Elsevier

Clinical Breast Cancer

Volume 20, Issue 5, October 2020, Pages e600-e611
Clinical Breast Cancer

Original Study
Oncotype DX Predictive Nomogram for Recurrence Score Output: The Novel System ADAPTED01 Based on Quantitative Immunochemistry Analysis

https://doi.org/10.1016/j.clbc.2020.04.012Get rights and content

Abstract

Purpose

Oncotype DX (ODX) predicts breast cancer recurrence risk, guiding the choice of adjuvant treatment. In many countries, access to the test is not always available. We used correlation between phenotypical tumor characteristics, quantitative classical immunohistochemistry (IHC), and recurrence score (RS) assessed by ODX to develop a decision supporting system for clinical use.

Patients and Methods

Breast cancer patients who underwent ODX testing between 2014 and 2018 were retrospectively included in the study. The data selected for analysis were age, menopausal status, and pathologic and IHC features. IHC was performed with standardized quantitative methods. The data set was split into two subsets: 70% for the training set and 30% for the internal validation set. Statistically significant features were included in logistic models to predict RS ≤ 25 or ≤ 20. Another set was used for external validation to test reproducibility of prediction models.

Results

The internal set included 407 patients. Mean (range) age was 53.7 (31-80) years, and 222 patients (54.55%) were > 50 years old. ODX results showed 67 patients (16.6%) had RS between 0 and 10, 272 patients between 11 and 25 (66.8%), and 68 patients > 26 (16.6%). Logistic regression analysis showed that RS score (for threshold ≤ 25) was significantly associated with estrogen receptor (P = .004), progesterone receptor (P < .0001), and Ki-67 (P < .0001). Generalized linear regression resulted in a model that had an area under the receiver operating characteristic curve (AUC) of 92.2 (sensitivity 84.2%, specificity 80.1%) and that was well calibrated. The external validation set (183 patients) analysis confirmed the model performance, with an AUC of 82.3 and a positive predictive value of 91%. A nomogram was generated for further prospective evaluation to predict RS ≤ 25.

Conclusion

RS was related to quantitative IHC in patients with RS ≤ 25, with a good performance of the statistical model in both internal and external validation. A nomogram for enhancing clinical approach in a cost-effective manner was developed. Prospective studies must test this application in clinical practice.

Introduction

Breast cancer is a heterogeneous disease, and the choice of adjuvant treatments is based on specific recurrence risk of relapse due to phenotypical and pathologic tumor characteristics.1, 2, 3 Luminal subtype (estrogen receptor [ER] positive, human epidermal growth factor receptor 2 [HER2] negative) presents itself a variable risk of relapse, so it is difficult for clinicians to escalate or de-escalate therapies in these patients. In clinical practice, most common biomarkers used to define treatments are currently clinicopathologic and immunochemistry features.4 However, while using these tumor characteristics, it is not possible to address patients to optimized, tailored therapies.

Since the 2000s, microarray DNA analysis has improved our knowledge of breast cancer, identifying subtypes corresponding to different clinical outcomes.2,3 On the basis of microarray analysis, further simplified genomic studies permitted instruments to be developed and validated to support clinical decision making.2,3,5, 6, 7 Further analyses developed simplified tests that were based on fewer gene parameters for clinical use. Oncotype DX (ODX) is based on a system of 21-gene reverse transcription (RT) PCR analysis and is correlated retrospectively with prognosis; recent prospective randomized studies have been performed on a node-negative ER+HER2− series of patients.7, 8, 9, 10 Thanks to ODX, adjuvant systemic therapies for early breast cancer have been implemented as a result of identification of patients at a higher risk of cancer who undergo chemotherapy and endocrine therapy, while patients without a significative change in prognosis are not addressed to chemotherapy, thus reducing health costs and sparing patients adverse events.

To date, ODX is the only test included in the definition of adjuvant treatments in the criteria used for classifying risk of relapse (tumor, node, metastasis classification system; American Joint Committee on Cancer staging). In the National Comprehensive Cancer Network guidelines, ODX is preferred for prediction and prognosis, with a category of evidence and consensus of 1.11 In many studies of cost-effectiveness, ODX has been deemed advantageous with regard to standard care as a result of gain in terms of quality-adjusted life-year per year.12,13 However, in many health systems, patients themselves pay for ODX. Of course sometimes patients cannot bear this cost, so they must undergo chemotherapy with no further genotypical data available.

In this study, we retrospectively collected clinicopathologic and immunohistochemical (IHC) features of breast cancer of patients who underwent the ODX test. From these data, which are commonly available in clinical practice, we developed a machine learning model to predict the recurrence score (RS) of ODX. This model led us to create a decision supporting system (DSS), which gives as output a prediction of RS ≤ 25, which in turn addresses requests for the ODX test for cost optimization in systems that cannot afford this health care spending. We called the DSS we created ADAPTED01, and it is freely available online (https://kbolab-tools.shinyapps.io/ADAPTED01/).

Section snippets

Patient Selection

In our analysis, we included all patients who underwent ODX in Fondazione Policlinico Agostino Gemelli IRCCS after surgery for invasive breast cancer. Inclusion criteria were multidisciplinary team indication for the test, ER+HER2 subtype, invasive breast cancer, stage pT1/T2 with pN0-1 disease, and no neoadjuvant systemic therapies provided before surgery. Retrospectively collected clinical data were sex, age at diagnosis, fertility status, clinical staging, and surgery procedure description.

Epidemiologic Results

For the internal data set, 407 patients were included and underwent ODX analysis. Mean (range) age was 53.7 (31-80) years, and 222 patients (54.55%) were > 50 years old. At diagnosis, patients’ menopausal status was as follows: 160 premenopausal (39.3%), 44 perimenopausal (10.8%), 199 postmenopausal (48.9%), and 4 not available—male patients (1%). Tumor characteristics of the internal set are provided in Table 1. ODX results showed the following: 68 patients (16.7%) had scores between 0 and 10,

Rationale and ADAPTED01 Development

ODX is an RT-PCR DNA genomic test that has permitted implementing breast cancer adjuvant treatments, thereby avoiding chemotherapy and its adverse effects, to patients with a low RS.7,19 In 2018, Sparano et al9 published the 10-year results of the TailorX trial, showing that a midrange RS of 11 to 25 presents the same efficacy from adjuvant endocrine therapy and chemoendocrine therapy (hazard ratio, 1.08; 95% confidence interval, 0.94-1.25; P = .26) for women aged > 50 years. The patients in

Conclusion

Quantitative IHC presented a good correlation with RS score in patients with RS ≤ 25 in both the internal and external validation sets. A TRIPOD 3 nomogram for physicians that enhances good and cost-effective clinical practice has been developed with a high positive predictive value that was confirmed in an external validation set, with possibility of 9% to have false positive. Prospective validation of this nomogram in clinical practice could allow resource savings by avoiding unnecessary

Disclosure

The authors have stated that they have no conflict of interest.

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      Histologic grade and tumor subtype in nomograms for RS ≥ 16 and PR status and Ki-67 for RS ≥ 26 were common, regardless of SWE parameters. This finding agrees with previously reported nomograms for RS showing histologic grade, PR status, and Ki-67 as the most common predictors [24,26–30]. Regarding the performance of nomograms, all nomograms in our study except for the Eratio model for RS ≥ 16 were well calibrated and discriminative with AUROC of 0.74 for RS ≥ 16 in two models and 0.81 to 0.86 for RS ≥ 26 in three models [31].

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