Practical implications of gene-expression-based assays for breast oncologists

Molecular imaging using PET and SPECT for identification of breast cancer subtypes

Breast cancer is a major disease with high morbidity and mortality in women. As a highly heterogeneous tumor, it contains different molecular subtypes: luminal A, luminal B, human epidermal growth factor 2-positive, and triple-negative subtypes. As each subtype has unique features, it may not be universal to the optimal treatment and expected response for individual patients. Therefore, it is critical to identify different breast cancer subtypes. Targeting subcellular levels, molecular imaging, especially PET and single photon emission computed tomography, has become a promising means to identify breast cancer subtypes and monitor treatment. Different biological processes between various subtypes, including changes correlated with receptor expression, cell proliferation, or glucose metabolism, have the potential for imaging with PET and single photon emission computed tomography radiopharmaceuticals. Receptor imaging, with radiopharmaceuticals targeting estrogen receptor, progesterone receptor, or human epidermal growth factor 2, is available to distinguish receptor-positive tumors from receptor-negative ones. Cell proliferation imaging with fluorine-18 fluorothymidine PET aids identification of luminal A and B subtypes on the basis of the correlation with the immunohistochemical biomarker Ki-67. Glucose metabolism imaging with fluorine-18 fluorodeoxyglucose PET may have potential to discriminate triple-negative subtypes from others. With increasing numbers of novel radiopharmaceuticals, noninvasive molecular imaging will be applied widely for the identification of different subtypes and provide more in-vivo information on individualized management of breast cancer patients.

Intrinsic Subtypes and Gene Expression Profiles in Primary and Metastatic Breast Cancer

Biological changes that occur during metastatic progression of breast cancer are still incompletely characterized. In this study, we compared intrinsic molecular subtypes and gene expression in 123 paired primary and metastatic tissues from breast cancer patients. Intrinsic subtype was identified using a PAM50 classifier and χ² tests determined the differences in variable distribution. The rate of subtype conversion was 0% in basal-like tumors, 23.1% in HER2-enriched (HER2-E) tumors, 30.0% in luminal B tumors, and 55.3% in luminal A tumors. In 40.2% of cases, luminal A tumors converted to luminal B tumors, whereas in 14.3% of cases luminal A and B tumors converted to HER2-E tumors. We identified 47 genes that were expressed differentially in metastatic versus primary disease. Metastatic tumors were enriched for proliferation-related and migration-related genes and diminished for luminal-related genes. Expression of proliferation-related genes were better at predicting overall survival in metastatic disease (OSmet) when analyzed in metastatic tissue rather than primary tissue. In contrast, a basal-like gene expression signature was better at predicting OSmet in primary disease compared with metastatic tissue. We observed correlations between time to tumor relapse and the magnitude of changes of proliferation, luminal B, or HER2-E signatures in metastatic versus primary disease. Although the intrinsic subtype was largely maintained during metastatic progression, luminal/HER2-negative tumors acquired a luminal B or HER2-E profile during metastatic progression, likely reflecting tumor evolution or acquisition of estrogen independence. Overall, our analysis revealed the value of stratifying gene expression by both cancer subtype and tissue type, providing clinicians more refined tools to evaluate prognosis and treatment. Cancer Res; 77(9); 2213-21. ©2017 AACR.

Advancement of mass spectrometry-based proteomics technologies to explore triple negative breast cancer

Understanding the complexity of cancer biology requires extensive information about the cancer proteome over the course of the disease. The recent advances in mass spectrometry-based proteomics technologies have led to the accumulation of an incredible amount of such proteomic information. This information allows us to identify protein signatures or protein biomarkers, which can be used to improve cancer diagnosis, prognosis and treatment. For example, mass spectrometry-based proteomics has been used in breast cancer research for over two decades to elucidate protein function. Breast cancer is a heterogeneous group of diseases with distinct molecular features that are reflected in tumour characteristics and clinical outcomes. Compared with all other subtypes of breast cancer, triple-negative breast cancer is perhaps the most distinct in nature and heterogeneity. In this review, we provide an introductory overview of the application of advanced proteomic technologies to triple-negative breast cancer research.

Androgen Receptor: A Complex Therapeutic Target for Breast Cancer

Molecular and histopathological profiling have classified breast cancer into multiple sub-types empowering precision treatment. Although estrogen receptor (ER) and human epidermal growth factor receptor (HER2) are the mainstay therapeutic targets in breast cancer, the androgen receptor (AR) is evolving as a molecular target for cancers that have developed resistance to conventional treatments. The high expression of AR in breast cancer and recent discovery and development of new nonsteroidal drugs targeting the AR provide a strong rationale for exploring it again as a therapeutic target in this disease. Ironically, both nonsteroidal agonists and antagonists for the AR are undergoing clinical trials, making AR a complicated target to understand in breast cancer. This review provides a detailed account of AR’s therapeutic role in breast cancer.

Breast Cancer Immunology and Immunotherapy: Current Status and Future Perspectives

Cancer immunology has gained renewed interest in the past few years due to emerging findings on mechanisms involved in tumoral immune evasion. Indisputably, immune edition is currently considered a critical hallmark of cancer. Basic research has revealed new targets which can be modulated in the clinical setting with new compounds and strategies. As recent evidence confirms, breast cancer (BC) is a complex and heterogeneous disease in which host immune responses play a substantial role. T-infiltrating lymphocytes measurement is suggested as a powerful new tool necessary to predict early BC evolution, especially in HER2-positive and triple negative subtypes. However, T-infiltrating lymphocytes, genomic platforms, and many other biomarkers in tissue and peripheral blood (e.g., regulatory T cells and myeloid-derived suppressor cells) are not the only factors being evaluated regarding their potential role as prognostic and/or predictive factors. Many ongoing clinical trials are exploring the activity of immune checkpoint modulators in BC treatment, both in the advanced and neoadjuvant setting. Although this field is expanding with exciting new discoveries and promising clinical results-and creating great expectations-there remain many uncertainties yet to be addressed satisfactorily before this long awaited therapeutic promise can come to fruition.

Lgr6 labels a rare population of mammary gland progenitor cells that are able to originate luminal mammary tumours

The mammary gland is composed of a complex cellular hierarchy with unusual postnatal plasticity. The identities of stem/progenitor cell populations, as well as tumour-initiating cells that give rise to breast cancer, are incompletely understood. Here we show that Lgr6 marks rare populations of cells in both basal and luminal mammary gland compartments in mice. Lineage tracing analysis showed that Lgr6+ cells are unipotent progenitors, which expand clonally during puberty but diminish in adulthood. In pregnancy or following stimulation with ovarian hormones, adult Lgr6+ cells regained proliferative potency and their progeny formed alveoli over repeated pregnancies. Oncogenic mutations in Lgr6+ cells resulted in expansion of luminal cells, culminating in mammary gland tumours. Conversely, depletion of Lgr6+ cells in the MMTV-PyMT model of mammary tumorigenesis significantly impaired tumour growth. Thus, Lgr6 marks mammary gland progenitor cells that can initiate tumours, and cells of luminal breast tumours required for efficient tumour maintenance.

Molecular Changes During Breast Cancer and Mechanisms of Endocrine Therapy Resistance

Estrogen receptors (ERs) are expressed in 75% of breast cancers. ERs and their estrogen ligands play a key role in the development and progression of breast cancer. ERs have a genomic activity involving direct modulation of expression of genes vital to cell growth and survival by their classic nuclear receptors. The nongenomic activity is mediated by membrane receptor tyrosine kinases that activate signaling pathways resulting in activation of ER pathway modulators. Endocrine therapies inhibit the growth promoting activity of estrogen. ERs-positive breast cancers can exhibit de novo or acquired endocrine resistance. The mechanisms of endocrine therapy resistance are complex include deregulation of ER pathway, growth factor receptor signaling, cell cycle machinery, and tumor microenvironment. In this chapter, we will review the literature on the biology of ERs, the postulated mechanisms of endocrine therapy resistance, and their clinical implications.

Intrinsic subtypes and bladder cancer metastasis

Recent studies demonstrated that bladder cancers can be grouped into basal and luminal molecular subtypes that possess distinct biological and clinical characteristics. Basal bladder cancers express biomarkers characteristic of cancer stem cells and epithelial-to-mesenchymal transition (EMT). Patients with basal cancers tend have more advanced stage and metastatic disease at presentation. In preclinical models basal human orthotopic xenografts are also more metastatic than luminal xenografts are, and they metastasize via an EMT-dependent mechanism. However, preclinical and clinical data suggest that basal cancers are also more sensitive to neoadjuvant chemotherapy (NAC), such that most patients with basal cancers who are aggressively managed with NAC have excellent outcomes. Importantly, luminal bladder cancers can also progress to become invasive and metastatic, but they appear to do so via mechanisms that are much less dependent on EMT and may involve help from stromal cells, particularly cancer-associated fibroblasts (CAFs). Although patients with luminal cancers do not appear to derive much clinical benefit from NAC, the luminal tumors that are infiltrated with stromal cells appear to be sensitive to anti-PDL1 antibodies and possibly other immune checkpoint inhibitors. Therefore, neoadjuvant and/or adjuvant immunotherapy may be the most effective approach in treating patients with advanced or metastatic infiltrated luminal bladder cancers.

Ki67/SATB1 ratio is an independent prognostic factor of overall survival in patients with early hormone receptor-positive invasive ductal breast carcinoma

Biological diversity of breast cancer presents challenges for personalized therapy and necessitates multiparametric approaches to understand and manage the disease. Multiple protein biomarkers tested by immunohistochemistry (IHC), followed by digital image analysis and multivariate statistics of the data, have been shown to be effective in exploring latent profiles of tumor tissue immunophenotype. In this study, based on tissue microarrays of 107 patients with hormone receptor (HR) positive invasive ductal breast carcinoma, we investigated the prognostic value of the integrated immunophenotype to predict overall survival (OS) of the patients. A set of 10 IHC markers (ER, PR, HER2, Ki67, AR, BCL2, HIF-1α, SATB1, p53, and p16) was used. The main factor of the variance was characterized by opposite loadings of ER/PR/AR/BCL2 and Ki67/HIF-1α; it was associated with histological grade but did not predict OS. The second factor was driven by SATB1 expression along with moderate positive HIF-1α and weak negative Ki67 loadings. Importantly, this factor did not correlate with any clinicopathologic parameters, but was an independent predictor of better OS. Ki67 and SATB1 did not reach statistical significance as single predictors; however, high Ki67/SATB1 ratio was an independent predictor of worse OS. In addition, our data indicate potential double prognostic meaning of HIF-1α expression in breast cancer and necessitate focused studies, taking into account the immunophenotype interactions and tissue heterogeneity aspects.

Novel biomarkers in cancer: The whole is greater than the sum of its parts

The major issues hampering progress in the treatment of cancer patients are distant metastases and drug resistance to chemotherapy. Metastasis formation is a very complex process, and looking at gene signatures alone is not enough to get deep insight into it. This paper reviews traditional and novel approaches to identify gene signature biomarkers and intratumoural fluid pressure both as a novel way of creating predictive markers and as an obstacle to cancer therapy. Finally recently developed in vitro systems to predict the response of individual patient derived cancer explants to chemotherapy are discussed.

Clinicopathological implications of Tiam1 overexpression in invasive ductal carcinoma of the breast

BACKGROUND

T-lymphoma invasion and metastasis-inducing protein 1 (Tiam1) has been implicated in tumor occurrence and progression. Recent studies have shown that high expression levels of Tiam1 protein appear to be associated with the progression of numerous human tumors. This study attempted to explore the role of Tiam1 protein in tumor progression and the prognostic evaluation of breast cancer.

METHODS

The localization of the Tiam1 protein was determined in the MDA-MB-231 breast cancer cell line using immunofluorescence (IF) staining. In addition, a total of 283 breast tissue samples, including 153 breast cancer tissues, 67 ductal carcinoma in situ (DCIS) and 63 adjacent non-tumor breast tissues, were analyzed by immunohistochemical (IHC) staining of the Tiam1 protein. The correlation between Tiam1 expression and clinicopathological characteristics was evaluated by Chi-square test and Fisher's exact tests. Disease-free survival (DFS) and 10-year overall survival (OS) rates were calculated by the Kaplan-Meier method. Additionally, univariate and multivariate analyses were performed by the Cox proportional hazards regression models.

RESULTS

Tiam1 protein showed a mainly cytoplasmic staining pattern in breast cancer cells; however, nuclear staining was also observed. Tiam1 protein expression was significantly higher in breast cancers (42.5 %, 65/153) and DCIS (40.3 %, 27/67) than in adjacent non-tumor tissues (12.7 %, 8/63). In addition, Tiam1 associated with tumor stage and Ki-67 expression, but negatively correlated with receptor tyrosine-protein kinase erbB-2 (Her2) expression. Moreover, survival analyses showed that DFS and 10-year OS rates were significantly lower in breast cancer patients with high Tiam1 expression than those with low Tiam1 expression. Univariate analysis suggested that molecular types, clinical stage, Her2 expression levels and Tiam1 expression levels were also significantly associated with DFS and 10-year OS rates of breast cancer patients. Furthermore, multivariate analysis suggested that Tiam1 expression is a significant independent prognostic factor along with tumor stage in patients with breast cancer.

CONCLUSIONS

Tiam1 expression is frequently up-regulated in breast cancer. Tiam1 expression correlated with clinicopathological parameters, suggesting that it may be a useful prognostic biomarker and potential therapeutic target for patients with breast cancer.

Prognostic Impact of Time to Ipsilateral Breast Tumor Recurrence after Breast Conserving Surgery

Background

The poor prognosis of patients who experience ipsilateral breast tumor recurrence (IBTR) after breast conserving surgery (BCS) is established. A short time between primary cancer and IBTR is a prognostic factor but no clinically relevant threshold was determined. Classification of IBTR may help tailor treatment strategies.

Purpose

We determined a specific time frame, which differentiates IBTR into early and late recurrence, and identified prognostic factors for patients with IBTR at time of the recurrence.

Methods

We analyzed 2209 patients with IBTR after BCS. We applied the optimal cut-points method for survival data to determine the cut-off times to IBTR. A subgroup analysis was performed by hormone receptor (HR) status. Survival analyses were performed using a Cox proportional hazard model to determine clinical features associated with distant-disease-free survival (DDFS) after IBTR. We therefor built decision trees.

Results

On the 828 metastatic events observed, the majority occurred within the first 3 months after IBTR: 157 in the HR positive group, 98 in the HR negative group. We found different prognostic times to IBTR: 49 months in the HR positive group, 33 in the HR negative group. After multivariate analysis, time to IBTR was the first discriminant prognostic factor in both groups (HR 0.65 CI95% [0.54–0.79] and 0.42 [0.30–0.57] respectively). The other following variables were significantly correlated with the DDFS: the initial number of positive lymph nodes for both groups, the initial tumor size and grade for HR positive tumors.

Conclusion

A short interval time to IBTR is the strongest factor of poor prognosis and reflects occult distant disease. It would appear that prognosis after IBTR depends more on clinical and histological parameters than on surgical treatment. A prospective trial in a low-risk group of patients to validate the safety of salvage BCS instead of mastectomy in IBTR is needed.

Breast cancer subtype predictors revisited: from consensus to concordance?

BACKGROUND

At the molecular level breast cancer comprises a heterogeneous set of subtypes associated with clear differences in gene expression and clinical outcomes. Single sample predictors (SSPs) are built via a two-stage approach consisting of clustering and subtype predictor construction based on the cluster labels of individual cases. SSPs have been criticized because their subtype assignments for the same samples were only moderately concordant (Cohen's κ<0.6).

METHODS

We propose a semi-supervised approach where for five datasets, consensus sets were constructed consisting of those samples that were concordantly subtyped by a number of different predictors. Next, nine subtype predictors - three SSPs, three subtype classification models (SCMs) and three novel rule-based predictors based on the St. Gallen surrogate intrinsic subtype definitions (STGs) - were constructed on the five consensus sets and their associated consensus subtype labels. The predictors were validated on a compendium of over 4,000 uniformly preprocessed Affymetrix microarrays. Concordance between subtype predictors was assessed using Cohen's kappa statistic.

RESULTS

In this standardized setup, subtype predictors of the same type (either SCM, SSP, or STG) but with a different gene list and/or consensus training set were associated with almost perfect levels of agreement (median κ>0.8). Interestingly, for a given predictor type a change in consensus set led to higher concordance than a change to another gene list. The more challenging scenario where the predictor type, gene list and training set were all different resulted in predictors with only substantial levels of concordance (median κ=0.74) on independent validation data.

CONCLUSIONS

Our results demonstrate that for a given subtype predictor type stringent standardization of the preprocessing stage, combined with carefully devised consensus training sets, leads to predictors that show almost perfect levels of concordance. However, predictors of a different type are only substantially concordant, despite reaching almost perfect levels of concordance on training data.

Stromal Activation by Tumor Cells: An in Vitro Study in Breast Cancer

Background: The tumor microenvironment participates in the regulation of tumor progression and influences treatment sensitivity. In breast cancer, it also may play a role in determining the fate of non-invasive lesions such as ductal carcinoma in situ (DCIS), a non-obligate precursor of invasive diseases, which is aggressively treated despite its indolent nature in many patients since no biomarkers are available to predict the progression of DCIS to invasive disease. In vitro models of stromal activation by breast tumor cells might provide clues as to specific stromal genes crucial for the transition from DCIS to invasive disease. Methods: normal human dermal fibroblasts (NHDF) were treated under serum-free conditions with cell culture media conditioned by breast cancer cell lines (SkBr3, MDA-MB-468, T47D) for 72 h and subjected to gene expression profiling with Illumina platform. Results: TGM2, coding for a tissue transglutaminase, was identified as candidate gene for stromal activation. In public transcriptomic datasets of invasive breast tumors TGM2 expression proved to provide prognostic information. Conversely, its role as an early biosensor of tumor invasiveness needs to be further investigated by in situ analyses. Conclusion: Stromal TGM2 might probably be associated with precancerous evolution at earlier stages compared to DCIS.

Comparing Breast Cancer Multiparameter Tests in the OPTIMA Prelim Trial: No Test Is More Equal Than the Others

BACKGROUND

Previous reports identifying discordance between multiparameter tests at the individual patient level have been largely attributed to methodological shortcomings of multiple in silico studies. Comparisons between tests, when performed using actual diagnostic assays, have been predicted to demonstrate high degrees of concordance. OPTIMA prelim compared predicted risk stratification and subtype classification of different multiparameter tests performed directly on the same population.

METHODS

Three hundred thirteen women with early breast cancer were randomized to standard (chemotherapy and endocrine therapy) or test-directed (chemotherapy if Oncotype DX recurrence score >25) treatment. Risk stratification was also determined with Prosigna (PAM50), MammaPrint, MammaTyper, NexCourse Breast (IHC4-AQUA), and conventional IHC4 (IHC4). Subtype classification was provided by Blueprint, MammaTyper, and Prosigna.

RESULTS

Oncotype DX predicted a higher proportion of tumors as low risk (82.1%, 95% confidence interval [CI] = 77.8% to 86.4%) than were predicted low/intermediate risk using Prosigna (65.5%, 95% CI = 60.1% to 70.9%), IHC4 (72.0%, 95% CI = 66.5% to 77.5%), MammaPrint (61.4%, 95% CI = 55.9% to 66.9%), or NexCourse Breast (61.6%, 95% CI = 55.8% to 67.4%). Strikingly, the five tests showed only modest agreement when dichotomizing results between high vs low/intermediate risk. Only 119 (39.4%) tumors were classified uniformly as either low/intermediate risk or high risk, and 183 (60.6%) were assigned to different risk categories by different tests, although 94 (31.1%) showed agreement between four of five tests. All three subtype tests assigned 59.5% to 62.4% of tumors to luminal A subtype, but only 121 (40.1%) were classified as luminal A by all three tests and only 58 (19.2%) were uniformly assigned as nonluminal A. Discordant subtyping was observed in 123 (40.7%) tumors.

CONCLUSIONS

Existing evidence on the comparative prognostic information provided by different tests suggests that current multiparameter tests provide broadly equivalent risk information for the population of women with estrogen receptor (ER)-positive breast cancers. However, for the individual patient, tests may provide differing risk categorization and subtype information.

Genetic subtypes of invasive bladder cancer

PURPOSE OF REVIEW

Recently completed cancer genomics projects identified intrinsic subtypes in muscle-invasive bladder cancers. Here we will describe the studies that led to their discovery and review their biological and clinical properties.

RECENT FINDINGS

Whole genome mRNA expression profiling and unsupervised hierarchical cluster analyses identified intrinsic basal and luminal subtypes in muscle-invasive bladder cancers that are similar to the ones found in breast cancer. Tumors within each subtype have distinct responses to conventional cisplatin-based combination chemotherapy, and they contain gene expression signatures and DNA alterations that may render them vulnerable to clinically available targeted therapies.

SUMMARY

Like their breast cancer counterparts, basal bladder cancers are characterized by poor clinical outcomes in the absence of effective systemic therapy, but a large fraction of them do respond to neoadjuvant chemotherapy, suggesting that the tumors should be managed aggressively. On the contrary, tumors that belong to the 'p53-like' subtype tend to be chemoresistant, so patients with these tumors should probably be managed differently. It seems likely that prospective identification of tumor intrinsic subtype membership could complement the use of DNA-based biomarkers to identify the groups of patients who will benefit the most from chemotherapy and targeted agents.

Development of breast cancer therapy: biomarker-driven and response-guided approaches in a neoadjuvant setting

A therapeutic strategy, biomarker-driven and response-guided approach has been investigated in cancer therapy where the treatment targets heterogeneous and unstable disease. Neoadjuvant chemotherapy, for instance, is indicated based on tumor stage and subtype and its therapeutic outcomes like pathological responses are associated with the long-term prognostic probability in subgroups such as hormone receptor (HR) negative and HR-positive patients with high-grade cancers. Therefore, it would be reasonable to consider a treatment plan according to the short-time response in the stratified subgroups. It is also applicable for new therapy development, and in fact many clinical trials are under investigation in the post-neoadjuvant setting. In order to increase the therapeutic efficacy, it is recognized as necessary to incorporate biomarkers that enable us to classify conventional subtypes further including genetic mutations and epigenetic phenotypes into the planning of treatment. It is also crucial to analyze tumor biology particularly tumor evolution in the metastasis and the clonal selection by the treatment in these clinical settings.

Prognostic ability of EndoPredict compared to research-based versions of the PAM50 risk of recurrence (ROR) scores in node-positive, estrogen receptor-positive, and HER2-negative breast cancer. A GEICAM/9906 sub-study

There are several prognostic multigene-based tests for managing breast cancer (BC), but limited data comparing them in the same cohort. We compared the prognostic performance of the EndoPredict (EP) test (standardized for pathology laboratory) with the research-based PAM50 non-standardized qRT-PCR assay in node-positive estrogen receptor-positive (ER+) and HER2-negative (HER2−) BC patients receiving adjuvant chemotherapy followed by endocrine therapy (ET) in the GEICAM/9906 trial. EP and PAM50 risk of recurrence (ROR) scores [based on subtype (ROR-S) and on subtype and proliferation (ROR-P)] were compared in 536 ER+/HER2− patients. Scores combined with clinical information were evaluated: ROR-T (ROR-S, tumor size), ROR-PT (ROR-P, tumor size), and EPclin (EP, tumor size, nodal status). Patients were assigned to risk-categories according to prespecified cutoffs. Distant metastasis-free survival (MFS) was analyzed by Kaplan–Meier. ROR-S, ROR-P, and EP scores identified a low-risk group with a relative better outcome (10-year MFS: ROR-S 87 %; ROR-P 89 %; EP 93 %). There was no significant difference between tests. Predictors including clinical information showed superior prognostic performance compared to molecular scores alone (10-year MFS, low-risk group: ROR-T 88 %; ROR-PT 92 %; EPclin 100 %). The EPclin-based risk stratification achieved a significantly improved prediction of MFS compared to ROR-T, but not ROR-PT. All signatures added prognostic information to common clinical parameters. EPclin provided independent prognostic information beyond ROR-T and ROR-PT. ROR and EP can reliably predict risk of distant metastasis in node-positive ER+/HER2− BC patients treated with chemotherapy and ET. Addition of clinical parameters into risk scores improves their prognostic ability.

Study design requirements for RNA sequencing-based breast cancer diagnostics

Sequencing-based molecular characterization of tumors provides information required for individualized cancer treatment. There are well-defined molecular subtypes of breast cancer that provide improved prognostication compared to routine biomarkers. However, molecular subtyping is not yet implemented in routine breast cancer care. Clinical translation is dependent on subtype prediction models providing high sensitivity and specificity. In this study we evaluate sample size and RNA-sequencing read requirements for breast cancer subtyping to facilitate rational design of translational studies. We applied subsampling to ascertain the effect of training sample size and the number of RNA sequencing reads on classification accuracy of molecular subtype and routine biomarker prediction models (unsupervised and supervised). Subtype classification accuracy improved with increasing sample size up to N = 750 (accuracy = 0.93), although with a modest improvement beyond N = 350 (accuracy = 0.92). Prediction of routine biomarkers achieved accuracy of 0.94 (ER) and 0.92 (Her2) at N = 200. Subtype classification improved with RNA-sequencing library size up to 5 million reads. Development of molecular subtyping models for cancer diagnostics requires well-designed studies. Sample size and the number of RNA sequencing reads directly influence accuracy of molecular subtyping. Results in this study provide key information for rational design of translational studies aiming to bring sequencing-based diagnostics to the clinic.

PAM50 gene signatures and breast cancer prognosis with adjuvant anthracycline- and taxane-based chemotherapy: correlative analysis of C9741 (Alliance)

PAM50 intrinsic breast cancer subtypes are prognostic independent of standard clinicopathologic factors. CALGB 9741 demonstrated improved recurrence-free (RFS) and overall survival (OS) with 2-weekly dose-dense (DD) versus 3-weekly therapy. A significant interaction between intrinsic subtypes and DD-therapy benefit was hypothesized. Suitable tumor samples were available from 1,471 (73%) of 2,005 subjects. Multiplexed gene-expression profiling generated the PAM50 subtype call, proliferation score, and risk of recurrence score (ROR-PT) for the evaluable subset of 1,311 treated patients. The interaction between DD-therapy benefit and intrinsic subtype was tested in a Cox proportional hazards model using two-sided alpha = 0.05. Additional multivariable Cox models evaluated the proliferation and ROR-PT scores as continuous measures with selected clinical covariates. Improved outcomes for DD therapy in the evaluable subset mirrored results from the complete data set (RFS; hazard ratio = 1.20; 95% confidence interval = 0.99-1.44) with 12.3-year median follow-up. Intrinsic subtypes were prognostic of RFS (P < 0.0001) irrespective of treatment assignment. No subtype-specific treatment effect on RFS was identified (interaction P = 0.44). Proliferation and ROR-PT scores were prognostic for RFS (both P < 0.0001), but no association with treatment benefit was seen (P = 0.14 and 0.59, respectively). Results were similar for OS. The prognostic value of PAM50 intrinsic subtype was greater than estrogen receptor/HER2 immunohistochemistry classification. PAM50 gene signatures were highly prognostic but did not predict for improved outcomes with DD anthracycline- and taxane-based therapy. Clinical validation studies will assess the ability of PAM50 and other gene signatures to stratify patients and individualize treatment based on expected risks of distant recurrence.

Response and survival of breast cancer intrinsic subtypes following multi-agent neoadjuvant chemotherapy

BACKGROUND

Predicting treatment benefit and/or outcome before any therapeutic intervention has taken place would be clinically very useful. Herein, we evaluate the ability of the intrinsic subtypes and the risk of relapse score at diagnosis to predict survival and response following neoadjuvant chemotherapy. In addition, we evaluated the ability of the Claudin-low and 7-TNBCtype classifications to predict response within triple-negative breast cancer (TNBC).

METHODS

Gene expression and clinical-pathological data were evaluated in a combined dataset of 957 breast cancer patients, including 350 with TNBC, treated with sequential anthracycline and anti-microtubule-based neoadjuvant regimens. Intrinsic subtype, risk of relapse score based on subtype and proliferation (ROR-P), the Claudin-low subtype and the 7-TNBCtype subtype classification were evaluated. Logistic regression models for pathological complete response (pCR) and Cox models for distant relapse-free survival (DRFS) were used.

RESULTS

Basal-like, Luminal A, Luminal B, and HER2-enriched subtypes represented 32.7%, 30.6%, 18.2%, and 10.3% of cases, respectively. Intrinsic subtype was independently associated with pCR in all patients, in hormone receptor-positive/HER2-negative disease, in HER2-positive disease, and in TNBC. The pCR rate of Basal-like disease was >35% across all clinical cohorts. Neither the Claudin-low nor the 7-TNBCtype subtype classifications predicted pCR within TNBCs after accounting for intrinsic subtype. Finally, intrinsic subtype and ROR-P provided independent prognostic information beyond clinicopathological variables and type of pathological response. A 5-year DRFS of 97.5% (92.8-100.0%) was observed in these neoadjuvant-treated and clinically node-negative patients predicted to be low risk by ROR-P (i.e. 57.4% of Luminal A tumors with clinically node-negative disease).

CONCLUSIONS

Intrinsic subtyping at diagnosis provides prognostic and predictive information for patients receiving neoadjuvant chemotherapy. Although we could not exclude a survival benefit of neoadjuvant chemotherapy in patients with early breast cancer with clinically node-negative and ROR-low disease at diagnosis, the absolute benefit of cytotoxic therapy in this group might be rather small (if any).

Genefu: an R/Bioconductor package for computation of gene expression-based signatures in breast cancer

Breast cancer is one of the most frequent cancers among women. Extensive studies into the molecular heterogeneity of breast cancer have produced a plethora of molecular subtype classification and prognosis prediction algorithms, as well as numerous gene expression signatures. However, reimplementation of these algorithms is a tedious but important task to enable comparison of existing signatures and classification models between each other and with new models. Here, we present the genefu R/Bioconductor package, a multi-tiered compendium of bioinformatics algorithms and gene signatures for molecular subtyping and prognostication in breast cancer.

AVAILABILITY AND IMPLEMENTATION

The genefu package is available from Bioconductor. http://www.bioconductor.org/packages/devel/bioc/html/genefu.html Source code is also available on Github https://github.com/bhklab/genefu

CONTACT

bhaibeka@uhnresearch.ca

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Role of MRI in the staging of breast cancer patients: does histological type and molecular subtype matter?

OBJECTIVE

To assess the role of MRI in the pre-operative staging of patients with different histological types and molecular subtypes of breast cancer, by the assessment of the dimensions of the main tumour and identification of multifocal and/or multicentric disease.

METHODS

The study included 160 females diagnosed with breast cancer who underwent breast MRI for pre-operative staging. The size of the primary tumour evaluated by MRI was compared with the pathology (gold standard) using the Pearson's correlation coefficient (r). The presence of multifocal and/or multicentric disease was also evaluated.

RESULTS

The mean age of patients was 52.6 years (range 30-81 years). Correlation between the largest dimension of the main tumour measured by MRI and pathology was worse for non-special type/invasive ductal carcinoma than for other histological types and was better for luminal A and triple-negative than for luminal B and Her-2 molecular subtypes. Multifocal and/or multicentric disease was present in 48 patients (30.0%), and it was more common in breast carcinomas classified as Her-2 molecular subtype. There was no statistically significant difference in the frequency of multifocal and/or multicentric tumours identified only by MRI in relation to histological type or molecular subtype.

CONCLUSION

The results of this retrospective study demonstrated that histological types and molecular subtypes might influence the MRI assessment of breast cancers, especially in the evaluation of tumour size.

ADVANCES IN KNOWLEDGE

The real benefit of MRI for treatment planning in patients with breast cancer may be different according to the histological type and molecular subtype.

A Prognostic Gene Expression Signature in the Molecular Classification of Chemotherapy-naïve Urothelial Cancer is Predictive of Clinical Outcomes from Neoadjuvant Chemotherapy: A Phase 2 Trial of Dose-dense Methotrexate, Vinblastine, Doxorubicin, and Cisplatin with Bevacizumab in Urothelial Cancer

BACKGROUND

Gene expression profiling (GEP) suggests there are three subtypes of muscle-invasive urothelial cancer (UC): basal, which has the worst prognosis; p53-like; and luminal. We hypothesized that GEP of transurethral resection (TUR) and cystectomy specimens would predict subtypes that could benefit from chemotherapy.

OBJECTIVE

To explore clinical outcomes for patients treated with dose-dense (DD) methotrexate, vinblastine, doxorubicin, and cisplatin (MVAC) and bevacizumab (B) and the impact of UC subtype.

DESIGN, SETTING, AND PARTICIPANTS

Sixty patients enrolled in a neoadjuvant trial of four cycles of DDMVAC + B between 2007 and 2010. TUR and cystectomy specimens for GEP were available from 38 and 23 patients, respectively, and from an additional confirmation cohort of 49 patients treated with perioperative MVAC.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Relationships with outcomes were analyzed using multivariable Cox regression and log-rank tests.

RESULTS AND LIMITATIONS

Chemotherapy was active, with pT0N0 and ≤pT1N0 downstaging rates of 38% and 53%, respectively, and 5-yr overall survival (OS) of 63%. Bevacizumab had no appreciable impact on outcomes. Basal tumors had improved survival compared to luminal and p53-like tumors (5-yr OS 91%, 73%, and 36%, log-rank p=0.015), with similar findings on multivariate analysis. Bone metastases within 2 yr were exclusively associated with the p53-like subtype (p53-like 100%, luminal 0%, basal 0%; p ≤ 0.001). Tumors enriched with the p53-like subtype at cystectomy suggested chemoresistance for this subtype. A separate cohort treated with perioperative MVAC confirmed the UC subtype survival benefit (5-yr OS 77% for basal, 56% for luminal, and 56% for p53-like; p=0.021). Limitations include the small number of pretreatment specimens with sufficient tissue for GEP.

CONCLUSION

GEP was predictive of clinical UC outcomes. The basal subtype was associated with better survival, and the p53-like subtype was associated with bone metastases and chemoresistant disease.

PATIENT SUMMARY

We can no longer think of urothelial cancer as a single disease. Gene expression profiling identifies subtypes of urothelial cancer that differ in their natural history and sensitivity to chemotherapy.

Using quantitative systems pharmacology for novel drug discovery

INTRODUCTION

Over the past three decades, the predominant paradigm in drug discovery was designing selective ligands for a specific target to avoid unwanted side effects. However, in the last 5 years, the aim has shifted to take into account the biological network in which they interact. Quantitative and Systems Pharmacology (QSP) is a new paradigm that aims to understand how drugs modulate cellular networks in space and time, in order to predict drug targets and their role in human pathophysiology.

AREAS COVERED

This review discusses existing computational and experimental QSP approaches such as polypharmacology techniques combined with systems biology information and considers the use of new tools and ideas in a wider 'systems-level' context in order to design new drugs with improved efficacy and fewer unwanted off-target effects.

EXPERT OPINION

The use of network biology produces valuable information such as new indications for approved drugs, drug-drug interactions, proteins-drug side effects and pathways-gene associations. However, we are still far from the aim of QSP, both because of the huge effort needed to model precisely biological network models and the limited accuracy that we are able to reach with those. Hence, moving from 'one molecule for one target to give one therapeutic effect' to the 'big systems-based picture' seems obvious moving forward although whether our current tools are sufficient for such a step is still under debate.

Reactivation of multipotency by oncogenic PIK3CA induces breast tumour heterogeneity

Breast cancer is the most frequent cancer in women and consists of heterogeneous types of tumours that are classified into different histological and molecular subtypes. PIK3CA and P53 (also known as TP53) are the two most frequently mutated genes and are associated with different types of human breast cancers. The cellular origin and the mechanisms leading to PIK3CA-induced tumour heterogeneity remain unknown. Here we used a genetic approach in mice to define the cellular origin of Pik3ca-derived tumours and the impact of mutations in this gene on tumour heterogeneity. Surprisingly, oncogenic Pik3ca(H1047R) mutant expression at physiological levels in basal cells using keratin (K)5-CreER(T2) mice induced the formation of luminal oestrogen receptor (ER)-positive/progesterone receptor (PR)-positive tumours, while its expression in luminal cells using K8-CReER(T2) mice gave rise to luminal ER(+)PR(+) tumours or basal-like ER(-)PR(-) tumours. Concomitant deletion of p53 and expression of Pik3ca(H1047R) accelerated tumour development and induced more aggressive mammary tumours. Interestingly, expression of Pik3ca(H1047R) in unipotent basal cells gave rise to luminal-like cells, while its expression in unipotent luminal cells gave rise to basal-like cells before progressing into invasive tumours. Transcriptional profiling of cells that underwent cell fate transition upon Pik3ca(H1047R) expression in unipotent progenitors demonstrated a profound oncogene-induced reprogramming of these newly formed cells and identified gene signatures characteristic of the different cell fate switches that occur upon Pik3ca(H1047R) expression in basal and luminal cells, which correlated with the cell of origin, tumour type and different clinical outcomes. Altogether our study identifies the cellular origin of Pik3ca-induced tumours and reveals that oncogenic Pik3ca(H1047R) activates a multipotent genetic program in normally lineage-restricted populations at the early stage of tumour initiation, setting the stage for future intratumoural heterogeneity. These results have important implications for our understanding of the mechanisms controlling tumour heterogeneity and the development of new strategies to block PIK3CA breast cancer initiation.

Clinical implications of the intrinsic molecular subtypes of breast cancer

Gene-expression profiling has had a considerable impact on our understanding of breast cancer biology. During the last 15 years, 5 intrinsic molecular subtypes of breast cancer (Luminal A, Luminal B, HER2-enriched, Basal-like and Claudin-low) have been identified and intensively studied. In this review, we will focus on the current and future clinical implications of the intrinsic molecular subtypes beyond the current pathological-based classification endorsed by the 2013 St. Gallen Consensus Recommendations. Within hormone receptor-positive and HER2-negative early breast cancer, the Luminal A and B subtypes predict 10-year outcome regardless of systemic treatment administered as well as residual risk of distant recurrence after 5 years of endocrine therapy. Within clinically HER2-positive disease, the 4 main intrinsic subtypes can be identified and dominate the biological and clinical phenotype. From a clinical perspective, patients with HER2+/HER2-enriched disease seem to benefit the most from neoadjuvant trastuzumab, or dual HER2 blockade with trastuzumab/lapatinib, in combination with chemotherapy, and patients with HER2+/Luminal A disease seem to have a relative better outcome compared to the other subtypes. Finally, within triple-negative breast cancer (TNBC), the Basal-like disease predominates (70-80%) and, from a biological perspective, should be considered a cancer-type by itself. Importantly, the distinction between Basal-like versus non-Basal-like within TNBC might predict survival following (neo)adjvuvant multi-agent chemotherapy, bevacizumab benefit in the neoadjuvant setting (CALGB40603), and docetaxel vs. carboplatin benefit in first-line metastatic disease (TNT study). Overall, this data suggests that intrinsic molecular profiling provides clinically relevant information beyond current pathology-based classifications.

Differential β₂-adrenergic receptor expression defines the phenotype of non-tumorigenic and malignant human breast cell lines

Breast cancer is the most frequent malignancy in women. Several reports demonstrated that adrenergic receptors (ARs) are involved in breast cancer. Here we observed that epinephrine (Epi), an endogenous AR agonist, caused opposite effects in non-tumorigenic (MCF-10A and HBL-100) and tumor cells (MCF-7 and MDA-MB-231). Thus, Epi, in non-tumor breast cells, as well as isoproterenol (β-agonist), in all cell lines, maintained a benign phenotype, decreasing cell proliferation and migration, and stimulating cell adhesion. β-AR expression and cAMP levels were higher in MCF-10A than in MCF-7 cells. β₂-AR knock-down caused a significant increase of cell proliferation and migration, and a decrease of cell adhesion both in basal and in Iso-stimulated conditions. Coincidently, β₂-AR over-expression induced a significant decrease of cell proliferation and migration, and an increase of cell adhesion. Therefore, β₂-AR is implied in cell phenotype and its agonists or antagonists could eventually complement cancer therapy.

The prognostic impact of age in different molecular subtypes of breast cancer

Breast cancer is a heterogeneous entity composed of distinct molecular subgroups with different molecular and clinical features. We analyzed the association between molecular breast cancer subgroups, age at diagnosis, and prognosis in a compilation of publicly available gene expression datasets. Affymetrix gene expression data (U133A or U133Plus2.0 arrays) of 4467 breast cancers from 40 datasets were compiled and homogenized. Breast cancer subgroups were defined based on expression of ESR1, PR, HER2, and Ki67. Event-free survival was calculated as recurrence-free survival or distant metastasis-free survival if recurrence-free survival was not available. Young age at diagnosis is associated with higher frequency of triple negative and HER2 subtypes and lower frequency of luminal A breast cancers. The 5-year event-free survival rates of patients aged less than 40, between 40 and 50, and >50 years were 54.3 ± 3.5, 68.5 ± 1.9, and 70.4 ± 1.3 %, respectively. When controlling for breast cancer subtype, we found that age <40 years remained significantly associated with poor prognosis in triple negative breast cancer. The effect was modest in luminal tumors and not found in HER2 subtype. Both subtypes and age retained their significances in multivariate analysis. Association of age at diagnosis with molecular breast cancer subtype contributes to its important role as prognostic factor among patients with breast cancer. Still, within the group of triple negative breast cancer, young age <40 years has a significant prognostic value which was retained in multivariate analysis.

Prediction of Response to Neoadjuvant Chemotherapy Using Core Needle Biopsy Samples with the Prosigna Assay

PURPOSE

Most hormone receptor (HR)(+)/HER2(-) breast cancer patients respond unfavorably to neoadjuvant chemotherapy (NAC); however, genomic tests may identify those patients who are likely to benefit. Using the Prosigna assay, we first evaluated the technical performance of core needle biopsy (CNB) tissues. We then determined whether Prosigna risk of relapse (ROR) score and intrinsic subtype predicted response to NAC in HR(+)/HER2(-) patients using CNB samples.

EXPERIMENTAL DESIGN

Using the NanoString's nCounter Dx analysis system and a development tissue sample set, we established tissue requirements and assay output variance. We then evaluated the concordance in subtype and correlation in ROR between CNBs and corresponding surgical resection specimens (SRS) in a second independent sample set. Finally, we analyzed 180 independent CNB samples from HR(+)/HER2(-) patients who were treated with NAC and correlated ROR and intrinsic subtype with pathologic response.

RESULTS

Intra- and interbiopsy variabilities were 2.2 and 6.8 ROR units, respectively. Subtype concordance within multiple CNBs was high for the 4- and 3-subtype classifications (k = 0.885 and 0.889, respectively). Correlation in Prosigna ROR score observed between paired CNBs and SRS was high (r ≥ 0.90), and subtype concordance was also high for the 4- and 3-subtype classifications (kappa = 0.81 and 0.91, respectively). Prosigna results obtained from the HR(+)/HER2(-) patient samples showed that both ROR (P = 0.047) and intrinsic subtype (OR LumA vs. non-LumA = 0.341, P = 0.037) were significant predictors of response to NAC.

CONCLUSIONS

Prosigna ROR and intrinsic subtype are readily obtained from CNB samples in normal practice and reliably predict response to NAC in HR(+)/HER2(-) patients.

The Discovery of Novel Biomarkers Improves Breast Cancer Intrinsic Subtype Prediction and Reconciles the Labels in the METABRIC Data Set

BACKGROUND

The prediction of breast cancer intrinsic subtypes has been introduced as a valuable strategy to determine patient diagnosis and prognosis, and therapy response. The PAM50 method, based on the expression levels of 50 genes, uses a single sample predictor model to assign subtype labels to samples. Intrinsic errors reported within this assay demonstrate the challenge of identifying and understanding the breast cancer groups. In this study, we aim to: a) identify novel biomarkers for subtype individuation by exploring the competence of a newly proposed method named CM1 score, and b) apply an ensemble learning, as opposed to the use of a single classifier, for sample subtype assignment. The overarching objective is to improve class prediction.

METHODS AND FINDINGS

The microarray transcriptome data sets used in this study are: the METABRIC breast cancer data recorded for over 2000 patients, and the public integrated source from ROCK database with 1570 samples. We first computed the CM1 score to identify the probes with highly discriminative patterns of expression across samples of each intrinsic subtype. We further assessed the ability of 42 selected probes on assigning correct subtype labels using 24 different classifiers from the Weka software suite. For comparison, the same method was applied on the list of 50 genes from the PAM50 method.

CONCLUSIONS

The CM1 score portrayed 30 novel biomarkers for predicting breast cancer subtypes, with the confirmation of the role of 12 well-established genes. Intrinsic subtypes assigned using the CM1 list and the ensemble of classifiers are more consistent and homogeneous than the original PAM50 labels. The new subtypes show accurate distributions of current clinical markers ER, PR and HER2, and survival curves in the METABRIC and ROCK data sets. Remarkably, the paradoxical attribution of the original labels reinforces the limitations of employing a single sample classifiers to predict breast cancer intrinsic subtypes.

CCR 20th Anniversary Commentary: The Development of Breast Cancer Molecular Subtyping

In the August 15, 2004, issue of Clinical Cancer Research, Nielsen and colleagues demonstrated how a cancer subtype identified by gene expression profiling could be validated using a widely accessible technology (immunohistochemistry). This opened the door to large-scale studies of archival cohorts and clinical trials, which allowed detailed clinical understanding of a new genomic discovery. Clin Cancer Res; 21(8); 1779-81. ©2015 AACR. See related article by Nielsen et al., Clin Cancer Res 2004;10(16) Aug 15, 2004;5367-74.

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.

Molecular characterization and targeted therapeutic approaches in breast cancer

Despite the wide improvements in breast cancer (BC) detection and adjuvant treatment, BC is still responsible for approximately 40,000 deaths annually in the United States. Novel biomarkers are fundamental to assist clinicians in BC detection, risk stratification, disease subtyping, prediction of treatment response, and surveillance, allowing a more tailored approach to therapy in both primary and metastatic settings. In primary BC, the development of molecular profiling techniques has added prognostic and predictive information to conventional biomarkers--estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2. Moreover, the application of next-generation sequencing and reverse-phase protein microarray methods in the metastatic setting holds the promise to further advance toward a personalized management of cancer. The improvement in our understanding on BC biology associated with the study of the genomic aberrations characterizing the most common molecular subtypes allows us to explore new targets for drug development. Finally, the integration of cancer stem cell-targeted therapies and immune therapies in future combination regimens increases our chances to successfully treat a larger proportion of women with more aggressive and resistant metastatic disease. This article reviews the current state of novel biological markers for BC, the evidence to demonstrate their clinical validity and utility, and the implication for therapeutic targeting.

Use of formalin-fixed paraffin-embedded samples for gene expression studies in breast cancer patients

To obtain gene expression profiles from samples collected in clinical trials, we conducted a pilot study to assess feasibility and estimate sample attrition rates when profiling formalin-fixed, paraffin-embedded specimens. Ten matched fresh-frozen and fixed breast cancer samples were profiled using the Illumina HT-12 and Ref-8 chips, respectively. The profiles obtained with Ref 8, were neither technically nor biologically reliable since they failed to yield the expected separation between estrogen receptor positive and negative samples. With the use of Affymetrix HG-U133 2.0 Plus chips on fixed samples and a quantitative polymerase chain reaction -based sample pre-assessment step, results were satisfactory in terms of biological reliability, despite the low number of present calls (M = 21%±5). Compared with the Illumina DASL WG platform, Affymetrix data showed a wider interquartile range (1.32 vs 0.57, P<2.2 E-16,) and larger fold changes. The Affymetrix chips were used to run a pilot study on 60 fixed breast cancers. By including in the workflow the sample pre-assessment steps, 96% of the samples predicted to give good results (44/46), were in fact rated as satisfactory from the point of view of technical and biological meaningfulness. Our gene expression profiles showed strong agreement with immunohistochemistry data, were able to reproduce breast cancer molecular subtypes, and allowed the validation of an estrogen receptor status classifier derived in frozen samples. The approach is therefore suitable to profile formalin-fixed paraffin-embedded samples collected in clinical trials, provided that quality controls are run both before (sample pre-assessment) and after hybridization on the array.

Global Transcriptional Changes Following Statin Treatment in Breast Cancer

BACKGROUND

Statins purportedly exert antitumoral effects, but the underlying mechanisms are currently not fully elucidated. The aim of this study was to explore potential statin-induced effects on global gene expression profiles in primary breast cancer.

EXPERIMENTAL DESIGN

This window-of-opportunity phase II trial enrolled 50 newly diagnosed breast cancer patients prescribed atorvastatin (80 mg/day) for 2 weeks presurgically. Pre- and posttreatment tumor samples were analyzed using Significance Analysis of Microarrays (SAM) to identify differentially expressed genes. Similarly, SAM and gene ontology analyses were applied to gene expression data derived from atorvastatin-treated breast cancer cell lines (MCF7, BT474, SKBR3, and MDAMB231) comparing treated and untreated cells. The Systematic Motif Analysis Retrieval Tool (SMART) was used to identify enriched transcription factor-binding sites. Literature Vector Analysis (LitVAn) identified gene module functionality, and pathway analysis was performed using GeneGo Pathways Software (MetaCore; https://portal.genego.com/).

RESULTS

Comparative analysis of gene expression profiles in paired clinical samples revealed 407 significantly differentially expressed genes (FDR = 0); 32 upregulated and 375 downregulated genes. Restricted filtration (fold change ≥1.49) resulted in 21 upregulated and 46 downregulated genes. Significantly upregulated genes included DUSP1, RHOB1, GADD45B, and RGS1. Pooled results from gene ontology, LitVAn and SMART analyses identified statin-induced effects on the apoptotic and MAPK pathways among others. Comparative analyses of gene expression profiles in breast cancer cell lines showed significant upregulation of the mevalonate and proapoptotic pathways following atorvastatin treatment.

CONCLUSIONS

We report potential statin-induced changes in global tumor gene expression profiles, indicating MAPK pathway inhibition and proapoptotic events.

Gene expression profiling of sequential metastatic biopsies for biomarker discovery in breast cancer

The feasibility of longitudinal metastatic biopsies for gene expression profiling in breast cancer is unexplored. Dynamic changes in gene expression can potentially predict efficacy of targeted cancer drugs. Patients enrolled in a phase III trial of metastatic breast cancer with docetaxel monotherapy versus combination of docetaxel + sunitinib were offered to participate in a translational substudy comprising longitudinal fine needle aspiration biopsies and Positron Emission Tomography imaging before (T1) and two weeks after start of treatment (T2). Aspirated tumor material was used for microarray analysis, and treatment-induced changes (T2 versus T1) in gene expression and standardized uptake values (SUV) were investigated and correlated to clinical outcome measures. Gene expression profiling yielded high-quality data at both time points in 14/18 patients. Unsupervised clustering revealed specific patterns of changes caused by monotherapy vs. combination therapy (p = 0.021, Fisher's exact test). A therapy-induced reduction of known proliferation and hypoxia metagene scores was prominent in the combination arm. Changes in a previously reported hypoxia metagene score were strongly correlated to the objective responses seen by conventional radiology assessments after 6 weeks in the combination arm, Spearman's ρ = 1 (p = 0.017) but not in monotherapy, ρ = -0.029 (p = 1). Similarly, the Predictor Analysis of Microarrays 50 (PAM50) proliferation metagene correlated to tumor changes merely in the combination arm at 6 and 12 weeks (ρ = 0.900, p = 0.083 and ρ = 1, p = 0.017 respectively). Reductions in mean SUV were a reliable early predictor of objective response in monotherapy, ρ = 0.833 (p = 0.008), but not in the combination arm ρ = -0.029 (p = 1). Gene expression profiling of longitudinal metastatic aspiration biopsies was feasible, demonstrated biological validity and provided predictive information.

Circulating miR-200c and miR-141 and outcomes in patients with breast cancer

Background

The deregulation of microRNAs in both tumours and blood has led to the search for microRNAs to indicate the presence of cancer and predict prognosis. We hypothesize the deregulation of miR-200c/miR-141 in the whole blood can identify breast cancer (BC), and could be developed into a prognostic signature.

Methods

The expression of miR-200c and miR-141 were examined in bloods (57 stage I-IV BC patients and 20 age-matched controls) by quantitative reverse-transcription PCR. The associations of circulating microRNAs with clinic and pathological characteristics were analysed. Their effects on survival were analysed by the Kaplan-Meier method and Cox regressions.

Results

MiR-200c was down regulated (P < 0.0001) in the blood of BC patients, yielded an area under the ROC curve of 0.79 (90% sensitivity, 70.2% specificity) in discriminating BC from controls. Circulating miR-141 was not discriminating. MiR-200c and miR-141 in the blood of BC patients were inversely correlated (P = 0.019). The miR-200c levels were numerically higher in stage IV and tumours with lower MIB-1. MiR-141 was significantly higher in the blood of patients with stage I-III, lymph node metastasis, and HER2 negative tumours. High blood expression of miR-200c and/or low expression of miR-141 was associated with unfavourable overall survival (hazard ratio, 3.89; [95% CI: 1.28-11.85]) and progression-free survival (3.79 [1.41–10.16]) independent of age, stage and hormonal receptors.

Conclusions

Circulating miR-200c and miR-141 were deregulated in BC comparing with controls. Furthermore, miR-200c and miR-141 were independent prognostic factors and associated with distinct outcomes of BC patients.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-015-1238-5) contains supplementary material, which is available to authorized users.

Evaluation of public cancer datasets and signatures identifies TP53 mutant signatures with robust prognostic and predictive value

Background

Systematic analysis of cancer gene-expression patterns using high-throughput transcriptional profiling technologies has led to the discovery and publication of hundreds of gene-expression signatures. However, few public signature values have been cross-validated over multiple studies for the prediction of cancer prognosis and chemosensitivity in the neoadjuvant setting.

Methods

To analyze the prognostic and predictive values of publicly available signatures, we have implemented a systematic method for high-throughput and efficient validation of a large number of datasets and gene-expression signatures. Using this method, we performed a meta-analysis including 351 publicly available signatures, 37,000 random signatures, and 31 breast cancer datasets. Survival analyses and pathologic responses were used to assess prediction of prognosis, chemoresponsiveness, and chemo-drug sensitivity.

Results

Among 31 breast cancer datasets and 351 public signatures, we identified 22 validation datasets, two robust prognostic signatures (BRmet50 and PMID18271932Sig33) in breast cancer and one signature (PMID20813035Sig137) specific for prognosis prediction in patients with ER-negative tumors. The 22 validation datasets demonstrated enhanced ability to distinguish cancer gene profiles from random gene profiles. Both prognostic signatures are composed of genes associated with TP53 mutations and were able to stratify the good and poor prognostic groups successfully in 82%and 68% of the 22 validation datasets, respectively. We then assessed the abilities of the two signatures to predict treatment responses of breast cancer patients treated with commonly used chemotherapeutic regimens. Both BRmet50 and PMID18271932Sig33 retrospectively identified those patients with an insensitive response to neoadjuvant chemotherapy (mean positive predictive values 85%-88%). Among those patients predicted to be treatment sensitive, distant relapse-free survival (DRFS) was improved (negative predictive values 87%-88%). BRmet50 was further shown to prospectively predict taxane-anthracycline sensitivity in patients with HER2-negative (HER2-) breast cancer.

Conclusions

We have developed and applied a high-throughput screening method for public cancer signature validation. Using this method, we identified appropriate datasets for cross-validation and two robust signatures that differentiate TP53 mutation status and have prognostic and predictive value for breast cancer patients.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-015-1102-7) contains supplementary material, which is available to authorized users.

Predictive markers in breast cancer: An update on ER and HER2 testing and reporting

Gene expression profiling of human tumors has provided a new paradigm for classifying breast carcinomas, predicting response to treatment, and risk of recurrence. Estrogen receptor (ER), human epidermal growth factor 2 (HER2) receptor, and proliferation-related genes are the main drivers of classification in many of the gene expression profiling tests for breast cancer. However, ER, progesterone receptor (PR), and HER2 receptor status remain essential in determining the need and type of adjuvant therapy. These biomarkers are routinely tested for in all invasive breast carcinomas; ER testing is also performed on cases of ductal carcinoma in situ (DCIS). This article will provide an update on current guidelines from the American Society of Clinical Oncologists (ASCO) and the College of American Pathologists (CAP) for ER and HER2 testing by immunohistochemistry (IHC) and in situ hybridization (ISH). The populations to be tested, antibody selection, criteria for interpretation, and reporting are discussed. The molecular alterations that correlate with IHC results, alternative methods of testing, and the current approach to complex aspects of HER2 testing, including heterogeneity and polysomy, also are summarized.

Gene expression-based classifications of fibroadenomas and phyllodes tumours of the breast

Fibroepithelial tumors (FTs) of the breast are a heterogeneous group of lesions ranging from fibroadenomas (FAD) to phyllodes tumors (PT) (benign, borderline, malignant). Further understanding of their molecular features and classification might be of clinical value. In this study, we analysed the expression of 105 breast cancer‐related genes, including the 50 genes of the PAM50 intrinsic subtype predictor and 12 genes of the Claudin‐low subtype predictor, in a panel of 75 FTs (34 FADs, 5 juvenile FADs, 20 benign PTs, 5 borderline PTs and 11 malignant PTs) with clinical follow‐up. In addition, we compared the expression profiles of FTs with those of 14 normal breast tissues and 49 primary invasive ductal carcinomas (IDCs). Our results revealed that the levels of expression of all breast cancer‐related genes can discriminate the various groups of FTs, together with normal breast tissues and IDCs (False Discovery Rate < 5%). Among FTs, the levels expression of proliferation‐related genes (e.g. CCNB1 and MKI67) and mesenchymal/epithelial‐related (e.g. CLDN3 and EPCAM) genes were found to be most discriminative. As expected, FADs showed the highest and lowest expression of epithelial‐ and proliferation‐related genes, respectively, whereas malignant PTs showed the opposite expression pattern. Interestingly, the overall profile of benign PTs was found more similar to FADs and normal breast tissues than the rest of tumours, including juvenile FADs. Within the dataset of IDCs and normal breast tissues, the vast majority of FADs, juvenile FADs, benign PTs and borderline PTs were identified as Normal‐like by intrinsic breast cancer subtyping, whereas 7 (63.6%) and 3 (27.3%) malignant PTs were identified as Claudin‐low and Basal‐like, respectively. Finally, we observed that the previously described PAM50 risk of relapse prognostic score better predicted outcome in FTs than the morphological classification, even within PTs‐only. Our results suggest that classification of FTs using gene expression‐based data is feasible and might provide clinically useful biological and prognostic information.

The levels expression of proliferation‐ and mesenchymal/epithelial‐related genes were found to be the most discriminative.

The overall profile of benign phyllodes was very similar to fibroadenomas.

The vast majority of fibroepithelial tumors (FTs) were identified as Normal‐like by the PAM50 and Claudin‐low predictors.

The PAM50 risk of relapse prognostic score better predicted outcome in FTs than the morphological classification.

Classification of FTs using gene expression‐based data provides clinically useful information.

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.