A lower Allred score for progesterone receptor is strongly associated with a higher recurrence score of 21-gene assay in breast cancer

Epidemiology of early vs. Late recurrence among women with Early-Stage estrogen Receptor-Positive breast cancer in the pathways study

BACKGROUND

Relatively little is known about the differences in prognostic factors for early vs late recurrence among women with early-stage estrogen receptor-positive (ER+) breast cancer.

METHODS

We analyzed factors related to early (<5 years) vs late (≥5 years) recurrence in 2,992 women with stage I-IIB ER+ breast cancer in the Pathways Study, a prospective cohort of women with breast cancer enrolled between 2006 and 2013, with ascertainment of recurrence and death through December 2021.

RESULTS

After a median follow-up of 13.3 years, 341 (13.8%) women had recurrences, including 181 (53.7%) with late recurrence. Higher stage and grade were associated with recurrence regardless of timing, whereas progesterone receptor (PR) negativity was associated with early but not late recurrence. Receipt of endocrine therapy was associated with reduced risk of overall recurrence, but the length of endocrine therapy was not significant in multivariable models. Minoritized racial and ethnic groups, including Asian, Black, and Hispanic women, had higher risk of early but not late recurrence, compared with non-Hispanic White women. The trend of higher risk of early recurrence among these groups remained after adjustment for clinical, demographic, and socioeconomic factors, but was statistically significant only in Asian women.

CONCLUSIONS

Our study revealed potentially important distinctions for early vs late recurrence, including the associations with PR-negativity and self-identified race and ethnicity. Possible higher risk of early recurrence among Asian, Black, and Hispanic women provides novel evidence for the existence of disparities in cancer outcomes, even within the breast cancer subtype indicative of generally good prognosis.

Prediction of the MammaPrint Risk Group Using MRI Features in Women With Estrogen Receptor-Positive, HER2-Negative, and 1 to 3 Node-Positive Invasive Breast Cancer

BACKGROUND

MammaPrint assigns chemotherapeutic benefits to patients with estrogen receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative, and 1 to 3 node-positive invasive breast cancer. However, its cost and time-consuming nature limit its use in certain clinical settings. We aimed to develop and validate the prediction models for the low MammaPrint risk group using clinicopathologic and MRI features.

PATIENTS AND METHODS

Overall, 352 women with ER-positive, HER2-negative, and 1 to 3 node-positive invasive breast cancer were retrospectively reviewed and assigned to development (n = 235) and validation sets (n = 117). Univariate and multivariate analyses identified features associated with the low MammaPrint risk group. The area under the receiver operating characteristic curves (AUROCs) of models based on clinicopathologic, MRI, and combined features were evaluated.

RESULTS

Development set multivariate analysis showed that clinicopathologic features including low histologic grade (odds ratio [OR], 5.29; P = .02), progesterone receptor-positivity (OR, 3.23; P = .01), and low Ki-67 (OR, 6.05; P < .001) and MRI features, including peritumoral edema absence (OR, 2.24; P = .04) and a high proportion of persistent components (OR, 1.15; P = .004) were significantly associated with the low MammaPrint risk group. The AUROCs of models based on clinicopathologic, MRI, and combined features were 0.77, 0.64, and 0.80 in the development and 0.66, 0.60, and 0.70 in the validation sets, respectively.

CONCLUSION

The combined model incorporating clinicopathologic and MRI features showed potential in predicting the low MammaPrint risk group, and may support decision-making in clinical settings with limited access to MammaPrint.

Discordance of Oncotype DX scores in synchronous bilateral and unilateral multifocal breast cancers

PURPOSE

Oncotype DX, a 21-gene expression profiling test, has become standard of care in the management of estrogen receptor (ER)-positive breast cancer. In multifocal tumors, it is unclear whether testing of the different foci is necessary. We evaluated the concordance of Oncotype DX recurrence scores (RS) between 2 tumor foci in synchronous bilateral or unilateral multifocal tumors and characterized pathological predictors of discordance.

METHODS

We reviewed 713 ER+, HER2- primary invasive breast cancer patients with Oncotype RS and identified 17 bilateral synchronous patients (34 tumors) and 13 unilateral multifocal patients (26 tumors) with available Oncotype RS on all foci. Discordance in Oncotype RS between synchronous tumors was recorded and associations with clinicopathologic features including tumor size, histology, Nottingham histologic grade, progesterone receptor staining, and Ki67 index were analyzed.

RESULTS

Bilateral synchronous tumors were present in older patients (median age 59 years) and had larger tumor (median size 17 mm) and more discordant histology (10/17, 59%) as compared to unilateral multifocal tumors (median age 49 years, p < 0.01; median tumor size 12 mm, p = 0.01; discordant histology 2/13, 15%, p = 0.03). Oncotype RS were discordant in 47% (8/17) of bilateral and 54% (7/13) of unilateral multifocal tumors. Concordant Oncotype RS was associated with similar histologic grade and Ki67 index in 78% (7/9) of bilateral and 100% (6/6) of multifocal tumors. In contrast, only 25% (2/8) of bilateral (p = 0.06) and 14% (1/7) of unilateral multifocal (p < 0.01) cases with discordant Oncotype RS had concordant histology grades and Ki67 levels. In synchronous tumors with discordant Oncotype RS and Ki67 index, all (4/4) foci with higher RS had higher Ki67 index.

CONCLUSION

Discordance of Oncotype RS is common in both bilateral and unilateral multifocal breast cancer and is likely associated with discordant histologic grade or Ki67.

Progesterone Receptor Expression Level Predicts Prognosis of Estrogen Receptor-Positive/HER2-Negative Young Breast Cancer: A Single-Center Prospective Cohort Study

BACKGROUND

Although estrogen receptor (ER) expression levels affect the prognosis of breast cancer, studies about progesterone receptor (PR) expression levels are insufficient, especially in young breast cancer (YBC). The purpose of this study was to compare clinical characteristics and prognosis according to PR expression levels in invasive breast cancer patients.

METHODS

A prospective cohort study was conducted to identify YBC patients with invasive carcinoma diagnosed at an age of less than 40 years old between 2013 and 2018. Clinicopathologic features and prognosis of ER-positive and human epidermal growth factor receptor 2 (HER2)-negative patients were investigated. Patients were stratified into strong PR (PR-positive cell proportion > 10%), low PR (PR-positive cell proportion = 1~10%), and PR-negative (PR-positive cell proportion < 1%).

RESULTS

Among 458 patients enrolled, 386 (84.3%), 26 (5.7%), and 46 (10.0%) were categorized into strong PR, low PR, and PR-negative groups, respectively. The median follow-up duration was 58.6 months. Compared with the strong PR group, low PR and PR-negative groups were more likely to have high Ki-67 and a high nuclear grade. Low R and PR-negative groups had significantly worse disease-free survival (DFS) and distant metastasis-free survival (DMFS) than the strong PR group (p = 0.0033, p = 0007). Low PR group had an even higher risk of distant metastasis than PR-negative patients. Low PR patients and PR-negative had significantly lower overall survival (OS) rates than strong PR.

CONCLUSION

Low PR might be a prognostic factor of ER-positive/HER2-negative in YBC.

Prognoses of Patients with Hormone Receptor-Positive and Human Epidermal Growth Factor Receptor 2-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy before Surgery: A Retrospective Analysis

PURPOSE

To evaluate the clinical characteristics, pathological response, and prognostic significance of hormone receptor-positive and human epidermal growth factor receptor 2-negative (HR+/HER2-) breast cancer (BC) after neoadjuvant chemotherapy (NAC).

METHODS

A survival analysis was performed to detect the factors related to recurrence and death in 3070 consecutive patients with HR+/HER2- BC who received NAC from 2011 to 2022. All patients received current "standard of care" following neoadjuvant therapy based on guidelines, including surgery and adjuvant endocrine therapy. HER2-low was defined as immunohistochemistry (IHC) 1+ or IHC 2+ and fluorescence in-situ hybridization-negative.

RESULTS

The complete pathological response (pCR) (ypT0/is ypN0) rate was 14.5%. The clinical tumor size (cT), ER scores, PR status, and Ki67 levels were related to pCR. The 5-year disease-free survival (DFS) and overall survival (OS) rates were 82.6% and 90.4%, respectively. PR, Ki67 levels, and postmastectomy radiotherapy were independent factors for DFS and OS, and the extranodal extension (ENE) correlated with DFS. However, pCR and HER2 status were related to OS. The pCR rate in PR negativity BC was significantly higher than that in PR positivity BC (21.1% vs. 12.2%, p = 0.000), but PR negativity BC had a poorer prognosis than PR positivity BC. HER2-low BC showed high ER scores (over 50%), PR positivity, large ypT, ENE, and lymphovascular invasion but a lower pCR rate than HER2-zero BC. Patients with HER2-low BC had shorter OS than those with HER2-zero BC (p = 0.037). However, there was no difference in DFS.

CONCLUSIONS

Depending on PR status and HER2 status, patients with ER positivity and HER2 negativity exhibit different pathologic complete response rates to neoadjuvant chemotherapy and long-term outcomes, especially patients with PR negativity or HER2-low status.

The Rochester Modified Magee Algorithm (RoMMa): An Outcomes Based Strategy for Clinical Risk-Assessment and Risk-Stratification in ER Positive, HER2 Negative Breast Cancer Patients Being Considered for Oncotype DX® Testing

INTRODUCTION

Multigene genomic profiling has become the standard of care in the clinical risk-assessment and risk-stratification of ER⁺, HER2^- breast cancer (BC) patients, with Oncotype DX^® (ODX) emerging as the genomic profile test with the most support from the international community. The current state of the health care economy demands that cost-efficiency and access to testing must be considered when evaluating the clinical utility of multigene profile tests such as ODX. Several studies have suggested that certain lower risk patients can be identified more cost-efficiently than simply reflexing all ER⁺, HER2^- BC patients to ODX testing. The Magee equations^TM use standard histopathologic data in a set of multivariable models to estimate the ODX recurrence score. Our group published the first outcome data in 2019 on the Magee equations^TM, using a modification of the Magee equations^TM combined with an algorithmic approach-the Rochester Modified Magee algorithm (RoMMa). There has since been limited published outcome data on the Magee equations^TM. We present additional outcome data, with considerations of the TAILORx risk-stratification recommendations.

METHODS

355 patients with an ODX recurrence score, and at least five years of follow-up or a BC recurrence were included in the study. All patients received either Tamoxifen or an aromatase inhibitor. None of the patients received adjuvant systemic chemotherapy.

RESULTS

There was no significant difference in the risk of recurrence in similar risk categories (very low risk, low risk, and high risk) between the average Modified Magee score and ODX recurrence score with the chi-square test of independence (p > 0.05) or log-rank test (p > 0.05). Using the RoMMa, we estimate that at least 17% of individuals can safely avoid ODX testing.

CONCLUSION

Our study further reinforces that BC patients can be confidently stratified into lower and higher-risk recurrence groups using the Magee equations^TM. The RoMMa can be helpful in the initial clinical risk-assessment and risk-stratification of BC patients, providing increased opportunities for cost savings in the health care system, and for clinical risk-assessment and risk-stratification in less-developed geographies where multigene testing might not be available.

Deep Learning-Based Pathology Image Analysis Enhances Magee Feature Correlation With Oncotype DX Breast Recurrence Score

Background

Oncotype DX Recurrence Score (RS) has been widely used to predict chemotherapy benefits in patients with estrogen receptor-positive breast cancer. Studies showed that the features used in Magee equations correlate with RS. We aimed to examine whether deep learning (DL)-based histology image analyses can enhance such correlations.

Methods

We retrieved 382 cases with RS diagnosed between 2011 and 2015 from the Emory University and the Ohio State University. All patients received surgery. DL models were developed to detect nuclei of tumor cells and tumor-infiltrating lymphocytes (TILs) and segment tumor cell nuclei in hematoxylin and eosin (H&E) stained histopathology whole slide images (WSIs). Based on the DL-based analysis, we derived image features from WSIs, such as tumor cell number, TIL number variance, and nuclear grades. The entire patient cohorts were divided into one training set (125 cases) and two validation sets (82 and 175 cases) based on the data sources and WSI resolutions. The training set was used to train the linear regression models to predict RS. For prediction performance comparison, we used independent variables from Magee features alone or the combination of WSI-derived image and Magee features.

Results

The Pearson's correlation coefficients between the actual RS and predicted RS by DL-based analysis were 0.7058 (p-value = 1.32 × 10-13) and 0.5041 (p-value = 1.15 × 10-12) for the validation sets 1 and 2, respectively. The adjusted R 2 values using Magee features alone are 0.3442 and 0.2167 in the two validation sets, respectively. In contrast, the adjusted R 2 values were enhanced to 0.4431 and 0.2182 when WSI-derived imaging features were jointly used with Magee features.

Conclusion

Our results suggest that DL-based digital pathological features can enhance Magee feature correlation with RS.

Prognostic Relevance of Progesterone Receptor Levels in Early Luminal-Like HER2 Negative Breast Cancer Subtypes: A Retrospective Analysis

Introduction

In luminal-like early breast cancer (BC), the lack of Progesterone Receptor (PR) expression generally correlates with more aggressive behavior but the clinical validity of low PR levels remains a debated issue.

Methods

The main aim of this retrospective analysis was to assess the survival outcome (Breast cancer specific survival, BCSS) in a cohort of 687 luminal-like HER2 negative early BC patients treated at our Institutions from January 2000 to December 2018, using a sub-classification of tumors in subgroup 1 (PR high/Ki67 low), subgroup 2 (PR high/Ki67 high), subgroup 3 (PR low/Ki67 low), subgroup 4 (PR low/Ki67 high) according to PR and Ki67 values.

Results

At a median follow-up of 7 years, BCSS rates were 96.3%, 89%, 86.8% and 85% in the subgroup 1, 2, 3, 4 respectively. Overall, a statistically significant difference in BCSS rates was observed among the 4 subgroups (p=0.0036). On univariate analysis, post-menopause, older age (≥ 50 years), low PR and high Ki67 expression, poorly differentiated grade and size ≥ 2 cm as well as luminal B-like tumors (subgroups 2, 3, 4) were significantly associated with a worse BCSS. Multivariate analysis identified grade, size and subgroup classification of BC as independent prognostic markers of poorer outcome. In particular, subgroups 4, 3 and 2 displayed a significantly higher risk of BC-related death (HR=4.11; p=0.008; HR=3.43; p=0-007; HR=2.57; p=0.020, respectively) when compared to subgroup 1.

Conclusions

Our results support the usefulness of PR and Ki67 levels as prognostic markers, corroborating their crucial role in the decision-making process of patients with luminal-like HER2 negative early BC. Clinical application of these parameters should be assessed prospectively.

A nomogram for predicting probability of low risk of MammaPrint results in women with clinically high-risk breast cancer

We aimed to develop a prediction MammaPrint (MMP) genomic risk assessment nomogram model for hormone-receptor positive (HR+) and human epidermal growth factor receptor-2 negative (HER2–) breast cancer and minimal axillary burden (N0-1) tumors using clinicopathological factors of patients who underwent an MMP test for decision making regarding adjuvant chemotherapy. A total of 409 T1-3 N0-1 M0 HR + and HER2– breast cancer patients whose MMP genomic risk results and clinicopathological factors were available from 2017 to 2020 were analyzed. With randomly selected 306 patients, we developed a nomogram for predicting a low-risk subgroup of MMP results and externally validated with remaining patients (n = 103). Multivariate analysis revealed that the age at diagnosis, progesterone receptor (PR) score, nuclear grade, and Ki-67 were significantly associated with MMP risk results. We developed an MMP low-risk predictive nomogram. With a cut off value at 5% and 95% probability of low-risk MMP, the nomogram accurately predicted the results with 100% positive predictive value (PPV) and negative predictive value respectively. When applied to cut-off value at 35%, the specificity and PPV was 95% and 86% respectively. The area under the receiver operating characteristic curve was 0.82 (95% confidence interval [CI] 0.77 to 0.87). When applied to the validation group, the nomogram was accurate with an area under the curve of 0.77 (95% CI 0.68 to 0.86). Our nomogram, which incorporates four traditional prognostic factors, i.e., age, PR, nuclear grade, and Ki-67, could predict the probability of obtaining a low MMP risk in a cohort of high clinical risk patients. This nomogram can aid the prompt selection of patients who does not need additional MMP testing.

Mammography-based radiomics for predicting the risk of breast cancer recurrence: a multicenter study

OBJECTIVE

This study aimed to establish a mammography-based radiomics model for predicting the risk of estrogen receptor (ER)-positive, lymph node (LN)-negative invasive breast cancer recurrence based on Oncotype DX and validated it by using multicenter data.

METHODS

A total of 304 potentially eligible patients with pre-operative mammography images and available Oncotype DX score were retrospectively enrolled from two hospitals. The patients were grouped as training set (168 patients), internal test set (72 patients), and external test set (64 patients). Radiomics features were extracted from the mammography images of each patient. Spearman correlation analysis, analysis of variance, and least absolute shrinkage and selection operator regression were performed to reduce the redundant features in the training set, and the least absolute shrinkage and selection operator algorithm was used to construct the radiomics signature based on selected features. Multivariate logistic regression was utilized to construct classification models that included radiomics signature and clinical risk factors to predict low vs intermediate and high recurrence risk of ER-positive, LN-negative invasive breast cancer in the training set. The models were evaluated with the receiver operating characteristic curve in the training set. The internal and external test sets were used to confirm the discriminatory power of the models. The clinical usefulness was evaluated by using decision curve analysis.

RESULTS

The radiomics signature consisting of three radiomics features achieved favorable prediction performance. The multivariate logistic regression model including radiomics signature and clinical risk factors (tumor grade and HER 2) showed good performance with areas under the curve of 0.92 (95% confidence interval [CI] 0.86 to 0.97), 0.88 (95% CI 0.75 to 1.00), and 0.84 (95% CI 0.69 to 0.99) in the training, internal and external test sets, respectively. The DCA indicated that when the threshold probability is ranges from 0.1 to 1.0, the radiomics model adds more net benefit than the "treat all" or "treat none" scheme in internal and external test sets.

CONCLUSION

As a non-invasive pre-operative prediction tool, the mammography-based radiomics model incorporating radiomics and clinical factors show favorable predictive performance for predicting the risk of ER-positive, LN-negative invasive breast cancer recurrence based on Oncotype DX.

ADVANCES IN KNOWLEDGE

The mammography-based radiomics model incorporating radiomics and clinical factors shows favorable predictive performance for predicting the risk of ER-positive, LN-negative invasive breast cancer recurrence.

Predictability of 21-Gene Recurrence Score Assay by Using Pathological and Immunohistochemical Parameters in Breast Cancer

Background:

Oncotype Dx is used to predict the long-term recurrence risk in patients with estrogen receptor (ER)-positive and human epidermal growth factor receptor 2 (HER2)-negative invasive breast cancer (BC). This study aimed at establishing a correlation between clinicopathological parameters and recurrence score (RS), subsequently improving predictability and ultimately justifying the use of the multigene assay.

Materials and methods:

A retrospective analysis of the pathology and clinical data of 114 female patients with BC who had Oncotype Dx testing between 2012 and 2019. The pathological parameters included are tumor cell type, tumor grade, pathological stage, and mitotic index (MI). The expression of ER, progesterone receptor (PR), HER2, and Ki67 was assessed by immunohistochemistry. A univariate and multivariate linear regression analysis was performed to assess the correlation between these parameters and the RS.

Results:

In univariate analysis, age (˂40 years), higher tumor grade, and low PR expression were significantly associated with higher RS (P = .02; ˂.001; and ˂.001, respectively). Both MI and Ki67 were also strongly correlated with an increase in the RS with a P value of .01 (Spearman correlation 0.34 and 0.33). In multivariate linear regression analysis, age, MI, and Ki67 lost their significance, but both higher grade and PR remained significantly associated with a higher RS along with the tumor stage (P ˂ .001; ˂.001; and .04, respectively).

Conclusions:

Tumor grade and PR immunohistochemical expression are the main predictors of RS in our study population. Other clinicopathological features were not significant predictors of change in RS in multivariate analysis.

Quantitative assessment of distant recurrence risk in early stage breast cancer using a nonlinear combination of pathological, clinical and imaging variables

Use of genomic assays to determine distant recurrence risk in patients with early stage breast cancer has expanded and is now included in the American Joint Committee on Cancer staging manual. Algorithmic alternatives using standard clinical and pathology information may provide equivalent benefit in settings where genomic tests, such as OncotypeDx, are unavailable. We developed an artificial neural network (ANN) model to nonlinearly estimate risk of distant cancer recurrence. In addition to clinical and pathological variables, we enhanced our model using intraoperatively determined global mammographic breast density (MBD) and local breast density (LBD). LBD was measured with optical spectral imaging capable of sensing regional concentrations of tissue constituents. A cohort of 56 ER+ patients with an OncotypeDx score was evaluated. We demonstrated that combining MBD/LBD measurements with clinical and pathological variables improves distant recurrence risk prediction accuracy, with high correlation (r = 0.98) to the OncotypeDx recurrence score.

Correlation between modified Magee equation-2 and Oncotype-Dx recurrence scores using both traditional and TAILORx cutoffs and the clinical application of the Magee Decision Algorithm: a single institutional review

BACKGROUND

Oncotype Dx (ODX) is used to predict recurrence risk for estrogen-positive (ER +), HER2-negative and lymph node negative breast cancer, however, due to the cost its use may be limited in low-resource areas. The aim of this study is to assess the concordance between the modified Magee Equation-2 (MME-2) and ODX recurrence scores (RS). The secondary aim is to apply the Magee Decision Algorithm (MDA) using the MME-2 to determine which patients are unlikely to benefit from ODX testing.

METHODS

All newly diagnosed ER + , HER2 negative, lymph node negative breast cancer patients with available ODX-RS from 2008-2018 were included. The original pathology reports were reviewed and chart review was performed. The MME-2 scores were calculated and correlated with the ODX-RS. The MDA was applied to our cohort to assess which patients would not benefit from ODX testing.

RESULTS

A total of 579 patients were included. There was an overall moderate correlation between ODX-RS and MME-2 score (Pearson correlation coefficient = 0.635). The overall concordance between ODX and MME-2 scores was similar when using both the traditional and TAILORx cutoffs (63.3% vs. 63.7%, respectively). Applying the MDA, for patients with MME-2 scores < 18, 96.8% of patients had the expected ODX-RS of < 25. For patients with MME-2 RS > 30, 90% had the expected ODX-RS of > 25. Concordance was highest in the high-risk category using both cutoffs. For patients with MME-2 18-25 and a mitotic score of 1, 88.8% had the expected ODX-RS of > 25.

CONCLUSION

There is a moderate correlation between MME-2 score and ODX-RS. The overall concordance was similar for both traditional and TAILORx cutoffs. The strongest concordance was found in the high-risk category for both cutoffs. The MME-2 can be used to identify patients unlikely to benefit from ODX testing using the MDA.

Prediction of Oncotype DX recurrence score using deep multi-layer perceptrons in estrogen receptor-positive, HER2-negative breast cancer

Oncotype DX (ODX) is a multi-gene expression signature designed for estrogen receptor (ER)-positive and human epidermal growth factor receptor 2 (HER2)-negative breast cancer patients to predict the recurrence score (RS) and chemotherapy (CT) benefit. The aim of our study is to develop a prediction tool for the three RS's categories based on deep multi-layer perceptrons (DMLP) and using only the morphoimmunohistological variables. We performed a retrospective cohort of 320 patients who underwent ODX testing from three French hospitals. Clinico-pathological characteristics were recorded. We built a supervised machine learning classification model using Matlab software with 152 cases for the training and 168 cases for the testing. Three classifiers were used to learn the three risk categories of the ODX, namely the low, intermediate, and high risk. Experimental results provide the area under the curve (AUC), respectively, for the three risk categories: 0.63 [95% confidence interval: (0.5446, 0.7154), p < 0.001], 0.59 [95% confidence interval: (0.5031, 0.6769), p < 0.001], 0.75 [95% confidence interval: (0.6184, 0.8816), p < 0.001]. Concordance rate between actual RS and predicted RS ranged from 53 to 56% for each class between DMLP and ODX. The concordance rate of low and intermediate combined risk group was 85%.We developed a predictive machine learning model that could help to define patient's RS. Moreover, we integrated histopathological data and DMLP results to select tumor for ODX testing. Thus, this process allows more relevant use of histopathological data, and optimizes and enhances this information.

The Correlation of Magee EquationsTM and Oncotype DX® Recurrence Score From Core Needle Biopsy Tissues in Predicting Response to Neoadjuvant Chemotherapy in ER+ and HER2- Breast Cancer

Objective

Oncotype DX^® recurrence score (RS) can be predicted from Magee Equations^TM (MS) postoperatively. The aim of this study is to investigate correlation of MS with RS from pretreatment core needle biopsy (CNB) tissues, and their clinical usefulness in prediction of response to neoadjuvant chemotherapy (NCT) in estrogen receptor-positive and human epidermal growth factor receptor 2-negative (ER+/HER2-) breast cancer (BC).

Materials and Methods

Pretreatment CNB tissue samples from 60 patients with ER+/HER2- invasive BC were analyzed for MS and RS correlation. MS and RS were categorized as follows: low (<18), intermediate (18-30), and high (≥ 31). Percentage Tumor size Reduction (%TR) was used to assess tumor response to NCT, and substantial %TR was defined as at least 50% reduction (≥50%TR). Correlation between MS and RS, and predictive factors for the ≥50%TR achievement were assessed.

Results

MS and RS represented a strong correlation (Spearman's correlation; r=0.58, p<0.0001) as a continuous variable. As a categorical variable, the concordance between MS and RS was 43.3%, and it increased to 80% (r=0.61, p=0.003) with the exclusion of the intermediate risk categories. Although, there was pathologic complete response (pCR), MS showed the highest predictive power for the ≥50% TR achievement, none of the factors were statistically significant (p≥0.07).

Conclusion

Our study demonstrated that there was a strong correlation between MS and RS from pretreatment biopsy tissue samples in ER+ and HER2- invasive BC.

The healthcare value of the Magee Decision Algorithm™: use of Magee Equations™ and mitosis score to safely forgo molecular testing in breast cancer

Magee Equations™ are multivariable models that can estimate oncotype DX® Recurrence Score, and Magee Equation 3 has been shown to have chemopredictive value in the neoadjuvant setting as a standalone test. The current study tests the accuracy of Magee Decision Algorithm™ using a large in-house database. According to the algorithm, if all Magee Equation scores are <18, or 18-25 with a mitosis score of 1, then oncotype testing is not required as the actual oncotype recurrence score is expected to be ≤25 (labeled "do not send"). If all Magee Equation scores are 31 or higher, then also oncotype testing is not required as the actual score is expected to be >25 (also "do not send"). All other cases could be considered for testing (labeled "send"). Of the 2196 ER+, HER2-negative cases sent for oncotype testing, 1538 (70%) were classified as "do not send" and 658 (30%) as "send". The classification accuracy in the "do not send" group was 95.1%. Of the 75 (4.9%) discordant cases (expected score ≤25 by decision algorithm but the actual oncotype score >25), 26 received endocrine therapy alone. None of these 26 patients experienced distant recurrence (average follow-up of 73 months). The Magee Decision Algorithm accurately identifies cases that will not benefit from oncotype testing. Such cases constitute ~70% of the routine clinical oncotype requests, an estimated saving of $300,000 per 100 test requests. The occasional discordant cases (expected ≤25, but actual oncotype score >25) appears to have an excellent outcome on endocrine therapy alone.

Development of a Nomogram to Predict the Recurrence Score of 21-Gene Prediction Assay in Hormone Receptor-Positive Early Breast Cancer

INTRODUCTION

A 21-gene prediction assay (Oncotype DX) is helpful to estimate benefit from adjuvant chemotherapy in patients with hormone receptor-positive, lymph node-negative early breast cancer. This study was conducted to develop a model to estimate high recurrence score (RS) using easily available clinicopathologic parameters in limited-resource countries.

PATIENTS AND METHODS

Hormone receptor-positive, lymph node-negative early breast cancer patients who underwent Oncotype DX were enrolled onto the training set (n = 192). The risk category range of the RS was the same as in the TAILORx study. The multivariable logistic regression model was used to identify significant variables associated with high RS. The independent validation set (n = 264) was established from patients of a different time period.

RESULTS

The median age in the training set was 47 years, and 78.0% were premenopausal. The number of patients with low RS (< 11), intermediate RS (11-25), and high RS (> 25) were 42 (22.0%), 122 (63.9%), and 27 (14.1%), respectively. High nuclear grade, no progesterone receptor expression, and high Ki-67 were associated with high RS, and these variables were used to construct the nomogram. It had significant discriminatory power in internal validation (area under the curve = 0.856) and in the validation set (area under the curve = 0.828). The calibration plot showed optimal agreement between predicted and actual probabilities in both sets.

CONCLUSION

A nomogram was successfully developed with 3 simple parameters. The probability of high RS can be easily and conveniently estimated using our nomogram. It might be useful to determine whether or not Oncotype DX is conducted in the TAILORx era. Future large-scale prospective studies are warranted.

A Nomogram for Predicting the Oncotype DX Recurrence Score in Women with T1-3N0-1miM0 Hormone Receptor‒Positive, Human Epidermal Growth Factor 2 (HER2)‒Negative Breast Cancer

PURPOSE

This preliminary study was conducted to evaluate the association between Oncotype DX (ODX) recurrence score and traditional prognostic factors. We also developed a nomogram to predict subgroups with low ODX recurrence scores (less than 25) and to avoid additional chemotherapy treatments for those patients.

MATERIALS AND METHODS

Clinicopathological and immunohistochemical variables were retrospectively retrieved and analyzed from a series of 485 T1-3N0-1miM0 hormone receptor-positive, human epidermal growth factor 2‒negative breast cancer patients with available ODX test results at Asan Medical Center from 2010 to 2016. One hundred twenty-seven patients (26%) had positive axillary lymph node micrometastases, and 408 (84%) had ODX recurrence scores of ≤25. Logistic regression was performed to build a nomogram for predicting a low-risk subgroup of the ODX assay.

RESULTS

Multivariate analysis revealed that estrogen receptor (ER) score, progesterone receptor (PR) score, histologic grade, lymphovascular invasion (LVI), and Ki-67 had a statistically significant association with the low-risk subgroup. With these variables, we developed a nomogram to predict the low-risk subgroup with ODX recurrence scores of ≤25. The area under the receiver operating characteristic curve was 0.90 (95% confidence interval [CI], 0.85 to 0.96). When applied to the validation group the nomogram was accurate with an area under the curve = 0.88 (95% CI, 0.83 to 0.95).

CONCLUSION

The low ODX recurrence score subgroup can be predicted by a nomogram incorporating five traditional prognostic factors: ER, PR, histologic grade, LVI, and Ki-67. Our nomogram, which predicts a low-risk ODX recurrence score, will be a useful tool to help select patients who may or may not need additional ODX testing.

The Oncotype Dx Assay in ER-Positive, HER2-Negative Breast Cancer Patients: A Real Life Experience from a Single Cancer Center

Objective

To determine the influence of the Oncotype Dx assay on the treatment of patients with Estrogen Receptor (ER)-positive, Human Epidermal Growth Factor Receptor 2 (HER2)-negative, axillary lymph node-negative or micrometastatic carcinoma of the breast in a single cancer center. In addition, patients with intermediate Oncotype Dx recurrence scores were analyzed to assess the factors influencing therapeutic decisions for adjuvant chemotherapy.

Materials and Methods

Data from medical records of women diagnosed with carcinoma of the breast and qualified for the Oncotype Dx assay were extracted (OncoDx cohort). Patient demographic and cancer characteristics, genomic report, and course of treatment data, including survival outcomes and treatment decision-making, were analyzed. A matched cohort of patients with similar tumor stage and biology (ER-positive, HER2-negative) from the era before the introduction of the Oncotype Dx assay was analyzed for comparison (pre-OncoDx cohort).

Results

Two hundred and one patients were included in the OncoDx cohort and one hundred and sixty patients were included in the pre-OncoDx cohort. Oncotype Dx recurrence score (RS) was low (<11) in fifty-six patients (28%), intermediate (11-25) in one hundred and twenty-three patients (61.5%) and high (>25) in twenty one patients (10.5%). Demographic and cancer clinicopathologic characteristics between OncoDx and pre-OncoDx cohorts were similar. Overall, 10.9% of the patients in the OncoDx cohort received adjuvant chemotherapy, versus 23.8% of the patients in the pre-OncoDx cohort (Fisher exact p=0.003). Fewer patients were recommended adjuvant chemotherapy in the OncoDx era compared to the pre-OncoDx era (17.9% vs 30.6%, respectively, Fisher exact p=0.006). The decision to recommend chemotherapy within the intermediate-risk cohort was influenced by the patient's RS. The mean RS of patients in the intermediate-risk cohort who did not receive chemotherapy was 21.5 while the score of those that received chemotherapy was 24.6 (p=0.000). The series confirmed excellent PFS and OS for both OncoDx and pre-OncoDx cohorts.

Conclusion

This single cancer center analysis confirms the avoidance of chemotherapy in the great majority of patients with early ER-positive, HER2-negative, lymph node-negative or micrometastatic carcinoma of the breast since the introduction of the Oncotype Dx assay. A higher recurrence risk score within the intermediate group may influence the decision for chemotherapy inclusion in the adjuvant treatment plan. A lower PR percentage by IHC and higher grade may predict higher Oncotype Dx scores.

Risk stratification of ER-positive breast cancer patients: A multi-institutional validation and outcome study of the Rochester Modified Magee algorithm (RoMMa) and prediction of an Oncotype DX® recurrence score <26

The skyrocketing cost of health-care demands that we question when to use multigene assay testing in the planning of treatment for breast cancer patients. A previously published algorithmic model gave recommendations for which cases to send out for Oncotype DX^® (ODX) testing. This study is a multi-institutional validation of that algorithmic model in 620 additional estrogen receptor positive breast cancer cases, with outcome data on 310 cases, named in this study as the Rochester Modified Magee algorithm (RoMMa). RoMMa correctly predicted 85% (140/164) and 100% (17/17) of cases to have a low- or high-risk ODX recurrence score, respectively, consistent with the original publication. Applying our own risk stratification criteria, in patients who received appropriate hormonal therapy, only one of the 45 (2.0%) patients classified as low risk by our original algorithm have been associated with a breast cancer recurrence over 5-10 years of follow-up. Eight of 116 (7.0%) patients classified as low risk by ODX have been associated with a breast cancer recurrence with up to 11 years of follow-up. In addition, 524 of 537 (98%) cases from our total population (n = 903) with an average modified Magee score ≤18 had an ODX recurrence score <26. Patients with an average modified Magee score ≤18 or >30 may not need to be sent out for ODX testing. By avoiding these cases sending out for ODX testing, the potential cost savings to the health-care system in 2018 are estimated to have been over $100,000,000.

A simple immunohistochemical bio-profile incorporating Bcl2 curbs those cases of invasive breast carcinoma for which an Oncotype Dx characterization is needed

AIM

Our goal has been to evaluate the importance that the incorporation of Bcl2 in the ER/PGR/Her2/Ki67 bio-profile can have as predictor of the Oncotype Dx categories.

MATERIAL AND METHODS

156 consecutive cases of HR+/Her2- pN0/1 primary breast carcinoma were sent to the Oncotype Dx test. Immunohistochemical determination of Bcl2/ER/PGR/Ki67/Her2 expression was evaluated for each case. After the selection of the appropriate cut-off values for PGR and Ki67, explorative as well as confirmative statistical analyses were performed to build and validate predictive risk-of-recurrence immunohistochemical only bio-profiles.

RESULTS

The predictive capacity of these immunohistochemical profiles was compared with both traditional and TAILORx Oncotype Dx risk class classification. This comparison showed that immunohistochemical bio-profiles select those cases not associated with high risk-of-recurrence of disease (luminal-A/B and luminal A/B Bcl2) and those that are instead at high risk and therefore worthy of chemotherapy (luminal-B ki67 and luminal-B Bcl2/Ki67), strongly suggesting to only submit PGR-positive/Bcl2-Ki67 altered cases to Oncotype Dx, thus reducing the number of cases to be tested.

CONCLUSIONS

Our results indicate that the addition of Bcl2 to an immunohistochemical bio-profile definitely improves its predictive capacity to correctly select which cases to send to the Oncotype Dx test. We have also suggested that institutions with a significant number of breast carcinomas sent to the Oncotype Dx test can use these latter to derive their own PGR and Ki67 cut-off values, overcoming the drawbacks of sharing common inter-laboratory values. Validation of these bio-profiles as predictors of the Oncotype Dx categories is ongoing in a prospective series of new cases.

Does integration of Magee equations into routine clinical practice affect whether oncologists order the Oncotype DX test? A prospective randomized trial

OBJECTIVE

The three Magee Equations provide an estimate of the Oncotype DX recurrence score using commonly available clinicopathologic information (tumour size, grade, oestrogen receptor, progesterone receptor, HER2, and Ki67). We assessed whether integration of Magee Equations into routine clinical practice affected the frequency of Oncotype DX requests.

METHODS

Patients with newly diagnosed, node negative, hormone receptor positive, and HER2 negative invasive breast cancer were randomized to undergo a Magee calculation or not. At the first clinic assessment, the oncologist was provided with all routinely available clinicopathologic information (including Ki67) either with or without the results of Magee Equations. Primary outcome was frequency of Oncotype DX ordering. Secondary outcomes included frequency of chemotherapy use, time to commencement of radiotherapy, or systemic therapy. Physician comfort with systemic therapy choices and the use of Ki67 and Magee Equations was also assessed.

RESULTS

Data from 175 randomized patients was available, 84 patients (48%) with and 91 (52%) without calculated Magee Equations. Oncotype DX was ordered in 10 (12.05%) and 13 (14.44%) (RR 0.83, 0.39-1.80; P = 0.64) in the Magee and no Magee groups, respectively. There were no statistically or clinically significant differences between the randomized groups for any of the secondary outcomes. Availability of both Ki67 and Magee Equations was associated with increased physician comfort around systemic treatment decisions.

CONCLUSIONS

In a practice where Ki67 is routinely available, addition of Magee Equations into routine clinic practice was not associated with a reduction in Oncotype DX use. Availability of both Ki67 and Magee Equations did however increase physician comfort with systemic therapy decisions.

Breast Cancers With Magee Equation Score of Less Than 18, or 18-25 and Mitosis Score of 1, Do Not Require Oncotype DX Testing: A Value Study

Objectives

To investigate use of Magee equations (MEs) to determine which breast cancer cases can be excluded from Oncotype DX testing.

Methods

A prospective value study was carried out using data from pathology reports.

Results

If all three MEs scores were less than 18 or 31 or higher, the cases were labeled do not send for testing. If any or all scores were 18 to 25, cases were labeled do not send if mitosis score was 1. Of the total 205 cases, 146 (71%) were labeled do not send; of these, the correct call was made in 143 (98%) cases. Two of the three discordant cases had associated nontumor factors, likely resulting in higher scores.

Conclusions

Cases with ME scores less than 18, or 18 to 25 and mitosis score 1, do not require Oncotype DX testing, an estimated saving of US$280,000 per 100 clinical requests.

BCL-2 expression aids in the immunohistochemical prediction of the Oncotype DX breast cancer recurrence score

Background

The development of molecular techniques to estimate the risk of breast cancer recurrence has been a significant addition to the suite of tools available to pathologists and breast oncologists. It has previously been shown that immunohistochemistry can provide a surrogate measure of tumor recurrence risk, effectively providing a less expensive and more rapid estimate of risk without the need for send-out. However, concordance between gene expression-based and immunohistochemistry-based approaches has been modest, making it difficult to determine when one approach can serve as an adequate substitute for the other. We investigated whether immunohistochemistry-based methods can be augmented to provide a useful therapeutic indicator of risk.

Methods

We studied whether the Oncotype DX breast cancer recurrence score can be predicted from routinely acquired immunohistochemistry of breast tumor histology. We examined the effects of two modifications to conventional scoring measures based on ER, PR, Ki-67, and Her2 expression. First, we tested a mathematical transformation that produces a more diagnostic-relevant representation of the staining attributes of these markers. Second, we considered the expression of BCL-2, a complex involved in regulating apoptosis, as an additional prognostic marker.

Results

We found that the mathematical transformation improved concordance rates over the conventional scoring model. By establishing a measure of prediction certainty, we discovered that the difference in concordance between methods was even greater among the most certain cases in the sample, demonstrating the utility of an accompanying measure of prediction certainty. Including BCL-2 expression in the scoring model increased the number of breast cancer cases in the cohort that were considered high certainty, effectively expanding the applicability of this technique to a greater proportion of patients.

Conclusions

Our results demonstrate an improvement in concordance between immunohistochemistry-based and gene expression-based methods to predict breast cancer recurrence risk following two simple modifications to the conventional scoring model.

Multifocal/Multicentric Ipsilateral Invasive Breast Carcinomas with Similar Histology: Is Multigene Testing of All Individual Foci Necessary?

BACKGROUND

Multiple synchronous ipsilateral invasive breast carcinomas (BCs) with similar histology usually have concordant receptor status. It is unknown whether individual foci with similar histology also share molecular and biological similarities or are heterogenous. This study examined the concordance of the 21-gene recurrence score (RS) in multiple synchronous morphologically similar ipsilateral BCs.

PATIENTS AND METHODS

We identified patients with multiple ipsilateral BCs and available RS treated at our institution from 1/2014 to 6/2018. BCs were divided into three groups based on RS: (1) RS in same risk category, (2) RS in different risk categories but within 2-unit difference (e.g., RS 17 and RS 19), and (3) RS in different risk categories and a change of > 2 units. BCs in groups 1 and 2 were considered as concordant (no significant clinical impact) and BCs in group 3 as discordant (variation affects management).

RESULTS

A total of 53 patients met the study criteria. RS was concordant in 46 (87%) cases. Seven (13%) cases were discordant (group 3). Of these, three (43%, 3/7) had biopsy cavity changes (BXC) adjacent to the BC with highest RS. In two cases the focus with higher RS had a lower percentage of progesterone receptor-positive tumor cells. In two cases, extensive ductal carcinoma in situ was associated with the BC focus with lower RS.

CONCLUSIONS

Morphologically similar multifocal ipsilateral BCs have concordant RS in 87% (46/53) of cases. Our results suggest that, in cases of morphologically similar multifocal BCs, testing of a single focus provides accurate prognostic and predictive information.

Only estrogen receptor "positive" is not enough to predict the prognosis of breast cancer

PURPOSE

Beginning in 2018, biomarkers including estrogen receptor (ER) status were incorporated in the 8th AJCC staging system. ER expression levels were not considered in these changes. We hypothesized that the levels of ER expression could affect the prognosis of breast cancer.

METHODS

A retrospective review was conducted to identify all female patients with invasive breast cancer between 2003 and 2012. ER negative (group I), weakly ER-positive (group II), and strongly ER-positive (group III) were defined as Allred total scores of 0-2, 3-5, and 6-8, respectively. We examined a multigene panel, designated the BCT score, which is a newly developed prognostic model for predicting the risk of a distant metastasis.

RESULTS

Among the 4949 patients enrolled in this study, 1310 (26.5%), 361 (7.3%), and 3277 (66.2%) were categorized as group I, II, and III, respectively. Median F/U duration was 57.8 months. Compared to group III, patients in group II were younger, had larger tumors, and were also more likely to have PR-negative tumors, HER-2 amplification, high Ki-67, and high nuclear grade. Between group II and III, there was a significant difference in OS (P = 0.0764, 0.909, and 0.010, respectively). After adjusting for additional factors that may affect OS, the HR for OS showed higher in group II than in group III. The baseline median BCT score indicated that lower ER expression was associated with significantly higher BCT score (P < 0.0001) and significantly more likely to have high risk group (P < 0.0001) relative to higher levels of ER expression group.

CONCLUSION

ER expression levels affect the prognosis of breast cancer. The risk for patients with weakly ER-positive breast cancer should not be underestimated.

Pathologist's health-care value in the triage of Oncotype DX® testing: a value-based pathology study of tumour biology with outcomes

AIMS

Pathologists provide expert tissue assessment of breast cancer, yet their value to guide the appropriate use of breast cancer gene expression profile tests (GEPT) is underutilised. The specific aims of this study are to report morpho-immunohistological characteristics of breast tumours with Oncotype DX^® (ODx) recurrence scores (RS) of 10 or fewer (ultra-low risk) and 25 or fewer (low risk) in order to determine if pathologists can identify prospectively patient tumours that do not require ODx testing.

METHODS AND RESULTS

Oncotype DX^® cases with RS < 10 from 2005 to 2010 comprised 441 of 2594 (17%) of clinical cases; this cohort had 5 years' follow-up and was treated with endocrine therapy alone. Tumours were analysed for tumour type, Nottingham grade, mitosis score (MS) semi-quantitative (H-score) hormone receptor content and Magee equation 3. Knowledge derived from this data set was used to develop algorithms in order to identify prospectively tumours with RS of 10 or fewer or 25 or fewer. Thirty-four per cent of tumours were low-grade special types, while the remainder were enriched with high hormone receptor content with MS of 1. These algorithmic selection criteria identified correctly all patient cases below the chemotherapy cut-point, i.e. RS < 25, indicating that these oncotype test orders were an unnecessary cost.

CONCLUSIONS

This unique study demonstrates that (i) pathologists add great value to triage breast cancer for GEPT; and (ii) can identify prospectively low-grade tumour biology with high sensitivity and high specificity for those cases which do not require chemotherapy (RS < 25) using MS and hormone receptor content.

Practical Consequences Resulting from the Analysis of a 21-Multigene Array in the Interdisciplinary Conference of a Breast Cancer Center

During the multidisciplinary planning of postoperative therapy after breast cancer, borderline cases can arise with no clear rationale for or against adjuvant chemotherapy. In 50 hormone- receptor-positive, Her2neu-negative carcinomas of the breast with no or only minimal lymph node involvement (max. pT1a) we initiated an Oncotype DX® multigene assay in addition to the evaluation of usual parameters. In the oncology conference a vote for or against chemotherapy was taken on the basis of the conventional criteria for decision-making before the test results were available. The final recommendation was made after the multigene test. In 32 breast carcinomas (64%) a low recurrence score could be documented, while 26 (32%) showed an intermediate RS and 3 (6%) showed a high RS. In most cases the result of the test could validate the choice of therapy established using conventional criteria. In 5 cases the initial recommendation for adjuvant therapy was revised, and in 3 cases chemotherapy was secondarily recommended after evaluation of the test results. Conversely, in some cases a low or intermediate risk constellation did not argue against a recommendation for adjuvant chemotherapy. Altogether, the results of our study do not indicate that a multigene assay should be used as a routine diagnostic tool. Instead a thorough compilation and careful analysis of conventional parameters for therapeutic decision-making should take precedence, with special emphasis on histopathological and immunohistochemical results. In selected cases, however, a multigene assay can be a useful tool in the deliberation for or against a therapeutic pathway.

A study of association of Oncotype DX recurrence score with DCE-MRI characteristics using multivariate machine learning models

PURPOSE

To determine whether multivariate machine learning models of algorithmically assessed magnetic resonance imaging (MRI) features from breast cancer patients are associated with Oncotype DX (ODX) test recurrence scores.

METHODS

A set of 261 female patients with invasive breast cancer, pre-operative dynamic contrast enhanced magnetic resonance (DCE-MR) images and available ODX score at our institution was identified. A computer algorithm extracted a comprehensive set of 529 features from the DCE-MR images of these patients. The set of patients was divided into a training set and a test set. Using the training set we developed two machine learning-based models to discriminate (1) high ODX scores from intermediate and low ODX scores, and (2) high and intermediate ODX scores from low ODX scores. The performance of these models was evaluated on the independent test set.

RESULTS

High against low and intermediate ODX scores were predicted by the multivariate model with AUC 0.77 (95% CI 0.56-0.98, p < 0.003). Low against intermediate and high ODX score was predicted with AUC 0.51 (95% CI 0.41-0.61, p = 0.75).

CONCLUSION

A moderate association between imaging and ODX score was identified. The evaluated models currently do not warrant replacement of ODX with imaging alone.

Applying new Magee equations for predicting the Oncotype Dx recurrence score

BACKGROUND

Breast cancer is one of the most prevalent cancers in women. Oncotype Dx is a multi-gene assay frequently used to predict the recurrence risk for estrogen receptor-positive early breast cancer, with values < 18 considered low risk; ≥ 18 and ≤ 30, intermediate risk; and > 30, high risk. Patients at a high risk for recurrence are more likely to benefit from chemotherapy treatment.

METHODS

In this study, clinicopathological parameters for 37 cases of early breast cancer with available Oncotype Dx results were used to estimate the recurrence score using the three new Magee equations. Correlation studies with Oncotype Dx results were performed. Applying the same cutoff points as Oncotype Dx, patients were categorized into low-, intermediate- and high-risk groups according to their estimated recurrence scores.

RESULTS

Pearson correlation coefficient (R) values between estimated and actual recurrence score were 0.73, 0.66, and 0.70 for Magee equations 1, 2 and 3, respectively. The concordance values between actual and estimated recurrence scores were 57.6%, 52.9%, and 57.6% for Magee equations 1, 2 and 3, respectively. Using standard pathologic measures and immunohistochemistry scores in these three linear Magee equations, most low and high recurrence risk cases can be predicted with a strong positive correlation coefficient, high concordance and negligible two-step discordance.

CONCLUSIONS

Magee equations are user-friendly and can be used to predict the recurrence score in early breast cancer cases.

Utilization of the 21-Gene Recurrence Score in a Diverse Breast Cancer Patient Population: Development of a Clinicopathologic Model to Predict High-Risk Scores and Response to Neoadjuvant Chemotherapy

INTRODUCTION

The 21-gene expression profile [Oncotype DX Recurrence Score (RS)] stratifies benefit from adjuvant chemotherapy in hormone receptor (HR)-positive, HER2/neu-negative, node-negative breast cancer. It is not routinely applied to predict neoadjuvant chemotherapy (NACT) response; data in diverse patient populations also are limited. We developed a statistical model based on standard clinicopathologic features to identify high-risk cases (RS > 30) and then evaluated ability of predicted high RS to predict for NACT downstaging.

METHODS

Primary surgery patients with Oncotype DX RS testing 2012-2016 were identified from a prospectively-maintained database. A RS predictive model was created and applied to a dataset of comparable NACT patients. Response was defined as tumor size decrease ≥ 1 cm.

RESULTS

Of 394 primary surgery patients-60.4% white American; 31.0% African American-RS distribution was similar for both groups. No single feature reliably identified high RS patients; however, a model accounting for age, HR expression, proliferative index (MIB1/Ki67), histology, and tumor size was generated, with receiver operator area under the curve 0.909. Fifty-six NACT patients were identified (25 African American). Of 21 cases with all relevant clinicopathology, 14 responded to NACT and the model generated high-risk RS in 14 (100%); conversely, of 16 cases generating high-risk RS, only 2 did not respond.

CONCLUSIONS

Predictive modelling can identify high RS patients; this model also can identify patients likely to experience primary tumor downstaging with NACT. Until this model is validated in other datasets, we recommend that Oncotype-eligible patients undergo primary surgery with decisions regarding chemotherapy made in the adjuvant setting.

Magee Equation 3 predicts pathologic response to neoadjuvant systemic chemotherapy in estrogen receptor positive, HER2 negative/equivocal breast tumors

Magee Equations were derived as an inexpensive, rapid alternative to Oncotype DX. The Magee Equation 3 utilizes immunohistochemical and FISH data for estrogen receptor (ER), progesterone receptor (PR), HER2 and Ki-67 for its calculation (24.30812+ERIHC × (-0.02177)+PRIHC × (-0.02884)+(0 for HER2 negative, 1.46495 for equivocal, 12.75525 for HER2 positive)+Ki-67 × 0.18649). We hypothesize that Magee Equation 3 scores from pre-therapy core biopsy can predict response to neoadjuvant systemic chemotherapy. A prospectively-maintained database of patients who received neoadjuvant systemic therapy from 2010 to 2014 at a single institution was retrospectively reviewed. Pathologic complete response was defined as absence of invasive tumor in the breast and regional lymph nodes. Of the 614 cases, tumors with missing immunohistochemical results and those that were ER negative or HER2 positive were excluded. This resulted in 237 ER positive, HER2 negative/equivocal tumors that formed the basis of this study. Magee Equation 3 scores were divided into 3 categories similar to Oncotype DX, ie, 0 to <18 (low), 18 to <31 (intermediate), and 31 or higher (high) scores. The pathologic complete response rate for low, intermediate and high Magee Equation 3 scores was 0%, 4%, and 36%, respectively. Patients with high Magee Equation 3 scores were 13 times more likely to achieve pathologic complete response compared to those with Magee Equation 3 scores less than 31 (95% CI 5.09-32.87, P<0.0001). For patients that did not achieve pathologic complete response, high Magee Equation 3 correlated with higher recurrence rate, with the majority occurring in patients with positive lymph nodes in the resection specimen. Magee Equation 3 score ≥31 predicts pathologic complete response in the neoadjuvant setting and for tumor recurrence, when pathologic complete response is not achieved. These results show the utility of Magee Equation 3 in predicting patients who will benefit from chemotherapy but warrant prospective multi-institutional validation.

Using the Modified Magee Equation to Identify Patients Unlikely to Benefit From the 21-Gene Recurrence Score Assay (Oncotype DX Assay)

OBJECTIVES

This study aimed to compare a modified Magee equation with Oncotype DX (Genomic Health, Redwood City, CA) recurrence score (RS) and identify patients who are unlikely to benefit from Oncotype DX.

METHODS

Magee equation RS was calculated in 438 cases and correlated with Oncotype DX RS.

RESULTS

The Pearson correlation coefficient ( r ) for the Magee equation and Oncotype DX RS was 0.6645 ( P  < .00001), and the overall agreement was 66.4%. All cases (11.6%) with a Magee equation RS greater than 30 or 11 or less had been correctly predicted to have either high Oncotype DX RS or low Oncotype DX RS, respectively.

CONCLUSIONS

The modified Magee equation is able to identify up to 12% patients who are unlikely to benefit from Oncotype DX testing. Using the modified Magee equation RS on these patients would be an alternative to Oncotype DX, leading to cost savings.

Oncotype DX breast cancer recurrence score can be predicted with a novel nomogram using clinicopathologic data

PURPOSE

Oncotype DX (ODX) recurrence score (RS) breast cancer (BC) assay is costly, and performed in only ~1/3 of estrogen receptor (ER)-positive BC patients in the USA. We have now developed a user-friendly nomogram surrogate prediction model for ODX based on a large dataset from the National Cancer Data Base (NCDB) to assist in selecting patients for which further ODX testing may not be necessary and as a surrogate for patients for which ODX testing is not affordable or available.

METHODS

Six clinicopathologic variables of 27,719 ODX-tested ER+/HER2-/lymph node-negative patients with 6-50 mm tumor size captured by the NCDB from 2010 to 2012 were assessed with logistic regression to predict high-risk or low-risk ODXRS test results with TAILORx-trial and commercial cut-off values; 12,763 ODX-tested patients in 2013 were used for external validation. The predictive accuracy of the regression model was yielded using a Receiver Operator Characteristic analysis. Model fit was analyzed by plotting the predicted probabilities against the actual probabilities. A user-friendly calculator version of nomograms is available online at the University of Tennessee Medical Center website (Knoxville, TN).

RESULTS

Grade and progesterone receptor status were the highest predictors of both low-risk and high-risk ODXRS, followed by age, tumor size, histologic tumor type and lymph-vascular invasion (C-indexes-.0.85 vs. 0.88 for TAILORx-trial vs. commercial cut-off values, respectively).

CONCLUSION

This is the first study of this scale showing confidently that clinicopathologic variables can be used for prediction of low-risk or high-risk ODXRS using our nomogram models. These novel nomograms will be useful tools to help physicians and patients decide whether further ODX testing is necessary and are excellent surrogates for patients for which ODX testing is not affordable or available.

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.

Immunohistochemical Surrogates for Molecular Classification of Breast Carcinoma: A 2015 Update

CONTEXT

-The pioneering works on molecular classification (MC) by Perou and Sorlie et al in the early 2000s using global gene expression profiling identified 5 intrinsic subtypes of invasive breast cancers (IBCs): luminal A, luminal B, normal breast-like, HER2-enriched, and basal-like subtypes, each unique in incidence, survival, and response to therapy. Because the application of gene expression profiling in daily practice is not economical or practical at the present time, many investigators have studied the use of immunohistochemical (IHC) surrogates as a substitute for determining the MC of IBC.

OBJECTIVE

-To discuss the continuing efforts that have been made to develop clinically significant and readily available IHC surrogates for the MC of IBC.

DATA SOURCES

-Data were obtained from pertinent peer-reviewed English-language literature.

CONCLUSIONS

-The most commonly used IHC surrogates are estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER2), dividing IBC into luminal, HER2, and triple-negative subtypes. The addition of Ki-67, cytokeratin 5, and epidermal growth factor receptor (EGFR) separates luminal B from luminal A subtypes, and basal-like subtype from triple-negative breast cancer. More recently, biomarkers such as androgen receptor and p53 have been shown to further stratify these molecular subtypes. Although many studies of IHC-based MC have shown clinical significance similar to gene expression profiling-defined MC, its critical limitations are: (1) a lack of standardization in terminology, (2) a lack of standardization in biomarkers used for each subtype, and (3) the lack of a uniform cutoff for each biomarker. A panel of IHC surrogates for each subtype of IBC is proposed.

Correlation of Oncotype DX Recurrence Score with Histomorphology and Immunohistochemistry in over 500 Patients

Oncotype Dx is used to determine the recurrence risk (RR) in patients with estrogen receptor positive (ER+) and lymph node negative (LN−) breast cancer. The RR is divided into low (0–17), intermediate (18–30), and high (31) to predict chemotherapy benefit. Our goal was to determine the association between histomorphology, immunohistochemistry, and RR. We retrospectively identified 536 patients with ER+ and LN− breast cancers that underwent Oncotype testing from 2006 to 2013. Tumor size ranged from 0.2 cm to 6.5 cm (mean = 1.3 cm) and was uniform in all 3 categories. The carcinomas were as follows: ductal = 63.2%, lobular = 11.1%, and mixed = 35.7%. The RR correlated with the Nottingham grade. Increasing RR was inversely related to PR positivity but directly to Her2 positivity. Of the morphologic parameters, a tubular(lobular) morphology correlated only with low-intermediate scores and anaplastic type with intermediate-high scores. Other morphologies like micropapillary and mucinous were uniformly distributed in each category. Carcinomas with comedo intraductal carcinoma were more likely associated with high RR. Forty-four patients with either isolated tumor cells or micrometastases were evenly distributed amongst the 3 RR. While there was only 1 ER discrepancy between our immunohistochemistry (3+ 80%) and Oncotype, up to 8% of PR+ cases (mean = 15%, median = 5%) and 2% of HER2+ cases were undervalued by Oncotype.

Algorithms for prediction of the Oncotype DX recurrence score using clinicopathologic data: a review and comparison using an independent dataset

PURPOSE

Given the potential savings in cost and resource utilization, several algorithms have been proposed to predict Oncotype DX recurrence score (ODX RS) using commonly acquired histopathologic variables. Although it is promising, additional independent validation of these surrogate markers is needed prior to guide the patient management.

METHODS

In this retrospective study, we analyzed 305 patients with invasive breast cancer at our institution who had ODX RS available. We selected five equations that provide a surrogate measure of ODX as previously published by Klein et al. (Magee equations 1-3), Gage et al., and Tang et al. All equations used estrogen receptor status and progesterone receptor status along with different combinations of grade, proliferation indices (Ki-67, mitotic rate), HER2 status, and tumor size.

RESULTS

Of all surrogate scores tested, the Magee equation 2 provided the highest correlation with ODX both with regard to raw score (Pearson's correlation coefficient = 0.66 95% CI 0.59-0.72) and categorical correlation (Cohen's kappa = 0.43, 95% CI 0.33-0.53). Although Magee equation 2 provided a way to reliably identify high-risk disease by assigning 95% of the patients with high ODX RS to either the intermediate- or high-risk group, it was unable to reliably identify the potential for patients to have intermediate- or high-risk disease by ODX (66% of such patients identified).

CONCLUSIONS

Although commonly available surrogates for ODX appear to predict high-risk ODX RS, they are unable to reliably rule out the presence of patients with intermediate-risk disease by ODX. Given the potential benefit of adjuvant chemotherapy in women with intermediate-risk disease by ODX, current surrogates are unable to safely substitute for ODX. Characterizing the true recurrence risk in patients with intermediate-risk disease by ODX is critical to the clinical adoption of current surrogate markers and is an area of ongoing clinical trials.

Optimizing the Use of Gene Expression Profiling in Early-Stage Breast Cancer

Purpose Gene expression profiling assays are frequently used to guide adjuvant chemotherapy decisions in hormone receptor-positive, lymph node-negative breast cancer. We hypothesized that the clinical value of these new tools would be more fully realized when appropriately integrated with high-quality clinicopathologic data. Hence, we developed a model that uses routine pathologic parameters to estimate Oncotype DX recurrence score (ODX RS) and independently tested its ability to predict ODX RS in clinical samples. Patients and Methods We retrospectively reviewed ordered ODX RS and pathology reports from five institutions (n = 1,113) between 2006 and 2013. We used locally performed histopathologic markers (estrogen receptor, progesterone receptor, Ki-67, human epidermal growth factor receptor 2, and Elston grade) to develop models that predict RS-based risk categories. Ordering patterns at one site were evaluated under an integrated decision-making model incorporating clinical treatment guidelines, immunohistochemistry markers, and ODX. Final locked models were independently tested (n = 472). Results Distribution of RS was similar across sites and to reported clinical practice experience and stable over time. Histopathologic markers alone determined risk category with > 95% confidence in > 55% (616 of 1,113) of cases. Application of the integrated decision model to one site indicated that the frequency of testing would not have changed overall, although ordering patterns would have changed substantially with less testing of estimated clinical risk-high or clinical risk-low cases and more testing of clinical risk-intermediate cases. In the validation set, the model correctly predicted risk category in 52.5% (248 of 472). Conclusion The proposed model accurately predicts high- and low-risk RS categories (> 25 or ≤ 25) in a majority of cases. Integrating histopathologic and molecular information into the decision-making process allows refocusing the use of new molecular tools to cases with uncertain risk.

Relevance of progesterone receptor immunohistochemical staining to Oncotype DX recurrence score

OBJECTIVE/BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Use of modified Magee equations and histologic criteria to predict the Oncotype DX recurrence score

Oncotype DX (Genomic Health, Redwood City, CA, USA, current list price $4,350.00) is a multigene quantitative reverse transcription-polymerase chain reaction-based assay that estimates the risk of distant recurrence and predicts chemotherapy benefit for patients with estrogen receptor (ER)-positive breast cancers. Studies have suggested that standard histologic variables can provide similar information. Klein and Dabbs et al have shown that Oncotype DX recurrence scores can be estimated by incorporating standard histologic variables into equations (Magee equations). Using a simple modification of the Magee equation, we predict the Oncotype DX recurrence score in an independent set of 283 cases. The Pearson correlation coefficient (r) for the Oncotype DX and average modified Magee recurrence scores was 0.6644 (n=283; P<0.0001). 100% of cases with an average modified Magee recurrence score>30 (n=8) or an average modified Magee recurrence score<9 (with an available Ki-67, n=5) would have been correctly predicted to have a high or low Oncotype DX recurrence score, respectively. 86% (38/44) of cases with an average modified Magee recurrence score≤12, and 89% (34/38) of low grade tumors (NS<6) with an ER and PR≥150, and a Ki-67<10%, would have been correctly predicted to have a low Oncotype DX recurrence score. Using an algorithmic approach to eliminate high and low risk cases, between 5% and 23% of cases would potentially not have been sent by our institution for Oncotype DX testing, creating a potential cost savings between $56,550.00 and $282,750.00. The modified Magee recurrence score along with histologic criteria may be a cost-effective alternative to the Oncotype DX in risk stratifying certain breast cancer patients. The information needed is already generated by many pathology laboratories during the initial assessment of primary breast cancer, and the equations are free.

A Validated Model for Identifying Patients Unlikely to Benefit From the 21-Gene Recurrence Score Assay

BACKGROUND

Predicting recurrence risk and chemotherapy benefit in early-stage breast cancer can be challenging, and Oncotype DX (ODX) is often used to gain insight. However, it is still unclear whether ODX can benefit in all cases. To clarify ODX's usefulness we sought to develop a model using readily available pathologic markers to help clinicians make that determination.

PATIENTS AND METHODS

Clinical pathologic data from 221 hormone receptor-positive, HER2-negative invasive breast cancer patients was used to create a model. The model was then validated on a second institution's set of 319 patients.

RESULTS

The model has 2 simple rules: low grade and positive progesterone receptor tumors (LG+PR) are low risk, and high grade or low estrogen receptor (ER) (ER < 20%) tumors (HG/LER) are high risk. The TAILORx (Trial Assigning Individualized Options for Treatment (Rx)) trial thresholds of Recurrence Score (RS) ≤ 10, when chemotherapy is of little benefit, and RS ≥ 26 when chemotherapy might be beneficial were used to judge model performance. Impressively, the misclassifications of an HG/LER patient who has an RS ≤ 10 were 0% and 2%, and for LG+PR patients who had an RS ≥ 26 were 0% and 2.6%. In the validation set, 28% (66 of 232) of the indeterminate group (neither in the HG/LER nor the LG+PR groups) had an RS ≤ 10 or an RS ≥ 26; this group might clinically benefit from ODX.

CONCLUSION

A simple 2-rule model based on readily available pathologic data was developed and validated, which categorized patients into high and low risk for recurrence. Identification of patients who are unlikely to benefit from ODX testing could result in significant cost avoidance.

Pathologic diagnosis, immunohistochemistry, multigene assays and breast cancer treatment: progress toward "precision" cancer therapy

Clinical decisions regarding the suitability of adjuvant systemic therapy for individual patients with breast cancer depends on comprehensive assessment of the underlying biology of each patient's tumor. The previous clinical-pathologic paradigm for treatment, which had been used for decades, now has been augmented by significant advances in molecular analysis of breast tumor tissue samples. Molecular testing has the potential to understand better both tumor biology and clinical behavior, which enables more appropriate therapy choices to be made. We review the rapid evolution in profiling breast cancer tissues, and discuss the current evidence for clinical use of this information and how the emerging molecular paradigm can be integrated into the clinical-pathologic context as we progress toward "precision" therapy for patients with breast cancer and other solid tumors.