Risk stratification in luminal-type breast cancer: Comparison of Ki-67 with EndoPredict test results

Potential biomarkers in breast cancer drug development: application of the biomarker qualification evidentiary framework

Breast cancer treatments have evolved rapidly, and clinically meaningful biomarkers have been used to guide therapy. These biomarkers hold utility within the drug development process to increase the efficiency and effectiveness. To this purpose, the US FDA developed an evidentiary framework. Literature searches conducted of literature published between 2016 and 2022 identified biomarkers in breast cancer. These biomarkers were reviewed for drug development utility through the biomarker qualification evidentiary framework. In the breast cancer setting, several promising biomarkers (ctDNA, Ki-67 and PIK3CA) were identified. There is a need for increased transparency regarding the requirements for qualification of specific biomarkers and increased awareness of the processes involved in biomarker qualification.

Use of the Gene Expression Test Prosigna® in Premenopausal Patients with HR+, HER2- Early Breast Cancer: Correlation of the Results with the Proliferation Marker Ki-67

Introduction

In hormone receptor-positive (ER+/PR+) and human epidermal growth factor receptor 2-negative (HER2-) early-stage breast cancer (EBC), gene expression tests such as the Prosigna are increasingly used since classic clinicopathological parameters and the proliferation factor Ki-67 often do not allow a definite therapy decision regarding an adjuvant chemotherapy. While the Prosigna test has been validated for postmenopausal patients, few data are available regarding its use in premenopausal patients. The present study compared the Prosigna test with the Ki-67 index in premenopausal patients.

Materials and Methods

Premenopausal patients with HR+ HER2-, pN0-1, G1-2 EBC were retrospectively enrolled (n = 55). The Prosigna assay was performed in formalin-fixed paraffin-embedded tumor samples of surgical resection specimens. Ki-67 was reassessed in original diagnostic core needle biopsy specimens and defined as low, intermediate, or high with the threshold of <10%, 10-24%, ≥25%.

Results

According to Ki-67, patients were in the low (LR)-, intermediate (IR)-, and high-risk (HR) groups in 40%, 36%, and 24% of the cases. The Prosigna gene signature assay assessed the risk of recurrence as LR for 45% of the patients, IR for 35%, and HR for 20%. The most frequent intrinsic subtypes were luminal A in 73% and luminal B in 24% of the patients. A moderate correlation was found between Prosigna and Ki-67 scores with a Pearson correlation coefficient of 0.51. In the overall cohort, 47% of the Ki-67-based therapy decision would correspond to those based on the Prosigna score. After exclusion of IR patients, matching of low/low or high/high results was observed in 57% of the cases.

Conclusion

According to the present study, there is only limited concordance regarding the risk group stratification between Ki-67 and Prosigna-based risk assessment. The relevance and frequency of premenopausal breast cancer emphasizes the need for further evaluation of gene expression analyses in this setting and the correlation with classic clinicopathological parameters regarding therapy decision-making.

Discordance between PAM50 intrinsic subtyping and immunohistochemistry in South African women with breast cancer

PURPOSE

Breast cancer is a heterogeneous disease with different gene expression profiles, treatment options and outcomes. In South Africa, tumors are classified using immunohistochemistry. In high-income countries multiparameter genomic assays are being utilized with implications for tumor classification and treatment.

METHODS

In a cohort of 378 breast cancer patients from the SABCHO study, we investigated the concordance between tumor samples classified by IHC and the PAM50 gene assay.

RESULTS

IHC classified patients as ER-positive (77.5%), PR-positive (70.6%), and HER2-positive (32.3%). These results, together with Ki67, were used as surrogates for intrinsic subtyping, and showed 6.9% IHC-A-clinical, 72.7% IHC-B-clinical, 5.3% IHC-HER2-clinical and 15.1% triple negative cancer (TNC). Typing using the PAM50 gave 19.3% luminal-A, 32.5% luminal-B, 23.5% HER2-enriched and 24.6% basal-like. The basal-like and TNC had the highest concordance, while the luminal-A and IHC-A group had the lowest concordance. By altering the cutoff for Ki67, and realigning the HER2/ER/PR-positive patients to IHC-HER2, we improved concordance with the intrinsic subtypes.

CONCLUSION

We suggest that the Ki67 be changed to a cutoff of 20-25% in our population to better reflect the luminal subtype classifications. This change would inform treatment options for breast cancer patients in settings where genomic assays are unaffordable.

Expression, assessment and significance of Ki67 expression in breast cancer: an update

Ki67 expression is one of the most important and cost-effective surrogate markers to assess for tumour cell proliferation in breast cancer (BC). The Ki67 labelling index has prognostic and predictive value in patients with early-stage BC, particularly in the hormone receptor-positive, HER2 (human epidermal growth factor receptor 2)-negative (luminal) tumours. However, many challenges exist in using Ki67 in routine clinical practice and it is still not universally used in the clinical setting. Addressing these challenges can potentially improve the clinical utility of Ki67 in BC. In this article, we review the function, immunohistochemical (IHC) expression, methods for scoring and interpretation of results as well as address several challenges of Ki67 assessment in BC. The prodigious attention associated with use of Ki67 IHC as a prognostic marker in BC resulted in high expectation and overestimation of its performance. However, the realisation of some pitfalls and disadvantages, which are expected with any similar markers, resulted in an increasing criticism of its clinical use. It is time to consider a pragmatic approach and weigh the benefits against the weaknesses and identify factors to achieve the best clinical utility. Here we highlight the strengths of its performance and provide some insights to overcome the existing challenges.

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.

[uPA/PAI-1 and EPClin®: Comparison of their impact on the management of intermediate-prognosis breast cancers]

OBJECTIVE

The uPA/PAI-1 assay and the EPClin® test are useful tools that add to clinico-anatomical characteristics to determine the indication of adjuvant chemotherapy in case of intermediate-prognosis invasive breast cancer. The principal purpose of our study was to analyze the concordance of uPA/PAI-1 and EPClin® in classification of patients into two groups: low and high risk of relapse.

METHODS

We prospectively included 63 patients treated for intermediate-prognosis invasive breast cancer. All of these patients received a uPA/PAI-1 assay and an EPClin® test.

RESULTS

The uPA/PAI-1 assay and EPClin® test were consistent for 56.2% and inconsistent for 43.8%. In the event of a discrepancy, the treatment decision was based in 95.2% of patients on the EPClin® test result. In total, 38 patients were selected for adjuvant chemotherapy after achievement of the two tests. The mean time to report results after surgery was 9 days for the uPA/PAI-1 assay and 35 days for the EPClin® test. No cases of recurrence or death were found, with an average follow-up of 32 months.

CONCLUSION

The EPClin® test resulted in more chemotherapy prescriptions than indicated by uPA/PAI-1. However, we can't conclude to the superiority of one of these two tests, survival data and the effectiveness of our study being insufficient. In general, studies comparing different signatures useful to the therapeutic decision of intermediate prognosis breast cancers should be encouraged.

Ki67 as Proliferative Marker in Patients with Early Breast Cancer and Its Association with Clinicopathological Factors

INTRODUCTION

Ki67 as a proliferative marker has prognostic and therapeutic relevance in early breast cancer (EBC). However, standard cutoffs for distinguishing low and high Ki67 do not exist.

MATERIAL AND METHODS

Data from all patients treated at the University Hospital Ulm for EBC between January 2013 and December 2015 with documented results for internal Ki67 assessment of the primary (n = 917) tumor were retrospectively analyzed evaluating the associations between Ki67 and other clinicopathological factors.

RESULTS

595 (64.9%) patients had a Ki67 <20% and 322 (35.1%) a Ki67 ≥20%. The median Ki67 was 10% (range 1-90%). Median Ki67 values according to the hormone receptor (HR)/ human epidermal growth factor receptor 2 (HER2) subtypes were 10% for HR-positive/HER2 negative (HR+/HER2-) disease (n = 717), 20% for HR+/HER2+ (n = 76), 30% for HR-/HER2+ (n = 45), and 60% for HR-/HER2- (n = 75). 75.2% or 89.3% of all patients with HER2-positive or triple-negative disease had a Ki67 ≥20%, respectively. Using a multivariable logistic regression with Ki67 (<20% vs. ≥20%) as binary dependent variable, younger age, positive nodal status, higher grading, histological nonspecific type carcinoma, negative HR status, and positive HER2 status were shown to be significantly associated with a higher proliferative index (Ki67 ≥20%).

CONCLUSION

This analysis described Ki67 in different subtypes in EBC and its association with clinicopathological factors. According to more aggressive tumor biology, the respective subgroups also showed higher median Ki67 levels. However, definition of low and high proliferation index itself is difficult. It is essential to interpret Ki67 indices carefully with regard to the own institutional values and other clinicopathological factors.

Comparison of breast cancer surrogate subtyping using a closed-system RT-qPCR breast cancer assay and immunohistochemistry on 100 core needle biopsies with matching surgical specimens

Background

Routine clinical management of breast cancer (BC) currently depends on surrogate subtypes according to estrogen- (ER) and progesterone (PR) receptor, Ki-67, and HER2-status. However, there has been growing demand for reduced immunohistochemistry (IHC) turnaround times. The Xpert® Breast Cancer STRAT4* Assay (STRAT4)*, a standardized test for ESR1/PGR/MKi67/ERBB2 mRNA biomarker assessment, takes less than 2 hours. Here, we compared the concordance between the STRAT4 and IHC/SISH, thereby evaluating the effect of method choice on surrogate subtype assessment and adjuvant treatment decisions.

Methods

In total, 100 formalin-fixed paraffin-embedded core needle biopsy (CNB) samples and matching surgical specimens for 98 patients with primary invasive BC were evaluated using the STRAT4 assay. The concordance between STRAT4 and IHC was calculated for individual markers for the CNB and surgical specimens. In addition, we investigated whether changes in surrogate BC subtyping based on the STRAT4 results would change adjuvant treatment recommendations.

Results

The overall percent agreement (OPA) between STRAT4 and IHC/SISH ranged between 76 and 99% for the different biomarkers. Concordance for all four biomarkers in the surgical specimens and CNBs was only 66 and 57%, respectively. In total, 74% of surgical specimens were concordant for subtype, regardless of the method used. IHC- and STRAT4-based subtyping for the surgical specimen were shown to be discordant for 25/98 patients and 18/25 patients would theoretically have been recommended a different adjuvant treatment, primarily receiving more chemotherapy and trastuzumab.

Conclusions

A comparison of data from IHC/in situ hybridization and STRAT4 demonstrated that subsequent changes in surrogate subtyping for the surgical specimen may theoretically result in more adjuvant treatment given, primarily with chemotherapy and trastuzumab.

Risk-adapted adjuvant therapy of luminal early breast cancer in 2020

PURPOSE OF REVIEW

The present review summarizes recent original publications addressing the topic of risk-adapted adjuvant therapy in early breast cancer (EBC). As neoadjuvant therapy has become a standard for triple negative and HER2+ EBC, it focusses on luminal EBC.

RECENT FINDINGS

Gene expression assays have become standard of care in luminal EBC, at least for patients with node negative disease. Two prospective randomized clinical trials, TAILORx (Oncotype DX) and MINDACT (MammaPrint) have presented additional analyses underlining the clinical utility of the tests. In times of COVID-19, immunohistochemically determined ER, PR, and Ki67 and early Ki67 response to endocrine therapy can be used to safely allocate patients for preoperative endocrine therapy and delay surgeries if resources are scarce. In patients with luminal high-risk disease, adding a CDK 4/6 inhibitor (abemaciclib) improves patient outcome already after short-term follow-up.

SUMMARY

Determination of recurrence risk will remain important in luminal EBC for optimal therapy decisions. In the future, risk-adapted treatment concepts will include decision making for chemotherapy but also for endocrine-based approaches.

Correlation between apparent diffusion coefficient and pathological characteristics of patients with invasive breast cancer

Background

There is insufficient research on the correlation between the apparent diffusion coefficient and clinicopathological characteristics of breast cancer patients. The present study is to investigate the correlation between the apparent diffusion coefficient and pathological characteristics of patients with invasive breast cancer.

Methods

From January 2019 to September 2020, 122 cases of invasive breast cancer and 21 cases of benign tumors were retrospectively enrolled. The apparent diffusion coefficient was compared between the two groups, and the correlation between the apparent diffusion coefficient and the pathological characteristics of the patients with invasive breast cancer were analyzed.

Results

Compared with the benign tumor group, the apparent diffusion coefficient in the invasive breast cancer group was significantly lower (0.89±0.17 vs. 1.47±0.27 10⁻³ mm²/s, P=0.000). Using the apparent diffusion coefficient to diagnose patients with invasive breast cancer, the area under receiver operating characteristic (ROC) curve was 0.966±0.021 [95% confidence interval (CI): 0.924–1.000, P=0.000], and the best diagnostic cut-off value was 1.16 (10⁻³ mm²/s), with sensitivity and specificity of 0.905 and 0.902, respectively. The apparent diffusion coefficient was used to diagnose vascular tumor thrombus in patients with invasive breast cancer. The area under the ROC curve was 0.641±0.068 (95% CI: 0.508–0.774, P=0.047), and the best diagnostic threshold was 0.835 (10⁻³ mm²/s), with sensitivity and specificity of 0.676 and 0.650, respectively. The apparent diffusion coefficient in patients with high expression of Ki-67 (%) was significantly reduced (0.87±0.17 vs. 1.00±0.16 10⁻³ mm²/s, P=0.000). The apparent diffusion coefficient was not significantly correlated with age, menopause, lesion size, estrogen receptor, progesterone receptor, or lymph node metastasis in patients with invasive breast cancer (P>0.05).

Conclusions

In patients with invasive breast cancer the apparent diffusion coefficient was significantly reduced. It was able to differentiate invasive breast cancer and vascular tumor thrombus, and was also related to Ki-67 (%) high expression.

Detection of Malignant and Benign Breast Cancer Using the ANOVA-BOOTSTRAP-SVM

Purpose

The aim of this research is to propose a modification of the ANOVA-SVM method that can increase accuracy when detecting benign and malignant breast cancer.

Methodology

We proposed a new method ANOVA-BOOTSTRAP-SVM. It involves applying the analysis of variance (ANOVA) to support vector machines (SVM) but we use the bootstrap instead of cross validation as a train/test splitting procedure. We have tuned the kernel and the C parameter and tested our algorithm on a set of breast cancer datasets.

Findings

By using the new method proposed, we succeeded in improving accuracy ranging from 4.5 percentage points to 8 percentage points depending on the dataset.

Research limitations

The algorithm is sensitive to the type of kernel and value of the optimization parameter C.

Practical implications

We believe that the ANOVA-BOOTSTRAP-SVM can be used not only to recognize the type of breast cancer but also for broader research in all types of cancer.

Originality/value

Our findings are important as the algorithm can detect various types of cancer with higher accuracy compared to standard versions of the Support Vector Machines.