Multicenter validation of a gene expression-based prognostic signature in lymph node-negative primary breast cancer

RNA-seq RNAaccess identified as the preferred method for gene expression analysis of low quality FFPE samples

Clinical tumor tissues that are preserved as formalin-fixed paraffin-embedded (FFPE) samples result in extensive cross-linking, fragmentation, and chemical modification of RNA, posing significant challenges for RNA-seq-based gene expression profiling. This study sought to define an optimal RNA-seq protocol for FFPE samples. We employed a common RNA extraction method and then compared RNA-seq library preparation protocols including RNAaccess, RiboZero and PolyA in terms of sequencing quality and concordance of gene expression using FFPE and case-matched fresh-frozen (FF) triple-negative breast cancer (TNBC) tissues. We found that RNAaccess, a method based on exome capture, produced the most concordant results. Applying RNAaccess to FFPE gastric cancer tissues, we established a minimum RNA DV200 requirement of 10% and a RNA input amount of 10ng that generated highly reproducible gene expression data. Lastly, we demonstrated that RNAaccess and NanoString platforms produced highly concordant expression profiles from FFPE samples for shared genes; however, RNA-seq may be preferred for clinical biomarker discovery work because of the broader coverage of the transcriptome. Taken together, these results support the selection of RNA-seq RNAaccess method for gene expression profiling of FFPE samples. The minimum requirements for RNA quality and input established here may allow for inclusion of clinical FFPE samples of sub-optimal quality in gene expression analyses and ultimately increasing the statistical power of such analyses.

The early-stage triple-negative breast cancer landscape derives a novel prognostic signature and therapeutic target

PURPOSE

Triple-negative breast cancer (TNBC) is a highly heterogeneous disease. Patients with early-stage TNBCs have distinct likelihood of distant recurrence. This study aimed to develop a prognostic signature of early-stage TNBC patients to improve risk stratification.

METHODS

Using RNA-sequencing data, we analyzed 189 pathologically confirmed pT1-2N0M0 TNBC patients and identified 21 mRNAs that were highly expressed in tumor and related to relapse-free survival. All-subset regression program was used for constructing a 7-mRNA signature in the training set (n = 159); the accuracy and prognostic value were then validated using an independent validation set (n = 158).

RESULTS

Here, we profiled the transcriptome data from 189 early-stage TNBC patients along with 50 paired normal tissues. Early-stage TNBCs mainly consisted of basal-like immune-suppressed subtype and had higher homologous recombination deficiency scores. We developed a prognostic signature including seven mRNAs (ACAN, KRT5, TMEM101, LCA5, RPP40, LAGE3, CDKL2). In both the training (n = 159) and validation set (n = 158), this signature could identify patients with relatively high recurrence risks and served as an independent prognostic factor. Time-dependent receiver operating curve showed that the signature had better prognostic value than traditional clinicopathological features in both sets. Functionally, we showed that TMEM101 promoted cell proliferation and migration in vitro, which represented a potential therapeutic target.

CONCLUSIONS

Our 7-mRNA signature could accurately predict recurrence risks of early-stage TNBCs. This model may facilitate personalized therapy decision-making for early-stage TNBCs individuals.

A Phenomic Perspective on Factors Influencing Breast Cancer Treatment: Integrating Aging and Lifestyle in Blood and Tissue Biomarker Profiling

Breast cancer is the most common malignancy among women worldwide. Over the last four decades, diagnostic and therapeutic procedures have improved substantially, giving patients with localized disease a better chance of cure, and those with more advanced cancer, longer periods of disease control and survival. However, understanding and managing heterogeneity in the clinical response exhibited by patients remains a challenge. For some treatments, biomarkers are available to inform therapeutic options, assess pathological response and predict clinical outcomes. Nevertheless, some measurements are not employed universally and lack sensitivity and specificity, which might be influenced by tissue-specific alterations associated with aging and lifestyle. The first part of this article summarizes available and emerging biomarkers for clinical use, such as measurements that can be made in tumor biopsies or blood samples, including so-called liquid biopsies. The second part of this article outlines underappreciated factors that could influence the interpretation of these clinical measurements and affect treatment outcomes. For example, it has been shown that both adiposity and physical activity can modify the characteristics of tumors and surrounding tissues. In addition, evidence shows that inflammaging and immunosenescence interact with treatment and clinical outcomes and could be considered prognostic and predictive factors independently. In summary, changes to blood and tissues that reflect aging and patient characteristics, including lifestyle, are not commonly considered clinically or in research, either for practical reasons or because the supporting evidence base is developing. Thus, an aim of this article is to encourage an integrative phenomic approach in oncology research and clinical management.

A Competing Risk Analysis Model to Determine the Prognostic Value of Isolated Tumor Cells in Axillary Lymph Nodes for T1N0M0 Breast Cancer Patients Based on the Surveillance, Epidemiology, and End Results Database

Introduction

Knowledge of the association between isolated tumor cells (ITCs) in breast cancer patients and the outcome is very limited. We aimed to determine the prognostic value of axillary lymph node ITCs for T1N0M0 female breast cancer (FBC) patients.

Methods

Data for T1N0M0 FBC patients staged ITCs negative [pN0(i−)] and positive [pN0(i+)] were extracted from the Surveillance, Epidemiology, and End Results database from 2004 to 2015. Prognostic predictors were identified by Kaplan–Meier analysis, competing risk model, and Fine–Gray multivariable regression model.

Results

A total of 94,599 subjects were included, 88,632 of whom were staged at pN0(i−) and 5,967 were pN0(i+). Patients staged pN0(i+) had worse breast cancer-specific survival (BCSS) [hazard ratio (HR): 1.298, 95% CI = 1.069–1.576, P = 0.003] and higher breast cancer-specific death (BCSD) rate (Gray’s test, P = 0.002) than pN0(i−) group. In the Fine–Gray multivariable regression analysis, the pN0(i+) group had higher BCSD rate (HR: 1.321, 95% CI = 1.109–1.575, P = 0.002) than pN0(i−) group. In subgroup analyses, no significant difference in BCSD was shown between the chemotherapy and non-chemotherapy subgroup (Gray’s test, P = 0.069) or radiotherapy and non-radiotherapy subgroup (Gray’s test, P = 0.096).

Conclusion

ITC was independently related to the increase of the BCSD rate and could be identified as a reliable survival predictor for T1N0M0 FBC patients.

Gene expression signatures: A tool for analysis of breast cancer prognosis and therapy

Breast Cancer is the most predominant female cancer in developed as well as developing countries. The treatment strategies of breast cancers depends on an array of factors like age at diagnosis, menstrual status, dietary pattern, immunological response, genetic variations of the cancer cells etc. Recent technological advancements in cancer diagnosis lead to the emergence of gene expression pattern for better understanding of the tumor behavior. It has not only bolstered the prognosis, but also the early diagnosis and therapy. The accuracy in disease prognosis can be boosted when gene expression signatures are combined with traditional clinicopathological features. This review explains how the evolution of gene expression signatures for breast cancers, its advantages and future prospects. In addition, an overview of currently available gene expression signature analysis tools and consolidated information on their current status and specific benefits, that can be availed for breast cancer diagnosis are also discussed.

The prognostic and predictive role of 21-gene recurrence scores in hormone receptor-positive early-stage breast cancer

Over the past two decades, gene expression profiling of breast cancer has emerged as an important tool in early-stage breast cancer management. The approach provides important information on underlying biological mechanisms, breast cancer classification, future risk potential of developing recurrent metastatic disease, and provides beneficial clues for adjuvant chemotherapy in hormone receptor (HR) positive breast cancer. Of the commercially available genomic tests for breast cancer, the prognostic and predictive value of 21-gene recurrence score tests have been validated using both retrospective data and prospective clinical trials. In this paper, we reviewed the current evidence on 21-gene expression profiles for HR-positive HER2-negative early-stage breast cancer management. We show that current evidence supports endocrine therapy alone as an appropriate adjuvant systemic therapy for approximately 70% of women with HR-positive, HER2-negative, node-negative breast cancer. Evolving evidence also suggests that 21-gene recurrence scores have predictive values for node-positive breast cancer and that chemotherapy can be avoided in more than half of women with nodes 1 to 3 positive HR-positive breast cancer. Furthermore, retrospective data also supports the predictive role of 21-gene recurrence scores for adjuvant radiation therapy. A prospective trial in this area is ongoing.

Etiopathogenic Correlations in Breast Cancer

The classic treatises, as well as the latest studies regarding the breast neoplasms emphasize the importance of several favorable factors in the genesis of the breast neoplasm.

The physiological personal history (age of the patient, age of first menstruation, late menopause, late-life sex, reduced breastfeeding, etc.), the personal pathological history, the heredocolateral history (breast, uterine neoplasia, other neoplasms) play a significant role, as well as the living and working conditions (stress, smoking, coffee, alcohol consumption), and dietary factors (hyper-lipidemic and hypoproteinemia regimens).

In order to evaluate the impact of these factors in the etiopathogenesis of breast cancer, we fol-lowed their incidence in a prospective study performed on the cases of breast neoplasm hospital-ized and surgically performed in the period between 2012-2018 in the 1st Surgery Clinic.

Upcoming Revolutionary Paths in Preclinical Modeling of Pancreatic Adenocarcinoma

To date, PDAC remains the cancer having the worst prognosis with mortality rates constantly on the rise. Efficient cures are still absent, despite all attempts to understand the aggressive physiopathology underlying this disease. A major stumbling block is the outdated preclinical modeling strategies applied in assessing effectiveness of novel anticancer therapeutics. Current in vitro preclinical models have a low fidelity to mimic the exact architectural and functional complexity of PDAC tumor found in human set, due to the lack of major components such as immune system and tumor microenvironment with its associated chemical and mechanical signals. The existing PDAC preclinical platforms are still far from being reliable and trustworthy to guarantee the success of a drug in clinical trials. Therefore, there is an urgent demand to innovate novel in vitro preclinical models that mirrors with precision tumor-microenvironment interface, pressure of immune system, and molecular and morphological aspects of the PDAC normally experienced within the living organ. This review outlines the traditional preclinical models of PDAC namely 2D cell lines, genetically engineered mice, and xenografts, and describing the present famous approach of 3D organoids. We offer a detailed narration of the pros and cons of each model system. Finally, we suggest the incorporation of two off-center newly born techniques named 3D bio-printing and organs-on-chip and discuss the potentials of swine models and in silico tools, as powerful new tools able to transform PDAC preclinical modeling to a whole new level and open new gates in personalized medicine.

Amplification of Stem Genes: New Potential Metastatic Makers in Patients with an Early Form of Breast Cancer

BACKGROUND

According to our previous studies, the presence of amplifications of stem genes can lead to their ectopic expression and this is associated with an increased activity of tumor stem cells in these patients. This leads to a high aggressiveness of the tumor and the development of metastatic disease. The aim was to evaluate the prognostic significance of the presence of amplifications of stem genes and their expression in patients with early breast cancer (BC).

METHODS

The study included 28 patients with T₁NxM₀ BC. We used surgical specimens, including formalin-fixed paraffin-embedded archive materials, for 8 patients. A microarray analysis was performed on high-density DNA chips from CytoScanHDArray to assess the status of copy number aberration (CNA) of stem genes locus. Gene expression was assessed using RT-qPCR.

RESULTS

CNA analysis of the studied tumors of patients without chemotherapy showed that 17/18 patients without metastases did not have two or more amplifications of chromosomal regions. Ten patients had visceral metastases. In 9/10 of these patients in the primary tumor there were two or more amplifications of the stem genes locus. Two or more amplifications of stem genes locus were found in 12 patients with stage I. Hematogenous metastases did not develop in all patients. Comparison of metastasis-free survival rates in groups of patients with 1 or without amplifications and with two or more amplifications showed statistically significant differences (P = 0.01).

CONCLUSION

Our studies have shown that the presence of clones with two or more amplifications of stem gene in patients with BC T₁NxM₀ has a significant prognostic value and determines an unfavorable prognosis for distant metastasis.

Differential impact of classical and non-canonical NF-κB pathway-related gene expression on the survival of breast cancer patients

Inflammation is a well-known driver of carcinogenesis and cancer progression, often attributed to the tumor microenvironment. However, tumor cells themselves are capable of secreting a variety of inflammatory molecules, leading to the activation of specific signaling pathways that promote tumor progression. The NF-κB signaling pathway is one of the most important connections between inflammation and tumorigenesis. NF-κB is a superfamily of transcription factors that plays an important role in several types of hematological and solid tumors, including breast cancer. However, the role of the NF-κB pathway in the survival of breast cancer patients is poorly studied. In this study, we analyzed and related the expression of both canonical and alternative NF-κB pathways and selected target genes with the relapse-free and overall survival of breast cancer patients. We used the public database Kaplan-Meier plotter (KMplot) which includes gene expression data and survival information of 3951 breast cancer patients. We found that the expression of IKKα was associated with poor relapse-free survival in patients with ER-positive tumors. Moreover, the expression of IL-8 and MMP-1 was associated with poor relapse-free and overall survival. In contrast, expression of IKKβ, p50, and p65 from the canonical pathway, and NIK and RELB from the alternative pathway correlated with better relapse-free survival also when the patients were classified by their hormonal and nodal status. Our study suggests that the expression of genes of the canonical and alternative NF-κB pathways is ultimately critical for tumor persistence. Understanding the communication between both pathways would help to find better therapeutic and prophylactic targets to prevent breast cancer progression and relapse.

A multiple breast cancer stem cell model to predict recurrence of T1-3, N0 breast cancer

Background

Local or distant relapse is the key event for the overall survival of early-stage breast cancer after initial surgery. A small subset of breast cancer cells, which share similar properties with normal stem cells, has been proven to resist to clinical therapy contributing to recurrence.

Methods

In this study, we aimed to develop a prognostic model to predict recurrence based on the prevalence of breast cancer stem cells (BCSCs) in breast cancer. Immunohistochemistry and dual-immunohistochemistry were performed to quantify the stem cells of the breast cancer patients. The performance of Cox proportional hazard regression model was assessed using the holdout methods, where the dataset was randomly split into two exclusive sets (70% training and 30% testing sets). Additionally, we performed bootstrapping to overcome a possible biased error estimate and obtain confidence intervals (CI).

Results

Four groups of BCSCs (ALDH1A3, CD44⁺/CD24⁻, integrin alpha 6 (ITGA6), and protein C receptor (PROCR)) were identified as associated with relapse-free survival (RFS). The correlated biomarkers were integrated as a prognostic panel to calculate a relapse risk score (RRS) and to classify the patients into different risk groups (high-risk or low-risk). According to RRS, 67.81 and 32.19% of patients were categorized into low-risk and high-risk groups respectively. The relapse rate at 5 years in the low-risk group (2.67, 95% CI: 0.72–4.63%) by Kaplan-Meier method was significantly lower than that of the high-risk group (19.30, 95% CI: 12.34–26.27%) (p <  0.001). In the multiple Cox model, the RRS was proven to be a powerful classifier independent of age at diagnosis or tumour size (p <  0.001). In addition, we found that high RRS score ER-positive patients do not benefit from hormonal therapy treatment (RFS, p = 0.860).

Conclusion

The RRS model can be applied to predict the relapse risk in early stage breast cancer. As such, high RRS score ER-positive patients do not benefit from hormonal therapy treatment.

Electronic supplementary material

The online version of this article (10.1186/s12885-019-5941-5) contains supplementary material, which is available to authorized users.

The 21-gene Recurrence Score® assay predicts distant recurrence in lymph node-positive, hormone receptor-positive, breast cancer patients treated with adjuvant sequential epirubicin- and docetaxel-based or epirubicin-based chemotherapy (PACS-01 trial)

Background

The 21-gene Recurrence Score (RS) result predicts outcome and chemotherapy benefit in node-negative and node-positive (N+), estrogen receptor-positive (ER+) patients treated with endocrine therapy. The purpose of this study was to evaluate the prognostic impact of RS results in N+, hormone receptor-positive (HR+) patients treated with adjuvant chemotherapy (6 cycles of FEC100 vs. 3 cycles of FEC100 followed by 3 cycles of docetaxel 100 mg/m²) plus endocrine therapy (ET) in the PACS-01 trial (J Clin Oncol 2006;24:5664-5671).

Methods

The current study included 530 HR+/N+ patients from the PACS-01 parent trial for whom specimens were available. The primary objective was to evaluate the relationship between the RS result and distant recurrence (DR).

Results

There were 209 (39.4%) patients with low RS (< 18), 159 (30%) with intermediate RS (18-30) and 162 (30.6%) with high RS (≥ 31). The continuous RS result was associated with DR (hazard ratio = 4.14; 95% confidence interval: 2.67-6.43; p <  0.001), adjusting for treatment. In multivariable analysis, the RS result remained a significant predictor of DR (p <  0.001) after adjustment for number of positive nodes, tumor size, tumor grade, Ki-67 (immunohistochemical status), and chemotherapy regimen. There was no statistically significant interaction between RS result and treatment in predicting DR (p = 0.79).

Conclusions

After adjustment for clinical covariates, the 21-gene RS result is a significant prognostic factor in N+/HR+ patients receiving adjuvant chemoendocrine therapy.

Trial registration

Not applicable.

Factors Predictive of Response to Hormone Therapy in Breast Cancer

Aims and Background

Approximately half of metastatic breast cancers expressing estrogen and/or progesterone receptors responds to endocrine therapy, and postoperative adjuvant endocrine therapy provides about a 50% reduction in the development of recurrent disease. A number of publications have focused on the correlation of biomarkers, in particular estrogen and progesterone receptors and HER-2/neu status as well as different gene profiles, multigene assays and genetic polymorphisms with response to hormone therapy. The purpose of this article is to review the literature to identify biological markers predictive of response to tamoxifen and aromatase inhibitors.

Methods

A computerized literature search through Medline and ASCO abstract databases was performed, applying the words “endocrine therapy” and “predictive markers” and each of the following: early and metastatic breast cancer, estrogen receptors, progesterone receptors, HER2/neu, multigene assays, polymorphisms. The last search was updated in June 2007. In the examined literature, biological markers were retrospectively assayed to establish whether such variables were predictive for endocrine therapy efficacy.

Results

The role of estrogen receptor content as a predictor of response to endocrine treatment was confirmed: benefit from endocrine treatment was directly proportional to estrogen receptor levels. Progesterone receptor status was only a strong time-dependent prognostic value, and it has not yet been validated as a predictive factor of tamoxifen efficacy. Retrospective clinical data from upfront and sequential studies of aromatase inhibitors were discordant regarding the degree of benefit of these drugs over tamoxifen according to progesterone receptor status. HER-2 positivity was associated with a significantly greater risk of endocrine therapy failure in metastatic and neoadjuvant settings. The current generation of genomic assays for tamoxifen sensitivity all contain a combination of prognostic information that it is difficult to integrate into clinical practice.

Conclusions

Available clinical data are inconclusive to support preferential use of aromatase inhibitors over tamoxifen in progesterone-receptor-negative and HER-2-positive tumors, but it was also clear that lower estrogen receptors, lower progesterone receptors, and positive HER-2 are associated with lower responsiveness to any type of endocrine therapy. Tumors overexpressing HER-2 are endocrine resistant and they require the blockage of the HER-2 pathway in addition to estrogen deprivation. Recent molecular studies have shown that endocrine responsiveness is to a large extent influenced by estrogen-receptor-related pathways. In the future, the key to the correct tailoring of hormone therapy will probably be the ability to subtype estrogen-receptor-positive breast cancer.

21-Gene Recurrence Score for prognosis and prediction of taxane benefit after adjuvant chemotherapy plus endocrine therapy: results from NSABP B-28/NRG Oncology

BACKGROUND

The 21-gene recurrence score (RS) predicts outcome and benefit from adjuvant chemotherapy benefit in breast cancer patients treated with adjuvant endocrine therapy. In the NSABP B-28 study, we evaluated the 21-gene RS for its prognostic impact and its ability to predict benefit from paclitaxel (P) in node-positive, estrogen receptor-positive (ER+) breast cancer patients treated with adjuvant chemotherapy plus tamoxifen.

METHODS

The B-28 trial compared doxorubicin/cyclophosphamide (AC) with AC followed by P in 3060 patients. Tamoxifen for 5 years was also given to patients > 50 years and those < 50 years with ER+ and/or progesterone receptor-positive (PR+) tumors. The present study includes 1065 ER-positive, tamoxifen-treated patients with RS assessment. Median follow-up time was 11.2 years.

RESULTS

In univariate analyses, RS was a significant predictor of outcome. In multivariate analyses, RS remained a significant independent predictor of outcome beyond clinico-pathologic factors, age, and type of surgery (p < 0.001). In the study population (n = 1065), the disease-free survival (DFS) hazard ratio (HR) with adding P to AC was 0.87 (95% CI 0.72-1.05; p = 0.14). RS was not a significant predictor of P benefit: for DFS, HRs for adding P to AC in RS low, intermediate, and high subgroups were 1.01 (95% CI 0.69-1.47; p = 0.99), 0.84 (95% CI 0.62-1.14; p = 0.26), and 0.81 (95% CI 0.60-1.10; p = 0.21), respectively (interaction p = 0.64). Similar findings were observed for the other study endpoints.

CONCLUSIONS

RS maintains significant prognostic impact in ER-positive, node-positive patients treated with adjuvant chemotherapy plus tamoxifen. However, RS did not significantly predict benefit from adding paclitaxel to AC chemotherapy. (Trial Registration: PDQ: NSABP-B-28).

Clinical utility of multigene profiling assays in early-stage breast cancer

BACKGROUND

This clinical practice guideline was developed to determine the level of evidence supporting the clinical utility of commercially available multigene profiling assays and to provide guidance about whether certain breast cancer patient populations in Ontario would benefit from alternative tests in addition to Oncotype dx (Genomic Health, Redwood City, CA, U.S.A.).

METHODS

A systematic electronic Ovid search of the medline and embase databases sought out systematic reviews and primary literature. A systematic review and practice guideline was written by a working group and was then reviewed and approved by Cancer Care Ontario's Molecular Oncology Advisory Committee.

RESULTS

Twenty-four studies assessing the clinical utility of Oncotype dx, Prosigna (NanoString Technologies, Seattle, WA, U.S.A.), EndoPredict (Myriad Genetics, Salt Lake City, U.S.A.), and MammaPrint (Agendia, Irvine, CA, U.S.A.) were included in the evidence base.

CONCLUSIONS

The clinical utility of multigene profiling assays is currently established for an appropriate subset of patients with estrogen receptor-positive, her2-negative, node-negative breast cancer for whom a decision to give chemotherapy is difficult to make. For patients with estrogen receptor-positive tumours who receive tamoxifen alone, Oncotype dx, Prosigna, and EndoPredict validly identify a low-risk population with favourable outcomes, indicating that a low-risk assay result is actionable and the decision to withhold chemotherapy is supported. Clinical evidence indicates that a high Oncotype dx recurrence score can predict for chemotherapy benefit, but a high Prosigna or EndoPredict score, although prognostic, is not, based on clinical trial evidence, directly actionable. Prosigna and EndoPredict are statistically more likely to identify a population at risk for recurrence beyond 5 years, but that information is currently not actionable because of a lack of interventional studies.

Personalized chemotherapy selection for breast cancer using gene expression profiles

Choosing the optimal chemotherapy regimen is still an unmet medical need for breast cancer patients. In this study, we reanalyzed data from seven independent data sets with totally 1079 breast cancer patients. The patients were treated with three different types of commonly used neoadjuvant chemotherapies: anthracycline alone, anthracycline plus paclitaxel, and anthracycline plus docetaxel. We developed random forest models with variable selection using both genetic and clinical variables to predict the response of a patient using pCR (pathological complete response) as the measure of response. The models were then used to reassign an optimal regimen to each patient to maximize the chance of pCR. An independent validation was performed where each independent study was left out during model building and later used for validation. The expected pCR rates of our method are significantly higher than the rates of the best treatments for all the seven independent studies. A validation study on 21 breast cancer cell lines showed that our prediction agrees with their drug-sensitivity profiles. In conclusion, the new strategy, called PRES (Personalized REgimen Selection), may significantly increase response rates for breast cancer patients, especially those with HER2 and ER negative tumors, who will receive one of the widely-accepted chemotherapy regimens.

OPTIMA prelim: a randomised feasibility study of personalised care in the treatment of women with early breast cancer

BACKGROUND

There is uncertainty about the chemotherapy sensitivity of some oestrogen receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative breast cancers. Multiparameter assays that measure the expression of several tumour genes simultaneously have been developed to guide the use of adjuvant chemotherapy for this breast cancer subtype. The assays provide prognostic information and have been claimed to predict chemotherapy sensitivity. There is a dearth of prospective validation studies. The Optimal Personalised Treatment of early breast cancer usIng Multiparameter Analysis preliminary study (OPTIMA prelim) is the feasibility phase of a randomised controlled trial (RCT) designed to validate the use of multiparameter assay directed chemotherapy decisions in the NHS.

OBJECTIVES

OPTIMA prelim was designed to establish the acceptability to patients and clinicians of randomisation to test-driven treatment assignment compared with usual care and to select an assay for study in the main RCT.

DESIGN

Partially blinded RCT with adaptive design.

SETTING

Thirty-five UK hospitals.

PARTICIPANTS

Patients aged ≥ 40 years with surgically treated ER-positive HER2-negative primary breast cancer and with 1-9 involved axillary nodes, or, if node negative, a tumour at least 30 mm in diameter.

INTERVENTIONS

Randomisation between two treatment options. Option 1 was standard care consisting of chemotherapy followed by endocrine therapy. In option 2, an Oncotype DX(®) test (Genomic Health Inc., Redwood City, CA, USA) performed on the resected tumour was used to assign patients either to standard care [if 'recurrence score' (RS) was > 25] or to endocrine therapy alone (if RS was ≤ 25). Patients allocated chemotherapy were blind to their randomisation.

MAIN OUTCOME MEASURES

The pre-specified success criteria were recruitment of 300 patients in no longer than 2 years and, for the final 150 patients, (1) an acceptance rate of at least 40%; (2) recruitment taking no longer than 6 months; and (3) chemotherapy starting within 6 weeks of consent in at least 85% of patients.

RESULTS

Between September 2012 and 3 June 2014, 350 patients consented to join OPTIMA prelim and 313 were randomised; the final 150 patients were recruited in 6 months, of whom 92% assigned chemotherapy started treatment within 6 weeks. The acceptance rate for the 750 patients invited to participate was 47%. Twelve out of the 325 patients with data (3.7%, 95% confidence interval 1.7% to 5.8%) were deemed ineligible on central review of receptor status. Interviews with researchers and recordings of potential participant consultations made as part of the integral qualitative recruitment study provided insights into recruitment barriers and led to interventions designed to improve recruitment. Patient information was changed as the result of feedback from three patient focus groups. Additional multiparameter analysis was performed on 302 tumour samples. Although Oncotype DX, MammaPrint(®)/BluePrint(®) (Agendia Inc., Irvine, CA, USA), Prosigna(®) (NanoString Technologies Inc., Seattle, WA, USA), IHC4, IHC4 automated quantitative immunofluorescence (AQUA(®)) [NexCourse BreastTM (Genoptix Inc. Carlsbad, CA, USA)] and MammaTyper(®) (BioNTech Diagnostics GmbH, Mainz, Germany) categorised comparable numbers of tumours into low- or high-risk groups and/or equivalent molecular subtypes, there was only moderate agreement between tests at an individual tumour level (kappa ranges 0.33-0.60 and 0.39-0.55 for tests providing risks and subtypes, respectively). Health economics modelling showed the value of information to the NHS from further research into multiparameter testing is high irrespective of the test evaluated. Prosigna is currently the highest priority for further study.

CONCLUSIONS

OPTIMA prelim has achieved its aims of demonstrating that a large UK clinical trial of multiparameter assay-based selection of chemotherapy in hormone-sensitive early breast cancer is feasible. The economic analysis shows that a trial would be economically worthwhile for the NHS. Based on the outcome of the OPTIMA prelim, a large-scale RCT to evaluate the clinical effectiveness and cost-effectiveness of multiparameter assay-directed chemotherapy decisions in hormone-sensitive HER2-negative early breast would be appropriate to take place in the NHS.

TRIAL REGISTRATION

Current Controlled Trials ISRCTN42400492.

FUNDING

This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 20, No. 10. See the NIHR Journals Library website for further project information. The Government of Ontario funded research at the Ontario Institute for Cancer Research. Robert C Stein received additional support from the NIHR University College London Hospitals Biomedical Research Centre.

Genomic Adequacy from Solid Tumor Core Needle Biopsies of ex Vivo Tissue and in Vivo Lung Masses: Prospective Study

Purpose To identify the variables and factors that affect the quantity and quality of nucleic acid yields from imaging-guided core needle biopsy. Materials and Methods This study was approved by the institutional review board and compliant with HIPAA. The authors prospectively obtained 232 biopsy specimens from 74 patients (177 ex vivo biopsy samples from surgically resected masses were obtained from 49 patients and 55 in vivo lung biopsy samples from computed tomographic [CT]-guided lung biopsies were obtained from 25 patients) and quantitatively measured DNA and RNA yields with respect to needle gauge, number of needle passes, and percentage of the needle core. RNA quality was also assessed. Significance of correlations among variables was assessed with analysis of variance followed by linear regression. Conditional probabilities were calculated for projected sample yields. Results The total nucleic acid yield increased with an increase in the number of needle passes or a decrease in needle gauge (two-way analysis of variance, P < .0001 for both). However, contrary to calculated differences in volume yields, the effect of needle gauge was markedly greater than the number of passes. For example, the use of an 18-gauge versus a 20-gauge biopsy needle resulted in a 4.8-5.7 times greater yield, whereas a double versus a single pass resulted in a 2.4-2.8 times greater yield for 18- versus 20-gauge needles, respectively. Ninety-eight of 184 samples (53%) had an RNA integrity number of at least 7 (out of a possible score of 10). Conclusion With regard to optimizing nucleic acid yields in CT-guided lung core needle biopsies used for genomic analysis, there should be a preference for using lower gauge needles over higher gauge needles with more passes. ^©RSNA, 2016 Online supplemental material is available for this article. An earlier incorrect version of this article appeared online. This article was corrected on October 21, 2016.

CCR 20th Anniversary Commentary: Gene-Expression Signature in Breast Cancer--Where Did It Start and Where Are We Now?

Desmedt and colleagues published two articles, one in the June 1, 2007 issue, and the other in the August 15, 2008, issue of Clinical Cancer Research, that showed gene-expression signatures to be proliferation driven and time dependent, with their prognostic power decreasing with increasing follow-up years. Moreover, the articles showed that immune response is a crucial determinant of prognosis in the HER2-positive and estrogen receptor-negative/HER2-negative subtypes, providing a rationale to further explore the role of the antitumor immune response in these breast cancer subtypes.

Indications for prognostic gene expression profiling in early breast cancer

OPINION STATEMENT

Breast cancer is a heterogeneous disease. While breast cancer mortality has dropped substantially over the past three decades due to early detection and adjuvant systemic therapy (AST), the risk of recurrence is highly dependent upon numerous factors including tumor size, involvement of regional lymph nodes, histologic grade, expression of hormone receptors (estrogen and progesterone), and human epidermal growth factor receptor 2 (HER2) amplification. We use these factors to determine which early breast cancer (EBC) patients should be treated with AST, including endocrine therapy (ET), chemotherapy, and HER2-directed treatments. While these factors aid in this determination, it remains challenging to identify those patients unlikely to benefit from adjuvant chemotherapy, resulting in over-treatment of patients. Given this dilemma, there has been great interest in the development of prognostic and predictive gene expression profiles. The most extensively studied profile, the 21-gene recurrence score (Oncotype Dx®), estimates 10-year risk of breast cancer recurrence in patients with estrogen receptor (ER)-positive, HER2-negative, node-negative EBC and is likely predictive of chemotherapy benefit. This assay has established analytic validity, clinical validity, and clinical utility for this patient group and, therefore, is indicated in this patient population to help inform decisions regarding administration of adjuvant chemotherapy. Several other assays may have utility in this clinical context or perhaps to identify patients who do not require extended adjuvant ET. These assays include the following: PAM 50 Risk of Recurrence (ROR) Score (Prosigna™), Breast Cancer Index, and EndoPredict®.

Relevance of progesterone receptor immunohistochemical staining to Oncotype DX recurrence score

OBJECTIVE/BACKGROUND

Progesterone-receptor negativity (PR-) is predictive of adverse outcomes in estrogen receptor-positive (ER+) breast cancer. The Oncotype DX assay provides risk stratification for hormone receptor-positive (HR+) invasive breast cancer; however, the association of PR status and Oncotype DX recurrence scores (RSs) is less clear.

METHODS

We designed an analysis to determine whether a significant difference exists in the RS for ER+/PR- tumors when compared with ER+/PR+ breast cancer. Three hundred and fifty patients with HR+ invasive breast cancer who underwent Oncotype DX testing at our institution from December 2006 to October 2013 were included. We also examined the concordance in the HR status reported by immunohistochemical (IHC) and reverse transcriptase-polymerase chain reaction (RT-PCR) analyses. The data were analyzed by analysis of variance, F test, t test, and chi-square tests. Multivariate linear regression was used to determine significant predictors of Oncotype DX RS.

RESULTS

A total of 301 patients had ER+/PR+ tumors and 47 patients had ER+/PR- tumors by IHC. PR- tumors had a significantly higher RS than PR+ tumors (24.7±8.53 vs. 17.3±7.38; p<.001), predicting a greater 10-year risk of distant recurrence. Multivariate linear regression showed PR status and tumor grade to be significant predictors of Oncotype DX RS (p<.0001). A total of 284 patients had HR status reported by Oncotype DX assay. Concordance between IHC and RT-PCR was 99.3% for ER and 88.7% for PR.

CONCLUSION

Our study shows that ER+/PR- breast cancer tumors are associated with a significantly higher Oncotype DX scores; this interprets into a higher risk of recurrence. Our data also show that the concordance between IHC and RT-PCR was 99.3% for ER and lower at 88.7% for PR.

Identifying patients at high risk of breast cancer recurrence: strategies to improve patient outcomes

Identifying patients at high risk of breast cancer recurrence has important implications not only for enabling the ability to provide accurate information to patients but also the potential to improve patient outcomes. Patients at high recurrence risk can be offered appropriate treatment to improve the overall survival. However, the major challenge is identifying patients with early-stage breast cancer at lower risk who may be spared potentially toxic therapy. The successful integration of molecular assays into clinical practice may address the problem of overtreatment and improve overall patient outcomes.

Gene-Expression-Based Predictors for Breast Cancer

An important and often complicated management decision in early stage hormone receptor (HR)-positive breast cancer relates to the use of adjuvant systemic chemotherapy. Although traditional clinicopathologic markers exist, tremendous progress has been achieved in the field of predictive biomarkers and genomics with both prognostic and predictive capabilities to identify patients who will potentially benefit from additional therapy. The use of these genomic tests in the neoadjuvant setting is also being studied and may lead to these tests providing clinical benefit even earlier in the disease course. Landmark articles published in the last few years have expanded our knowledge of breast cancer genomics to an unprecedented level, and mutational analysis via next-generation sequencing methods allows the identification of molecular targets for novel targeted therapeutic agents and clinical trials testing efficacy of targeted therapies, such as PI3K inhibitors, in addition to endocrine therapy for HR-positive breast cancer, are ongoing. We provide an in-depth review on the role of gene expression-based predictors in early stage breast cancer and an overview of future directions, including next-generation sequencing. Over the coming years, we anticipate a significant increase in utilization of genomic-based predictors for individualized selection and duration of endocrine therapy with and without genotype-driven targeted therapy, and a major decrease in the use of chemotherapy, possibly even leading to a chemotherapy-free road for early stage HR-positive breast cancer.

[Prevalence of breast cancer sub-types by immunohistochemistry in patients in the Regional General Hospital 72, Instituto Mexicano del Seguro Social]

BACKGROUND

Breast cancer mortality has increased in women 25 years and over, and since 2006 it has surpassed cervical cancer. Breast cancer is a heterogeneous disease, with several clinical and histological presentations that require a thorough study of all clinical and pathological parameters, including immunohistochemistry to classify it into subtypes, have a better prognosis, provide individualised treatment, increase survival, and reduce mortality.

OBJECTIVE

To evaluate the prevalence of sub-types of breast cancer and the association with the clinical and histopathological features of the tumour.

MATERIAL AND METHODS

An observational, retrospective, cross-sectional and analytical study conducted on 1380 patients with a diagnosis of breast cancer have been classified by immunohistochemistry into four subtypes: luminal A, triple negative, luminal B and HER2. An analysis was performed on the association with age, risk factors, and the clinical and histopathological features of the tumour.

RESULTS

The mean age of the patients was 53.3 ± 11.4. The frequency was luminal A (65%), triple negative (14%), luminal B (12%), and HER2 (9%). The most frequent characteristics were the 50 to 59 age range, late menopause, the right side, upper external quadrant, stage II, metastatic lymph nodes, and mastectomy.

CONCLUSION

The most frequent sub-type was luminal A, and together with the luminal B are those which have better prognosis compared with the triple negative and HER2.

Tumor cell migration screen identifies SRPK1 as breast cancer metastasis determinant

Tumor cell migration is a key process for cancer cell dissemination and metastasis that is controlled by signal-mediated cytoskeletal and cell matrix adhesion remodeling. Using a phagokinetic track assay with migratory H1299 cells, we performed an siRNA screen of almost 1,500 genes encoding kinases/phosphatases and adhesome- and migration-related proteins to identify genes that affect tumor cell migration speed and persistence. Thirty candidate genes that altered cell migration were validated in live tumor cell migration assays. Eight were associated with metastasis-free survival in breast cancer patients, with integrin β3-binding protein (ITGB3BP), MAP3K8, NIMA-related kinase (NEK2), and SHC-transforming protein 1 (SHC1) being the most predictive. Examination of genes that modulate migration indicated that SRPK1, encoding the splicing factor kinase SRSF protein kinase 1, is relevant to breast cancer outcomes, as it was highly expressed in basal breast cancer. Furthermore, high SRPK1 expression correlated with poor breast cancer disease outcome and preferential metastasis to the lungs and brain. In 2 independent murine models of breast tumor metastasis, stable shRNA-based SRPK1 knockdown suppressed metastasis to distant organs, including lung, liver, and spleen, and inhibited focal adhesion reorganization. Our study provides comprehensive information on the molecular determinants of tumor cell migration and suggests that SRPK1 has potential as a drug target for limiting breast cancer metastasis.

A new vision of evaluating gene expression signatures

Gene expression profiles based on high-throughput technologies contribute to molecular classifications of different cell lines and consequently to clinical diagnostic tests for cancer types and other diseases. Statistical techniques and dimension reduction methods have been devised for identifying minimal gene subset with maximal discriminative power. For sets of in silico candidate genes, assuming a unique gene signature or performing a parsimonious signature evaluation seems to be too restrictive in the context of in vitro signature validation. This is mainly due to the high complexity of largely correlated expression measurements and the existence of various oncogenic pathways. Consequently, it might be more advantageous to identify and evaluate multiple gene signatures with a similar good predictive power, which are referred to as near-optimal signatures, to be made available for biological validation. For this purpose we propose the bead-chain-plot approach originating from swarm intelligence techniques, and a small scale computational experiment is conducted in order to convey our vision. We simulate the acquisition of candidate genes by using a small pool of differentially expressed genes derived from microarray-based CNS tumour data. The application of the bead-chain-plot provides experimental evidence for improved classifications by using near-optimal signatures in validation procedures.

Predicting clinical response to anticancer drugs using an ex vivo platform that captures tumour heterogeneity

Predicting clinical response to anticancer drugs remains a major challenge in cancer treatment. Emerging reports indicate that the tumour microenvironment and heterogeneity can limit the predictive power of current biomarker-guided strategies for chemotherapy. Here we report the engineering of personalized tumour ecosystems that contextually conserve the tumour heterogeneity, and phenocopy the tumour microenvironment using tumour explants maintained in defined tumour grade-matched matrix support and autologous patient serum. The functional response of tumour ecosystems, engineered from 109 patients, to anticancer drugs, together with the corresponding clinical outcomes, is used to train a machine learning algorithm; the learned model is then applied to predict the clinical response in an independent validation group of 55 patients, where we achieve 100% sensitivity in predictions while keeping specificity in a desired high range. The tumour ecosystem and algorithm, together termed the CANScript technology, can emerge as a powerful platform for enabling personalized medicine.

Molecular diagnostic testing in breast cancer

OBJECTIVES

An overview of molecular tests used in the treatment of breast cancer, organized by stage and clinical condition.

DATA SOURCES

Systematic review of scientific literature, guideline recommendations, and data published by test manufacturers.

CONCLUSION

Several molecular tests that analyze expression of cancer-related genes have been validated in clinical trials and are recommended by clinical practice guidelines to inform diagnosis and treatment decisions for personalized interventions.

IMPLICATIONS FOR NURSING PRACTICE

Molecular testing has become an important part of patient care for those with breast cancer. Oncology nurses must understand this methodology to prescribe tests, interpret the results, and provide guidance to patients.

The value of multigene predictors of clinical outcome in breast cancer: an analysis of the evidence

OBJECTIVE

Multigene predictors are being used increasingly in early-stage breast cancer patients for prediction and prognosis. However, one consequence of the increased use of multigene predictors, and the heightened efforts toward their incorporation into routine clinical practice, is the potential for future malpractice litigation. It is, therefore, important to ascertain the strength of the evidence for using the different commercially available multigene predictor assays clinically. We evaluated the literature for evidence of clinical validity of four currently available gene signatures and to assess the influence of the 21-gene-expression assay on changes in treatment recommendations.

METHODS

A systematic search of the peer-reviewed literature from January 2002 to March 2014 for multigene predictor assays was carried out, and a meta-analysis was conducted.

RESULTS

The adjusted Cox hazard ratio average for studies that met the eligibility criteria was 3.538 (95% CI: 1.513-8.469). The 21-gene signature showed the highest stability in the estimation of likelihood of distant risk of recurrence. Using the recurrence scores resulted in changes in treatment recommendations in 31.8% of all patients in the studies.

CONCLUSION

This study may provide insight about the use of multigene predictors in clinical practice for prediction and prognosis of breast cancer.

ATAD2 is highly expressed in ovarian carcinomas and indicates poor prognosis

The purpose of this study was to explore the expression of ATAD2 in ovarian tumor tissue as well as its relationship with degree of malignancy. Tumor tissue from 110 cases of ovarian cancer was collected in accordance with the Declaration of Helsinki for evaluation of ATAD2 expression immunohistochemistry, quantitative PCR (qPCR) and Western blotting. The correlation between the ATAD2 expression and and the prognosis of ovarian cancer was evaluated by Cox regression model. In addition, HO-8910 and OVCAR-3 cells were transfected with two siRNAs targeting ATAD2. Cell viability was evaluated with MTT assay, and cell migration by transwell migration assay. ATAD2 was shown to be highly expressed in 65.5% (72/110) of ovarian cancer cases, both at transcriptional and protein levels. Moreover, highly expression was positively correlated with degree of malignancy. Knock-down of ATAD2 in HO-8910 and OVCAR-3 cells was found to reduce cell migration. In addition, follow-up visits of the patients demonstrated that the 5-year survival rate was lower in patients with high expression of ATAD2. Our study suggested that ovarian tumor tissue may have highly expressed ATAD2, which is associated with tumor stage, omentum-metastasis, ascites and CA-125. Increased ATAD2 may play important roles in tumor proliferation and migration. ATAD2 could serve in particular as a prognostic marker and a therapeutic target for ovarian cancer.

Cross-study validation for the assessment of prediction algorithms

Motivation: Numerous competing algorithms for prediction in high-dimensional settings have been developed in the statistical and machine-learning literature. Learning algorithms and the prediction models they generate are typically evaluated on the basis of cross-validation error estimates in a few exemplary datasets. However, in most applications, the ultimate goal of prediction modeling is to provide accurate predictions for independent samples obtained in different settings. Cross-validation within exemplary datasets may not adequately reflect performance in the broader application context.

Methods: We develop and implement a systematic approach to ‘cross-study validation’, to replace or supplement conventional cross-validation when evaluating high-dimensional prediction models in independent datasets. We illustrate it via simulations and in a collection of eight estrogen-receptor positive breast cancer microarray gene-expression datasets, where the objective is predicting distant metastasis-free survival (DMFS). We computed the C-index for all pairwise combinations of training and validation datasets. We evaluate several alternatives for summarizing the pairwise validation statistics, and compare these to conventional cross-validation.

Results: Our data-driven simulations and our application to survival prediction with eight breast cancer microarray datasets, suggest that standard cross-validation produces inflated discrimination accuracy for all algorithms considered, when compared to cross-study validation. Furthermore, the ranking of learning algorithms differs, suggesting that algorithms performing best in cross-validation may be suboptimal when evaluated through independent validation.

Availability: The survHD: Survival in High Dimensions package (http://www.bitbucket.org/lwaldron/survhd) will be made available through Bioconductor.

Contact:levi.waldron@hunter.cuny.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

Más-o-menos: a simple sign averaging method for discrimination in genomic data analysis

MOTIVATION

The successful translation of genomic signatures into clinical settings relies on good discrimination between patient subgroups. Many sophisticated algorithms have been proposed in the statistics and machine learning literature, but in practice simpler algorithms are often used. However, few simple algorithms have been formally described or systematically investigated.

RESULTS

We give a precise definition of a popular simple method we refer to as más-o-menos, which calculates prognostic scores for discrimination by summing standardized predictors, weighted by the signs of their marginal associations with the outcome. We study its behavior theoretically, in simulations and in an extensive analysis of 27 independent gene expression studies of bladder, breast and ovarian cancer, altogether totaling 3833 patients with survival outcomes. We find that despite its simplicity, más-o-menos can achieve good discrimination performance. It performs no worse, and sometimes better, than popular and much more CPU-intensive methods for discrimination, including lasso and ridge regression.

AVAILABILITY AND IMPLEMENTATION

Más-o-menos is implemented for survival analysis as an option in the survHD package, available from http://www.bitbucket.org/lwaldron/survhd and submitted to Bioconductor.

Bioinformatics for precision medicine in oncology: principles and application to the SHIVA clinical trial

Precision medicine (PM) requires the delivery of individually adapted medical care based on the genetic characteristics of each patient and his/her tumor. The last decade witnessed the development of high-throughput technologies such as microarrays and next-generation sequencing which paved the way to PM in the field of oncology. While the cost of these technologies decreases, we are facing an exponential increase in the amount of data produced. Our ability to use this information in daily practice relies strongly on the availability of an efficient bioinformatics system that assists in the translation of knowledge from the bench towards molecular targeting and diagnosis. Clinical trials and routine diagnoses constitute different approaches, both requiring a strong bioinformatics environment capable of (i) warranting the integration and the traceability of data, (ii) ensuring the correct processing and analyses of genomic data, and (iii) applying well-defined and reproducible procedures for workflow management and decision-making. To address the issues, a seamless information system was developed at Institut Curie which facilitates the data integration and tracks in real-time the processing of individual samples. Moreover, computational pipelines were developed to identify reliably genomic alterations and mutations from the molecular profiles of each patient. After a rigorous quality control, a meaningful report is delivered to the clinicians and biologists for the therapeutic decision. The complete bioinformatics environment and the key points of its implementation are presented in the context of the SHIVA clinical trial, a multicentric randomized phase II trial comparing targeted therapy based on tumor molecular profiling versus conventional therapy in patients with refractory cancer. The numerous challenges faced in practice during the setting up and the conduct of this trial are discussed as an illustration of PM application.

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.

Emerging technologies in extracellular vesicle-based molecular diagnostics

Extracellular vesicles (EVs), including exosomes and microvesicles, have been shown to carry a variety of biomacromolecules including mRNA, microRNA and other non-coding RNAs. Within the past 5 years, EVs have emerged as a promising minimally invasive novel source of material for molecular diagnostics. Although EVs can be easily identified and collected from biological fluids, further research and proper validation is needed in order for them to be useful in the clinical setting. In addition, innovative and more efficient means of nucleic acid profiling are needed to facilitate investigations into the cellular and molecular mechanisms of EV function and to establish their potential as useful clinical biomarkers and therapeutic tools. In this article, we provide an overview of recent technological improvements in both upstream EV isolation and downstream analytical technologies, including digital PCR and next generation sequencing, highlighting future prospects for EV-based molecular diagnostics.

Theranostic and molecular classification of breast cancer

CONTEXT

Despite advances in breast cancer management, women continue to relapse and die of breast cancer. Traditionally, evaluation for hormone receptors (estrogen and progesterone), as well as HER2 overexpression, have guided therapy-related decision-making because they are both prognostic and predictive indicators. However, there are limitations with those studies, which can lead to improper treatment. Gene signatures have recently been shown to be of value in identifying molecular portraits of breast carcinoma and are beginning to play role in management and treatment algorithms.

OBJECTIVE

To provide a summary of the prognostic and predictive indicators of breast cancer, such as hormone receptors, HER2, and molecular gene signatures that currently help guide clinical decision making.

DATA SOURCES

Published articles from peer-reviewed journals in PubMed (US National Library of Medicine).

CONCLUSIONS

Emerging evidence shows promise that, in addition to hormone receptors and HER2 studies, evaluating tumors with gene expression profiling can provide additional prognostic and predictive information, further aiding clinical management and leading to a more personalized approach to treating breast cancer.

A genetic signature can predict prognosis and response to therapy in breast cancer: OncotypeDX

We now recognize that not all breast cancers are the same. Different characteristics in gene expression profiles result in differential clinical behavior. With the use of gene microarrays, different subtypes of breast cancer have been characterized. These subtypes include the basal, the ERBB2+, and the luminal A, B and C subtypes. The importance of these different subtypes lies in the fact that they differ in clinical outcome, with the basal and ERBB2+ subtypes having the worst prognosis and the luminal A group having the best prognosis. However, identification of these subtypes is still not clinically used. Other strategies for evaluating tumors in a clinical setting have been developed using smaller sets of genes. One such strategy is the 21-gene assay (Oncotype DX), which is currently in commercial use in the USA. One advantage of this test is the use of paraffin-embedded blocks instead of previous methods, which required fresh frozen tissue. Oncotype DX has been shown to predict 10-year distant recurrence in patients with estrogen receptor-positive, axillary lymph node-negative breast cancer. This genomic assay has also been shown to predict chemotherapy and endocrine therapy response. Large, prospective, randomized clinical trials are currently underway using this genomic test. Other similar tests are also finding their way in clinical practice. A 70-gene assay, which has been developed by a group in The Netherlands, is currently being used as a tool to assign treatment in women with early stage breast cancer. In the near future, clinical decisions will most likely be dictated by the genetic characteristics of the tumor, with the clinical characteristics becoming less important. Tailoring our treatment based on individual tumor characteristics will help us develop better therapeutic strategies and save many of our patients from receiving unnecessary toxic therapy.

Breast carcinoma: is molecular evaluation a necessary part of current pathological analysis?

Breast cancer is the most common women cancer and is the second leading cause of cancer-related mortality in women. While the last two decades revolutionized breast cancer treatment with the development and use of therapies targeting steroid receptors and HER2/neu, there are limits to the risk estimation provided by traditional clinicopathologic parameters and IHC. Therefore, there is continued potential for inaccurate risk stratification of breast cancer patients which may lead to over- or under-treatment. In this review, we discuss the latest developments in the area of breast cancer research which have lead to better understanding of the breast cancer mechanisms, provided more accurate risk stratification, and identified potential new treatment targets. Specifically, we review the new dualistic model of breast carcinogenesis, which can inform pathologic diagnosis and tumor grading; we also discuss the intrinsic molecular classification of breast cancer and its impact on diagnosis and treatment; lastly, we compare the most common commercial molecular prognostic and predictive assays, with their respective strengths and weaknesses, and their clinical utility.

Molecular and clinicopathological markers of prognosis in breast cancer

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

Evaluation and correlation of risk recurrence in early breast cancer assessed by Oncotype DX®, clinicopathological markers and tumor cell dissemination in the blood and bone marrow

The Oncotype DX® assay is a validated genomic test that predicts the likelihood of breast cancer recurrence, patient survival within ten years of diagnosis and the benefit of chemotherapy in early-stage, node-negative, estrogen receptor-positive breast cancer. Further markers of recurrence include disseminated tumor cells (DTCs) in the bone marrow (BM) and circulating tumor cells (CTCs) in the blood, particularly stemness-like tumor cells (slCTCs). In this study, Oncotype DX, DTCs, CTCs and slCTCs were used to evaluate the risk of recurrence in 68 patients with human epidermal growth factor receptor 2-negative, early-stage breast cancer. Formalin-fixed, paraffin-embedded tissue sections were analyzed for the expression of 16 cancer genes and 5 reference genes by Oncotype DX, yielding a recurrence score (RS). G2 tumors were evaluated for urokinase-type plasminogen activator (uPA)/plasminogen activator inhibitor type 1 (PAI1) and Ki-67. Two BM aspirates were analyzed by immunocytochemistry for DTCs using the pan-cytokeratin antibody A45-B/B3. CTCs and slCTCs in the blood were detected using the AdnaTest BreastCancer, AdnaTest EMT and the AdnaTest TumorStemCell. Oncotype DX was performed in 68 cases, yielding a low RS in 30/68 patients (44%), an intermediate RS in 29/68 patients (43%) and a high RS in 9/68 patients (13%). DTCs were detected in 19/68 patients (28%), CTCs in 13/68 patients (19%) and slCTCs in 26/68 (38%) patients. Moreover, 8/68 patients (12%) with G2 tumors were positive for uPA, 6/68 (9%) for PAI1 and 21/68 (31%) for Ki-67. Ki-67, progesterone receptor (PR) and G3 tumors were significantly correlated with RS (P<0.001; P=0.006; and P=0,002, respectively), whereas no correlation was identified between DTCs, CTCs, slCTCs and RS. Ki-67 may support therapeutic decisions in cases where Oncotype DX is not feasible. Larger patient cohorts are required to estimate the additional detection of DTCs and CTCs for the determination of risk recurrence.

Molecular profiling for breast cancer: a comprehensive review

In recent years advances in molecular biology have launched disruptive innovations in breast cancer diagnostics and therapeutics. The advent of genomics has revolutionized our understanding of breast cancer as several different biologically and molecularly distinct diseases. This research has led to commercially available polymerase chain reaction (PCR) and microarray tests that have begun to fundamentally change the way medical oncologists quantify recurrence risk in early stage breast cancer patients. The Genomics era has altered the clinicopathologic paradigm of selecting patients for adjuvant cytotoxic chemotherapy. Sufficiently powered prospective studies are underway that may establish these molecular assays as elements of standard clinical practice in breast cancer treatment. In this article, we review the strengths and limitations of currently available breast cancer-specific molecular tests.

KLF6-SV1 drives breast cancer metastasis and is associated with poor survival

Metastasis is the major cause of cancer mortality. A more thorough understanding of the mechanisms driving this complex multistep process will aid in the identification and characterization of therapeutically targetable genetic drivers of disease progression. We demonstrate that KLF6-SV1, an oncogenic splice variant of the KLF6 tumor suppressor gene, is associated with increased metastatic potential and poor survival in a cohort of 671 lymph node-negative breast cancer patients. KLF6-SV1 overexpression in mammary epithelial cell lines resulted in an epithelial-to-mesenchymal-like transition and drove aggressive multiorgan metastatic disease in multiple in vivo models. Additionally, KLF6-SV1 loss-of-function studies demonstrated reversion to an epithelial and less invasive phenotype. Combined, these findings implicate KLF6-SV1 as a key driver of breast cancer metastasis that distinguishes between indolent and lethal early-stage disease and provides a potential therapeutic target for invasive breast cancer.

Molecular and protein markers for clinical decision making in breast cancer: today and tomorrow

In early breast cancer (eBC), established clinicopathological factors are not sufficient for clinical decision making particularly regarding adjuvant chemotherapy since substantial over- or undertreatment may occur. Thus, novel protein- and molecular markers have been put forward as decision aids. Since these potential prognosis and/or predictive tests differ substantially regarding their methodology, analytical and clinical validation, this review attempts to summarize the essential facts for clinicians. This review focuses on those markers which are the most advanced so far in their development towards routine clinical application, i.e. two protein markers (i.e. uPA/PAI-1 and IHC4) and six molecular multigene tests (i.e. Mammaprint®, Oncotype DX®, PAM50, Endopredict®, the 97-gene genomic grade, and 76 gene Rotterdam signatures). Next to methodological aspects, we summarized the clinical evidences, in particular the main prospective clinical trials which have already been fully recruited (i.e. MINDACT, TAILORx, WSG PLAN B) or are still ongoing (i.e. RxPONDER/SWOG S1007, WSG-ADAPT). Last but not least, this review points out the key elements for clinicians to select one test among the wide panel of proposed assays, for a specific population of patients in term of level of evidence, analytical and clinical validity as well as cost effectiveness.

Detection of cancer before distant metastasis

Background

To establish a distant metastasis (DM) cells must disseminate from the primary tumor and overcome a series of obstacles, the metastatic cascade. In this study we develop a mathematical model for this cascade to estimate the tumor size and the circulating tumor cell (CTC) load before the first metastasis has formed from a primary breast cancer tumor.

Methods

The metastatic cascade is described in discrete steps: 1. local tumor growth; 2. dissemination into circulation; 3. survival in circulation; 4. extravasation into tissue; and 5. growth into a metastasis. The model was built using data and relationships described in the literature to predict the relationship between tumor size and probability of distant metastasis for 38715 patients with surgically removed T_XN_XM₀ primary breast cancer from the Netherlands Cancer Registry. The model was calibrated using primary tumor size, probability of distant metastasis and time to distant metastasis for 1489 patients with stage T_1BN_XM₀ (25% of total patients with T_1BN_XM₀). Validation of the model was done with data for all patients.

Results

From the time to distant metastasis of these 38715 breast cancer patients, we determined a tumor doubling time of 1.7 ± 0.9 months. Fitting the data for 25% of T_1B patients estimates a metastatic efficiency of 1 metastasis formed per 60 million disseminated tumor cells. Validation of the model to data of patients in all T-stages shows good agreement between model and epidemiological data. To reduce the 5-year risk of distant metastasis for T_XN_XM₀ from 9.2% to 1.0%, the primary tumor needs to be detected and removed before it reaches a diameter of 2.7 ± 1.6 mm. At this size, the model predicts that there will be 9 ± 6 CTC/L blood.

Conclusions

To reduce the rate of distant metastasis in surgically treated T_XN_XM₀ breast cancer to 1%, imaging technology will need to be able to detect lesions of 2.7 mm in diameter or smaller. Before CTC detection can be applied in the early disease setting, sensitivity will need to be improved by at least 15-fold and combined with technology that minimizes false positives.

Gene expression profiling in breast cancer: a clinical perspective

Gene expression profiling tests are used in an attempt to determine the right treatment for the right person with early-stage breast cancer that may have spread to nearby lymph nodes but not to distant parts of the body. These new diagnostic approaches are designed to spare people who do not need additional treatment (adjuvant therapy) the side effects of unnecessary treatment, and allow people who may benefit from adjuvant therapy to receive it. In the present review we discuss in detail the major diagnostic tests available such as MammaPrint dx, Oncotype dx, PAM50, Mammostrat, IHC4, MapQuant DX, Theros-Breast Cancer Gene Expression Ratio Assay, and their potential clinical applications.

A data similarity-based strategy for meta-analysis of transcriptional profiles in cancer

Background

Robust transcriptional signatures in cancer can be identified by data similarity-driven meta-analysis of gene expression profiles. An unbiased data integration and interrogation strategy has not previously been available.

Methods and Findings

We implemented and performed a large meta-analysis of breast cancer gene expression profiles from 223 datasets containing 10,581 human breast cancer samples using a novel data similarity-based approach (iterative EXALT). Cancer gene expression signatures extracted from individual datasets were clustered by data similarity and consolidated into a meta-signature with a recurrent and concordant gene expression pattern. A retrospective survival analysis was performed to evaluate the predictive power of a novel meta-signature deduced from transcriptional profiling studies of human breast cancer. Validation cohorts consisting of 6,011 breast cancer patients from 21 different breast cancer datasets and 1,110 patients with other malignancies (lung and prostate cancer) were used to test the robustness of our findings. During the iterative EXALT analysis, 633 signatures were grouped by their data similarity and formed 121 signature clusters. From the 121 signature clusters, we identified a unique meta-signature (BRmet50) based on a cluster of 11 signatures sharing a phenotype related to highly aggressive breast cancer. In patients with breast cancer, there was a significant association between BRmet50 and disease outcome, and the prognostic power of BRmet50 was independent of common clinical and pathologic covariates. Furthermore, the prognostic value of BRmet50 was not specific to breast cancer, as it also predicted survival in prostate and lung cancers.

Conclusions

We have established and implemented a novel data similarity-driven meta-analysis strategy. Using this approach, we identified a transcriptional meta-signature (BRmet50) in breast cancer, and the prognostic performance of BRmet50 was robust and applicable across a wide range of cancer-patient populations.

Multidisciplinary care of patients with early-stage breast cancer

There is a compelling need for close coordination and integration of multiple specialties in the management of patients with early-stage breast cancer. Optimal patient care and outcomes depend on the sequential and often simultaneous participation and dialogue between specialists in imaging, pathologic and molecular diagnostic and prognostic stratification, and the therapeutic specialties of surgery, radiation oncology, and medical oncology. These are but a few of the various disciplines needed to provide modern, sophisticated management. The essential role for coordinated involvement of the entire health care team in optimal management of patients with early-stage breast cancer is likely to increase further.