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

Development and Validation of a Prognostic Nomogram for HR+ HER- Breast Cancer

Purpose

We aimed to develop a nomogram to predict prognosis of HR+ HER2- breast cancer patients and guide the application of postoperative adjuvant chemotherapy.

Methods

We identified 310 eligible HR+ HER- breast cancer patients and randomly divided the database into a training group and a validation group. The endpoint was disease free survival (DFS). Concordance index (C-index), area under the curve (AUC) and calibration curves were used to evaluate predictive accuracy and discriminative ability of the nomogram. We also compared the predictive accuracy and discriminative ability of our nomogram with the eighth AJCC staging system using overall data.

Results

According to the training group, platelet-to-lymphocyte ratio (PLR), tumor size, positive lymph nodes and Ki-67 index were used to construct the nomogram of DFS. The C-index of DFS was 0.708 (95% CI: 0.623-0.793) in the training group and 0.67 (95% CI: 0.544-0.796) in the validation group. The calibration curves revealed great consistencies in both groups.

Conclusion

We have developed and validated a novel and practical nomogram that can provide individual prediction of DFS for patients with HR+ HER- breast cancer. This nomogram may help clinicians in risk consulting and guiding the application of postoperative adjuvant chemotherapy.

Nomogram Update to Predict the High Genomic Risk Breast Cancer by Different Races

PURPOSE

To develop a nomogram to predict the high-risk recurrence score (RS) and to customize the nomogram for different races in early-stage hormone receptor (HoR)-positive, human epidermal growth factor receptor-2 (HER2)-negative breast cancer.

METHODS

Patients diagnosed between 2010 and 2015 were included from the surveillance, epidemiology, and end results oncotype DX database. The nomogram was assessed with a receiver operating characteristic curve to measure the area under the curve (AUC) with a 95% confidence interval (95% CI). The nomogram was developed and internally validated for discrimination and calibration, and then validated in different races.

RESULTS

A total of 48,464 patients were included and randomly assigned to the training cohort (n = 36370, 75.0%) and validation cohort (n = 12,094, 25.0%). Patients in the training cohort were identified to develop the nomogram, including 32,683 (89.9%) White women, 3135 (8.6%) Black women, and 552 (1.5%) Chinese women. Five independent predictive factors for high-risk RS were included to develop the nomogram, including tumor grade, progesterone receptor status, histological subtype, race, and tumor stage. The AUC was 0.696 (95% CI, 0.682-0.710) in the training cohort and 0.700 (95% CI, 0.676-0.724) in the validation cohort. There was no significant difference between the training cohort and the validation cohort. When validating the nomogram classified by race, the AUC was 0.694 (95% CI, 0.682-0.706) for the White cohort, 0.708 (95% CI, 0.673-0.743) for the Black cohort, and 0.653 (95% CI, 0.565-0.741) for the Chinese cohort.

CONCLUSION

The developed nomogram for predicting high-risk RS is available for different races in patients with HoR+/HER2- breast cancer, which could be used as qualified surrogates before ordering the 21-gene RS testing.

The Magee 3 Equation Predicts Favorable Pathologic Response to Neoadjuvant Endocrine Therapy in Breast Cancer Patients

BACKGROUND

Breast cancer (BC) remains a significant health care challenge, and treatment approaches continue to evolve. Among these, neoadjuvant endocrine therapy (NET) has gained prominence, particularly for postmenopausal, hormone-receptor positive, HER2-negative (HR+/HER2-) BC patients. Despite this, a significant gap exists in identifying patients who stand to benefit from NET. The objective of this study was to assess whether Magee equations (MEs) could serve as predictors of response to NET.

METHODS

This retrospective study included adult patients with invasive BC who underwent NET followed by curative surgery. Assessment of sociodemographic, clinical, and tumor-related variables was conducted. The ME1, ME2, ME3, and ME mean were analyzed to explore their predictive role for NET response. Receiver operating characteristic (ROC) curves were employed, along with the determination of optimal cutoff points. Logistic regression models were utilized to identify the most significant predictors of pathological response.

RESULTS

Among the 75 female participants, the mean age was 69.4 years, with the majority being postmenopausal (n = 72, 96%) and having an ECOG-PS of 0/1 (n = 63, 84%). Most patients were classified as luminal A (n = 41, 54.7%). ME3 emerged as a promising predictor, boasting an AUC of 0.734, with sensitivity of 90.62% and specificity of 57.50% when the threshold was ≤ 19.97. In univariate analysis, clinical staging (p = 0.002), molecular subtype (p = 0.001), and ME3 (continuous = 0.001, original 3-tier: p = 0.013, new 2-tier: <0.001) categories exhibited significant associations with pathological response. In the multivariate model, clinical staging and new 2-tier ME3 (<20 vs. ≥20) were included as significant variables.

CONCLUSIONS

Patients with ME3 < 20 have a higher likelihood of presenting a pathological response, offering a cost-effective alternative tool to Oncotype DX. Larger future studies with a prospective design are awaited to confirm our findings.

Adjuvant chemotherapy for pT1-3N0-1 breast cancer patients with HR+, HER2- subtype: a propensity-score matched study with competing risk analysis

OBJECTIVE

To wholly evaluate the prognostic value of CHT for pT1-3N0-1 breast cancer patients with HR+, HER2- subtype using the Surveillance, Epidemiology, and End Results (SEER) database.

METHOD

A total of 126,102 eligible cases diagnosed between January 2010 and December 2018 were included in the SEER database. A propensity-score matched (PSM) study with competing risk analysis was conducted. The Kaplan-Meier method was used to visualize the survival disparities between chemotherapy (CHT) and no CHT groups. The cumulative incidences of different subgroups were compared by Fine-Gray's test.

RESULTS

Before PSM, patients in the CHT group had worse OS and CSS (both P < 0.001). After PSM, we were surprised that patients in the CHT group had a better OS than those in the no CHT group (HR 0.74, 95% CI 0.68-0.80, P < 0.001), while no significant survival disparities were observed for CSS (HR 1.00, 95% CI 0.89-1.12, P = 0.952). In the competing risk analysis, the OS disparities between the CHT and no CHT groups were mainly attributed to deaths of other causes (subdistribution HR [95% CI] 0.50 [0.44-0.57]). After adjusting for other competitive risk events, there was no significant difference in cumulative death risk of breast cancer between the CHT and no CHT groups (subdistribution HR [95% CI] 1.01 [0.90-0.1.13]).

CONCLUSION

The present study is the first, to our knowledge, to wholly evaluate the prognostic value of CHT for pT1-3N0-1 breast cancer patients with HR+, HER2- subtype using a propensity-score matched study with competing risk analysis. All pT1-3N0-1 breast cancer patients with HR+, HER2- subtype do not benefit from CHT. Genetic testing may be the only effective tool to determine the need for CHT at the present.

Differential distribution of actual and surrogate oncotype DX recurrence scores in breast cancer patients by age, menopausal status, race, and body mass index

PURPOSE

The Oncotype DX Recurrence Score (RS) is a widely used prognostic tool for estrogen receptor-positive breast cancer patients. Multiple surrogate models can predict RS with good accuracy. In this study we aimed to determine whether the RS and two surrogate indices were differentially distributed by age, menopausal status, race, and body mass index (BMI).

METHODS

516 breast cancer cases treated at a single institution were analyzed. Epidemiologic data, RS, tumor size, grade, and biomarker data were abstracted. Breast Cancer Prognostic Score (BCPS) and modified Magee equation 2 were used to calculate surrogate RS. Patients were stratified into different groups based on age, menopausal status, race, BMI, or a combination of strata. Mean and standard deviation were calculated for each group/subgroup.

RESULTS

Age below median (< 63) was associated with higher RS, especially in obese and Black patients. RS was also higher in obese and Black patients in the premenopausal subgroup. Black patients had a higher RS compared to White women in the premenopausal and non-obese subgroups. BMI < 30 was associated with higher RS, especially in older, postmenopausal, and Black patients. Some of these observations were replicated by the two surrogate models. The surrogate recurrence scores were higher in the younger age group, in non-obese older/postmenopausal women, and in younger/premenopausal obese individuals.

CONCLUSIONS

Higher RS was observed in younger and premenopausal breast cancer patients, especially among the Black and obese subgroups, and in non-obese patients, especially among Black and older/postmenopausal women, suggesting more aggressive disease in these subgroups. Some statistical differences could be replicated by both surrogate models, suggesting that they may have utility in breast cancer epidemiology studies that do not have access to Oncotype DX RS or patient outcome data.

Applications of Artificial Intelligence in Breast Pathology

CONTEXT.—

Increasing implementation of whole slide imaging together with digital workflow and advances in computing capacity enable the use of artificial intelligence (AI) in pathology, including breast pathology. Breast pathologists often face a significant workload, with diagnosis complexity, tedious repetitive tasks, and semiquantitative evaluation of biomarkers. Recent advances in developing AI algorithms have provided promising approaches to meet the demand in breast pathology.

OBJECTIVE.—

To provide an updated review of AI in breast pathology. We examined the success and challenges of current and potential AI applications in diagnosing and grading breast carcinomas and other pathologic changes, detecting lymph node metastasis, quantifying breast cancer biomarkers, predicting prognosis and therapy response, and predicting potential molecular changes.

DATA SOURCES.—

We obtained data and information by searching and reviewing literature on AI in breast pathology from PubMed and based our own experience.

CONCLUSIONS.—

With the increasing application in breast pathology, AI not only assists in pathology diagnosis to improve accuracy and reduce pathologists' workload, but also provides new information in predicting prognosis and therapy response.

The Story of the Magee Equations: The Ultimate in Applied Immunohistochemistry

Magee equations (MEs) are a set of multivariable models that were developed to estimate the actual Onco type DX (ODX) recurrence score in invasive breast cancer. The equations were derived from standard histopathologic factors and semiquantitative immunohistochemical scores of routinely used biomarkers. The 3 equations use slightly different parameters but provide similar results. ME1 uses Nottingham score, tumor size, and semiquantitative results for estrogen receptor (ER), progesterone receptor, HER2, and Ki-67. ME2 is similar to ME1 but does not require Ki-67. ME3 includes only semiquantitative immunohistochemical expression levels for ER, progesterone receptor, HER2, and Ki-67. Several studies have validated the clinical usefulness of MEs in routine clinical practice. The new cut-off for ODX recurrence score, as reported in the Trial Assigning IndividuaLized Options for Treatment trial, necessitated the development of Magee Decision Algorithm (MDA). MEs, along with mitotic activity score can now be used algorithmically to safely forgo ODX testing. MDA can be used to triage cases for molecular testing and has the potential to save an estimated $300,000 per 100 clinical requests. Another potential use of MEs is in the neoadjuvant setting to appropriately select patients for chemotherapy. Both single and multi-institutional studies have shown that the rate of pathologic complete response (pCR) to neoadjuvant chemotherapy in ER+/HER2-negative patients can be predicted by ME3 scores. The estimated pCR rates are 0%, <5%, 14%, and 35 to 40% for ME3 score <18, 18 to 25, >25 to <31, and 31 or higher, respectively. This information is similar to or better than currently available molecular tests. MEs and MDA provide valuable information in a time-efficient manner and are available free of cost for anyone to use. The latter is certainly important for institutions in resource-poor settings but is also valuable for large institutions and integrated health systems.

Evaluation oncotype DX® 21-gene recurrence score and clinicopathological parameters: a single institutional experience

AIMS

Oncotype DX recurrence score (RS) is a clinically validated assay, which predicts the likelihood of disease recurrence in oestrogen receptor-positive/HER2-negative (ER+/HER2-) breast cancer (BC). In this study we aimed to compare the performance of Oncotype DX against the conventional clinicopathological parameters using a large BC cohort diagnosed in a single institution.

METHODS AND RESULTS

A cohort (n = 430) of ER+/HER2- BC patients who were diagnosed at the Nottingham University Hospitals NHS Trust and had Oncotype DX testing was included. Correlation with the clinicopathological and other biomarkers, including the proliferation index, was analysed. The median Oncotype DX RS was 17.5 (range = 0-69). There was a significant association between high RS and grade 3 tumours. No grade 1 BC or grade 2 tumours with mitosis score 1 showed high RS. Low RS was significantly associated with special tumour types where none of the patients with classical lobular or tubular carcinomas had a high RS. There was an inverse association between RS and levels of ER and progesterone receptor (PR) expression and a positive linear correlation with Ki67 labelling index. Notably, six patients who developed recurrence had an intermediate RS; however, four of these six cases (67%) were identified as high-risk disease when the conventional clinical and molecular parameters were considered.

CONCLUSION

Oncotype DX RS is correlated strongly with the conventional clinicopathological parameters in BC. Some tumour features such as tumour grade, type, PR status and Ki67 index can be used as surrogate markers in certain scenarios.

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.

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.

The Evolution of Ki-67 and Breast Carcinoma: Past Observations, Present Directions, and Future Considerations

The 1983 discovery of a mouse monoclonal antibody-the Ki-67 antibody-that recognized a nuclear antigen present only in proliferating cells represented a seminal discovery for the pathologic assessment of cellular proliferation in breast cancer and other solid tumors. Cellular proliferation is a central determinant of prognosis and response to cytotoxic chemotherapy in patients with breast cancer, and since the discovery of the Ki-67 antibody, Ki-67 has evolved as an important biomarker with both prognostic and predictive potential in breast cancer. Although there is universal recognition among the international guideline recommendations of the value of Ki-67 in breast cancer, recommendations for the actual use of Ki-67 assays in the prognostic and predictive evaluation of breast cancer remain mixed, primarily due to the lack of assay standardization and inconsistent inter-observer and inter-laboratory reproducibility. The treatment of high-risk ER-positive/human epidermal growth factor receptor-2 (HER2) negative breast cancer with the recently FDA-approved drug abemaciclib relies on a quantitative assessment of Ki-67 expression in the treatment decision algorithm. This further reinforces the urgent need for standardization of Ki-67 antibody selection and staining interpretation, which will hopefully lead to multidisciplinary consensus on the use of Ki-67 as a prognostic and predictive marker in breast cancer. The goals of this review are to highlight the historical evolution of Ki-67 in breast cancer, summarize the present literature on Ki-67 in breast cancer, and discuss the evolving literature on the use of Ki-67 as a companion diagnostic biomarker in breast cancer, with consideration for the necessary changes required across pathology practices to help increase the reliability and widespread adoption of Ki-67 as a prognostic and predictive marker for breast cancer in clinical practice.

Establishment and validation of an ultrasound-based nomogram with risk stratification for short disease-free survival in breast cancer

PURPOSE

This retrospective study aimed to develop and validate an Ultrasound (US)-based nomogram to predict short disease-free survival (short-DFS, less than 120 months DFS) in breast cancer (BC).

METHODS

Nomogram was established based on a training data of 311 BC patients by multivariable logistic regression, and were assessed by discrimination, calibration, and clinical usefulness. Risk stratification was performed by X-tile. An independent testing data of 200 patients with BC was used for external validation.

RESULTS

Nine predictors including three US features and six clinical parameters were screened into the nomogram by Lasso (log λ = -3.594) in training data. Better performance was obtained in the training data (C-index: 0.942) and testing data (C-index: 0.914). Calibration analysis indicated optimal agreement between nomogram predictions and actual observations (p = 0.67). Decision curve analysis showed a great clinical benefit (Youden index: 0.634). Three risk levels are low-risk (<184.0), moderate-risk (184.0-345.3) and high-risk (>345.3). Our nomograms had larger area under the receiver operating characteristic (ROC) curves compared with Magee Equation and Nottingham Prognostic models (0.942 vs. 0.824, 0.790).

CONCLUSION

The US-based nomogram and the practical score system facilitate individualized prediction of short-DFS to optimize clinical decisions and improve prognosis in patients with BC.

Use of a supervised machine learning model to predict Oncotype DX risk category in node-positive patients older than 50 years of age

PURPOSE

The use of the Oncotype DX recurrence score (RS) to predict chemotherapy benefit in patients with hormone receptor-positive/HER2 negative (HR+/HER2-) breast cancer has recently expanded to include postmenopausal patients with N1 disease. RS availability is limited in resource-poor settings, however, prompting the development of statistical models that predict RS using clinicopathologic features. We sought to assess the performance of our supervised machine learning model in a cohort of patients > 50 years of age with N1 disease.

METHODS

We identified patients > 50 years of age with pT1-2N1 HR+/HER2- breast cancer and applied the statistical model previously developed in a node-negative cohort, which uses age, pathologic tumor size, histology, progesterone receptor expression, lymphovascular invasion, and tumor grade to predict RS. We measured the model's ability to predict RS risk category (low: RS ≤ 25; high: RS > 25).

RESULTS

Our cohort included 401 patients, 60.6% of whom had macrometastases, with a median of 1 positive node. The majority of patients had a low-risk observed RS (85.8%). For predicting RS category, the model had specificity of 97.3%, sensitivity of 31.8%, a negative predictive value of 87.9%, and a positive predictive value of 70.0%.

CONCLUSION

Our model, developed in a cohort of node-negative patients, was highly specific for identifying cN1 patients > 50 years of age with a low RS who could safely avoid chemotherapy. The use of this model for identifying patients in whom genomic testing is unnecessary would help decrease the cost burden in resource-poor settings as reliance on RS for adjuvant treatment recommendations increases.

Predictive Value of Ultrasound Characteristics for Disease-Free Survival in Breast Cancer

The main objective of this study was to determine the predictive value of US characteristics for disease-free survival (DFS) in BC patients. We retrospectively analyzed the ultrasonic images and clinical data of BC patients who had previously undergone breast surgery at least 10 years before study enrollment and divided them into a case group and a control group according to the cutoff value of 120 months for DFS. Correlation analysis was performed to identify US characteristics as independent predictors for DFS by multivariable logistic regression and Kaplan−Meier survival analysis. A total of 374 patients were collected, including 174 patients in the case group with short-DFS and 200 patients in the control group with long-DFS. Three US characteristics (size on US, mass shape, mass growth orientation) and two clinical factors (axillary lymph node (ALN), molecular subtypes) were identified as independent predictors for DFS (p < 0.05). The ROC curve showed good performance of the multivariate linear regression model with the area under the curve being 0.777. The US characteristics of large size, irregular shape, and nonparallel orientation were significantly associated with short-DFS, which is a promising supplementary for clinicians to optimize clinical decisions and improve prognosis in BC patients.

Pathobiologic Stratification of Oncotype DX Recurrence Scores and Comparative Validation of 3 Surrogate Models

CONTEXT.—

The Oncotype DX Recurrence Score (RS) predicts recurrence and chemotherapy benefit in early-stage estrogen receptor positive breast cancer patients. Cost and unavailability are 2 major disadvantages of the assay. Multiple models have been developed to predict the RS.

OBJECTIVE.—

To predict RS based on histopathologic and biomarker features, and to measure concordance and correlation with RS of the following 3 algorithms: breast cancer prognostic score, Magee0, and Magee2.

DESIGN.—

Breast cancer cases with available RS were reviewed (n = 442). RS categories were stratified by pathologic and biomarker variables. Histopathologic and biomarker data were abstracted from pathology reports, and RS was calculated by each model. Correlation and concordance between models and RS were calculated.

RESULTS.—

Less than 5% of breast cancers with lobular features, low-grade tumors, carcinomas with high progesterone receptor content, or luminal A tumors had an RS greater than 25. Breast cancer prognostic score, Magee0, and Magee2 demonstrated correlation coefficients with RS of 0.63, 0.61, and 0.62, respectively. Two-step discordances were uncommon. When an RS of 25 was used to separate high-risk from non-high-risk cases, concordance rates of 86% to 88% were achieved.

CONCLUSIONS.—

High RS was observed only in a small percentage of pure or mixed lobular carcinomas, low-grade or luminal A tumors, and tumors with high progesterone receptor expression, suggesting that these cancers may not require Oncotype testing. All 3 surrogate models demonstrated comparable correlation and high concordance with the RS when a cutoff of 25 was used, suggesting their utility in cases where the actual RS is unavailable.

Supervised machine learning model to predict oncotype DX risk category in patients over age 50

PURPOSE

Routine use of the oncotype DX recurrence score (RS) in patients with early-stage, estrogen receptor-positive, HER2-negative (ER+/HER2-) breast cancer is limited internationally by cost and availability. We created a supervised machine learning model using clinicopathologic variables to predict RS risk category in patients aged over 50 years.

METHODS

From January 2012 to December 2018, we identified patients aged over 50 years with T1-2, ER+/HER2-, node-negative tumors. Clinicopathologic data and RS results were randomly split into training and validation cohorts. A random forest model with 500 trees was developed on the training cohort, using age, pathologic tumor size, histology, progesterone receptor (PR) expression, lymphovascular invasion (LVI), and grade as predictors. We predicted risk category (low: RS ≤ 25, high: RS > 25) using the validation cohort.

RESULTS

Of the 3880 tumors identified, 1293 tumors comprised the validation cohort in patients of median (IQR) age 62 years (56-68) with median (IQR) tumor size 1.2 cm (0.8-1.7). Most tumors were invasive ductal (80.3%) of low-intermediate grade (80.5%) without LVI (80.9%). PR expression was ≤ 20% in 27.3% of tumors. Specificity for identifying RS ≤ 25 was 96.3% (95% CI 95.0-97.4) and the negative predictive value was 92.9% (95% CI 91.2-94.4). Sensitivity and positive predictive value for predicting RS > 25 was lower (48.3 and 65.1%, respectively).

CONCLUSION

Our model was highly specific for identifying eligible patients aged over 50 years for whom chemotherapy can be omitted. Following external validation, it may be used to triage patients for RS testing, if predicted to be high risk, in resource-limited settings.

Correlation of manual semi-quantitative and automated quantitative Ki-67 proliferative index with OncotypeDXTM recurrence score in invasive breast carcinoma

BACKGROUND

Ki-67 immunohistochemistry (IHC) staining is a widely used cancer proliferation assay; however, its limitations could be improved with automated scoring. The OncotypeDXTM Recurrence Score (ORS), which primarily evaluates cancer proliferation genes, is a prognostic indicator for breast cancer chemotherapy response; however, it is more expensive and slower than Ki-67.

OBJECTIVE

To compare manual Ki-67 (mKi-67) with automated Ki-67 (aKi-67) algorithm results based on manually selected Ki-67 "hot spots" in breast cancer, and correlate both with ORS.

METHODS

105 invasive breast carcinoma cases from 100 patients at our institution (2011-2013) with available ORS were evaluated. Concordance was assessed via Cohen's Kappa (κ).

RESULTS

57/105 cases showed agreement between mKi-67 and aKi-67 (κ 0.31, 95% CI 0.18-0.45), with 41 cases overestimated by aKi-67. Concordance was higher when estimated on the same image (κ 0.53, 95% CI 0.37-0.69). Concordance between mKi-67 score and ORS was fair (κ 0.27, 95% CI 0.11-0.42), and concordance between aKi-67 and ORS was poor (κ 0.10, 95% CI -0.03-0.23).

CONCLUSIONS

These results highlight the limits of Ki-67 algorithms that use manual "hot spot" selection. Due to suboptimal concordance, Ki-67 is likely most useful as a complement to, rather than a surrogate for ORS, regardless of scoring method.

Concordance between results of inexpensive statistical models and multigene signatures in patients with ER+/HER2- early breast cancer

Multigene signatures (MGS) are used to guide adjuvant chemotherapy (aCT) decisions in patients diagnosed with estrogen receptor (ER)-positive HER2-negative early breast cancer. We used results from three MGS (Oncotype DX® (ODX), MammaPrint® (MP) or Prosigna®) and assessed the concordance between high or low risk of recurrence and the predicted risk of recurrence based on statistical models. In addition, we looked at the impact of MGS results on final aCT administration during the multidisciplinary meeting (MDM). We retrospectively included 129 patients with ER-positive HER2-negative early breast cancer for which MGS testing was performed after MDM at University Hospitals Leuven between May 2013 and April 2019 in case there was doubt about aCT recommendation. Tumor tissue was analyzed either by ODX (N = 44), MP (N = 28), or Prosigna® (N = 57). Eight statistical models were computed: Magee equations (ME), Memorial Sloan Kettering simplified risk score (MSK-SRS), Breast Cancer Recurrence Score Estimator (BCRSE), OncotypeDXCalculator (ODXC), new Adjuvant! Online (nAOL), Mymammaprint.com (MyMP), PREDICT, and SiNK. Concordance, negative percent agreement, and positive percent agreement were calculated. Of 129 cases, 53% were MGS low and 47% MGS high risk. Concordances of 100.0% were observed between risk results obtained by ODX and ME. For MP, BCRSE demonstrated the best concordance, and for Prosigna® the average of ME. Concordances of <50.0% were observed between risk results obtained by ODX and nAOL, ODX and MyMP, ODX and SiNK, MP and MSK-SRS, MP and nAOL, MP and MyMP, MP and SiNK, and Prosigna® and ODXC. Integration of MGS results during MDM resulted in change of aCT recommendation in 47% of patients and a 15% relative and 9% absolute reduction. In conclusion, statistical models, especially ME and BCRSE, can be useful in selecting ER-positive HER2-negative early breast cancer patients who may need MGS testing resulting in enhanced cost-effectiveness and reduced delay in therapeutic decision-making.

Developing a clinical-pathologic model to predict genomic risk of recurrence in patients with hormone receptor positive, human epidermal growth factor receptor-2 negative, node negative breast cancer

INTRODUCTION

Patients with hormone receptor (HR)-positive, human epidermal growth factor receptor-2 (HER2)-negative, node negative (NN) breast cancer may be offered a gene expression profiling (GEP) test to determine recurrence risk and benefit of adjuvant chemotherapy. We developed a clinical-pathologic (CP) model to predict genomic recurrence risk and examined its performance characteristics.

METHODS

Patients diagnosed with HR-positive, HER2-negative, NN breast cancer with a tumour size < 30 mm and who underwent a GEP test [OncotypeDX or Prosigna] in Alberta from October 2017 through March 2019 were identified. Patients were classified as low or high genomic risk. Multivariable logistic regression analysis was performed to examine the associations of CP factors with genomic risk. A CP model was developed using coefficients of regression and sensitivity analyses were performed.

RESULTS

A total of 366 patients were eligible (135 were tested using OncotypeDX and 231 with Prosigna). Of these, 64 (17.5%) patients were classified as high genomic risk. On multivariable logistic regression, tumour size > 20 mm (odds ratio [OR], 3.58; 95% confidence interval [CI], 1.84-6.98; P<0.001), low expression of progesterone receptor (OR, 3.46; 95% CI, 1.76-6.82; P<0.001), and histological grade III (OR, 7.24; 95% CI, 3.82-13.70; P<0.001) predicted high genomic risk. A CP model using these variables was developed to provide a score of 0-4. A CP cut-point of 0, identified 56% of genomic low risk patients with a specificity of 98.4%.

CONCLUSIONS

A CP model could be used to narrow the population of breast cancer patients undergoing GEP testing.

Magee Equations™ and response to neoadjuvant chemotherapy in ER+/HER2-negative breast cancer: a multi-institutional study

Magee Equations™ (ME) are multivariable models that can estimate oncotype DX^® recurrence score. One of the equations, Magee Equation 3 (ME3) which utilizes only semi-quantitative receptor results has been shown to provide chemopredictive value in the neoadjuvant setting in a single institutional study. This multi-institutional study (seven institutions contributed cases) was undertaken to examine the validity of ME3 in predicting response to neoadjuvant chemotherapy in estrogen receptor positive, HER2-negative breast cancers. Stage IV cases were excluded. The primary endpoint was the pathologic complete response (pCR) rate in different categories of ME3 scores calculated based on receptor results in the pre-therapy core biopsy. A total of 166 cases met the inclusion criteria. The patient age ranged from 24 to 83 years (median 53 years). The average pre-therapy tumor size was 3.9 cm, and axillary lymph nodes were confirmed positive by pre-therapy core biopsy in 85 of 166 cases (51%). The pCR rate according to ME3 scores was 0% (0 of 64) in ME3 < 18, 0% (0 of 46) in ME3 18-25, 14% (3 of 21) in ME3 > 25 to <31, and 40% (14 of 35) in ME3 score 31 or higher (p value: <0.0001). There were no distant recurrences and no deaths in the 17 patients with pCR. In the remaining 149 cases with residual disease, ME3 score of >25 was significantly associated with shorter distant recurrence-free survival and showed a trend for shorter breast cancer-specific survival. The results of this multi-institutional study are similar to previously published data from a single institution (PMID: 28548119) and confirm the chemo-predictive value of ME3 in the neoadjuvant setting. In addition, ME3 may provide prognostic information in patients with residual disease which should be further evaluated.

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.

The Influence of Histologic Grade on Outcomes of Elderly Women With Early Stage Breast Cancer Treated With Breast Conserving Surgery With or Without Radiotherapy

BACKGROUND

Two large randomized trials, CALGB 9343 and PRIME II, support omission of radiotherapy after breast conserving surgery (BCS) in elderly women with favorable-risk early stage breast cancer intending to take endocrine therapy. However, patients with grade 3 histology were underrepresented on these trials. We hypothesized that high-grade disease may be unsuitable for treatment de-escalation and report the oncologic outcomes for elderly women with favorable early stage breast cancer treated with BCS with or without radiotherapy.

MATERIALS AND METHODS

The Surveillance, Epidemiology, and End Results database was queried for women between 70 and 79 years of age with invasive ductal carcinoma diagnosed between 1998 and 2007. This cohort was narrowed to women with T1mic-T1c, N0, estrogen receptor-positive, invasive ductal carcinoma treated with BCS with or without external beam radiation (EBRT). The primary endpoints were 5- and 10-year cause-specific survival (CSS). Univariate and multivariate analyses were performed. Propensity-score matching of T-stage, year of diagnosis, and age was utilized to reduce selection bias while comparing treatment arms within the grade 3 subgroup.

RESULTS

A total of 12,036 women met inclusion criteria, and the median follow-up was 9.4 years. EBRT was omitted in 22% of patients, including 21% with grade 3 disease. Patients in the EBRT cohort were slightly younger (median, 74 vs. 75 years; P < .01) and had fewer T1a tumors (11% vs. 13%; P = .02). Histologic grades 1, 2, and 3 comprised 36%, 50%, and 14% of the cohort, respectively, and there were no differences in EBRT utilization by grade. Utilization of EBRT decreased following the publication of the CALGB trial in 2004 decreasing from 82% to 85% in 1998 to 2000 to 73% to 75% in 2005 to 2007 (P < .01). Unadjusted outcomes showed that in grade 1 disease, there were no differences in CSS with or without EBRT at 5 (99%) and 10 years (95%-96%). EBRT was associated with an improvement in CSS in grade 2 histology at 5 years (97% vs. 98%) and 10 years (92% vs. 95%) (P = .004). The benefit was more pronounced in grade 3 disease with CSS increasing from 93% to 96% at 5 years and from 87% to 92% at 10 years (P = .02) with EBRT. In the grade 3 subgroup, propensity-score matching confirmed EBRT was associated with superior CSS compared with surgery alone (hazard ratio, 0.58; 95% confidence interval, 0.34-0.98; P = .043).

CONCLUSION

In this database analysis, omission of radiotherapy after BCS in elderly women with favorable-risk, early stage, grade 3 breast cancer was associated with inferior CSS. Further prospective data in this patient population are needed to confirm our findings and conclusions.

Cost analysis of using Magee scores as a surrogate of Oncotype DX for adjuvant treatment decisions in women with early breast cancer

Breast cancer is the most common cancer in women worldwide. Most current guidelines recommend using multigene profiling assays to aid the decision on the addition of chemotherapy to adjuvant hormone therapy for women who present with early-stage, hormone receptor-positive, HER2-negative disease. One of these assays is the Oncotype DX, which predicts the disease recurrence risk and adjuvant chemotherapy benefits. Given its high cost, there is an economic incentive to evaluate its surrogates, such as the Magee equations. We assessed health system costs associated with the use of the Magee scores. A probabilistic decision tree was used to calculate the difference in mean health system costs based on data obtained from a randomized trial and the published literature. Costs were calculated from a perspective of Canada's publicly funded health care system. A series of sensitivity analysis was conducted to assess the robustness of the study findings. The Magee equations were associated with a total cost savings of C$100 per patient (95% CI, -C$3068 to C$5022) compared with standard of care. The difference in costs was highly sensitive to the extent that the Magee scores could reduce the frequency of adjuvant chemotherapy and Oncotype DX requests.

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.

Estrogen and Progesterone Receptor Testing in Breast Cancer: American Society of Clinical Oncology/College of American Pathologists Guideline Update

PURPOSE.—

To update key recommendations of the American Society of Clinical Oncology/College of American Pathologists estrogen receptor (ER) and progesterone receptor (PgR) testing in breast cancer guideline.

METHODS.—

A multidisciplinary international Expert Panel was convened to update the clinical practice guideline recommendations informed by a systematic review of the medical literature.

RECOMMENDATIONS.—

The Expert Panel continues to recommend ER testing of invasive breast cancers by validated immunohistochemistry as the standard for predicting which patients may benefit from endocrine therapy, and no other assays are recommended for this purpose. Breast cancer samples with 1% to 100% of tumor nuclei positive should be interpreted as ER positive. However, the Expert Panel acknowledges that there are limited data on endocrine therapy benefit for cancers with 1% to 10% of cells staining ER positive. Samples with these results should be reported using a new reporting category, ER Low Positive, with a recommended comment. A sample is considered ER negative if < 1% or 0% of tumor cell nuclei are immunoreactive. Additional strategies recommended to promote optimal performance, interpretation, and reporting of cases with an initial low to no ER staining result include establishing a laboratory-specific standard operating procedure describing additional steps used by the laboratory to confirm/adjudicate results. The status of controls should be reported for cases with 0% to 10% staining. Similar principles apply to PgR testing, which is used primarily for prognostic purposes in the setting of an ER-positive cancer. Testing of ductal carcinoma in situ (DCIS) for ER is recommended to determine potential benefit of endocrine therapies to reduce risk of future breast cancer, while testing DCIS for PgR is considered optional. Additional information can be found at www.asco.org/breast-cancer-guidelines .

The Evaluation of Magee Equation 2 in Predicting Response and Outcome in Hormone Receptor-Positive and HER2-Negative Breast Cancer Patients Receiving Neoadjuvant Chemotherapy

Background and Purpose

Magee Equations have been developed as accurate tools for predicting response and clinical outcomes in breast cancer patients treated with adjuvant systemic therapy using basic clinicopathological parameters. This study aims to evaluate the alternative application of Magee Equation 2 score in predicting pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in hormone receptor (HR)-positive, HER2-negative breast cancer.

Patients and Methods

Patients with HR-positive, HER2-negative breast cancer who received NAC from January 2010 to May 2018 at Siriraj Hospital, Mahidol University, Thailand, were recruited. Pre-treatment status of HR and HER2 was used to calculate the Magee Equation 2 scores. The pCR rates among different clinicopathological parameters were analyzed. Survival analysis was performed by Log-rank test. Kaplan-Meier survival curves were analyzed.

Results

A total of 215 patients were eligible. The pCR rates for low, intermediate, and high scores were 4.8%, 3.6%, and 23.8%, respectively. Patients with high scores had significantly higher size reduction and pCR rates compared to those with intermediate or low scores (p<0.001). Those with high scores had higher rates of locoregional recurrence and death. The patients with high score had significantly lower overall survival (p=0.034).

Conclusion

Among patients with HR-positive and HER2-negative breast cancer treated with NAC, Magee Equation 2 might be used as a tool for predicting the pCR and clinical outcome.

Estrogen and Progesterone Receptor Testing in Breast Cancer: ASCO/CAP Guideline Update

PURPOSE

To update key recommendations of the American Society of Clinical Oncology/College of American Pathologists estrogen (ER) and progesterone receptor (PgR) testing in breast cancer guideline.

METHODS

A multidisciplinary international Expert Panel was convened to update the clinical practice guideline recommendations informed by a systematic review of the medical literature.

RECOMMENDATIONS

The Expert Panel continues to recommend ER testing of invasive breast cancers by validated immunohistochemistry as the standard for predicting which patients may benefit from endocrine therapy, and no other assays are recommended for this purpose. Breast cancer samples with 1% to 100% of tumor nuclei positive should be interpreted as ER positive. However, the Expert Panel acknowledges that there are limited data on endocrine therapy benefit for cancers with 1% to 10% of cells staining ER positive. Samples with these results should be reported using a new reporting category, ER Low Positive, with a recommended comment. A sample is considered ER negative if < 1% or 0% of tumor cell nuclei are immunoreactive. Additional strategies recommended to promote optimal performance, interpretation, and reporting of cases with an initial low to no ER staining result include establishing a laboratory-specific standard operating procedure describing additional steps used by the laboratory to confirm/adjudicate results. The status of controls should be reported for cases with 0% to 10% staining. Similar principles apply to PgR testing, which is used primarily for prognostic purposes in the setting of an ER-positive cancer. Testing of ductal carcinoma in situ (DCIS) for ER is recommended to determine potential benefit of endocrine therapies to reduce risk of future breast cancer, while testing DCIS for PgR is considered optional. Additional information can be found at www.asco.org/breast-cancer-guidelines.

Ki-67 index, progesterone receptor expression, histologic grade and tumor size in predicting breast cancer recurrence risk: A consecutive cohort study

Background

The 21‐gene recurrence score (RS) assay has been recommended by major guidelines for treatment decision in hormone receptor (HR)‐positive early breast cancer (EBC). However, the genomic assay is not accessible and affordable worldwide. Alternatively, an increasing number of studies have shown that traditional immunohistochemistry (IHC) can partially or even completely replace the role of the 21‐gene genomic assay. Here, we developed and validated a predictive model (IHC3 model) combining the Ki‐67 index, progesterone receptor (PR) expression, histologic grade, and tumor size to predict the recurrence risk of HR‐positive EBC.

Methods

The data from 389 patients (development set) with HR‐positive, human epidermal growth factor receptor 2‐negative, lymph node non‐metastasized invasive breast cancer were used to construct the IHC3 model based on the Surexam^® 21‐gene RS and the TAILORx clinical trial criteria. An additional 146 patients with the same characteristics constituted the validation set. The predictive accuracy of the IHC3 model was compared with that of Orucevic et al.’s nomogram. Invasive disease‐free survival (IDFS) was analyzed in the IHC3 predictive low‐recurrence risk (pLR) group and the predictive high‐recurrence risk (pHR) group. The Pearson chi‐square test, Fisher exact test, and log‐rank test were used for analysis.

Results

The pLR and pHR group could be easily stratified using the decision tree model without network dependence. The accuracies of the IHC3 model were 86.1% in the development set and 87.7% in the validation set. The predictive accuracy of the IHC3 model and Orucevic et al.’s nomogram for the whole cohort was 86.5% and 86.9%, respectively. After a 52‐month of median follow‐up, a significant difference was found in IDFS between of the IHC3 pLR and the pHR groups (P = 0.001) but not in the IDFS between the low‐ and high‐recurrence risk groups according to the Surexam® 21‐gene RS and the TAILORx clinical trial criteria (P = 0.556) or 21‐gene binary RS group (P = 0.511).

Conclusions

The proposed IHC3 model could reliably predict low and high recurrence risks in most HR‐positive EBC patients. This easy‐to‐use predictive model may be a reliable replacement for the 21‐gene genomic assay in patients with EBC who have no access to or cannot afford the 21‐gene genomic assay.

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.

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.

Impact of Genomic Assay Testing and Clinical Factors on Chemotherapy Use After Implementation of Standardized Testing Criteria

BACKGROUND

For clinically appropriate early-stage breast cancer patients, reflex criteria for Oncotype DX ordering ("the intervention") were implemented at our comprehensive cancer center, which reduced time-to-adjuvant chemotherapy initiation. Our objective was to evaluate Oncotype DX ordering practices and chemotherapy use before and after implementation of the intervention.

MATERIALS AND METHODS

We examined medical records for 498 patients who had definitive breast cancer surgery at our center. The post-intervention cohort consisted of 232 consecutive patients who had Oncotype DX testing after reflex criteria implementation. This group was compared to a retrospective cohort of 266 patients who were diagnosed and treated prior to reflex criteria implementation, including patients who did and did not have Oncotype DX ordered. Factors associated with Oncotype DX ordering pre- and post-intervention were examined. We used multivariate logistic regression to evaluate factors associated with chemotherapy receipt among patients with Oncotype DX testing.

RESULTS

The distribution of Oncotype DX scores, the proportion of those having Oncotype DX testing (28.9% vs. 34.1%) and those receiving chemotherapy (14.3% vs. 19.4%), did not significantly change between pre- and post-intervention groups. Age ≤65 years, stage II, grade 2, 1-3+ nodes, and tumor size >2 cm were associated with higher odds of Oncotype DX testing. Among patients having Oncotype DX testing, node status and Oncotype DX scores were significantly associated with chemotherapy receipt.

CONCLUSION

Our criteria for reflex Oncotype DX ordering appropriately targeted patients for whom Oncotype DX would typically be ordered by providers. No significant change in the rate of Oncotype DX ordering or chemotherapy use was observed after reflex testing implementation.

IMPLICATIONS FOR PRACTICE

This study demonstrates that implementing multidisciplinary consensus reflex criteria for Oncotype DX ordering maintains a stable Oncotype DX ordering rate and chemotherapy rate, mirroring what was observed in a specific clinical practice, while decreasing treatment delays due to additional testing. These reflex criteria appropriately capture patients who would likely have had Oncotype DX ordered by their providers and for whom the test results are predicted to influence management. This intervention serves as a potential model for other large integrated, multidisciplinary oncology centers to institute processes targeting patient populations most likely to benefit from genomic assay testing, while mitigating treatment delays.

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.

A predictive model for high/low risk group according to oncotype DX recurrence score using machine learning

BACKGROUND

Oncotype DX(ODX) is a 21-gene breast cancer recurrence score(RS) assay that aids in decision-making for chemotherapy in early-stage hormone receptor-positive(HR+)breast cancer. We developed a prediction tool using machine learning for high- or low-risk ODX criteria (i.e., RS < 11 for low-risk; RS > 25 for high-risk).

METHODS

We performed a retrospective review of 301 breast cancer patients who underwent surgery between April 2011 and July 2017 and then an ODX test at Samsung Medical Center in Seoul, Korea. Among them, 208 cases were defined as the modeling group and 76 cases were defined as the validation group. We built a supervised machine learning classification model using the Azure ML platform.

RESULTS

For the high RS group, accuracy was 0.903 through Two-class Decision Jungle method in test set. For the low RS group, the accuracy was 0.726 when the Two-class Neural Network method was applied. The AUC of the ROC curve was 0.917 in the high RS group and 0.744 in the low RS group in test set. In addition, we conducted an internal validation using 76 patients who underwent ODX testing between January 2017 and July 2017. The accuracy of validation was 0.880 in the high RS group and 0.790 in the low RS group.

CONCLUSION

We developed a predictive model using machine learning that could represent a useful and easy-to-access tool for the selection of high ODX RS patients. After additional evaluation with large data and external validation, worldwide use of our model could be expected.

Ancillary Prognostic and Predictive Testing in Breast Cancer: Focus on Discordant, Unusual, and Borderline Results

Ancillary testing in breast cancer has become standard of care to determine what therapies may be most effective for individual patients with breast cancer. Single-marker tests are required on all newly diagnosed and newly metastatic breast cancers. Markers of proliferation are also used, and include both single-marker tests like Ki67 as well as panel-based gene expression tests, which have made more recent contributions to prognostic and predictive testing in breast cancers. This review focuses on pathologist interpretation of these ancillary test results, with a focus on expected versus unexpected results and troubleshooting borderline, unusual, or discordant results.

Breast cancer histopathology is predictive of low-risk Oncotype Dx recurrence score

BACKGROUND

Oncotype Dx is a genetic test that has been incorporated into the 2017 AJCC breast cancer staging system for ER positive, HER2-negative, lymph node-negative patients to predict the risk of recurrence. Recent data suggest that immunohistochemistry (ER, PR, HER2, and Ki-67) and histologic subtype may identify patients that will not benefit from Oncotype Dx testing.

METHODS

A total of 371 patients underwent Oncotype Dx testing at our institution from 2012 to 2016. Oncotype recurrence score was categorized as low- (ORS = 0-10), intermediate- (11-25), or high risk (26-100). Invasive carcinomas were categorized based on histologic subtype as "favorable" (mucinous, tubular, cribriform, tubulolobular, and lobular) and "unfavorable" (ductal, mixed ductal and lobular, and micropapillary carcinoma). All cases were estrogen receptor positive and HER2-negative. Clinical and histologic predictors of low-risk ORS were assessed in univariate and multivariate logistic regression.

RESULTS

A total of 371 patients were categorized by ORS as low risk (n = 85, 22.9%), intermediate risk (n = 244, 65.8%), and high risk (n = 42, 11.3%). The histologic subtypes with the highest percentage of high-risk ORS were invasive micropapillary (n = 4/17, 23.5%), pleomorphic lobular (n = 2/10, 20%), and ductal carcinoma (n = 28/235, 11.9%). Low-grade invasive carcinomas with favorable histology rarely had a high-risk ORS (n = 1/97, 1%). In a simple multivariable model, favorable histologic subtype (OR = 2.39, 95% CI: 1.10 to 5.15, P = 0.026), and histologic grade (OR = 1.76, 95% CI: 1.07 to 2.90, P = 0.025) were the only significant predictors of an ORS less than 11 in estrogen receptor positive, HER2-negative, and lymph node-negative patients.

CONCLUSION

We question the utility of performing Oncotype Dx in subtypes of invasive carcinoma that are associated with excellent prognosis. We propose that immunohistochemistry for ER, PR, and HER2 is sufficient for patients with low-grade invasive carcinomas and can be used as a surrogate for Oncotype Dx.

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.

Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer

Background

Gene-expression companion diagnostic tests, such as the Oncotype DX test, assess the risk of early stage Estrogen receptor (ER) positive (+) breast cancers, and guide clinicians in the decision of whether or not to use chemotherapy. However, these tests are typically expensive, time consuming, and tissue-destructive.

Methods

In this paper, we evaluate the ability of computer-extracted nuclear morphology features from routine hematoxylin and eosin (H&E) stained images of 178 early stage ER+ breast cancer patients to predict corresponding risk categories derived using the Oncotype DX test. A total of 216 features corresponding to the nuclear shape and architecture categories from each of the pathologic images were extracted and four feature selection schemes: Ranksum, Principal Component Analysis with Variable Importance on Projection (PCA-VIP), Maximum-Relevance, Minimum Redundancy Mutual Information Difference (MRMR MID), and Maximum-Relevance, Minimum Redundancy - Mutual Information Quotient (MRMR MIQ), were employed to identify the most discriminating features. These features were employed to train 4 machine learning classifiers: Random Forest, Neural Network, Support Vector Machine, and Linear Discriminant Analysis, via 3-fold cross validation.

Results

The four sets of risk categories, and the top Area Under the receiver operating characteristic Curve (AUC) machine classifier performances were: 1) Low ODx and Low mBR grade vs. High ODx and High mBR grade (Low-Low vs. High-High) (AUC = 0.83), 2) Low ODx vs. High ODx (AUC = 0.72), 3) Low ODx vs. Intermediate and High ODx (AUC = 0.58), and 4) Low and Intermediate ODx vs. High ODx (AUC = 0.65). Trained models were tested independent validation set of 53 cases which comprised of Low and High ODx risk, and demonstrated per-patient accuracies ranging from 75 to 86%.

Conclusion

Our results suggest that computerized image analysis of digitized H&E pathology images of early stage ER+ breast cancer might be able predict the corresponding Oncotype DX risk categories.

Electronic supplementary material

The online version of this article (10.1186/s12885-018-4448-9) contains supplementary material, which is available to authorized users.

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.

Will oncotype DX DCIS testing guide therapy? A single-institution correlation of oncotype DX DCIS results with histopathologic findings and clinical management decisions

Given the increased detection rates of ductal carcinoma in situ (DCIS) and the limited overall survival benefit from adjuvant breast irradiation after breast-conserving surgery, there is interest in identifying subsets of patients who have low rates of ipsilateral breast tumor recurrence such that they might safely forgo radiation. The Oncotype DCIS score is a reverse transcription-PCR (RT-PCR)-based assay that was validated to predict which DCIS cases are most likely to recur. Clinically, these results may be used to assist in selecting which patients with DCIS might safely forgo radiation therapy after breast-conserving surgery; however, little is currently published on how this test is being used in practice. Our study examines traditional histopathologic features used in predicting DCIS risk with Oncotype DCIS results and how these results affect clinical decision-making at our academic institution. Histopathologic features and management decisions for 37 cases with Oncotype DCIS results over the past 4 years were collected. Necrosis, high nuclear grade, biopsy site change, estrogen receptor and progesterone receptor positivity <90% on immunohistochemistry, and Van Nuys Prognostic Index score of 8 or greater were significant predictors of an intermediate-high recurrence score on multivariate regression analysis (P<0.02). Low Oncotype DCIS scores and low nuclear grade were associated with lower rate of radiation therapy (P<0.008). There were seven cases (19%) with Oncotype DCIS results that we considered unexpected in relation to the histopathologic findings (ie, high nuclear grade with comedonecrosis and a low Oncotype score, or hormone receptor discrepancies). Overall, pathologic features correlate with Oncotype DCIS scores but unexpected results do occur, making individual recommendations sometimes challenging.

Delay to Formalin Fixation (Cold Ischemia Time) Effect on Breast Cancer Molecules

OBJECTIVES

The gold standard of examining breast biomarkers, including estrogen receptor (ER)/progesterone receptor (PR)/human epidermal growth factor receptor 2 (HER2)/Ki-67, is to perform these assays on formalin-fixed, paraffin-embedded tissue. However, preanalytical variables may confound these assays. One of these factors is delay to formalin fixation (DFF). The purpose of this review is to evaluate each study that investigated the effect of DFF on breast biomarkers and other molecules.

METHODS

Thirteen primary research articles were identified by the literature search. The credibility of the studies was judged based on the degree of controlling other confounding factors. Nine studies had a prospective design with a high number of controlled variables.

RESULTS

Most of the studies concluded that DFF had an effect on ER/PR/HER2. Some of these studies showed that DFF had negative effect on other markers used either clinically or for research purposes.

CONCLUSIONS

The vast majority of the studies agree that DFF has negative effect on breast biomarkers.

The use of automated Ki67 analysis to predict Oncotype DX risk-of-recurrence categories in early-stage breast cancer

Ki67 is a commonly used marker of cancer cell proliferation, and has significant prognostic value in breast cancer. In spite of its clinical importance, assessment of Ki67 remains a challenge, as current manual scoring methods have high inter- and intra-user variability. A major reason for this variability is selection bias, in that different observers will score different regions of the same tumor. Here, we developed an automated Ki67 scoring method that eliminates selection bias, by using whole-slide analysis to identify and score the tumor regions with the highest proliferative rates. The Ki67 indices calculated using this method were highly concordant with manual scoring by a pathologist (Pearson’s r = 0.909) and between users (Pearson’s r = 0.984). We assessed the clinical validity of this method by scoring Ki67 from 328 whole-slide sections of resected early-stage, hormone receptor-positive, human epidermal growth factor receptor 2-negative breast cancer. All patients had Oncotype DX testing performed (Genomic Health) and available Recurrence Scores. High Ki67 indices correlated significantly with several clinico-pathological correlates, including higher tumor grade (1 versus 3, P<0.001), higher mitotic score (1 versus 3, P<0.001), and lower Allred scores for estrogen and progesterone receptors (P = 0.002, 0.008). High Ki67 indices were also significantly correlated with higher Oncotype DX risk-of-recurrence group (low versus high, P<0.001). Ki67 index was the major contributor to a machine learning model which, when trained solely on clinico-pathological data and Ki67 scores, identified Oncotype DX high- and low-risk patients with 97% accuracy, 98% sensitivity and 80% specificity. Automated scoring of Ki67 can thus successfully address issues of consistency, reproducibility and accuracy, in a manner that integrates readily into the workflow of a pathology laboratory. Furthermore, automated Ki67 scores contribute significantly to models that predict risk of recurrence in breast cancer.