EndoPredict® in early hormone receptor-positive, HER2-negative breast cancer

Genomics in Clinical trials for Breast Cancer

Breast cancer (B.C.) still has increasing incidences and mortality rates globally. It is known that B.C. and other cancers have a very high rate of genetic heterogeneity and genomic mutations. Traditional oncology approaches have not been able to provide a lasting solution. Targeted therapeutics have been instrumental in handling the complexity and resistance associated with B.C. However, the progress of genomic technology has transformed our understanding of the genetic landscape of breast cancer, opening new avenues for improved anti-cancer therapeutics. Genomics is critical in developing tailored therapeutics and identifying patients most benefit from these treatments. The next generation of breast cancer clinical trials has incorporated next-generation sequencing technologies into the process, and we have seen benefits. These innovations have led to the approval of better-targeted therapies for patients with breast cancer. Genomics has a role to play in clinical trials, including genomic tests that have been approved, patient selection and prediction of therapeutic response. Multiple clinical trials in breast cancer have been done and are still ongoing, which have applied genomics technology. Precision medicine can be achieved in breast cancer therapy with increased efforts and advanced genomic studies in this domain. Genomics studies assist with patient outcomes improvement and oncology advancement by providing a deeper understanding of the biology behind breast cancer. This article will examine the present state of genomics in breast cancer clinical trials.

Which Patients Need Chemotherapy? From Pathological Risk Factors to Gene Signatures and Evaluation of Endocrine Response

Background

Chemotherapy, used either before or after surgery, has significantly improved survival in early breast cancer. Accurate risk assessment is essential to avoid both overtreatment and undertreatment. This review provides an overview of the evolution of chemotherapy as well as risk factors for tailored systemic therapies in early breast cancer - from pathologic risk factors to gene expression signatures to endocrine response assessment.

Summary

Chemotherapy has improved dramatically in recent decades from its beginnings with conventionally dosed cyclophosphamide plus methotexate plus 5-fluorouracil to dose-dense anthracycline- and taxane-containing regimens. Similarly, risk assessment has evolved starting from traditional pathologic risk factors such as tumor size, axillary nodal status, and grading. In recent decades, gene expression signatures have improved prognostic accuracy with a high level of evidence. In turn, these signatures can be further improved by incorporating the aforementioned pathologic factors. As an important step away from this static assessment, dynamic assessment of proliferation factor Ki-67 after short-term preoperative endocrine treatment has gained interest to improve risk assessment in early hormone receptor-positive breast cancer.

Key Message

This review highlights advances in chemotherapy and risk assessment in early breast cancer, from pathologic risk factors for recurrence to gene expression signatures and endocrine response assessment. These developments are leading to better risk stratification and thus better adaptation of therapies.

Genetic Testing Enhances the Precision Diagnosis and Treatment of Breast Cancer

The contemporary comprehension of breast cancer has progressed to the molecular level. As a heterogeneous malignancy, conventional pathological diagnosis and histological classification could no longer meet the needs of precisely managing breast cancer. Genetic testing based on gene expression profiles and gene mutations has emerged and substantially contributed to the precise diagnosis and treatment of breast cancer. Multigene assays (MGAs) are explored for early-stage breast cancer patients, aiding the selection of adjuvant therapy and predicting prognosis. For metastatic breast cancer patients, testing specific genes indicates potentially effective antitumor agents. In this review, genetic testing in early-stage and metastatic breast cancer is summarized, as well as the advantages and challenges of genetic testing in breast cancer.

The Role of Nodes and Nodal Assessment in Diagnosis, Treatment and Prediction in ER+, Node-Positive Breast Cancer

The majority of breast cancers are oestrogen receptor-positive (ER+). In ER+ cancers, oestrogen acts as a disease driver, so these tumours are likely to be susceptible to endocrine therapy (ET). ET works by blocking the hormone's synthesis or effect. A significant number of patients diagnosed with breast cancer will have the spread of tumour cells into regional lymph nodes either at the time of diagnosis, or as a recurrence some years later. Patients with node-positive disease have a poorer prognosis and can respond less well to ET. The nodal metastases may be genomically similar or, as is becoming more evident, may differ from the primary tumour. However, nodal metastatic disease is often not assessed, and treatment decisions are almost always based on biomarkers evaluated in the primary tumour. This review will summarise the evidence in the field on ER+, node-positive breast cancer, including diagnosis, treatment, prognosis and predictive tools.

Resistance of breast cancer cells to paclitaxel is associated with low expressions of miRNA-186 and miRNA-7

Aim: Neo-adjuvant chemotherapy is a common approach for the complex treatment of breast cancer (BC) and paclitaxel (PTX) is frequently included in the therapeutic regimen. However, the effect of PTX-based treatment is hard to predict precisely based on routinely used markers. As microRNAs are considered a new promising class of biomarkers, the link between miRNA expression and PTX resistance of BC cells needs to be well investigated. This study aimed at the identification of miRNAs associated with responses of BC cells to PTX. Methods: Intrinsic PTX sensitivity and miRNA profiling were assayed in five BC cell lines to identify candidate miRNAs. Selected miRNA (n. 15) expressions were analyzed by real-time-quantitative polymerase chain reaction (RT-qPCR) in BC tissue samples (n. 31) obtained from a diagnostic biopsy. Results were analyzed in the context of the effect of two cycles of PTX and the effect of the completed scheme of neoadjuvant therapy. The study's design facilitated the evaluation of the effect of PTX on cells and the identification of features of the microRNA expression profiles associated exclusively with sensitivity to this drug. Results: miR-186 and miR-7 expression in BC tissues was higher in patients with better outcomes of PTX-based neoadjuvant therapy. Conclusion: High expressions of miR-186 and miR-7 are associated with good response to PTX, whereas their low expressions may be associated with resistance to PTX in BC, indicating the possibility of developing innovative test systems for the prediction of the PTX response, which can be used before the start of neo-adjuvant chemotherapy for BC.

Molecular Classification of Breast Cancer: Relevance and Challenges

CONTEXT.—

Appropriate patient management requires precise and meaningful tumor classification. Breast cancer classification continues to evolve from traditional morphologic evaluation to more sophisticated systems with the integration of new knowledge from research being translated into practice. Breast cancer is heterogeneous at the molecular level, with diversified patterns of gene expression, which is presumably responsible for the difference in tumor behavior and prognosis. Since the beginning of this century, new molecular technology has been gradually applied to breast cancer research on issues pertinent to prognosis (prognostic signature) and therapeutic prediction (predictive signature), and much progress has been made.

OBJECTIVE.—

To summarize the current state and the prospective future of molecular classification of breast cancer.

DATA SOURCES.—

Sources include recent medical literature on molecular classification of breast cancer.

CONCLUSIONS.—

Identification of intrinsic tumor subtypes has set a foundation for refining the breast cancer molecular classification. Studies have explored the genetic features within the intrinsic cancer subtypes and have identified novel molecular targets that led to the innovation of clinical assays to predict a patient's prognosis and to provide specific guidelines for therapeutic decisions. With the development and implication of these molecular tools, we have remarkably advanced our knowledge and enhanced our power to provide optimal management to patients. However, challenges still exist. Besides accurate prediction of prognosis, we are still in urgent need of more molecular predictors for tumor response to therapeutic regimes. Further exploration along this path will be critical for improving a patient's prognosis.

Genomic assays for lobular breast carcinoma

BACKGROUND

One of the current challenges in breast cancer is the appropriate treatment of invasive lobular breast cancer (ILC) and defining the high-risk group within ILC. The biological character of ILC typically translates to a good prognosis, however, several studies have indicated that the long-term prognosis is worse than for patients diagnosed with the more commonly invasive ductal carcinoma. Many genomic tests are now available to determine whether those patients are at high risk (HR) and enable tailored treatment. Unfortunately, most of the studies in which these genomic tests have been evaluated entail retrospective analysis of a prospective trial.

AIM

This review focuses on the validation of the available genomic assays based on trials performed in ILC patients, where in some instances, the various subtypes of ILC (classical, pleomorphic, and non-classic type) were taken into account.

RESULTS

Using Oncotype DX in retrospective studies, only 1.3%-8% of ILC tumors were categorized as HR tumors. For MammaPrint, 24% of patients were classified as HR, which was associated with poor outcome. In a recent sub-analysis of the MINDACT study comprising 487 ILC patients, 16.2% were high genomic risk. EndoPredict, Prosigna Breast Cancer Prognostic Gene Signature Assay, and the Breast Cancer Index have been validated in patients receiving only endocrine treatment.

CONCLUSION

Although ILC accounts for the second most common breast cancer subtype in women, none of these tests encompass tumor morphology in their algorithms. Prospective studies on ILC with genomic assays are warranted given the various subtypes of and treatment options for this underestimated, but frequently occurring cancer.

RELEVANCE FOR PATIENTS

Genomic assays can be employed in ILC patients to predict the risk of recurrence and identify those patients who might benefit from chemotherapy in addition to their standard treatment regimen.

MicroRNA-dependent mechanisms of taxane resistance in breast cancer

Breast cancer (BC) has a leading position in the statistics of oncological morbidity and mortality among women. Taxan-based polychemotherapy regimens are an essential component of the complex therapy of the BC. However, currently used algorithms of taxan-based regimens application do not always provide with desire effect. It indicates the need to identify new prognostic markers and to develop new approaches to modify response of BC cells to standard therapeutic regimens. MicroRNAs, small RNA molecules regulating protein synthesis, are considered as promising markers and potential modulators of the BC cells sensitivity to taxanes.

The review includes a brief summary of the molecular mechanisms of action of the taxanes and the mechanism BC resistance to the process of microtubules depolymerization, provides with analysis of recent experimental and observational studies of the role of microRNAs in control of these mechanisms, and evaluates prospects for the development of new approaches to predict and to improve the cytostatic effects of taxanes through the analysis and modification of cellular microRNAs.

ADAMTS8 Expression is a Potential Prognostic Biomarker for Postoperative Metastasis in Lymph Node-Negative Early-Stage Invasive Breast Carcinoma Patients

Purpose

Invasive breast carcinoma (BC) is the most common malignant breast tumor. Most lymph node-negative (LN-) early-stage BC patients usually have a good prognosis, but 7% of patients still develop metastasis after surgery. It is not yet clear how to screen candidates with poorer prognosis in LN- early-stage patients, so that they can receive intensive therapy. Hence, we expect to identify a prognostic biomarker to assess postoperative metastasis in LN- early-stage BC patients.

Patients and Methods

Screening and verifying of candidate genes by gene expression profiling of LN- early-stage BC samples (n = 640) from 3 independent public datasets. Univariable and multivariable Cox regression analyses showed the relation between the candidate genes and postoperative metastasis. Distant metastasis-free survival (DMFS) analysis was performed to examine the prognostic significance. Quantitative real-time polymerase chain reaction (qRT-PCR) assays were performed to examine ADAMTS8 expression and prognostic association in our clinical samples (n = 25).

Results

In the discovery cohort (TCGA and GSE20685 datasets), we found that ADAMTS8 tend to be low expression in LN- early-stage BC, and low ADAMTS8 expression was associated with postoperative metastasis and shortened DMFS. Moreover, the above finding was confirmed in the validation cohort (GSE6538 dataset). Lower ADAMTS8 expression was related to poorer prognostic clinical stage and PAM50 subtypes and shorter DMFS. Gene enrichment analysis indicated that ADAMTS8 may be correlated with BC metastasis. qRT-PCR assays of our clinical tumor sample showed that patients with low ADAMTS8 expression seem to be prone to developing metastasis and have a shorter DMFS time.

Conclusion

Our research shows that low ADAMTS8 expression is associated with postoperative metastasis and shortened DMFS in LN- early-stage BC patients, which suggests that ADAMTS8 may be a potential prognostic marker for postoperative metastasis in LN- early-stage BC patients.

The Clinical Research and Latest Application of Genomic Assays in Early-Stage Breast Cancer

Breast cancer is a kind of malignant tumor that seriously endangers women's life and health. Once diagnosed, most patients will receive a combination of treatments to achieve a cure. However, breast cancer is a heterogeneous disease. Even with the same clinical stage and pathological features, its response to treatment and postoperative recurrence risk may still be completely different. With the advent of genomic assay, some patients with early-stage breast cancer who originally needed treatment can still achieve long-term disease-free survival without adjuvant chemotherapy, so as to achieve personalized and accurate treatment mode to a certain extent. In this paper, we reviewed the 5 most widely used and studied genomic panel technologies in breast cancer, namely Oncotype DX, MammaPrint, RecurIndex, PAM50, and EndoPredict, according to accessibility and availability. Based on the results of the completed or ongoing clinical studies, we summarized the origin, applicable population, and clinical efficacy of each detection method, and discussed the potential development prospect of detection technology in the future.

Comparison of risk assessment in 1652 early ER positive, HER2 negative breast cancer in a real-world data set: classical pathological parameters vs. 12-gene molecular assay (EndoPredict)

BACKGROUND

Risk assessment on the molecular level is important in predictive pathology to determine the risk of metastatic disease for ERpos, HER2neg breast cancer. The gene expression test EndoPredict (EP) was trained and validated for prediction of a 10-year risk of distant recurrence to support therapy decisions regarding endocrine therapy alone or in combination with chemotherapy. The EP test provides the 12-gene Molecular Score (MS) and the EPclin-Score (EPclin), which combines the molecular score with tumor size and nodal status. In this project we investigated the correlation of 12-gene MS and EPclin scores with classical pathological markers.

METHODS

EndoPredict-based gene expression profiling was performed prospectively in a total of 1652 patients between 2017 and 2020. We investigated tumor grading and Ki67 cut-offs of 20% for binary classification as well as 10% and 30% for three classes (low, intermediate, high), based on national and international guidelines.

RESULTS

410 (24.8%) of 1652 patients were classified as 12-gene MS low risk and 626 (37.9%) as EPclin low risk. We found significant positive associations between 12-gene MS and grading (p < 0.001), EPclin and grading (p = 0.001), 12-gene MS and Ki67 (p < 0.001), and EPclin and Ki67 (p < 0.001). However, clinically relevant differences between EP test results, Ki67 and tumor grading were observed. For example, 118 (26.3%) of 449 patients with Ki67 > 20% were classified as low risk by EPclin. Same differences were seen comparing EP test results and tumor grading.

CONCLUSION

In this study we could show that EP risk scores are distributed differentially among Ki67 expression groups, especially in Ki67 low and high tumors with a substantial proportion of patients with EPclin high risk results in Ki67 low tumors and vice versa. This suggests that classical pathological parameters and gene expression parameters are not interchangeable, but should be used in combination for risk assessment.

Prognostic Impact of Immunoglobulin Kappa C in Breast Cancer Patients Treated with Adjuvant Chemotherapy

Introduction

Immunoglobulin κC (IGKC)-positive tumor-infiltrating plasma cells are associated with better prognosis in node-negative breast cancer patients without adjuvant systemic therapy. In the present study we evaluated the prognostic significance of IGKC in breast cancer patients treated with adjuvant chemotherapy ± tamoxifen.

Methods

IGKC expression was immunohistochemically analyzed in 193 breast cancer patients who were treated with adjuvant chemotherapy, either with cyclophosphamide, methotrexate, and 5-fluorouracil (CMF) or 5-fluorouracil, epirubicin, and cyclophosphamide (FEC) between 1993 and 2001 with a median follow-up of 11 years. The prognostic impact of IGKC expression was evaluated by Kaplan-Meier survival analyses as well as uni- and multivariate Cox regression. An interaction term was used to investigate a possible association between tamoxifen treatment and prognostic effect of IGKC expression.

Results

Kaplan-Meier analyses identified IGKC as a prognostic marker for metastasis-free survival (MFS): higher IGKC expression was associated with a better outcome (p = 0.02, log rank). Results of univariate Cox regression confirmed the prognostic impact of IGKC expression: patients with a strong IGKC expression had a longer MFS (hazard ratio [HR] 1.931; 95% confidence interval [CI] 1.087-3.431; p = 0.025). Multivariate Cox regression analysis showed the independent prognostic significance of IGKC expression (HR 2.070; 95% CI 1.088-3.938; p = 0.027). The interaction term confirmed a significant interaction between tamoxifen treatment and the prognostic impact of IGKC expression (p_interaction = 0.04).

Conclusion

IGKC expression had an independent prognostic impact in early breast cancer patients who received adjuvant chemotherapy. There was a significant interaction with the use of tamoxifen.

Survival analysis across the entire transcriptome identifies biomarkers with the highest prognostic power in breast cancer

Introduction

Extensive research is directed to uncover new biomarkers capable to stratify breast cancer patients into clinically relevant cohorts. However, the overall performance ranking of such marker candidates compared to other genes is virtually absent. Here, we present the ranking of all survival related genes in chemotherapy treated basal and estrogen positive/HER2 negative breast cancer.

Methods

We searched the GEO repository to uncover transcriptomic datasets with available follow-up and clinical data. After quality control and normalization, samples entered an integrated database. Molecular subtypes were designated using gene expression data. Relapse-free survival analysis was performed using Cox proportional hazards regression. False discovery rate was computed to combat multiple hypothesis testing. Kaplan-Meier plots were drawn to visualize the best performing genes.

Results

The entire database includes 7,830 unique samples from 55 independent datasets. Of those with available relapse-free survival time, 3,382 samples were estrogen receptor-positive and 696 were basal. In chemotherapy treated ER positive/ERBB2 negative patients the significant prognostic biomarker genes achieved hazard rates between 1.76 and 3.33 with a p value below 5.8E−04. The significant prognostic genes in adjuvant chemotherapy treated basal breast cancer samples reached hazard rates between 1.88 and 3.61 with a p value below 7.2E−04. Our integrated platform was extended enabling the validation of future biomarker candidates.

Conclusions

A reference ranking for all genes in two chemotherapy treated breast cancer cohorts is presented. The results help to neglect those with unlikely clinical significance and to focus future research on the most promising candidates.

Luminal Breast Cancer: Risk of Recurrence and Tumor-Associated Immune Suppression

Hormone-receptor positive (HR+) breast cancer (BC) (including the luminal A and the luminal B subtypes) is the most common type of tumor in women diagnosed with early-stage BC (EBC). It represents a highly heterogeneous subgroup that is characterized by different risks of relapse. The aim of this review is to discuss the possible role played by the immune response in predicting this risk, along with the most common clinical and pathological factors and molecular tools that have been developed and are already in use. As opposed to what has previously been observed in the most aggressive human epidermal growth factor receptor 2 (HER2)-positive and triple-negative breast cancer (TNBC) subtypes, a high proportion of tumor-infiltrating lymphocytes (TILs)-reflecting a spontaneous and pre-existing immune response to the tumor-has been linked to a worse prognosis in HR+ EBC. This work provides some immune biological rationale explaining these findings and provides the basics to understand the principal clinical trials that are testing immunotherapy in HR+ (luminal) BC.

The Signal Transducer IL6ST (gp130) as a Predictive and Prognostic Biomarker in Breast Cancer

Novel biomarkers are needed to continue to improve breast cancer clinical management and outcome. IL6-like cytokines, whose pleiotropic functions include roles in many hallmarks of malignancy, rely on the signal transducer IL6ST (gp130) for all their signalling. To date, 10 separate independent studies based on the analysis of clinical breast cancer samples have identified IL6ST as a predictor. Consistent findings suggest that IL6ST is a positive prognostic factor and is associated with ER status. Interestingly, these studies include 4 multigene signatures (EndoPredict, EER4, IRSN-23 and 42GC) that incorporate IL6ST to predict risk of recurrence or outcome from endocrine or chemotherapy. Here we review the existing evidence on the promising predictive and prognostic value of IL6ST. We also discuss how this potential could be further translated into clinical practice beyond the EndoPredict tool, which is already available in the clinic. The most promising route to further exploit IL6ST's promising predicting power will likely be through additional hybrid multifactor signatures that allow for more robust stratification of ER+ breast tumours into discrete groups with distinct outcomes, thus enabling greater refinement of the treatment-selection process.