Clinical evidence supporting genomic tests in early breast cancer: Do all genomic tests provide the same information?

Evaluation and Comparison of Prognostic Multigene Tests in Early-Stage Breast Cancer: Which Is the Most Effective? A Literature Review Exploring Clinical Utility to Enhance Therapeutic Management in Luminal Patients

Breast cancer is the most common malignancy affecting women, marked by significant complexity and heterogeneity. This disease includes multiple subtypes, each with unique biological features and treatment responses. Despite significant advancements in detection and therapy, challenges remain, particularly in managing aggressive forms like triple-negative breast cancer and overcoming drug resistance. Breast cancer classification and subtype determination are typically performed by immunohistochemistry (IHC) method, which assesses four key markers (ER, PR, HER2, KI67); however, due to the recognized issues with this approach-especially regarding the evaluation of Ki67-there is a risk of misclassification. Patients who may be suitable for chemotherapy could miss possible advantages and only experience needless toxicity as a result of improper treatment decisions. Molecular profiling has improved breast cancer management, enabling the creation of multigene prognostic tests (MPTs) like Oncotype Dx, MammaPrint, Prosigna, Endopredict, and Breast Cancer Index which assess gene expression profiles to more accurately predict recurrence risks. These tools help personalize treatment, identifying patients who can avoid chemotherapy and/or extended endocrine therapy. While many MPTs are available, only Oncotype Dx and MammaPrint have prospective validation, with Prosigna providing additional prognostic insights by incorporating clinical variables. Molecular tests are especially usefull in the "gray zone," which includes tumors measuring between 1 and 3 cm with 0-3 positive lymph nodes and an intermediate proliferation index. However, their clinical utility has not been definitively established, and significant differences exist between them. This article provides an in-depth analysis of established genomic assays, including testing procedures, clinical validity, utility, diagnostic frameworks, and methodologies. Our comparison aims to improve early breast cancer management by guiding pathologists and oncologists in optimizing the use of genomic assays in clinical practice. By presenting this information, we aim to enhance understanding of the clinical utility and effectiveness of these assays, supporting the development of personalized treatment strategies for early breast cancer patients. Genomic assays offer important insights that can support treatment decisions in early-stage breast cancer, especially when used alongside other clinical evaluations, predictive tools, and management guidelines. While multiple gene expression profiling tests are available, they classify patients differently and are not interchangeable; therefore, their application should be at the clinician's discretion during the decision-making process. It is essential that these tests are not the sole factor in determining the best treatment plan: other clinical considerations and patient preferences should also play a significant role in guiding treatment decisions.

Impact of race/ethnicity on MammaPrint genomic assay risk and prognosis in early breast cancer: A National Cancer Data Base analysis

BACKGROUND

Black race is associated with poorer prognosis in hormone receptor (HR)-positive, HER2-negative early breast cancer (EBC). The impact of race/ethnicity on the risk distribution and prognostic information provided by the 70-gene MammaPrint assay was evaluated.

METHODS

Women with HR-positive EBC with tumors up to 5 cm in size and zero to three involved lymph nodes, who were diagnosed between 2009 and 2018, and who had an available MammaPrint result were identified in the National Cancer Data Base (NCDB). t-tests and χ2 tests were used to compare patient characteristics, whereas log-rank tests assessed differences in overall survival (OS) between racial/ethnic subgroups.

RESULTS

Of the 6137 women included, 82.8% (n = 5084) were non-Hispanic White, 8.9% (n = 545) were non-Hispanic Black (NHB), 4.8% (n = 292) were Hispanic, and the remainder were other/unknown (n = 216). Of these women, 58.4% (n = 3587) were MammaPrint low risk and 41.6% (n = 2550) were MammaPrint high risk. NHB and Hispanic women were more likely to have a high-risk MammaPrint result (p < .001) than other racial/ethnic subgroups. Five-year OS was worse for the MammaPrint high-risk versus low-risk group (92.6% vs. 96.6%; p < .0001). There were no significant differences in OS on the basis of race/ethnicity in the MammaPrint low-risk (p = .34) or high-risk (p = .79) subgroups. However, Black race did not affect mortality in the overall population (hazard ratio, 1.15; 95% confidence interval, 0.84-1.58).

CONCLUSIONS

NHB and Hispanic women were more likely to have high-risk MammaPrint results in this NCDB cohort. Although there were no differences in survival by race/ethnicity in the MammaPrint low- and high-risk groups, these findings are limited by the lack of association between race/ethnicity and mortality in the overall population when adjusting for other clinicopathologic prognostic covariates.

The REMAR (Rhein-Main-Registry) real-world study: prospective evaluation of the 21-gene breast recurrence score® assay in addition to Ki-67 for adjuvant treatment decisions in early-stage breast cancer

PURPOSE

Ki-67 is recommended by international/national guidelines for risk stratification in early breast cancer (EBC), particularly for defining "intermediate risk," despite inter-laboratory/inter-observer variability and cutoff uncertainty. We investigated Ki-67 (> 10%- < 40%, determined locally) as a prognostic marker for intermediate/high risk in EBC, pN0-1 patients.

METHODS

This prospective, non-interventional, real-world study included females ≥ 18 years, with pN0/pN1mi/pN1, HR+ , HER2-negative EBC, and locally determined Ki-67 ranging 10%-40%. The primary outcome was changes in treatment recommendations after disclosing the Oncotype DX Breast Recurrence Score®(RS) assay result.

RESULTS

The analysis included 567 patients (median age, 57 [range, 29-83] years; 70%/1%/29%/ with pN0/pN1mi/pN1 disease; 81% and 19% with RS results 0-25 and 26-100, respectively). The correlations between local and central Ki-67, local Ki-67, and the RS, and central Ki-67 and the RS results were weak (r = 0.35, r = 0.3, and r = 0.46, respectively), and discrepancies were noted in both directions (e.g., local Ki-67 was lower or higher than central Ki-67). After disclosing the RS, treatment recommendations changed for 190 patients (34%). Changes were observed in pN0 and pN1mi/pN1 patients and in patients with centrally determined Ki-67 ≤ 10% and > 10%. Treatment changes were aligned with RS results (adding chemotherapy for patients with higher RS results, omitting it for lower RS results), and their net result was 8% reduction in adjuvant chemotherapy use (from 32% pre-RS results to 24% post-RS results).

CONCLUSION

The Oncotype DX® assay is a tool for individualizing treatments that adds to classic treatment decision factors. The RS result and Ki-67 are not interchangeable, and Ki-67, as well as nodal status, should not be used as gatekeepers for testing eligibility, to avoid under and overtreatment.

Nottingham prognostic x (NPx): a risk stratification tool in ER-positive HER2-negative breast cancer: a validation study

AIMS

In this study, we validate the use of Nottingham Prognostic x (NPx), consisting of tumour size, tumour grade, progesterone receptor (PR) and Ki67 in luminal BC.

MATERIALS AND METHODS

Two large cohorts of luminal early-stage BC (n = 2864) were included. PR and Ki67 expression were assessed using full-face resection samples using immunohistochemistry. NPx was calculated and correlated with clinical variables and outcome, together with Oncotype DX recurrence score (RS), that is frequently used as a risk stratifier in luminal BC.

RESULTS

In the whole cohort, 38% of patients were classified as high risk using NPx which showed significant association with parameters characteristics of aggressive tumour behaviour and shorter survival (P < 0.0001). NPx classified the moderate Nottingham Prognostic Index (NPI) risk group (n = 1812) into two distinct prognostic subgroups. Of the 82% low-risk group, only 3.8% developed events. Contrasting this, 14% of the high-risk patients developed events during follow-up. A strong association was observed between NPx and Oncotype Dx RS (P < 0.0001), where 66% of patients with intermediate risk RS who had subsequent distant metastases also had a high-risk NPx.

CONCLUSION

NPx is a reliable prognostic index in patients with luminal early-stage BC, and in selected patients may be used to guide adjuvant chemotherapy recommendations.

Introducing effective genes in lymph node metastasis of breast cancer patients using SHAP values based on the mRNA expression data

OBJECTIVE

Breast cancer, a global concern predominantly impacting women, poses a significant threat when not identified early. While survival rates for breast cancer patients are typically favorable, the emergence of regional metastases markedly diminishes survival prospects. Detecting metastases and comprehending their molecular underpinnings are crucial for tailoring effective treatments and improving patient survival outcomes.

METHODS

Various artificial intelligence methods and techniques were employed in this study to achieve accurate outcomes. Initially, the data was organized and underwent hold-out cross-validation, data cleaning, and normalization. Subsequently, feature selection was conducted using ANOVA and binary Particle Swarm Optimization (PSO). During the analysis phase, the discriminative power of the selected features was evaluated using machine learning classification algorithms. Finally, the selected features were considered, and the SHAP algorithm was utilized to identify the most significant features for enhancing the decoding of dominant molecular mechanisms in lymph node metastases.

RESULTS

In this study, five main steps were followed for the analysis of mRNA expression data: reading, preprocessing, feature selection, classification, and SHAP algorithm. The RF classifier utilized the candidate mRNAs to differentiate between negative and positive categories with an accuracy of 61% and an AUC of 0.6. During the SHAP process, intriguing relationships between the selected mRNAs and positive/negative lymph node status were discovered. The results indicate that GDF5, BAHCC1, LCN2, FGF14-AS2, and IDH2 are among the top five most impactful mRNAs based on their SHAP values.

CONCLUSION

The prominent identified mRNAs including GDF5, BAHCC1, LCN2, FGF14-AS2, and IDH2, are implicated in lymph node metastasis. This study holds promise in elucidating a thorough insight into key candidate genes that could significantly impact the early detection and tailored therapeutic strategies for lymph node metastasis in patients with breast cancer.

Selecting postoperative adjuvant systemic therapy for early-stage breast cancer: An updated assessment and systematic review of leading commercially available gene expression assays

Gene expression assays (GEAs) can guide treatment for early-stage breast cancer. Several large prospective randomized clinical trials, and numerous additional studies, now provide new information for selecting an appropriate GEA. This systematic review builds upon prior reviews, with a focus on five widely commercialized GEAs (Breast Cancer Index®, EndoPredict®, MammaPrint®, Oncotype DX®, and Prosigna®). The comprehensive dataset available provides a contemporary opportunity to assess each GEA's utility as a prognosticator and/or predictor of adjuvant therapy benefit.

Enhancing clinical decision support with genomic tools in breast cancer: A Scottish perspective

INTRODUCTION

The Oncotype DX Breast RS test has been adopted in Scotland and has been the subject of a large population-based study by a Scottish Consensus Group to assess the uptake of the recurrence score (RS), evaluate co-variates associated with the RS and to analyse the effect it may have had on clinical practice.

MATERIALS & METHODS

Pan-Scotland study between August 2018-August 2021 evaluating 833 patients who had a RS test performed as part of their diagnostic pathway. Data was extracted retrospectively from electronic records and analysis conducted to describe change in chemotherapy administration (by direct comparison with conventional risk assessment tools), and univariate/multivariate analysis to assess relationship between covariates and the RS.

RESULTS

Chemotherapy treatment was strongly influenced by the RS (p < 0.001). Only 30 % of patients received chemotherapy treatment in the intermediate and high risk PREDICT groups, where chemotherapy is considered. Additionally, 55.5 % of patients with a high risk PREDICT had a low RS and did not receive chemotherapy. There were 17 % of patients with a low risk PREDICT but high RS who received chemotherapy. Multivariate regression analysis showed the progesterone receptor Allred score (PR score) to be a strong independent predictor of the RS, with a negative PR score being associated with high RS (OR 4.49, p < 0.001). Increasing grade was also associated with high RS (OR 3.81, p < 0.001). Classic lobular pathology was associated with a low RS in comparison to other tumour pathology (p < 0.01). Nodal disease was associated with a lower RS (p = 0.012) on univariate analysis, with menopausal status (p = 0.43) not influencing the RS on univariate or multivariate analysis.

CONCLUSIONS

Genomic assays offer the potential for risk-stratified decision making regarding the use of chemotherapy. They can help reduce unnecessary chemotherapy treatment and identify a subgroup of patients with more adverse genomic tumour biology. A recent publication by Health Improvement Scotland (HIS) has updated guidance on use of the RS test for NHS Scotland. It suggests to limit its use to the intermediate risk PREDICT group. Our study shows the impact of the RS test in the low and high risk PREDICT groups. The implementation across Scotland has resulted in a notable shift in practice, leading to a significant reduction in chemotherapy administration in the setting of high risk PREDICT scores returning low risk RS. There has also been utility for the test in the low risk PREDICT group to detect a small subgroup with a high RS. We have found the PR score to have a strong independent association with high risk RS. This finding was not evaluated by the key RS test papers, and the potential prognostic information provided by the PR score as a surrogate biomarker is an outstanding question that requires more research to validate.

Clinical Utility of Genomic Assay in Node-Positive Early-Stage Breast Cancer

Breast cancer (BC) is the most common malignancy among women in Canada. Adjuvant treatment in early BC can reduce the risk of BC recurrence. Historically, the decision for adjuvant chemotherapy for early BC was made only based on clinical and tumour characteristics. In recent years, there has been an effort toward developing genomic assays as a predictive and prognostic tool to improve precision in estimating disease recurrence, sensitivity to systemic treatment and ultimately with clinical utility for guidance regarding adjuvant systemic treatment(s). There are various commercial genomic tests available for early-stage ER+/HER-2 negative BC. This paper will review the Oncotype DX 21-gene Recurrence Score (RS), MammaPrint, EndoPredict, Prosigna®, and Breast Cancer Index (BCI) genomic assays. We will also focus on these genomic assays’ clinical application and utility in node-positive early-stage BC based on the most recent evidence and guidance recommendations.

Breast cancer in the era of precision medicine

Breast cancer is a heterogeneous disorder with different molecular subtypes and biological characteristics for which there are diverse therapeutic approaches and clinical outcomes specific to any molecular subtype. It is a global health concern due to a lack of efficient therapy regimens that might be used for all disease subtypes. Therefore, treatment customization for each patient depending on molecular characteristics should be considered. Precision medicine for breast cancer is an approach to diagnosis, treatment, and prevention of the disease that takes into consideration the patient's genetic makeup. Precision medicine provides the promise of highly individualized treatment, in which each individual breast cancer patient receives the most appropriate diagnostics and targeted therapies based on the genetic profile of cancer. The knowledge about the molecular features and development of breast cancer treatment approaches has increased, which led to the development of new targeted therapeutics. Tumor genomic profiling is the standard of care for breast cancer that could contribute to taking steps to better management of malignancies. It holds great promise for accurate prognostication, prediction of response to common systemic therapies, and individualized monitoring of the disease. The emergence of targeted treatment has significantly enhanced the survival of patients with breast cancer and contributed to reducing the economic costs of the health system. In this review, we summarized the therapeutic approaches associated with the molecular classification of breast cancer to help the best treatment selection specific to the target patient.

Tumor cell intrinsic and extrinsic features predict prognosis in estrogen receptor positive breast cancer

Although estrogen-receptor-positive (ER+) breast cancer is generally associated with favorable prognosis, clinical outcome varies substantially among patients. Genomic assays have been developed and applied to predict patient prognosis for personalized treatment. We hypothesize that the recurrence risk of ER+ breast cancer patients is determined by both genomic mutations intrinsic to tumor cells and extrinsic immunological features in the tumor microenvironment. Based on the Cancer Genome Atlas (TCGA) breast cancer data, we identified the 72 most common genomic aberrations (including gene mutations and indels) in ER+ breast cancer and defined sample-specific scores that systematically characterized the deregulated pathways intrinsic to tumor cells. To further consider tumor cell extrinsic features, we calculated immune infiltration scores for six major immune cell types. Many individual intrinsic features are predictive of patient prognosis in ER+ breast cancer, and some of them achieved comparable accuracy with the Oncotype DX assay. In addition, statistical learning models that integrated these features predicts the recurrence risk of patients with significantly better performance than the Oncotype DX assay (our optimized random forest model AUC = 0.841, Oncotype DX model AUC = 0.792, p = 0.04). As a proof-of-concept, our study indicates the great potential of genomic and immunological features in prognostic prediction for improving breast cancer precision medicine. The framework introduced in this work can be readily applied to other cancers.

Clinical Potential of lncRNA PPP1R26-AS1 in Breast Cancer and Its Contribution to Cancer Progression

Breast cancer has become the most leading diagnosed tumor worldwide in 2020. In this study, the biomarker potential and influence on the cellular function of lncRNA PPP1R26-AS1 was investigated in breast cancer. Expression levels of lncRNA PPP1R26-AS1 in breast tissues and cells were detected by RT-qPCR. Association between lncRNA PPP1R26-AS1 level and clinical parameters was investigated by Chi-square analysis. The prognostic potential was assessed by Kaplan-Meier and multivariate Cox regression analysis. Knockdown of lncRNA PPP1R26-AS1 was subjected to study the effect on cell proliferation, invasion, and migration by CCK-8 and transwell assay. The bind between PPP1R26-AS1 and miR-1226-3p was investigated. LncRNA PPP1R26-AS1 was highly expressed in breast tissues and cell lines. This upregulation was correlated with pTNM, positive ER status, luminal B subtype, positive nodal status, and shorter overall survival in breast cancer patients. Significant decreases in proliferation, migration, and invasion activity were observed upon knockdown of lncRNA PPP1R26-AS1. lncRNA PPP1R26-AS1 could act as ceRNA to bind with miR-1226-3p in breast cancer. LncRNA PPP1R26-AS1, as oncogenic lncRNA, could provide a new perspective on the development of prognostic biomarkers and a new approach in tailoring the treatment personalized in breast cancer.

The clinician's perspective on the 21-gene assay in early breast cancer

Most patients with early HR+ and HER2- breast cancer receive a hormone therapy; the clinical question still open is how to identify patients who can really benefit from adjuvant chemotherapy. The accurate identification of these patients is essential to avoid an over-treatment, increasing the risk of an unnecessary toxicity; on the contrary, the omission of chemotherapy can deprive high risk patients of a potential life-saving treatment (under-treatment). Several multigene assays (MGAs), assessing the risk of relapse according to the biological characteristics of the tumor, have been developed. To date, the 21-gene assay (Oncotype DX Breast Recurrence Score^®) is the only test developed and validated to be actionable, i.e., able to predict the benefit of adjuvant chemotherapy. The different available tests can be classified according to their clinical utility based on their prognostic and predictive value. A prognostic test gives information about the outcome of the disease, regardless of the administered therapy. When the aim of the test is to drive the treatment decisions, the predictive component, and therefore the ability to accurately identify which patients could benefit from chemotherapy, is essential. This review summarizes the clinical evidences of the Oncotype DX^® test supporting its clinical utility.

Uncovering the roles of microRNAs/lncRNAs in characterising breast cancer subtypes and prognosis

Background

Accurate prognosis and identification of cancer subtypes at molecular level are important steps towards effective and personalised treatments of breast cancer. To this end, many computational methods have been developed to use gene (mRNA) expression data for breast cancer subtyping and prognosis. Meanwhile, microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) have been extensively studied in the last 2 decades and their associations with breast cancer subtypes and prognosis have been evidenced. However, it is not clear whether using miRNA and/or lncRNA expression data helps improve the performance of gene expression based subtyping and prognosis methods, and this raises challenges as to how and when to use these data and methods in practice.

Results

In this paper, we conduct a comparative study of 35 methods, including 12 breast cancer subtyping methods and 23 breast cancer prognosis methods, on a collection of 19 independent breast cancer datasets. We aim to uncover the roles of miRNAs and lncRNAs in breast cancer subtyping and prognosis from the systematic comparison. In addition, we created an R package, CancerSubtypesPrognosis, including all the 35 methods to facilitate the reproducibility of the methods and streamline the evaluation.

Conclusions

The experimental results show that integrating miRNA expression data helps improve the performance of the mRNA-based cancer subtyping methods. However, miRNA signatures are not as good as mRNA signatures for breast cancer prognosis. In general, lncRNA expression data does not help improve the mRNA-based methods in both cancer subtyping and cancer prognosis. These results suggest that the prognostic roles of miRNA/lncRNA signatures in the improvement of breast cancer prognosis needs to be further verified.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04215-3.

Multigene tests for breast cancer: the physician's perspective

Breast cancer is the most common tumour in women and the first cause of death for cancer in the female population. Preserving the quality of life has therefore become an important objective in the management of the disease. The benefits of adjuvant chemotherapy in patients with HR+ HER2- early breast cancer should always be balanced against its potential short and long-term adverse effects, and identifying the appropriate patients for whom chemotherapy can offer the highest clinical benefit is critical. Besides clinical and pathological factors, today four multigene tests able to guide the choice of the adjuvant therapy early breast cancer are available in Italy: Oncotype DX®, EndoPredict®, MammaPrint® e Prosigna®. This review evaluates the main characteristics of these diagnostic tests, the studies on clinical utility, their economic impact and their inclusion in international and national guidelines. The Oncotype DX Breast Recurrence Score® test is the only multigene test validated, with level IA evidence, to guide the adjuvant therapy decisions: hormone therapy alone for most patients with RS results 0-25, and chemotherapy for patients with RS results 26-100. Clinical data demonstrate that the Oncotype DX test is able to significantly impact therapeutic decisions, reducing chemotherapy use up to 49% and supporting the use of chemotherapy (up to 12%) in potentially under-treated patients. Based on the level of clinical evidence and established clinical utility, several multigene tests have been included in the main international guidelines, with recommendations ranging from "strong" to "moderate".

Personalization in Modern Radiation Oncology: Methods, Results and Pitfalls. Personalized Interventions and Breast Cancer

Breast cancer, the most frequent malignancy in women worldwide, is a heterogeneous group of diseases, characterized by distinct molecular aberrations. In precision medicine, radiation oncology for breast cancer aims at tailoring treatment according to tumor biology and each patient’s clinical features and genetics. Although systemic therapies are personalized according to molecular sub-type [i.e. endocrine therapy for receptor-positive disease and anti-human epidermal growth factor receptor 2 (HER2) therapy for HER2-positive disease] and multi-gene assays, personalized radiation therapy has yet to be adopted in the clinical setting. Currently, attempts are being made to identify prognostic and/or predictive factors, biomarkers, signatures that could lead to personalized treatment in order to select appropriate patients who might, or might not, benefit from radiation therapy or whose radiation therapy might be escalated or de-escalated in dosages and volumes. This overview focuses on what has been achieved to date in personalized post-operative radiation therapy and individual patient radiosensitivity assessments by means of tumor sub-types and genetics.

Genomic Assays in Node Positive Breast Cancer Patients: A Review

In recent years, developments in breast cancer have allowed yet another realization of individualized medicine in the field of oncology. One of these advances is genomic assays, which are considered elements of standard clinical practice in the management of breast cancer. These assays are widely used today not only to measure recurrence risk in breast cancer patients at an early stage but also to tailor treatment as well and minimize avoidable treatment side effects. At present, genomic tests are applied extensively in node negative disease. In this article, we review the use of these tests in node positive disease, explore their ramifications on neoadjuvant chemotherapy decisions, highlight sufficiently powered recent studies emphasizing their use and review the most recent guidelines.

Gene signature-based prediction of triple-negative breast cancer patient response to Neoadjuvant chemotherapy

Neoadjuvant chemotherapy is the current standard of care for large, advanced, and/or inoperable tumors, including triple‐negative breast cancer. Although the clinical benefits of neoadjuvant chemotherapy have been illustrated through numerous clinical trials, more than half of the patients do not experience therapeutic benefit and needlessly suffer from side effects. Currently, no clinically applicable biomarkers are available for predicting neoadjuvant chemotherapy response in triple‐negative breast cancer; the discovery of such a predictive biomarker or marker profile is an unmet need. In this study, we introduce a generic computational framework to calculate a response‐probability score (RPS), based on patient transcriptomic profiles, to predict their response to neoadjuvant chemotherapy. We first validated this framework in ER‐positive breast cancer patients and showed that it predicted neoadjuvant chemotherapy response with equal performance to several clinically used gene signatures, including Oncotype DX and MammaPrint. Then, we applied this framework to triple‐negative breast cancer data and, for each patient, we calculated a response probability score (TNBC‐RPS). Our results indicate that the TNBC‐RPS achieved the highest accuracy for predicting neoadjuvant chemotherapy response compared to previously proposed 143 gene signatures. When combined with additional clinical factors, the TNBC‐RPS achieved a high prediction accuracy for triple‐negative breast cancer patients, which was comparable to the prediction accuracy of Oncotype DX and MammaPrint in ER‐positive patients. In conclusion, the TNBC‐RPS accurately predicts neoadjuvant chemotherapy response in triple‐negative breast cancer patients and has the potential to be clinically used to aid physicians in stratifying patients for more effective neoadjuvant chemotherapy.

Breast cancer: Biology, biomarkers, and treatments

During the past recent years, various therapies emerged in the era of breast cancer. Breast cancer is a heterogeneous disease in which genetic and environmental factors are involved. Breast cancer stem cells (BCSCs) are the main player in the aggressiveness of different tumors and also, these cells are the main challenge in cancer treatment. Moreover, the major obstacle to achieve an effective treatment is resistance to therapies. There are various types of treatment for breast cancer (BC) patients. Therefore, in this review, we present the current treatments, novel approaches such as antibody-drug conjugation systems (ADCs), nanoparticles (albumin-, metal-, lipid-, polymer-, micelle-based nanoparticles), and BCSCs-based therapies. Furthermore, prognostic and predictive biomarkers will be discussed also biomarkers that have been applied by some tests such as Oncotype DX, Mamm αPrint, and uPA/PAI-1 are regarded as suitable prognostic and predictive factors in breast cancer.

Precision Medicine and the Role of Biomarkers of Radiotherapy Response in Breast Cancer

Radiotherapy remains an important treatment modality in nearly two thirds of all cancers, including the primary curative or palliative treatment of breast cancer. Unfortunately, largely due to tumor heterogeneity, tumor radiotherapy response rates can vary significantly, even between patients diagnosed with the same tumor type. Although in recent years significant technological advances have been made in the way radiation can be precisely delivered to tumors, it is proving more difficult to personalize radiotherapy regimens based on cancer biology. Biomarkers that provide prognostic or predictive information regarding a tumor's intrinsic radiosensitivity or its response to treatment could prove valuable in helping to personalize radiation dosing, enabling clinicians to make decisions between different treatment options whilst avoiding radiation-induced toxicity in patients unlikely to gain therapeutic benefit. Studies have investigated numerous ways in which both patient and tumor radiosensitivities can be assessed. Tumor molecular profiling has been used to develop radiosensitivity gene signatures, while the assessment of specific intracellular or secreted proteins, including circulating tumor cells, exosomes and DNA, has been performed to identify prognostic or predictive biomarkers of radiation response. Finally, the investigation of biomarkers related to radiation-induced toxicity could provide another means by which radiotherapy could become personalized. In this review, we discuss studies that have used these methods to identify or develop prognostic/predictive signatures of radiosensitivity, and how such assays could be used in the future as a means of providing personalized radiotherapy.

Region-specific glucocorticoid receptor promoter methylation has both positive and negative prognostic value in patients with estrogen receptor-positive breast cancer

BACKGROUND

The glucocorticoid receptor (NR3C1, GR) is frequently downregulated in breast tumors, and evidence suggests it acts as a tumor suppressor in estrogen receptor-positive (ER+) breast cancer. We previously found that methylation of the GR promoter CpG island represses gene expression and occurs in ER+ breast tumors. In this study, the prognostic and predictive value of GR methylation was examined in ER+ patients from the CCTG MA.12 clinical trial of tamoxifen versus placebo in women with early breast cancer.

METHODS

We developed a targeted multiplex bisulfite next-generation sequencing assay to detect methylation at multiple GR promoter regions in DNA from formalin-fixed paraffin-embedded (FFPE) samples. Following validation in a small cohort of breast tumors, ER+ FFPE tumor samples from MA.12 (n = 208) were tested. Survival analyses evaluated the impact of GR promoter methylation on patient overall survival (OS) and disease-free survival (DFS).

RESULTS

An analysis of TCGA data found that GR methylation is prevalent in ER+ tumors and is associated with decreased gene expression and analysis of public microarray data (KM Plotter) linked decreased GR expression to a poor outcome. In MA.12, two GR promoter regions (U and C) each had prognostic value, but with opposite effects on the outcome. U methylation was associated with poor OS (HR = 1.79, P = 0.041) whereas C methylation was associated with better OS (HR = 0.40, P = 0.040) and DFS (HR = 0.49, P = 0.037). The classification of patients based on the methylation status of the two regions was prognostic for OS (P = 0.006) and DFS (P = 0.041) and revealed a group of patients (U methylated, C unmethylated) with very poor outcomes. Placebo-treated patients in this high-risk group had worse OS (HR = 2.86, P = 0.002) and DFS (HR = 2.09, P = 0.014) compared to the rest of the cohort.

CONCLUSION

Region-specific GR promoter methylation was an independent prognostic marker for patient survival and identified a subset of patients with poor prognosis, particularly without tamoxifen treatment. These findings provide a foundation for future studies into GR methylation as a promising prognostic biomarker in ER+ breast cancer.

Food allergy and omics

Food allergy (FA) prevalence has been increasing over the last few decades and is now a global health concern. Current diagnostic methods for FA result in a high number of false-positive results, and the standard of care is either allergen avoidance or use of epinephrine on accidental exposure, although currently with no other approved treatments. The increasing prevalence of FA, lack of robust biomarkers, and inadequate treatments warrants further research into the mechanism underlying food allergies. Recent technological advances have made it possible to move beyond traditional biological techniques to more sophisticated high-throughput approaches. These technologies have created the burgeoning field of omics sciences, which permit a more systematic investigation of biological problems. Omics sciences, such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, microbiomics, and exposomics, have enabled the construction of regulatory networks and biological pathway models. Parallel advances in bioinformatics and computational techniques have enabled the integration, analysis, and interpretation of these exponentially growing data sets and opens the possibility of personalized or precision medicine for FA.

Cost effectiveness of Gene Expression Profiling in Patients with Early-Stage Breast Cancer in a Middle-Income Country, Turkey: Results of a Prospective Multicenter Study

Objective

Breast cancer is a heterogenous disease, and genetic profiling helps to individualize adjuvant treatment. The Oncotype DX is a validated test to predict benefit of adjuvant systemic treatment. The aims of this study are to determine the costs of chemotherapy in government hospitals in Turkey and evaluate the cost-effectiveness of the Oncotype DX from the national insurance perspective.

Materials and Methods

A Markov model was developed to make long term projections of distant recurrence, survival, quality adjusted life expectancy, and direct costs for patients with ER+, HER2-, node-negative or up to 3 node-positive early stage breast cancer. Turkish decision impact study patient data were captured for model reference. In that study, ten academic centers across Turkey participated in a prospective trial. Of 165 patients with pT1-3, pN0-N1mic, ER-positive, and HER-2 negative tumors, 57% had low recurrence score (RS), 35% had intermediate RS, and 8% had high RS, respectively. The overall rate of change in chemotherapy treatment decisions following Oncotype DX was 33%.

Results

The cost of adjuvant chemotherapy in public hospitals was estimated at $3.649, and Oncotype Dx test was $5.141. Based on the cost-effectiveness analysis, Oncotype DX testing was estimated to improve life expectancy (+0.86 years) and quality-adjusted life expectancy (+0.68 QALYs) versus standard care. The incremental cost-effectiveness ratio (ICERs) of Oncotype DX was estimated to be $7207.9 per QALY gained and $5720.6 per LY gained versus current clinical practice.

Conclusion

As Oncotype DX was found both cost-effective and life-saving from a national perspective, the test should be introduced to standard care in patients with ER+, HER-2 negative early-stage breast cancer in Turkey.

Risk stratification in early breast cancer in premenopausal and postmenopausal women: integrating genomic assays with clinicopathological features

PURPOSE OF REVIEW

There is growing consensus that genomic assays provide useful complementary information to clinicopathological features in oestrogen receptor-positive breast cancers. Here, ongoing research with multigene tests used for postmenopausal breast cancer and new emerging prognostic and predictive markers for pre and postmenopausal women are summarised.

RECENT FINDINGS

Results of the TAILORx trial have shown that women with an intermediate risk score do not benefit from adjuvant chemotherapy. Prosgina has been further investigated in a contemporary patient population in postmenopausal women and its use has been extended for premenopausal women. The EndoPredict was extensively used in decision-impact studies showing that its use can potentially reduce the need for adjuvant chemotherapy. Several new genomic assays have been developed, with some of them showing promising use for women with early oestrogen receptor-positive breast cancer.

SUMMARY

New areas of research for prediction of recurrence and risk stratification involve the development of immune gene signatures that carry modest but significant prognostic value. The recent expansion of high-throughput technology platforms including circulating tumour DNA/RNA and microRNA offer new opportunities to improve prediction models, particularly in women with oestrogen receptor-negative disease and premenopausal women. Genomic assays have clearly improved prognostication of early oestrogen receptor-positive breast cancer but it is clear that standard clinicopathological parameters are still very important when identifying patient for adjuvant chemotherapy.

Novel Predictors of Breast Cancer Survival Derived from miRNA Activity Analysis

Purpose: Breast cancer is among the leading causes of cancer-related death; discovery of novel prognostic markers is needed to improve outcomes. Combining systems biology and epidemiology, we investigated miRNA-associated genes and breast cancer survival in a well-characterized population-based study.Experimental Design: A recently developed algorithm, ActMiR, was used to identify key miRNA "activities" corresponding to target gene degradation, which were predictive of breast cancer mortality in published databases. We profiled miRNA-associated genes in tumors from our well-characterized population-based cohort of 606 women with first primary breast cancer. Cox proportional hazards models were used to estimate HRs and 95% confidence intervals (CI), after 15+ years of follow-up with 119 breast cancer-specific deaths.Results: miR-500a activity was identified as a key miRNA for estrogen receptor-positive breast cancer mortality using public databases. From a panel of 161 miR-500a-associated genes profiled, 73 were significantly associated with breast cancer-specific mortality (FDR < 0.05) in our population, among which two clusters were observed to have opposing directions of association. For example, high level of SUSD3 was associated with reduced breast cancer-specific mortality (HR = 0.3; 95% CI, 0.2-0.4), whereas the opposite was observed for TPX2 (HR = 2.7; 95% CI, 1.8-3.9). Most importantly, we identified set of genes for which associations with breast cancer-specific mortality were independent of known prognostic factors, including hormone receptor status and PAM50-derived risk-of-recurrence scores. These results are validated in independent datasets.Conclusions: We identified novel markers that may improve prognostic efficiency while shedding light on molecular mechanisms of breast cancer progression. Clin Cancer Res; 24(3); 581-91. ©2017 AACR.

Budget impact analysis of gene expression tests to aid therapy decisions for breast cancer patients in Germany

OBJECTIVES

Many women with early-stage, hormone receptor-positive breast cancer may not benefit from adjuvant chemotherapy. Gene expression tests can reduce chemotherapy over- and undertreatment by providing prognostic information on the likelihood of recurrence and, with Oncotype DX, predictive information on chemotherapy benefit. These tests are currently not reimbursed by German healthcare payers. An analysis was conducted to evaluate the budget impact of gene expression tests in Germany.

MATERIALS AND METHODS

Costs of gene expression tests and medical and non-medical costs associated with treatment were assessed from healthcare payer and societal perspectives. Costs were estimated from data collected at a university hospital and were combined with decision impact data for Oncotype DX, MammaPrint, Prosigna and EndoPredict (EPclin). Changes in chemotherapy use and budget impact were evaluated over 1 year for 20,000 women.

RESULTS

Chemotherapy was associated with substantial annual costs of EUR 19,003 and EUR 84,412 per therapy from the healthcare payer and societal perspective, respectively. Compared with standard care, only Oncotype DX was associated with cost savings to healthcare payers and society (EUR 5.9 million and EUR 253 million, respectively). Scenario analysis showed that both women at high clinical but low genomic risk and low clinical but high genomic risk were important contributors to costs.

CONCLUSIONS

Oncotype DX was the only gene expression test that was estimated to reduce costs versus standard care in Germany. The reimbursement of Oncotype DX testing in standard clinical practice in Germany should be considered.

Reducing chemotherapy use in clinically high-risk, genomically low-risk pN0 and pN1 early breast cancer patients: five-year data from the prospective, randomised phase 3 West German Study Group (WSG) PlanB trial

Background

The prospective phase 3 PlanB trial used the Oncotype DX^® Recurrence Score^® (RS) to define a genomically low-risk subset of clinically high-risk pN0-1 early breast cancer (EBC) patients for treatment with adjuvant endocrine therapy (ET) alone. Here, we report five-year data evaluating the prognostic value of RS, Ki-67, and other traditional clinicopathological parameters.

Methods

A central tumour bank was prospectively established within PlanB. Following an early amendment, hormone receptor (HR)+ , pN0-1 RS ≤ 11 patients were recommended to omit chemotherapy. Patients with RS ≥ 12, pN2-3, or HR-negative/HER2-negative disease were randomised to anthracycline-containing or anthracycline-free chemotherapy. Primary endpoint: disease-free survival (DFS). PlanB Clinicaltrials.gov identifier: NCT01049425.

Findings

From 2009 to 2011, PlanB enrolled 3198 patients (central tumour bank, n = 3073) with the median age of 56 years, 41.1% pN+, and 32.5% grade 3 EBC. Chemotherapy was omitted in 348/404 (86.1%) eligible RS ≤ 11 patients. After 55 months of median follow-up, five-year DFS in ET-treated RS ≤ 11 patients was 94% (in both pN0 and pN1) versus 94% (RS 12–25) and 84% (RS > 25) in chemotherapy-treated patients (p < 0.001); five-year overall survival (OS) was 99 versus 97% and 93%, respectively (p < 0.001). Nodal status, central/local grade, tumour size, continuous Ki-67, progesterone receptor (PR), IHC4, and RS were univariate prognostic factors for DFS. In a multivariate analysis including all univariate prognostic markers, only pN2-3, central and local grade 3, tumour size >2 cm, and RS, but not IHC4 or Ki-67 were independent adverse factors. If RS was excluded, IHC4 or both Ki-67 and PR entered the model. The impact of RS was particularly pronounced in patients with intermediate Ki-67 (>10%, <40%) tumours.

Interpretation

The excellent five-year outcomes in clinically high-risk, genomically low-risk (RS ≤ 11) pN0-1 patients without adjuvant chemotherapy support using RS with standardised pathology for treatment decisions in HR+ HER2-negative EBC. Ki-67 has the potential to support patient selection for genomic testing.

Pre-silencing of genes involved in the electron transport chain (ETC) pathway is associated with responsiveness to abatacept in rheumatoid arthritis

Background

In the current context of personalized medicine, one of the major challenges in the management of rheumatoid arthritis (RA) is to identify biomarkers that predict drug responsiveness. From the European APPRAISE trial, our main objective was to identify a gene expression profile associated with responsiveness to abatacept (ABA) + methotrexate (MTX) and to understand the involvement of this signature in the pathophysiology of RA.

Methods

Whole human genome microarrays (4 × 44 K) were performed from a first subset of 36 patients with RA. Data validation by quantitative reverse-transcription (qRT)-PCR was performed from a second independent subset of 32 patients with RA. Gene Ontology and WikiPathways database allowed us to highlight the specific biological mechanisms involved in predicting response to ABA/MTX.

Results

From the first subset of 36 patients with RA, a combination including 87 transcripts allowed almost perfect separation between responders and non-responders to ABA/MTX. Next, the second subset of patients 32 with RA allowed validation by qRT-PCR of a minimal signature with only four genes. This latter signature categorized 81% of patients with RA with 75% sensitivity, 85% specificity and 85% negative predictive value. This combination showed a significant enrichment of genes involved in electron transport chain (ETC) pathways. Seven transcripts from ETC pathways (NDUFA6, NDUFA4, UQCRQ, ATP5J, COX7A2, COX7B, COX6A1) were significantly downregulated in responders versus non-responders to ABA/MTX. Moreover, dysregulation of these genes was independent of inflammation and was specific to ABA response.

Conclusion

Pre-silencing of ETC genes is associated with future response to ABA/MTX and might be a crucial key to susceptibility to ABA response.

Electronic supplementary material

The online version of this article (doi:10.1186/s13075-017-1319-8) contains supplementary material, which is available to authorized users.

The Era of Multigene Panels Comes? The Clinical Utility of Oncotype DX and MammaPrint

The AJCC Cancer Staging Manual, eighth edition published in late 2016, will become the new global guideline for cancer diagnosis and treatment from January 1, 2018. The new edition for the tumor staging system has numerous updates, including building up the prognostic stage group of tumors for the first time and adding a large number of non-anatomical factors into the prognostic evaluation. Oncotype DX and MammaPrint are two of the genomic predictors that will be part of routine clinical practice in the future. Numerous studies have proved the clinical utility of multigene panels in predicting clinical outcome and treatment response. Here we present our review of the studies on these multigene panels and their application to breast cancer.

Paradigm Shift From Halstedian Radical Mastectomy to Personalized Medicine

Breast cancer management changed from radical mastectomy to precision medicine in a period longer than a century. The aims of these changes were to refrain from overdiagnoses and overtreatments as well as their harmful side effects and extra costs. Breast cancer is a heterogeneous disease and characterized by many morphological, clinical and molecular features. We now increasingly realise that a one-size-fits-all strategy does not apply to all breast cancer patients. Personalized medicine may be used for breast cancer screening, diagnosis and treatment. Individualized screening can decrease the number of unnecessary mammograms, additional radiologic studies, breast biopsies and false positivity rates. However, additional 15 to 20 years are necessary to reach the results of prospective randomized trials comparing low-risk and normal-risk women. We also should wait for outcomes of risk-based screening trials. The rates of overtreatment in patients with early-stage breast cancer have reached 40% in many studies. Personalized treatment has succeeded in reducing it substantially by using tumour genetic profiling and tumour receptors in early breast cancer patients. However, it has its limits and it is impossible to generalize it to all patients. New biomarkers and molecular classifications have also led to the development of novel therapies and treatment strategies. And, they can contribute to a more personalized management of breast cancer patients.

Specific gene expression profiles are associated with a pathologic complete response to neoadjuvant therapy in esophageal adenocarcinoma

BACKGROUND

Predicting treatment response to chemo-radiotherapy (CRT) in esophageal cancer remains an unrealized goal despite studies linking constellations of genes to prognosis. We aimed to determine if specific expression profiles are associated with pathologic complete response (pCR) after neoadjuvant CRT.

METHODS

Eleven genes previously associated with esophageal cancer prognosis were identified. Esophageal adenocarcinoma (EAC) patients treated with neoadjuvant CRT and esophagectomy were included. Patients were classified into two groups: pCR and no-or-incomplete response (NR). Polymerase chain reaction was used to evaluate gene expression. Omnibus testing was applied to overall gene expression differences between groups, and log-rank tests compared individual genes.

RESULTS

Eleven pCR and eighteen NR patients were analyzed. Combined expression profiles were significantly different between pCR and NR groups (p < 0.01). The gene CCL28 was over-expressed in pCR patients (Log-HR: 1.53, 95%CI: 0.46-2.59, p = 0.005), and DKK3 was under-expressed in pCR (Log-HR: -1.03 95%CI: -1.97, -0.10, p = 0.031).

CONCLUSION

EAC tumors that demonstrated a pCR have genetic profiles that are significantly different from typical NR profiles. The genes CCL28 and DKK3 are potential predictors of treatment response.

Pathologic diagnosis of breast cancer patients: evolution of the traditional clinical-pathologic paradigm toward "precision" cancer therapy

We present an updated account of breast cancer treatment and of progress toward "precision" cancer therapy; we focus on new developments in diagnostic molecular pathology and breast cancer that have emerged during the past 2 years. Increasing awareness of new prognostic and predictive methodologies, and introduction of next generation sequencing has increased understanding of both tumor biology and clinical behavior, which offers the possibility of more appropriate therapeutic choices. It remains unclear which of these testing methodologies provides the most informative and cost-effective actionable results for predictive and prognostic pathology. It is likely, however, that an integrated "step-wise" approach that uses the traditional clinical-pathologic paradigms coordinated with molecular characterization of breast tumor tissue, will offer the most comprehensive and cost-effective options for individualized, "precision" therapy for patients with breast cancer.