Relationship between IHC4 score and response to neo-adjuvant chemotherapy in estrogen receptor-positive breast cancer

Conventional Tools for Predicting Satisfactory Response to Neoadjuvant Chemotherapy in HR+/HER2- Breast Cancer Patients

Aim

The aim of the study was to assess the role of Magee Equation 3 (MagEq3), IHC4 score, and HER2-low status in predicting "satisfactory response (SR)" to neoadjuvant chemotherapy (NAC) in HR+/HER2- breast cancer (BC) patients.

Methods

In a retrospective study, female patients of any age with T1-4, N0-2, M0 HR+/HER2- BC who received NAC and underwent adequate locoregional surgical treatment were included. Patients were grouped according to 2 outcomes: (a) overall response to NAC in breast and axilla by using residual cancer burden (RCB) criteria and (b) axillary downstaging after NAC by using N staging. 2 cohorts for overall response were overall SR (RCB 0-1) and no SR (RCB 2-3). On the other hand, for axillary downstaging, 2 cohorts constituted from axillary SR (ypN0 and ypN0i+) and no SR (ypNmic-N3). MagEq3 and IHC4 scores were calculated from their pathological tumor slides in each patient. HER2 status was categorized as either "no" or "low." In addition, patient age, family history, tumor histology, stage at admission, and Ki-67 status were compared between cohorts according to predefined outcomes.

Results

In a total of 230 BC patients, 228 patients were included to compare according to their RCB levels. The mean age of patients with overall SR was significantly lower than those without. Patients with high Ki-67 expression, high (>30) MagEq3 score, high ICH4 quartile, and HER2-low status had significantly more overall SR. On the other hand, only patients with high Ki-67 expression had significantly more axillary SR. MagEq3 score levels, ICH4 quartiles, and HER2 status were similar between patients with axillary SR and not.

Conclusion

MagEq3 and IHC4 tools seemed to be useful to predict those HR+/HER2- BC patients who are most likely to get benefit from NAC. But, only high Ki-67 expression level significantly predicted satisfactory axillary downstaging in HR+/HER2- BC patients.

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.

Development of a nomogram for predicting pathological complete response in luminal breast cancer patients following neoadjuvant chemotherapy

Background

Given the low chance of response to neoadjuvant chemotherapy (NACT) in luminal breast cancer (LBC), the identification of predictive factors of pathological complete response (pCR) represents a challenge. A multicenter retrospective analysis was performed to develop and validate a predictive nomogram for pCR, based on pre-treatment clinicopathological features.

Methods

Clinicopathological data from stage I-III LBC patients undergone NACT and surgery were retrospectively collected. Descriptive statistics was adopted. A multivariate model was used to identify independent predictors of pCR. The obtained log-odds ratios (ORs) were adopted to derive weighting factors for the predictive nomogram. The receiver operating characteristic analysis was applied to determine the nomogram accuracy. The model was internally and externally validated.

Results

In the training set, data from 539 patients were gathered: pCR rate was 11.3% [95% confidence interval (CI): 8.6-13.9] (luminal A-like: 5.3%, 95% CI: 1.5-9.1, and luminal B-like: 13.1%, 95% CI: 9.8-13.4). The optimal Ki67 cutoff to predict pCR was 44% (area under the curve (AUC): 0.69; p < 0.001). Clinical stage I-II (OR: 3.67, 95% CI: 1.75-7.71, p = 0.001), Ki67 ⩾44% (OR: 3.00, 95% CI: 1.59-5.65, p = 0.001), and progesterone receptor (PR) <1% (OR: 2.49, 95% CI: 1.15-5.38, p = 0.019) were independent predictors of pCR, with high replication rates at internal validation (100%, 98%, and 87%, respectively). According to the nomogram, the probability of pCR ranged from 3.4% for clinical stage III, PR > 1%, and Ki67 <44% to 53.3% for clinical stage I-II, PR < 1%, and Ki67 ⩾44% (accuracy: AUC, 0.73; p < 0.0001). In the validation set (248 patients), the predictive performance of the model was confirmed (AUC: 0.7; p < 0.0001).

Conclusion

The combination of commonly available clinicopathological pre-NACT factors allows to develop a nomogram which appears to reliably predict pCR in LBC.

Prediction of pathologic complete response to neoadjuvant chemotherapy using machine learning models in patients with breast cancer

BACKGROUND

The aim of this study was to develop a machine learning (ML) based model to accurately predict pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) using pretreatment clinical and pathological characteristics of electronic medical record (EMR) data in breast cancer (BC).

METHODS

The EMR data from patients diagnosed with early and locally advanced BC and who received NAC followed by curative surgery were reviewed. A total of 16 clinical and pathological characteristics was selected to develop ML model. We practiced six ML models using default settings for multivariate analysis with extracted variables.

RESULTS

In total, 2065 patients were included in this analysis. Overall, 30.6% (n = 632) of patients achieved pCR. Among six ML models, the LightGBM had the highest area under the curve (AUC) for pCR prediction. After hyper-parameter tuning with Bayesian optimization, AUC was 0.810. Performance of pCR prediction models in different histology-based subtypes was compared. The AUC was highest in HR+HER2- subgroup and lowest in HR-/HER2- subgroup (HR+/HER2- 0.841, HR+/HER2+ 0.716, HR-/HER2 0.753, HR-/HER2- 0.653).

CONCLUSIONS

A ML based pCR prediction model using pre-treatment clinical and pathological characteristics provided useful information to predict pCR during NAC. This prediction model would help to determine treatment strategy in patients with BC planned NAC.

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.

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 .

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.

Prediction model of the response to neoadjuvant chemotherapy in breast cancers by a Naive Bayes algorithm

BACKGROUND AND OBJECTIVE

Chemotherapy is useful to many breast cancer patients, however, it is not therapeutic for some patients. Pathologic complete response (pCR) is an indicator to good response in Neoadjuvant chemotherapy (NAC). In this study, we aimed to develop a way to predict pCR before NAC.

METHODS

We retrospectively collected 287 stage II-III breast cancer cases either to a training set (N = 197) or to a test set (N = 90). Fourteen candidate genes were selected from four public microarray data sets. A prediction model was built, by using these fourteen candidate genes and three reference genes expression which were tested by TaqMan probe-based quantitative polymerase chain reaction, after selecting a better algorithm.

RESULTS

The Naive Bayes algorithm had a relatively higher predictive value, compared with random forest, support vector machine (SVM), and k-nearest neighbor (knn) algorithms (P < 0.05). This 17-gene prediction model showed a high positive correlation with pCR (odds ratio, 8.914, 95% confidence interval, 4.430-17.934, P < 0.001). By using this model, the enrolled patients were classified into sensitive (SE) and insensitive (INS) groups. The pCR rates between the SE and INS groups were highly different (42.3% vs.7.6%, P < 0.001). The sensitivity and specificity of this prediction model were 84.5% and 62.0%.

CONCLUSIONS

Instead of whole transcriptome-based technologies, panel gene expression with tens of essential genes implemented in a machine learning model has predictive potential for chemosensitivity in breast cancers.

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.

Low Estrogen Receptor (ER)-Positive Breast Cancer and Neoadjuvant Systemic Chemotherapy: Is Response Similar to Typical ER-Positive or ER-Negative Disease?

OBJECTIVES

Pathologic complete response (pCR) rate after neoadjuvant chemotherapy was compared between 141 estrogen receptor (ER)-negative (43%), 41 low ER+ (13%), 47 moderate ER+ (14%), and 98 high ER+ (30%) tumors.

METHODS

Human epidermal growth factor receptor 2-positive cases, cases without semiquantitative ER score, and patients treated with neoadjuvant endocrine therapy alone were excluded.

RESULTS

The pCR rate of low ER+ tumors was similar to the pCR rate of ER- tumors (37% and 26% for low ER and ER- respectively, P = .1722) but significantly different from the pCR rate of moderately ER+ (11%, P = .0049) and high ER+ tumors (4%, P < .0001). Patients with pCR had an excellent prognosis regardless of the ER status. In patients with residual disease (no pCR), the recurrence and death rate were higher in ER- and low ER+ cases compared with moderate and high ER+ cases.

CONCLUSIONS

Low ER+ breast cancers are biologically similar to ER- tumors. Semiquantitative ER H-score is an important determinant of response to neoadjuvant chemotherapy.