A Nomogram for Predicting Mammaprint Results in Women with T1-3N0-1M0 Hormone Receptor-Positive and Human Epidermal Growth Factor Receptor-2-Negative Breast Cancer

Results

Baseline characteristics

Data from patients (n = 409) whose tumor tissues were analyzed using the MMP test were used to develop the nomogram for predicting patients with a low risk of MMP. A detailed comparison of the clinical characteristics of all patients based on the MMP results is shown in Table 1. The average age at diagnosis was 53.3 ± 9.3 years in the MMP low-risk group vs. 47.9 ± 9.8 years in the MMP high-risk group (p < 0.001) and the average tumor size was 2.2 ± 1.1 cm vs. 2.4 ± 1.0 cm, p = 0.018. In the MMP high-risk group, a significantly higher rate of histological and nuclear grade and a higher Ki-67 level (≥ 20%), all p < 0.001, were observed. With respect to the receptor status, most of the patients had a strong Allred score (7–8) of ER. In the MMP high-risk group, 6 patients (3.6%) had an intermediate ER score (5‒6) compared with 2 (0.8%), in the low-risk group. None of the patients had a negative (0‒2) or weak positive (3‒4) ER score. MMP low-risk group had a better strong PR status (7‒8) rate (68.8%) compared with the MMP low-risk group (53.3%) and a lower rate of negative or weak PR status, p < 0.001. No difference was seen in the surgical methods. More T2 tumors in the high-risk group (46.2% vs. 58.7%, p = 0.045) without any significant difference in the pathological N stage and final stage were observed. There was no significant difference in the number of positive nodes, p = 0.942; however, MMP high-risk group had a slightly larger size of positive nodes (5.4 ± 3.9 mm vs. 6.3 ± 4.8 mm, p = 0.042). More women in the high-risk group were premenopausal (66.9%, p < 0.001).

Development of model predicting MMP results

We randomized the data into two groups of random sizes of about 312 patients at a ratio of 7:3 to develop nomograms. Detailed clinical characteristics of patients included in the training cohort and internal validation cohort are shown in Table 2. There were no significant differences in the clinical characteristics between these two cohorts. In multivariate analysis, patient age at diagnosis, nuclear grade, PR, and Ki-67 were all found to be independent predictors of MMP low risk in this population. The odds ratio and coefficient associated with four significant factors in the multivariate model are shown in Table 3. A strong PR status was a positive effect, and a higher nuclear grade, younger age, and a higher Ki-67 were negative effects in the MMP low-risk group. The significant variables of the explanatory model were used to develop a nomogram to predict low risk of MMP (Fig. 1). Values lower than 0.1 and higher or equal to 0.9 were not displayed. The total scores for individual patients ranged from 81 to 189. The overall predictive accuracy of the nomogram was measured based on the AUC, which was 0.82 (95% confidence interval [CI], 0.77 to 0.87) for the training dataset of 312 patients, and 0.77 (95% CI, 0.68 to 0.86) for the internal validation dataset of 97 patients (Fig. 2).

Sensitivity, specificity and positive and negative predictive values

Table 4 shows the sensitivity, specificity, and positive and negative predictive values according to each cutoff value. Each probability shown in the table represents the cutoff value of different probabilities of obtaining a low-risk MMP results; for example, when we apply a probability of 90% as a cutoff for binary risk results, it means that the nomogram results higher than 90% will have a higher chance of being associated with actual MMP low risk; the specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV) are shown. A positive predictive value refers to the ability to predict MMP low risk without MMP testing. Based on our results, use of a low cutoff value strongly predicted the actual MMP high s risk, with 0.90 (38/42) PPV and 98% sensitivity. With a high cutoff value of 90%, we predicted an actual low-risk MMP result with an NPV of 0.89 (4/34) and a 98% specificity. The specificity rate was also determined to investigate the clinical utility of the nomogram.

User-friendly calculator

The results obtained from our analysis were used to develop a user-friendly calculator using Microsoft Excel worksheets (Fig. 3). The interface allows the user to input the values of age at diagnosis, PR Allred score (0‒8), Ki-67 level as a percentage, and nuclear grade (1‒3). The standard output includes an estimate of the probability of detecting low MMP risk when actually tested. Our algorithm seems to help clinicians in identifying patients with a higher chance of getting a low risk or high risk MMP test and for whom gene testing accessibility is low.

Discussion

MMP is the one of the most widely used genomic assays for breast cancer testing in the world, especially for patients with one to three positive nodes. The recent National Comprehensive Cancer Network (NCCN) guideline recommends clinicians to consider MMP testing for decision making regarding adjuvant systemic chemotherapy with evidence of category 1, for patients with ER/PR positive, HER2 negative, and with negative or positive (1‒3) node metastasis breast cancer [8]. Although according to interim analysis of RxPONDER trial, the guideline recommends Oncotype DX for same subset of patients, however the interpretation of the results in premenopausal patients in complex. [9] The use of genomic signatures is recommended for this subset of patients by national and international clinical guidelines, i.e., St. Gallen Consensus Conference, European Society for Medical Oncology (ESMO), and American Society of Clinical Oncology [13–15]. However the economic burden on patients makes clinicians hesitate to recommend genomic testing. Although reports on the cost effectiveness of MMP show that MMP safely guided chemotherapy de-escalation in clinical high-risk patients with HR+/HER2- tumors compared (compared to clinical assessment alone) [16], the cost of MMP in South Korea (approximately $3,200) can still be a burden for patients. As this test originally designed in foreign countries, the South Korea National Health Insurance does not cover the test, only a few private health insurance companies covers MMP. Therefore, MMP imposes an economic burden in South Korea, regardless of whether patients have private insurance or not. This study was performed to evaluate MMP risk assessment, which was based on routine standard patient and tumor characteristics. Similar prediction models have been developed using clinicopathological data [17] or radiological phenotype results [18]. However, the best way to link these results with clinical practice has not yet been identified. We found that using this prediction tool with simple four combined clinicopathological factors can aid decision-making and serve as an alternative to the costly MMP test for patients by predicting high- or low-risk MMP results.

Four clinicopathological variables were used in our model, age, nuclear grade, PR, and Ki-67. The values for the latter three variables can be easily determined through examination in any pathological laboratory in health care centers and were used to predict prognosis before the gene testing era. Here, the Allred score of ER was not included in the final model because most of the patients had high (7‒8) ER scores with variation in the PR status only. This implies that clinicians have a tendency to perform fewer genomic tests to decide on adjuvant chemotherapy administration for patients with weak to intermediate ER scores (3‒6)[19, 20]. A higher ER status is related to a higher endocrine response and a lower chemotherapy response [21]. In contrast, a low ER status is known to be associated with a low chemotherapy response, which is similar to negative ER tumors, compared with strong ER-positive tumors in neoadjuvant settings [22]. PR status can also be used to predict the endocrine and chemotherapy responses. By analyzing 77 invasive breast cancers and their PR status and 21-gene testing recurrence score results, a strong negative correlation between both factors was revealed [20].

Another important clinical factor according to our model was the age at diagnosis. A negative association was observed between younger patients and a high nomogram score. The chemotherapy benefit for invasive disease-free survival varied when the recurrence score was combined with age (p = 0.004), with some chemotherapy benefits found in women < 50 years with a recurrence score of 16–25 [5]. According to a recent update on the long-term results of MINDACT trial (EORTC 10041/BIG3-04) presented at the American society of clinical oncology annual meeting in 2020, there is an absolute 5% ± 2.8% distant metastasis free survival gain with adjuvant chemotherapy in premenopausal women with a discordant clinical and genomic risk (clinical high risk/genomic low risk) [11]. The authors suggested that this result is due to chemotherapy-induced ovarian function suppression. In a recent phase III trial of 1,483 premenopausal women with ER-positive breast cancer with neoadjuvant or adjuvant chemotherapy, patients who recovered their ovarian function after chemotherapy showed a better overall survival upon adding ovarian function suppression with tamoxifen (compared with the tamoxifen-alone group) [23]. According to our prediction model, age was the second most powerful predictor of MMP risk results. Younger patients had a higher probability of receiving MMP high-risk results. These results should be interpreted with caution as even the same genomic scores can differ in their long-term outcome based on age. The recent analysis of MINDACT trial revealed that there was no significant difference in the long-term outcome between clinical-low/genomic-low risk and clinical-low/genomic-high risk groups. When applied to our model, if the clinicopathological indicators other than age are positively correlated with a low risk, endocrine and ovarian function suppression would be a better option than MMP or chemotherapy.

In our previous study on a prediction model for Oncotype DX recurrence score [12], Ki-67 was most strongly related to the Recurrence Score. The role of Ki-67 as an indicator of poor prognosis in the Oncotype Dx gene assay is well-known [24–27]. Similarly, a current study also revealed that a higher Ki-67 level was closely associated with MMP high risk. Ki-67 itself can be a strong prognostic index; however, recent analyses on intermediate Ki-67 and MMP results showed that for the patients with a low Ki-67 (< 15%) or a high Ki-67 (> 30%), the risk results of the MMP test mostly agreed with the Ki-67 level, while for the patients with an intermediate Ki-67 value (15‒30%), they were discordant with the MMP risk result [28]. It is expected that with a definitely high Ki-67, clinicians would be reluctant to forego adjuvant chemotherapy or make a decision after MMP testing. When we looked up our data closely, in 175 Ki-67 high (≥ 20%) patients, 135 patients (77.1%) had an intermediate Ki-67 level (20‒40) and only 40 patients (22.8%) had a Ki-67 level > 40. Our prediction model would give clinicians a better decision guide for a low to intermediate Ki-67 level, combined with the other factors. Grade has also been long regarded as a prognostic indicator of breast cancer outcome [29] by Nottingham Prognostic Index, whose association with genomic assays is proven [30].

This study has some strength. The majority of the patients were (94.8%) node positive, with up to three lymph nodes. The final results of the lymph-node-positive population with an Oncotype DX recurrence score (RxPONDER) [31] are pending, MMP is the only recommended genomic assay, in accordance with evidence category 1 in NCCN guideline regardless of menopausal status. We also analyzed age, which plays a key role in interpreting two major genomic tests according to recent publications [5, 11], making the decision to de-escalate treatment by adding ovarian suppression to conventional endocrine therapy—instead of chemotherapy—making it much easier for clinicians who hesitate to test premenopausal patients who can hardly afford the genomic testing. The MINDACT trial for MMP mainly comprised Caucasians [32], requiring the acquisition of more data by doing a retrospective analysis on Asian populations. Our nomogram can provide additional information to clinicians in Asian countries who are planning to use genomic assays to de-escalate or escalate adjuvant therapy. We also offer a user-friendly interfaced calculator with simple four robust factors which can be widely used by clinicians. This study has also some limitations. Due to its retrospective nature, our study might have selection bias with respect to the nature of the primary population, i.e., patients with a low MMP risk. However, this is also a strength, as this population reflects a subset about which clinicians ponder for performing MMP testing. Also, there is a concern that reproducibility of Ki-67 level due to variability of the assay which has not been validated by the St. Gallen guidelines. Because a prediction nomogram will never produce the same result as a genomic test, we suggest that the purpose of using this nomogram should be to decide on whether to perform genomic test for intermediate risk patients who cannot afford the associated medical expense. Further, the study endpoint was set based on the result of MMP, not the subsequent outcome of the patient. we recommend that clinicians interpret and apply the nomogram results carefully after sufficient agreement with patients.

In conclusion, our nomogram, which predicts a low-risk MMP result, will be a useful tool to help select patients with HR+/HER2- and node-positive tumors who may or may not need additional MMP testing, especially with intermediate clinicopathological characteristics. It may also be a useful replacement for MMP testing in cases where genomic testing can be costly or when MMP testing is not available.

Declarations

Acknowledgement

We would like to thank Editage (www.editage.co.kr) for English language editing.

Authors' contributions 

Conception or design of the work: Young Joo Lee, Junetae Kim and Sae Byul Lee

Data collection: Young Joo Lee, Sei-Hyun Ahn, Byung Ho Son, Hee Jeong Kim, Beom Seok Ko, Jisun Kim, Il Yong Chung, Jong Won Lee and Sae Byul Lee

Data analysis and interpretation: Young Joo Lee, Young Sol Hwang, Junetae Kim and Sae Byul Lee

Drafting the article: Young Joo Lee and Sae Byul Lee

Critical revision of the article: Sae Byul Lee

Final approval of the version to be published: All authors

Funding

This study was supported by a grant (grant number 2020-0037) from the Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea.

Conflicts of interest/Competing interests 

The authors declare that there is no conflict of interest.

Ethics approval 

The project was reviewed and approved by the Asan Medical Center institutional review board (2020-0037). Due to the retrospective nature of the study, the requirement for informed consent was waived.

Consent to participate 

Not applicable

Consent for publication

Not applicable

Availability of data and material 

Data archiving is not mandated but data will be made available on reasonable request

Code availability 

Not applicable

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