomorphic ILC had an IR score. Patients in the IR category were significantly more likely to undergo CT than patients in the LR category (54% versus 18%; p < 0.0001). In the LR category, those undergoing CT were significantly younger and more likely to have positive lymph nodes (p < 0.05). Qualitative analysis of medical oncologic assessments showed that RS played a role in decision-making on CT in 74% of cases overall. At our institution, Oncotype Dx RS currently plays a role in the management of a proportion of ILC and impacts on treatment decisions.

Use of modified Magee equations and histologic criteria to predict the Oncotype DX recurrence score

Oncotype DX (Genomic Health, Redwood City, CA, USA, current list price $4,350.00) is a multigene quantitative reverse transcription-polymerase chain reaction-based assay that estimates the risk of distant recurrence and predicts chemotherapy benefit for patients with estrogen receptor (ER)-positive breast cancers. Studies have suggested that standard histologic variables can provide similar information. Klein and Dabbs et al have shown that Oncotype DX recurrence scores can be estimated by incorporating standard histologic variables into equations (Magee equations). Using a simple modification of the Magee equation, we predict the Oncotype DX recurrence score in an independent set of 283 cases. The Pearson correlation coefficient (r) for the Oncotype DX and average modified Magee recurrence scores was 0.6644 (n=283; P<0.0001). 100% of cases with an average modified Magee recurrence score>30 (n=8) or an average modified Magee recurrence score<9 (with an available Ki-67, n=5) would have been correctly predicted to have a high or low Oncotype DX recurrence score, respectively. 86% (38/44) of cases with an average modified Magee recurrence score≤12, and 89% (34/38) of low grade tumors (NS<6) with an ER and PR≥150, and a Ki-67<10%, would have been correctly predicted to have a low Oncotype DX recurrence score. Using an algorithmic approach to eliminate high and low risk cases, between 5% and 23% of cases would potentially not have been sent by our institution for Oncotype DX testing, creating a potential cost savings between $56,550.00 and $282,750.00. The modified Magee recurrence score along with histologic criteria may be a cost-effective alternative to the Oncotype DX in risk stratifying certain breast cancer patients. The information needed is already generated by many pathology laboratories during the initial assessment of primary breast cancer, and the equations are free.

Effect of 21-Gene RT-PCR Assay on Adjuvant Therapy and Outcomes in Patients With Stage I Breast Cancer

BACKGROUND

Recurrence score (RS) derived from a 21-gene reverse transcriptase-polymerase chain reaction assay is used to stratify patients with early-stage estrogen receptor-positive, HER2-normal breast cancer into 3 groups on the basis of 10-year distant metastasis risk: low, intermediate, and high. Published data are limited regarding the effect of RS on choice of adjuvant therapy for T1 breast cancer. We investigated the relationship between RS and choice of adjuvant therapy, prognosis, and benefit of chemotherapy (CT) in stage I breast cancer.

MATERIALS AND METHODS

We reviewed the records of 1030 patients with estrogen receptor-positive, HER2-normal stage I breast cancer and RS available. RSs were correlated with clinicopathologic characteristics, treatment, and outcome.

RESULTS

Patients with pathologic (p)T1a, pT1b, and pT1c disease did not differ in distribution of low, intermediate, and high RS (P = .673). Overall, fewer than 10% of patients had a high RS. Histologic grade 1, nuclear grade 1, and low Ki-67 expression had only 1%, 0%, and 6% of high RSs, respectively. Among patients with intermediate RSs, 41% with pT1b and 46% with pT1c disease received CT. Among patients with intermediate RSs, for pT1b disease, distant disease-free survival (DDFS) did not differ between hormonal therapy (HT) alone and CT with HT (P = .752); for pT1c, DDFS was superior for CT with HT (P = .020). Histologic grade was the only independent prognostic factor of DDFS (P = .0007, 1 vs. 3; P = .035, 2 vs. 3); RS did not predict DDFS (P = .083, high vs. low; P = .066, intermediate vs. low).

CONCLUSION

The added value of RS to known prognostic factors appears limited to patients with pT1b breast cancer. However, this study lacked long-term follow-up.

A generic cycling hypoxia-derived prognostic gene signature: application to breast cancer profiling

Background

Temporal and local fluctuations in O₂ in tumors require adaptive mechanisms to support cancer cell survival and proliferation. The transcriptome associated with cycling hypoxia (CycHyp) could thus represent a prognostic biomarker of cancer progression.

Methods

We exposed 20 tumor cell lines to repeated periods of hypoxia/reoxygenation to determine a transcriptomic CycHyp signature and used clinical data sets from 2,150 breast cancer patients to estimate a prognostic Cox proportional hazard model to assess its prognostic performance.

Results

The CycHyp prognostic potential was validated in patients independently of the receptor status of the tumors. The discriminating capacity of the CycHyp signature was further increased in the ER+ HER2- patient populations including those with a node negative status under treatment (HR=3.16) or not (HR=5.54). The CycHyp prognostic signature outperformed a signature derived from continuous hypoxia and major prognostic metagenes (P<0.001). The CycHyp signature could also identify ER+HER2 node-negative breast cancer patients at high risk based on clinicopathologic criteria but who could have been spared from chemotherapy and inversely those patients classified at low risk based but who presented a negative outcome.

Conclusions

The CycHyp signature is prognostic of breast cancer and offers a unique decision making tool to complement anatomopathologic evaluation.

Translating genomics for precision cancer medicine

The Human Genome Project not only provided the essential reference map for the human genome but also stimulated the development of technology and analytic tools to process massive quantities of genomic data. As a result of this project, new technologies for DNA sequencing have improved in efficiency and cost by more than a millionfold over the past decade, and these technologies can now be routinely applied at a cost of less than $5,000 per genome. Although the application of these technologies in cancer genomics research has continued to contribute to basic discoveries, opportunities for translating them for individual patients have also emerged. This is especially important in clinical cancer research, where genetic alterations in a patient's tumor may be matched to molecularly targeted therapies. In this review, we discuss the integration of cancer genomics and clinical oncology and the opportunity to deliver precision cancer medicine.

Identification of drug-induced toxicity biomarkers for treatment determination

Drug-induced organ toxicity (DIOT) that leads to the removal of marketed drugs or termination of candidate drugs has been a leading concern for regulatory agencies and pharmaceutical companies. In safety studies, the genomic assays are conducted after the treatment so that drug-induced adverse effects can occur. Two types of biomarkers are observed: biomarkers of susceptibility and biomarkers of response. This paper presents a statistical model to distinguish two types of biomarkers and procedures to identify susceptible subpopulations. The biomarkers identified are used to develop classification model to identify susceptible subpopulation. Two methods to identify susceptibility biomarkers were evaluated in terms of predictive performance in subpopulation identification, including sensitivity, specificity, and accuracy. Method 1 considered the traditional linear model with a variable-by-treatment interaction term, and Method 2 considered fitting a single predictor variable model using only treatment data. Monte Carlo simulation studies were conducted to evaluate the performance of the two methods and impact of the subpopulation prevalence, probability of DIOT, and sample size on the predictive performance. Method 2 appeared to outperform Method 1, which was due to the lack of power for testing the interaction effect. Important statistical issues and challenges regarding identification of preclinical DIOT biomarkers were discussed. In summary, identification of predictive biomarkers for treatment determination highly depends on the subpopulation prevalence. When the proportion of susceptible subpopulation is 1% or less, a very large sample size is needed to ensure observing sufficient number of DIOT responses for biomarker and/or subpopulation identifications.

The 21-gene recurrence score and locoregional recurrence in breast cancer patients

PURPOSE

Although the 21-gene recurrence score (RS) assay has been validated to assess the risk of distant recurrence in hormone receptor-positive breast cancer patients, the relationship between RS and the risk of locoregional recurrence (LRR) remains unclear. The purpose of this study was to determine if RS is associated with LRR in breast cancer patients and whether this relationship varies based on the type of local treatment [mastectomy or breast-conserving therapy (BCT)].

METHODS

163 consecutive estrogen receptor-positive breast cancer patients at our institution had an RS generated from the primary breast tumor between August 2006 and October 2009. Patients were treated with lumpectomy and radiation (BCT) (n = 110) or mastectomy alone (n = 53). Patients were stratified using a pre-determined RS of 25 and then grouped according to local therapy type.

RESULTS

Median follow-up was 68.2 months. Patients who developed an LRR had stage I or IIA disease, >2 mm surgical margins, and received chemotherapy as directed by RS. While an RS > 25 did not predict for a higher rate of LRR, an RS > 24 was associated with LRR in our subjects. Among mastectomy patients, the 5-year LRR rate was 27.3 % in patients with an RS > 24 versus 10.7 % (p = 0.04) in those whose RS was ≤ 24. RS was not associated with LRR in patients who received BCT.

CONCLUSIONS

Breast cancer patients treated with mastectomy for tumors that have an RS > 24 are at high risk of LRR and may benefit from post-mastectomy radiation.

Improving the Care of Patients With Stage IB Non-Small-Cell Lung Cancer: Role of Prognostic Signatures and Use of Cell Cycle Progression Biomarkers

Patients with non-small-cell lung cancer have relatively poor survival outcomes after surgery (overall 5-year survival rate < 50%). Adjuvant chemotherapy adds only a small incremental survival benefit (hazard ratio, 0.89) with a 5% improvement in 5-year survival. There is no proven benefit to adjuvant chemotherapy in stage 1A or 1B disease. However, for patients with stage IB disease, outcomes after chemotherapy have been mixed; therefore, additional risk stratification measures are needed to guide decision-making in this patient population. Several significant prognostic indicators have been identified, including the presence of poorly differentiated tumors, tumors > 4 cm, blood vessel invasion, visceral pleural invasion, and incomplete lymph node dissection. A new risk stratification tool based on the expression of cell cycle genes recently has become available. Assessment of cell cycle gene expression may provide useful prognostic and predictive data when considered along with existing prognostic indicators to help identify patients with a poor prognosis and highly proliferative disease who would benefit the most from adjuvant chemotherapy.

Landscape of neoadjuvant therapy for breast cancer

Neoadjuvant chemotherapy provides clinical outcomes equivalent to those achieved when the same regimen is provided in the adjuvant setting. The therapeutic response to neoadjuvant treatment may include a reduction in tumor burden that alleviates the morbidity associated with locoregional therapy. Important prognostic information can be gained based on the response to treatment and knowing the quantity and biology of the residual disease. The evaluation of investigational agents in the neoadjuvant setting is of particular value for accelerating drug development. This review highlights landmark trials and contemporary perspectives on neoadjuvant chemotherapy and hormonal therapy, treatment response as a prognostic biomarker, use of the neoadjuvant paradigm for new drug development, and clinical advances in neoadjuvant therapy by molecular subtype of breast cancer.

Bcl-2 is a highly significant prognostic marker of hormone-receptor-positive, human epidermal growth factor receptor-2-negative breast cancer

B-cell lymphoma-2 (Bcl-2) is one of the most important anti-apoptotic genes. Although Bcl-2 promotes tumor cell survival in vitro, previous studies have shown conflicting results regarding the association between Bcl-2 and breast cancer survival. The aim of this study was to assess the prognostic significance of Bcl-2 according to the molecular tumor subtype in primary invasive breast cancer patients. The relationship between immunohistochemical Bcl-2 expression and overall survival was analyzed in 2399 primary invasive breast cancer patients treated by curative surgery. Patients were classified into four subtypes based on hormone receptor (HR) and human epidermal growth factor receptor-2 (HER2) status: HR+/HER2-, HR+/HER2+, HR-/HER2+, and HR-/HER2-. A total of 1304 patients (54.4 %) had Bcl-2 positive (+) tumors by immunohistochemistry. Bcl-2 (+) tumors were significantly associated with a younger age (<50 years), early stage, lower grade, positive expression of HR, and negative expression of HER2. In the HR+/HER2- group, patients with Bcl-2 (+) tumors showed a significantly better prognosis (p < 0.001). In contrast, there was no significant prognostic effect of Bcl-2 expression in other subtypes. In multivariate analysis, Bcl-2 positivity remained an independent, favorable prognostic factor in the HR+/HER2- subtype (hazard ratio, 0.609; 95 % confidence interval, 0.424-0.874; p < 0.007). The prognostic significance of Bcl-2 expression differed according to the molecular subtype of breast cancer. The expression of Bcl-2 was an independent, favorable prognostic factor in breast cancer patients with the HR+/HER2- subtype.

Classification of serous ovarian tumors based on microarray data using multicategory support vector machines

Ovarian cancer, the most fatal of reproductive cancers, is the fifth leading cause of death in women in the United States. Serous borderline ovarian tumors (SBOTs) are considered to be earlier or less malignant forms of serous ovarian carcinomas (SOCs). SBOTs are asymptomatic and progression to advanced stages is common. Using DNA microarray technology, we designed multicategory classification models to discriminate ovarian cancer subclasses. To develop multicategory classification models with optimal parameters and features, we systematically evaluated three machine learning algorithms and three feature selection methods using five-fold cross validation and a grid search. The study included 22 subjects with normal ovarian surface epithelial cells, 12 with SBOTs, and 79 with SOCs according to microarray data with 54,675 probe sets obtained from the National Center for Biotechnology Information gene expression omnibus repository. Application of the optimal model of support vector machines one-versus-rest with signal-to-noise as a feature selection method gave an accuracy of 97.3%, relative classifier information of 0.916, and a kappa index of 0.941. In addition, 5 features, including the expression of putative biomarkers SNTN and AOX1, were selected to differentiate between normal, SBOT, and SOC groups. An accurate diagnosis of ovarian tumor subclasses by application of multicategory machine learning would be cost-effective and simple to perform, and would ensure more effective subclass-targeted therapy.

Chemotherapy benefit for 'ER-positive' breast cancer and contamination of nonluminal subtypes—waiting for TAILORx and RxPONDER

BACKGROUND

Retrospective analyses of NSABP B20 and SWOG 8814 showed a large benefit of chemotherapy in patients with ER-positive tumors and high OncotypeDX Recurrence Score (RS≥31). However, it might be possible that both studies may be contaminated by non-luminal tumors, especially in high-risk RS group.

METHODS

We conducted simulations in order to obtain a better understanding of how the NSABP B20 and SWOG 8814 results would have been if non-luminal breast cancer would have been excluded. Simulations were done separately for the node-negative and node-positive cohorts.

RESULTS AND CONCLUSION

The results of the simulations suggest that the non-luminal tumors are augmenting the apparent benefit of chemotherapy, but do not appear to be responsible for the entire effect. These simulations could provide information about the potential influence of contamination by unexpected tumor subtypes on the future results of TAILORx and RxPONDER clinical trials.

A tumor DNA complex aberration index is an independent predictor of survival in breast and ovarian cancer

Complex focal chromosomal rearrangements in cancer genomes, also called "firestorms", can be scored from DNA copy number data. The complex arm-wise aberration index (CAAI) is a score that captures DNA copy number alterations that appear as focal complex events in tumors, and has potential prognostic value in breast cancer. This study aimed to validate this DNA-based prognostic index in breast cancer and test for the first time its potential prognostic value in ovarian cancer. Copy number alteration (CNA) data from 1950 breast carcinomas (METABRIC cohort) and 508 high-grade serous ovarian carcinomas (TCGA dataset) were analyzed. Cases were classified as CAAI positive if at least one complex focal event was scored. Complex alterations were frequently localized on chromosome 8p (n = 159), 17q (n = 176) and 11q (n = 251). CAAI events on 11q were most frequent in estrogen receptor positive (ER+) cases and on 17q in estrogen receptor negative (ER-) cases. We found only a modest correlation between CAAI and the overall rate of genomic instability (GII) and number of breakpoints (r = 0.27 and r = 0.42, p < 0.001). Breast cancer specific survival (BCSS), overall survival (OS) and ovarian cancer progression free survival (PFS) were used as clinical end points in Cox proportional hazard model survival analyses. CAAI positive breast cancers (43%) had higher mortality: hazard ratio (HR) of 1.94 (95%CI, 1.62-2.32) for BCSS, and of 1.49 (95%CI, 1.30-1.71) for OS. Representations of the 70-gene and the 21-gene predictors were compared with CAAI in multivariable models and CAAI was independently significant with a Cox adjusted HR of 1.56 (95%CI, 1.23-1.99) for ER+ and 1.55 (95%CI, 1.11-2.18) for ER- disease. None of the expression-based predictors were prognostic in the ER- subset. We found that a model including CAAI and the two expression-based prognostic signatures outperformed a model including the 21-gene and 70-gene signatures but excluding CAAI. Inclusion of CAAI in the clinical prognostication tool PREDICT significantly improved its performance. CAAI positive ovarian cancers (52%) also had worse prognosis: HRs of 1.3 (95%CI, 1.1-1.7) for PFS and 1.3 (95%CI, 1.1-1.6) for OS. This study validates CAAI as an independent predictor of survival in both ER+ and ER- breast cancer and reveals a significant prognostic value for CAAI in high-grade serous ovarian cancer.

Validation of the 12-gene colon cancer recurrence score as a predictor of recurrence risk in stage II and III rectal cancer patients

BACKGROUND

The 12-gene Recurrence Score assay is a validated predictor of recurrence risk in stage II and III colon cancer patients. We conducted a prospectively designed study to validate this assay for prediction of recurrence risk in stage II and III rectal cancer patients from the Dutch Total Mesorectal Excision (TME) trial.

METHODS

RNA was extracted from fixed paraffin-embedded primary rectal tumor tissue from stage II and III patients randomized to TME surgery alone, without (neo)adjuvant treatment. Recurrence Score was assessed by quantitative real time-polymerase chain reaction using previously validated colon cancer genes and algorithm. Data were analysed by Cox proportional hazards regression, adjusting for stage and resection margin status. All statistical tests were two-sided.

RESULTS

Recurrence Score predicted risk of recurrence (hazard ratio [HR] = 1.57, 95% confidence interval [CI] = 1.11 to 2.21, P = .01), risk of distant recurrence (HR = 1.50, 95% CI = 1.04 to 2.17, P = .03), and rectal cancer-specific survival (HR = 1.64, 95% CI = 1.15 to 2.34, P = .007). The effect of Recurrence Score was most prominent in stage II patients and attenuated with more advanced stage (P(interaction) ≤ .007 for each endpoint). In stage II, five-year cumulative incidence of recurrence ranged from 11.1% in the predefined low Recurrence Score group (48.5% of patients) to 43.3% in the high Recurrence Score group (23.1% of patients).

CONCLUSION

The 12-gene Recurrence Score is a predictor of recurrence risk and cancer-specific survival in rectal cancer patients treated with surgery alone, suggesting a similar underlying biology in colon and rectal cancers.

Endocrine Resistance in Breast Cancer

Around 70% of all breast cancers are estrogen receptor alpha positive and hence their development is highly dependent on estradiol. While the invention of endocrine therapies has revolusioned the treatment of the disease, resistance to therapy eventually occurs in a large number of patients. This paper seeks to illustrate and discuss the complexity and heterogeneity of the mechanisms which underlie resistance and the approaches proposed to combat them. It will also focus on the use and development of methods for predicting which patients are likely to develop resistance.

Biological subtypes of breast cancer: Prognostic and therapeutic implications

Breast cancer is a heterogeneous complex of diseases, a spectrum of many subtypes with distinct biological features that lead to differences in response patterns to various treatment modalities and clinical outcomes. Traditional classification systems regarding biological characteristics may have limitations for patient-tailored treatment strategies. Tumors with similar clinical and pathological presentations may have different behaviors. Analyses of breast cancer with new molecular techniques now hold promise for the development of more accurate tests for the prediction of recurrence. Gene signatures have been developed as predictors of response to therapy and protein gene products that have direct roles in driving the biology and clinical behavior of cancer cells are potential targets for the development of novel therapeutics. The present review summarizes current knowledge in breast cancer molecular biology, focusing on novel prognostic and predictive factors.

Breaking a vicious cycle

Despite prodigious advances in tumor biology research, few tumor-biomarker tests have been adopted as standard clinical practice. This lack of reliable tests stems from a vicious cycle of undervaluation, resulting from inconsistent regulatory standards and reimbursement, as well as insufficient investment in research and development, scrutiny of biomarker publications by journals, and evidence of analytical validity and clinical utility. We offer recommendations designed to serve as a roadmap to break this vicious cycle and call for a national dialogue, as changes in regulation, reimbursement, investment, peer review, and guidelines development require the participation of all stakeholders.

Subtype-dependent prognostic relevance of an interferon-induced pathway metagene in node-negative breast cancer

The majority of gene expression signatures developed to predict the likelihood to relapse in breast cancer (BC) patients assigns a high risk score to patients with Estrogen Receptor (ER) negative or highly proliferating tumors. We aimed to identify a signature of differentially expressed (DE) metagenes, rather than single DE genes, associated with distant metastases beyond classical risk factors. We used 105 gene expression profiles from consecutive BCs to identify metagenes whose prognostic role was defined on an independent series of 92 ESR1+/ERBB2- node-negative BCs (42 cases developing metastases within 5 years from diagnosis and 50 cases metastasis-free for more than 5 years, comparable for age, tumor size, ER status and surgery). Findings were validated on publicly available datasets of 684 node-negative BCs including all the subtypes. Only a metagene containing interferon-induced genes (IFN metagene) proved to be predictive of distant metastasis in our series of patients with ESR1+/ERBB2- tumors (P = 0.029), and such a finding was validated on 457 ESR1+/ERBB2- BCs from public datasets (P = 0.0424). Conversely, the IFN metagene was associated with a low risk of metastasis in 104 ERBB2+ tumors (P = 0.0099) whereas it did not prove to significantly affect prognosis in 123 ESR1-/ERBB2- tumors (P = 0.2235). A complex prognostic interaction was revealed in ESR1+/ERBB2- and ERBB2+ tumors when the association between the IFN metagene and a T-cell metagene was considered. The study confirms the importance of analyzing prognostic variables separately within BC subtypes, highlights the advantages of using metagenes rather than genes, and finally identifies in node-negative ESR1+/ERBB2- BCs, the unfavorable role of high IFN metagene expression.

Systematic assessment of prognostic gene signatures for breast cancer shows distinct influence of time and ER status

Background

The aim was to assess and compare prognostic power of nine breast cancer gene signatures (Intrinsic, PAM50, 70-gene, 76-gene, Genomic-Grade-Index, 21-gene-Recurrence-Score, EndoPredict, Wound-Response and Hypoxia) in relation to ER status and follow-up time.

Methods

A gene expression dataset from 947 breast tumors was used to evaluate the signatures for prediction of Distant Metastasis Free Survival (DMFS). A total of 912 patients had available DMFS status. The recently published METABRIC cohort was used as an additional validation set.

Results

Survival predictions were fairly concordant across most signatures. Prognostic power declined with follow-up time. During the first 5 years of followup, all signatures except for Hypoxia were predictive for DMFS in ER-positive disease, and 76-gene, Hypoxia and Wound-Response were prognostic in ER-negative disease. After 5 years, the signatures had little prognostic power. Gene signatures provide significant prognostic information beyond tumor size, node status and histological grade.

Conclusions

Generally, these signatures performed better for ER-positive disease, indicating that risk within each ER stratum is driven by distinct underlying biology. Most of the signatures were strong risk predictors for DMFS during the first 5 years of follow-up. Combining gene signatures with histological grade or tumor size, could improve the prognostic power, perhaps also of long-term survival.

[Personalized medicine and breast cancer: anticipatory medicine, prognostic evaluation and therapeutic targeting]

Breast cancer is now considered as a large collection of distinct biological entities, the management of which is increasingly personalized. Personalized medicine - defined as a medicine, which uses molecular profiles, notably genetic profiles, from patients and/or tumors to tailor therapeutic decisions - is now introduced in the management of breast cancer at any stages: screening and prevention of hereditary forms, prognostic and predictive evaluation of early breast cancer, and, more recently, novel clinical trials in advanced breast cancer, where genetic characterization of tumor tissue based on genomics, including next-generation sequencing tools, is used to drive specific therapeutic targeting.

Pharmacogenomic biomarkers for personalized medicine

Pharmacogenomics examines how the benefits and adverse effects of a drug vary among patients in a target population by analyzing genomic profiles of individual patients. Personalized medicine prescribes specific therapeutics that best suit an individual patient. Much current research focuses on developing genomic biomarkers to identify patients, to identify which patients would benefit from a treatment, have an adverse response, or no response at all, prior to treatment according to relevant differences in risk factors, disease types and/or responses to therapy. This review describes the use of the two personalized medicine biomarkers, prognostic and predictive, to classify patients into subgroups for treatment recommendation.

Neoadjuvant endocrine treatment for breast cancer: from bedside to bench and back again?

In recent years, considerable attention has been paid to the role of neoadjuvant chemotherapy as a pluripotential test bed for the treatment of breast cancer. Although traditionally reserved to render inoperable disease operable, neoadjuvant chemotherapy is increasingly being used to improve the chance for breast-conserving surgery, to gain information on pathologic response rates for a more rapid assessment of new chemotherapy-biologic regimens, and also to study in vivo tumour sensitivity or resistance to the agent being used. Similarly, use of neoadjuvant endocrine treatment was also traditionally restricted to elderly or frail patients who were felt to be unsuitable for chemotherapy. It is therefore not surprising that, given the increasing realization of the pivotal role of endocrine therapy in patient care, there is enhanced interest in neoadjuvant endocrine therapy not only as a less-toxic alternative to chemotherapy, but also to assess tumour sensitivity or resistance to endocrine agents. The availability of newer endocrine manipulations and increasing evidence that the benefits of chemotherapy are frequently marginal in many hormone-positive patients is making endocrine therapy increasingly important in the clinical setting. The hope is that, one day, instead of preoperative endocrine therapy being restricted to the infirm and the elderly, it will be used in the time between biopsy diagnosis and surgery to predict which patients will or will not benefit from chemotherapy in the adjuvant setting.

Understanding how breast cancer patients use risk information from genomic tests

We sought to examine how patients' treatment decisions incorporate potentially conflicting information from standard clinical indicators (e.g., tumor size) and genomic tests for breast cancer recurrence risk. Participants were 77 early stage breast cancer survivors who previously received genomic testing. They read six hypothetical vignettes that varied recurrence risk indicated by standard tests (low or high risk) coupled with the genomic test (low, intermediate or high risk). For each vignette, women reported their perceived recurrence risk and treatment preferences. Test results indicating high recurrence risk increased perception of risk and preference for chemotherapy (p < .001 for all). Perceived risk explained (i.e., mediated) the effect of test results on chemotherapy preferences. When test results conflicted, women gave more weight to genomic over standard test results. Hypothetical genomic test results had the intended effect of influencing women's perceptions of recurrence risk and interest in chemotherapy.

Personalized therapy for breast cancer: a dream or a reality?

Breast cancer oncology represents one of the disciplines where personalized cancer medicine has been most actively pursued. The class-discovery studies conceptually advanced the field, underlining the molecular heterogeneity governing this common disease. The advent of high-throughput molecular profiling technologies holds great promise for the advance of all aspects of personalized cancer medicine, namely accurate prognostication, prediction of response to common systemic therapies and individualized monitoring of the disease. Moreover, an ever-expanding arsenal of targeted therapeutic compounds under clinical development, coupled with emerging powerful tools for comprehensive molecular and functional characterization, pose significant promise for improved clinical outcomes for breast cancer patients. Interrogation of the germline genetic variation offers further promise towards tailoring of breast cancer management. Well-conducted prospective validation studies are needed if breast cancer personalized therapy is to transform from a dream into a reality.

Breast Cancer Biomarkers: Utility in Clinical Practice

Breast cancer is a heterogeneous disease. For the past decades, new technical tools have been developed for biomarkers at the DNA, RNA and protein levels to better understand the biology of breast cancer. This progress is essential to classify the disease into clinically relevant subtypes, which may lead to new therapeutic opportunities. Novel biomarker development is paramount to deliver personalized cancer therapies. Further, tumor evolution, being natural or under treatment pressure, should be monitored and "liquid biopsies" by detecting circulating tumor cells or circulating free tumor DNA in blood samples will become an important option. This paper reviews the new generation of biomarkers and the current evidence to demonstrate their analytical validity, clinical validity and clinical utility.

Multigene Assays for Classification, Prognosis, and Prediction in Breast Cancer: a Critical Review on the Background and Clinical Utility

Gene signatures which are based on multigene profiling assays have been developed for the purpose to better define the prognosis and prediction of therapy results in early-stage breast cancer. These assays were designed to be more specific than conventional clinico-pathologic parameters in the selection of patients for (neo-)adjuvant treatment and in effect help to avoid unnecessary cytotoxic treatment. In this review we describe molecular risk scores, for which tests are commercially available (PAM50®, MammaTyper®, MammaPrint®, Oncotype DX®, Endopredict®, Genomic Grade Index®) and IHC risk scores (Mammostrat® and IHC4), and discuss the current evidence of their clinical use.

Towards the integration, annotation and association of historical microarray experiments with RNA-seq

Background

Transcriptome analysis by microarrays has produced important advances in biomedicine. For instance in multiple myeloma (MM), microarray approaches led to the development of an effective disease subtyping via cluster assignment, and a 70 gene risk score. Both enabled an improved molecular understanding of MM, and have provided prognostic information for the purposes of clinical management. Many researchers are now transitioning to Next Generation Sequencing (NGS) approaches and RNA-seq in particular, due to its discovery-based nature, improved sensitivity, and dynamic range. Additionally, RNA-seq allows for the analysis of gene isoforms, splice variants, and novel gene fusions. Given the voluminous amounts of historical microarray data, there is now a need to associate and integrate microarray and RNA-seq data via advanced bioinformatic approaches.

Methods

Custom software was developed following a model-view-controller (MVC) approach to integrate Affymetrix probe set-IDs, and gene annotation information from a variety of sources. The tool/approach employs an assortment of strategies to integrate, cross reference, and associate microarray and RNA-seq datasets.

Results

Output from a variety of transcriptome reconstruction and quantitation tools (e.g., Cufflinks) can be directly integrated, and/or associated with Affymetrix probe set data, as well as necessary gene identifiers and/or symbols from a diversity of sources. Strategies are employed to maximize the annotation and cross referencing process. Custom gene sets (e.g., MM 70 risk score (GEP-70)) can be specified, and the tool can be directly assimilated into an RNA-seq pipeline.

Conclusion

A novel bioinformatic approach to aid in the facilitation of both annotation and association of historic microarray data, in conjunction with richer RNA-seq data, is now assisting with the study of MM cancer biology.

Statistical issues in the validation of prognostic, predictive, and surrogate biomarkers

BACKGROUND

Biomarkers have many distinct purposes, and depending on their intended use, the validation process varies substantially.

PURPOSE

The goal of this article is to provide an introduction to the topic of biomarkers, and then to discuss three specific types of biomarkers, namely, prognostic, predictive, and surrogate.

RESULTS

A principle challenge for biomarker validation from a statistical perspective is the issue of multiplicity. In general, the solution to this multiplicity challenge is well known to statisticians: pre-specification and replication. Critical requirements for prognostic marker validation include uniform treatment, complete follow-up, unbiased case selection, and complete ascertainment of the many possible confounders that exist in the context of an observational sample. In the case of predictive biomarker validation, observational data are clearly inadequate and randomized controlled trials are mandatory. Within the context of randomization, strategies for predictive marker validation can be grouped into two categories: retrospective versus prospective validation. The critical validation criteria for a surrogate endpoint is to ensure that if a trial uses a surrogate endpoint, the trial will result in the same inferences as if the trial had observed the true endpoint. The field of surrogate endpoint validation has now moved to the multi-trial or meta-analytic setting as the preferred method.

CONCLUSIONS

Biomarkers are a highly active research area. For all biomarker developmental and validation studies, the importance of fundamental statistical concepts remains the following: pre-specification of hypotheses, randomization, and replication. Further statistical methodology research in this area is clearly needed as we move forward.

The impact of the Oncotype Dx breast cancer assay in clinical practice: a systematic review and meta-analysis

The impact of the Oncotype Dx (ODX) breast cancer assay on adjuvant chemotherapy (ACT) treatment decisions has been evaluated in many previous studies. However, it can be difficult to interpret the collective findings, which were conducted in diverse settings with limited sample sizes. We conducted a systematic review and meta-analysis to synthesize the results and provide insights about ODX utility. Studies, identified from PubMed, Embase, ASCO, and SABCS, were included if patients had ER+, node -, early-stage breast cancer, reported use of ODX to inform actual ACT decisions. Information was summarized and pooled according to: (1) distribution of ODX recurrence scores (RS), (2) impact of ODX on ACT recommendations, (3) impact of ODX on ACT use, and (4) proportion of patients following the treatment suggested by the ODX RS. A total of 23 studies met inclusion criteria. The distribution of RS categories was 48.8 % low, 39.0 % intermediate, and 12.2 % high (21 studies, 4,156 patients). ODX changed the clinical-pathological ACT recommendation in 33.4 % of patients (8 studies, 1,437 patients). In patients receiving ODX, receipt of ACT were: 28.2 % overall, 5.8 % low, 37.4 % intermediate, and 83.4 % high. Low RS patients were significantly more likely to follow the treatment suggested by ODX versus high RS patients RR: 1.07 (1.01–1.14) [corrected].The pooled results are consistent with most individual studies to date. The increased proportion of intermediate scores relative to original estimates may have implications for the clinical utility and cost impacts of testing. In addition, low versus high RS patients were significantly more likely to follow the ODX results, suggesting a tendency toward less aggressive treatment, despite a high ODX RS. Finally, there was a lack of studies on the impact of ODX on ACT use versus standard approaches, suggesting that additional studies are warranted.

Genomic Grade Index (GGI): feasibility in routine practice and impact on treatment decisions in early breast cancer

Purpose

Genomic Grade Index (GGI) is a 97-gene signature that improves histologic grade (HG) classification in invasive breast carcinoma. In this prospective study we sought to evaluate the feasibility of performing GGI in routine clinical practice and its impact on treatment recommendations.

Methods

Patients with pT1pT2 or operable pT3, N0-3 invasive breast carcinoma were recruited from 8 centers in Belgium. Fresh surgical samples were sent at room temperature in the MapQuant Dx™ PathKit for centralized genomic analysis. Genomic profiles were determined using Affymetrix U133 Plus 2.0 and GGI calculated using the MapQuant Dx® protocol, which defines tumors as low or high Genomic Grade (GG-1 and GG-3 respectively).

Results

180 pts were recruited and 155 were eligible. The MapQuant test was performed in 142 cases and GGI was obtained in 78% of cases (n=111). Reasons for failures were 15 samples with <30% of invasive tumor cells (11%), 15 with insufficient RNA quality (10%), and 1 failed hybridization (<1%). For tumors with an available representative sample (≥ 30% inv. tumor cells) (n=127), the success rate was 87.5%. GGI reclassified 69% of the 54 HG2 tumors as GG-1 (54%) or GG-3 (46%). Changes in treatment recommendations occurred mainly in the subset of HG2 tumors reclassified into GG-3, with increased use of chemotherapy in this subset.

Conclusion

The use of GGI is feasible in routine clinical practice and impacts treatment decisions in early-stage breast cancer.

Trial Registration

ClinicalTrials.gov NCT01916837, http://clinicaltrials.gov/ct2/show/NCT01916837

[Gene expression analysis in breast cancer. A new diagnostic tool in pathology]

Molecular biomarker analysis is increasingly being used as a basis for individualized therapy selection. In breast cancer established standard biomarkers are hormone receptors, HER2 and if indicated Ki67. In particular for hormone receptor positive, HER2 negative tumors, gene expression analysis provides additional information on proliferation and hormone receptor signalling. The results of the gene expression tests can be used to identify patients with a very good prognosis under an exclusive endocrine therapy. This group of patients can then be treated without conventional chemotherapy. The EndoPredict assay was validated in two large cohorts from clinical studies of the Austrian breast cancer study group (ABCSG). Furthermore, using a round robin test, the test method was established in several German institutes of molecular pathology. The EndoPredict assay can be carried out in local institutes of pathology and offers additional information to existing standard prognostic parameters.

Who gets genomic testing for breast cancer recurrence risk?

BACKGROUND/AIMS

Our study examined whether patient characteristics, beliefs and decision-making styles were associated with uptake of genomic testing for breast cancer recurrence risk.

METHODS

Participants were 132 early-stage breast cancer patients eligible for the Oncotype DX genomic test. We interviewed patients in 2009-2010 and obtained information from medical charts.

RESULTS

Half of the women eligible for genomic testing for breast cancer recurrence risk received it. The most common reason for not getting the test was that women's physicians did not offer it (80%). Test recipients were more likely to be unsure about receiving chemotherapy treatment compared to women who did not receive the test (p < 0.05). Women who received the test had less advanced disease pathologies, recalled a lower objective recurrence risk, perceived lower recurrence risk, and were slightly younger (all p < 0.05). Most women who described their decision-making style as active received the test (75%), whereas few women who described their style as passive received the test (12%) (p < 0.01).

CONCLUSION

In the university clinic we studied, genomic testing appeared to be more common among patients who may benefit most from the information provided by results, but confirmation in larger studies is needed.

In situ sequencing for RNA analysis in preserved tissue and cells

Tissue gene expression profiling is performed on homogenates or on populations of isolated single cells to resolve molecular states of different cell types. In both approaches, histological context is lost. We have developed an in situ sequencing method for parallel targeted analysis of short RNA fragments in morphologically preserved cells and tissue. We demonstrate in situ sequencing of point mutations and multiplexed gene expression profiling in human breast cancer tissue sections.

Value of research and value of development in early assessments of new medical technologies

OBJECTIVES

In early stages of development of new medical technologies, there are conceptually separate but related societal decisions to be made concerning adoption, further development (i.e., technical improvement), and research (i.e., clinical trials) of new technologies. This article presents a framework to simultaneously support these three decisions from a societal perspective. The framework is applied to the 70-gene signature, a gene-expression profile for breast cancer, deciding which patients should receive adjuvant systemic therapy after surgery. The "original" signature performed on fresh frozen tissue (70G-FFT) could be further developed to a paraffin-based signature (70G-PAR) to reduce test failures.

METHODS

A Markov decision model comparing the "current" guideline Adjuvant Online (AO), 70G-FFT, and 70G-PAR was used to simulate 20-year costs and outcomes in a hypothetical cohort in The Netherlands. The 70G-PAR strategy was based on projected data from a comparable technology. Incremental net monetary benefits were calculated to support the adoption decision. Expected net benefit of development for the population and expected net benefit of sampling were calculated to support the development and research decision.

RESULTS

The 70G-PAR had the highest net monetary benefit, followed by the 70G-FFT. The population expected net benefit of development amounted to €91 million over 20 years (assuming €250 development costs per patient receiving the test). The expected net benefit of sampling amounted to €61 million for the optimal trial (n = 4000).

CONCLUSIONS

We presented a framework to simultaneously support adoption, development, and research decisions in early stages of medical technology development. In this case, the results indicate that there is value in both further development of 70G-FFT into 70G-PAR and further research.

Biomarkers for the clinical management of breast cancer: international perspective

The higher incidence of breast cancer in developed countries has been tempered by reductions in mortality, largely attributable to mammographic screening programmes and advances in adjuvant therapy. Optimal systemic management requires consideration of clinical, pathological and biological parameters. Oestrogen receptor alpha (ERα), progesterone receptor (PgR) and human epidermal growth factor receptor 2 (HER2) are established biomarkers evaluated at diagnosis, which identify cardinal subtypes of breast cancer. Their prognostic and predictive utility effectively guides systemic treatment with endocrine, anti-HER2 and chemotherapy. Hence, accurate and reliable determination remains of paramount importance. However, the goals of personalized medicine and targeted therapies demand further information regarding residual risk and potential benefit of additional treatments in specific circumstances. The need for biomarkers which are fit for purpose, and the demands placed upon them, is therefore expected to increase. Technological advances, in particular high-throughput global gene expression profiling, have generated multi-gene signatures providing further prognostic and predictive information. The rational integration of routinely evaluated clinico-pathological parameters with key indicators of biological activity, such as proliferation markers, also provides a ready opportunity to improve the information available to guide systemic therapy decisions. The additional value of such information and its proper place in patient management is currently under evaluation in prospective clinical trials. Expanding the utility of biomarkers to lower resource settings requires an emphasis on cost effectiveness, quality assurance and possible international variations in tumor biology; the potential for improved clinical outcomes should be justified against logistical and economic considerations.