Development and validation of a novel HER2RI assay for predicting the risk of recurrence and survival in HER2-positive early breast cancer

doi:10.21203/rs.3.rs-1964372/v1

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Development and validation of a novel HER2RI assay for predicting the risk of recurrence and survival in HER2-positive early breast cancer

https://doi.org/10.21203/rs.3.rs-1964372/v1

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De-escalated or escalated systemic therapy remains to be a controversial topic for HER2-positive early breast cancer. Here we presented a novel assay for predicting the relapse risk and survival in HER2-positive early breast cancer. The prognostic models based on the 20-gene signature (HER2RI model) and 20-gene signature combined with nodal stage (HER2RIclin model) were built using random forest, respectively. Totally 254 patients were used for training the models, and two independent cohorts for validating the accuracy. The HER2RI presented a better predictive accuracy than the HER2RIclin. Compared with high-risk patients, the low-risk patients stratified by the HER2RI showed a better 7-year relapse-free survival (RFS) rate. Multivariate analysis further confirmed the high risk stratified by the HER2RI was a significant risk factor for RFS. Overall, the HER2RI assay is predictive of the relapse risk in HER2-positive early breast cancer and can help the patient risk stratification to aid the treatment decisions.

HER2-positive breast cancer

Gene expression profiling

Risk of relapse

Relapse-free survival

Adjuvant treatment

As the leading cause of death in women worldwide, breast cancer is a highly heterogeneous disease with significant differences in treatment and prognosis across subtypes, and even within the same subtype, the prognosis and treatment benefits may vary greatly^1–4. At time of the initial diagnosis, human epidermal growth factor receptor 2 (HER2) is found to amplify and/or overexpress in 15%-20% of early breast cancer and is related to an aggressive clinical course⁵. Although new therapies have improved the prognosis of HER2-positive breast cancer in recent years, there still exists a significant proportion of morbidity and mortality.

Most HER2-positive early breast cancers are curable with chemotherapy and trastuzumab alone⁴. Given the cardiotoxicity of trastuzumab, there appears studies on the feasibility of shortening the duration of trastuzumab, most of which fail to prove non-inferiority^6–9, except for the PERSEPHONE¹⁰. For low-risk patients with HER2-positive disease, chemotherapy cycles can be reduced to minimize the incidence of adverse effects¹¹. There is a study suggesting that combining trastuzumab with pertuzumab could reduce the risk of relapse in patients with HER2-positive early breast cancer, with the most significant benefit in node-positive patients¹². Extended adjuvant therapy with 1-year neratinib after chemotherapy and trastuzumab significantly decreased the proportion of clinically relevant breast cancer recurrence¹³. Currently, the decision to escalate or de-escalate systemic therapy for HER2-positive early breast cancer is based primarily on traditional clinical factors, such as tumor size, nodal status, and hormone receptor (HR) expression. The percentage of stromal tumor-infiltrating lymphocytes (TILs) is emerging as a key prognostic and predictive biomarker for HER2-positive breast cancer¹⁴. However, all these factors are insufficient to reflect the molecular complexity and heterogeneity of HER2-positive breast cancer.

Over the past 20 years, gene expression profiling has become an important prognostic indicator for breast cancer, illustrating the heterogeneity of breast cancer at the molecular level. Common multigene expression signatures, such as Oncotype Dx^15,16, MammaPrint^17,18 and RecurIndex^19,20, can be used to assess the risk of recurrence in HR-positive, HER2-negative, node-negative/positive, early-stage breast cancers and to help assess the need for adjuvant chemotherapy/radiotherapy. Although a novel HER2DX assay capable of predicting the likelihood of achieving pathological complete response (pCR) and long-term prognosis in HER2-positive early breast cancer is developed²¹, the gene expression profiling tools for HER2-positive early breast cancer are still inadequate. For aiding the treatment decisions, we developed a novel prognostic assay for predicting the risk of recurrence and long-term survival in HER2-positive early breast cancer, and meanwhile two independent clinal cohorts were used for external validation. We discovered that this prognostic assay performed well in prediction of the recurrence risk and long-term survival in early HER2-positive breast cancer.

Study populations. In this study, 254 patients with HER2-positive breast cancer who were treated surgically at Cancer Hospital, Chinese Academy of Medical Sciences (CHCAMS) from June 2010 and September 2016 were collected as the training cohort; 125 HER2-positive patients from Cancer Hospital of the University of Chinese Academy of Sciences (validation cohort I) and 24 HER2-positive patients from Amwise database (validation cohort II) were both selected as external validation cohorts. The flow chart of patient selection and external validation is shown in Fig. 1.

Patient inclusion criteria: female aged 18–75 years; patients with the first diagnosis of pT1-2N0-1M0 primary invasive breast cancer who underwent breast-conserving surgery or mastectomy; completion of standard chemotherapy and 1 year of trastuzumab treatment after surgery; complete information (clinicopathological and follow-up data) and adequate formalin-fixed and paraffin-embedded (FFPE) tumor samples. Patients receiving preoperative neoadjuvant therapy such as chemotherapy, endocrine therapy, targeted therapy, and those with a history of other malignancies were excluded. Written consent forms were obtained from each patient included in the study. The study was approved by the ethics committee of CHCAMS and was conducted following local regulations and the Declaration of Helsinki.

Tumor sample procedures. Ribonucleic acid (RNA) was extracted from FFPE breast cancer samples. The expression profiles of 20 breast cancer-associated genes and 3 housekeeping genes (ACTB, RPLP0 and TFRC) were measured on the PanelStation platform using real-time fluorescent quantitative nucleic acid amplification technology with a minimum requirement of 800 ng of total RNA. The expression level of target genes was independently normalized to 3 housekeeping genes. Recurrence risk scores were calculated using analysis software based on gene expression profiles and clinical factors for breast cancer.

Model development and validation. We identified a 20-gene panel based on a previous study using the random forest (RF)²², including OBSL1, BUB1B, CCR1, STIL, BLM, ESR1, PTI1, DTX2, PIM1, RCHY1, DDX39, CKAP5, MKI67, CLCA2, SF3B5, PHACTR2, TPX2, PGR, ERBB2, and YWHAB. The function of 20-gene predictors associated with recurrence and survival in HER2-positive early breast cancer is summarized in Supplementary Table 1. The models based on the 20-gene signature alone and the 20-gene signature combined with nodal stage were named as HER2RI model and HER2RIclin model, respectively. The best-fit model was selected using the RF function in R package. In the beginning, we presented 500 trees in the RF to look for the best combination of parameters and determine the number of trees in the prediction model based on the minimum of Out of Bag (OOB) error rate. After the parameter tuning, 100 trees and 50 trees were fixed and developed to HER2RI and HER2RIclin, respectively. In addition, we estimated the feature importance with the OOB permutation during the model fitting to calculate the contribution of the predictors included. Receivers operating characteristic (ROC) curves were used to assess the area under the curve (AUC). Both models classified the patients as high and low risk based on the optimal cut-off determined by ROC, and then analyzed the entire population.

Data collection and follow-up. Patient demographics and tumor characteristics were collected by reviewing hospital electronic medical records, including age, tumor stage, nodal stage, histological grade, expression of estrogen receptor (ER), progesterone receptor (PR) and HER2, absence or presence of lymphovascular invasion (LVI), anti-HER2 therapy, adjuvant chemotherapy and recurrence, as well as survival status. All patients were followed up postoperatively by telephone, with a final follow-up cut-off date of August 2020. In the training dataset, the survival endpoint was recurrence-free survival (RFS), defined as the time between treatment and any recurrence of the ipsilateral chest, breast, regional lymph node recurrence and distant metastases, or death occurred.

Statistical analysis. Kaplan-Meier method was used to calculate 7-year estimates of RFS which were compared by Log-rank test. Univariate and multivariate Cox proportional hazards models examined the relationship between each variable and survival outcomes. The normality test was used to determine whether the data conformed to normal distribution. Normally distributed continuous variables were expressed as mean (standard deviation, SD) and compared by Student’s t-test, while abnormally distributed variables were expressed as median and interquartile [M (Q1, Q3)], and compared by Mann-Whitney U rank sum test. Categorical variables were described as frequencies (percentages) and compared using the χ² test or Fisher exact test. ROC curves were adopted as a performance test for models and evaluated the optimal cut-off point. Significant differences were shown at p < 0.05. All statistical analyses were performed using R version 4.0.2.

Patient characteristics. Between June 2010 and September 2016, there were 320 pT1-2N0-1M0 patients with HER2-positive breast cancer who underwent breast-conserving surgery or mastectomy in the training dataset. After excluding 6 cases of no ≥3 years of follow-up data, 18 cases with insufficient tumor proportion in H&E section and 42 cases with insufficient RNA/failure to pass quality control, 254 patients aged (49.3±9.6) years were finally eligible for the study to construct the prognostic model. As shown in Fig. 1, two independent clinical cohorts were used for external validation.

Among the patients enrolled in the training dataset, 52.8% were assessed for grade II, 52.8% for T2 and 62.6% for N0; 73.2% of patients had no LVI; 53.9% of patients showed ER-positive and PR-positive. All the patients received anti-HER2 therapy and adjuvant chemotherapy. Thirty-two patients experienced relapse, while 222 didn’t. The baseline characteristics of patients in training and two validation datasets are summarized in Table 1.

Table 1

Baseline characteristics of patients in training and validation datasets, n(%)

Characteristic	Training dataset (n=254)	External validation dataset
Characteristic	Training dataset (n=254)	Validation cohort I (n=125)	Validation cohort II (n=24)
Age, years, mean (SD)	49.3 (9.6)	50.3 (9.5)	53.9 (12.6)
Histologic grade
Ⅰ	1 (0.4)	0 (0.0)	4 (16.7)
Ⅱ	134 (52.8)	58 (46.4)	15 (62.5)
Ⅲ	119 (46.9)	67 (53.6)	5 (20.8)
Tumor stage
T1	120 (47.2)	45 (36.0)	11 (45.8)
T2	134 (52.8)	80 (64.0)	9 (33.4)
T3	0 (0.0)	0 (0.0)	2 (8.3)
Unknown	0 (0.0)	0 (0.0)	2 (12.5)
Nodal stage
N0	159 (62.6)	89 (71.2)	8 (33.3)
N1	95 (37.4)	36 (28.8)	14 (58.3)
N2	0 (0.0)	0 (0.0)	2 (8.3)
LVI
Yes	68 (26.8)	36 (28.8)	12 (50.0)
No	186 (73.2)	89 (71.2)	10 (41.7)
Unknown	0 (0.0)	0 (0.0)	2 (8.3)
HR status
ER-/PR-	81 (31.9)	57 (45.6)	9 (37.5)
ER-/PR+	17 (6.7)	4 (3.2)	1 (4.2)
ER+/PR-	19 (7.5)	15 (12.0)	2 (8.3)
ER+/PR+	137 (53.9)	49 (39.2)	12 (50.0)
Anti-HER2 therapy
Yes	254 (100.0)	125 (100.0)	0 (0.0)
No	0 (0.0)	0 (0.0)	0 (0.0)
Unknown	-	-	24 (100.0)
Adjuvant chemotherapy
Yes	254 (100.0)	125 (100.0)	22 (91.7)
No	0 (0.0)	0 (0.0)	2 (8.3)
Relapse
Yes	32 (12.6)	12 (9.6)	6 (25.0)
No	222 (87.4)	113 (90.4)	18 (75.0)

Notes: LVI, lymphovascular invasion; HR, hormone receptor; ER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal growth factor receptor 2.

Performance and validation of HER2RI and HER2RIclin models. In the training cohort, the accuracy (proportion of correct predictions) of the HER2RI model for predicting the risk of recurrence and survival was 100%, with the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 100% for each. The HER2RIclin model also achieved an unexceptionable accuracy of 100%, with sensitivity, specificity, PPV, and NPV of 100% for each (Table 2; Supplementary Figure 1). These results indicated the same excellent predictive performance of HER2RI and HER2RIclin models in the training cohort, which may be attributed to use of the RF based on machine learning method.

Two cohorts involving 125 and 24 HER2-poisitve patients were respectively used for external validation of both HER2RI and HER2RIclin models. The predictive accuracy of the HER2RI model was 90.4% in validation cohort I and 83.3% in validation cohort II, while that of the HER2RIclin model was 84.0% in validation cohort I and 81.8% in validation cohort II (Table 2; Supplementary Figure 1). Overall, the HER2RI model presented a superior predictive accuracy in both validation cohorts.

Table 2

Predictive performance of the HER2RI and HER2RIclin models in the training and validation datasets

Datasets	HER2RI model				HER2RIclin model
Datasets	Relapse	Yes (%)	No (%)	p-value	Relapse	Yes (%)	No (%)	p-value
Training dataset (n=254)	Risk category			<0.001	Risk category			<0.001
	High	32 (100.0)	0 (0.0)		High	32 (100.0)	0 (0.0)
	Low	0 (0.0)	222 (100.0)		Low	0 (0.0)	222 (100.0)
	Total	32	222		Total	32	222
	Accuracy=1.000; Sensitivity=1.000; Specificity=1.000; PPV=1.000; NPV=1.000				Accuracy=1.000; Sensitivity=1.000; Specificity=1.000; PPV=1.000; NPV=1.000
Validation dataset I (n=125)	Risk category			0.003	Risk category			0.323
	High	4 (50.0)	4 (50.0)		High	2 (16.7)	10 (83.3)
	Low	8 (6.8)	109 (93.2)		Low	10 (8.8)	103 (91.2)
	Total	12	113		Total	12	113
	Accuracy=0.904; Sensitivity=0.333; Specificity=0.965; PPV=0.500; NPV=0.932				Accuracy=0.840; Sensitivity=0.167; Specificity=0.912; PPV=0.167; NPV=0.912
Validation dataset II* (n=24)	Risk category			0.054	Risk category			0.046
	High	2 (100.0)	0 (0.0)		High	3 (75.0)	1 (25.0)
	Low	4 (18.2)	18 (81.8)		Low	3 (16.7)	15 (83.3)
	Total	6	18		Total	6	16
	Accuracy=0.833; Sensitivity=0.333; Specificity=1.000; PPV=1.000; NPV=0.818				Accuracy=0.818; Sensitivity=0.500; Specificity=0.938; PPV=0.750; NPV=0.833

Notes: “*” represents 2 cases are excluded from the HER2RIclin model due to lack of nodal stage.

PPV, Positive predictive value; NPV, negative predictive value.

Survival outcomes based on HER2RI and HER2RIclin models. In the training cohort, there were 222 patients assessed as low risk and 32 as high risk based on the cut-off 0.36 of the risk score in the HER2RI model or the cut-off 0.32 of the risk score in the HER2RIclin model. The difference was pronounced between low- and high-risk patients regarding nodal stage (p=0.018) (Table 3). In validation cohort I, 117 cases were assessed as low risk and 8 as high risk by the HER2RI model, while 113 cases were classified as low risk and 12 as high risk using the HER2RIclin model. In validation cohort II, there were 22 cases at low risk and 2 at high risk through the HER2RI model, 18 cases at low risk and 4 at high risk using the HER2RIclin model. The clinicopathological characteristics of low- and high-risk patients stratified by the HER2RI and HER2RIclin models in both validation datasets are compared in Supplementary Table 2-3.

Table 3

Clinicopathological characteristics of low- and high-risk patients stratified by the HER2RI/HER2RIclin model, n(%)

Characteristics	HER2RI/HER2RIclin model (n=254)
Characteristics	Low-risk (n=222)	High-risk (n=32)	p-value
Age, years, mean (SD)	49.6 (9.5)	47.6 (10.7)	0.274
Histologic grade			0.746
Ⅰ	1 (0.5)	0 (0.0)
Ⅱ	116 (52.3)	18 (56.3)
Ⅲ	105 (47.3)	14 (43.7)
Tumor stage			0.275
T1	102 (45.9)	18 (56.3)
T2	120 (54.1)	14 (43.7)
Nodal stage			0.018
N0	145 (65.3)	14 (43.7)
N1	77 (34.7)	18 (56.3)
LVI			0.853
Yes	59 (26.6)	9 (28.1)
No	163 (73.4)	23 (71.9)
HR status			0.534
ER-/PR-	69 (31.1)	12 (37.5)
ER-/PR+	14 (6.3)	3 (9.4)
ER+/PR-	16 (7.2)	3 (9.4)
ER+/PR+	123 (55.4)	14 (43.8)

Notes: LVI, lymphovascular invasion; HR, hormone receptor; ER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal growth factor receptor 2.

Fig. 2 depicts the 7-year RFS rates of patients at low and high risk assessed by the HER2RI model. It could be observed that the low-risk patients in the training dataset showed a better 7-year RFS rate than the high-risk patients (p<0.0001; Fig. 2a). As expected, statistically significant differences in 5-year RFS rates were shown between the low- and high-risk patients whether in validation cohort I (p<0.0001; Fig. 2b) or in validation cohort II (p<0.0001; Fig. 2c). Regarding the 7-year RFS rates of high- and low-risk patients stratified by the HER2RIclin model, the significant difference was shown in the training cohort (p<0.0001; Fig. 3a). Despite no statistically significant differences in validation cohort I, there existed a tendency of a higher 5-year RFS rate in low-risk patients than high-risk patients (p=0.183; Fig. 2b). Additionally, in the validation cohort II, the 5-year RFS rate of patients at low risk classified by the HER2RIclin model was also significantly higher than those at high risk (p=0.00083; Fig. 3c).

Association of the model predictors with prognosis. Uni- and multivariate analyses of the validation cohort I were performed to examine whether the predictors derived from the HER2RI and HER2RIclin models could be an independent factor for RFS (Table 4). It could be observed that RFS was not under the influence of the clinicopathological factors, such as age, histologic grade, tumor stage, nodal stage, LVI and HR status, while the HER2RI model was a significant determinant for RFS. Compared with low-risk patients, high-risk patients stratified by the HER2RI had a significantly shortened RFS (HR: 12.972, 95%CI: 3.124-53.868, p<0.001).

Table 4

Cox proportional hazards regression analysis for recurrence-free survival of patients included in validation cohort I

Variables	Univariate		Multivariate (HER2RI)		Multivariate (HER2RIclin)
Variables	HR (95% CI)	p-value	HR (95% CI)	p-value	HR (95% CI)	p-value
Age, years
≤40	Reference		Reference		Reference
>40	0.636 (0.134-3.018)	0.569	1.379 (0.166-11.449)	0.766	0.759 (0.143-4.020)	0.746
Histologic grade
Ⅱ	Reference		Reference		Reference
Ⅲ	3.929 (0.848-18.214)	0.080	2.783 (0.512-15.122)	0.236	2.657 (0.529-13.343)	0.235
Tumor stage
T1	Reference		Reference		Reference
T2	1.763 (0.464-6.697)	0.405	1.930 (0.299-12.465)	0.490	1.710 (0.300-9.730)	0.546
Nodal stage
N0	Reference		Reference		Reference
N1	2.706 (0.783-9.353)	0.116	1.543 (0.292-8.143)	0.610	1.111 (0.218-5.652)	0.899
LVI
No	Reference		Reference		Reference
Yes	3.810 (1.072-13.540)	0.039	3.536 (0.767-16.306)	0.105	3.394 (0.731-15.747)	0.119
HR status
HR-	Reference		Reference		Reference
HR+	0.301 (0.080-1.135)	0.076	0.334 (0.071-1.575)	0.166	0.260 (0.064-1.064)	0.061
HER2RI model
Low risk	Reference		Reference
High risk	13.644 (3.816-48.789)	<0.001	12.972 (3.124-53.868)	<0.001
HER2RIclin model
Low risk	Reference				Reference
High risk	2.375 (0.504-11.193)	0.274			0.730 (0.141-3.782)	0.708

Notes: LVI, lymphovascular invasion; HR, hormone receptor; HR (95%CI), hazard ratio (95% confidence interval).

In this study, we built a HER2RI model and a HER2RIclin model for assessing the risk of recurrence and long-term survival in HER2-positive early breast cancer using a machine leaning-based random forest algorithm, and two independent clinical cohorts were used to validate the predictive performance of these two models. Our results showed a superior predictive accuracy of the HER2RI model whether in validation cohort I or in validation cohort II. Specifically, HER2RI assay could stratify the patients with great differences in recurrence risk into high- and low-risk groups well and was significantly associated with long-term survival after standard therapy (chemotherapy and trastuzumab), suggesting HER2RI contributed to identifying the patients with HER2-positive early breast cancer who were candidates for de-escalation or escalation of systemic therapy.

In recent years, more and more studies have concentrated on developing de-escalated therapy for patients at low risk of relapse and escalated therapy for high-risk patients to improve the prognosis. The current standard of care for HER-positive early breast cancer is 1-year of adjuvant trastuzumab added to chemotherapy¹⁴. Adding trastuzumab to chemotherapy can lead to a 34% reduction in risk of recurrence and a 33% reduction in risk of death⁴. However, compared with 1-year of trastuzumab, 2-year of adjuvant trastuzumab after chemotherapy had no additional benefits for HER-positive early breast cancer and may bring additional cardiac toxicity^23,24. Given the cardiac toxicity of trastuzumab, several studies attempted to explore de-escalated therapy with trastuzumab, but most of them failed to prove the non-inferiority^{6–9, 25}. In the PHARE trial, the patients were categorized into 4 prognostic groups based on clinicopathological characteristics, and the results showed that for patients at very low risk, the potential absolute benefit of 1 year of trastuzumab was extremely small²⁶. Thus, this is an urgent need for defining the candidate populations for de-escalated systemic therapy. In our study, HER2RI, a model based on 20-gene signature, was developed, and found to be capable of identifying the candidates for de-escalated systemic therapy, especially for those at low risk of relapse and/or high risk of cardiac toxicity. In addition, the APT single-arm phase II study suggested that HER2-positive patients with N0 and small tumors (≤ 3 cm) that received short-term non-anthracycline chemotherapy and targeted therapy were associated with excellent long-term outcomes²⁷. Further studies are needed to investigate the predictive performance of the HER2RI in the low-risk patients for identifying those who may derive benefits from the de-escalated chemotherapy.

The APHINITY results exhibited that addition of pertuzumab to chemotherapy and trastuzumab significantly enhanced the rates of invasive disease-free survival (iDFS) in HER2-positive breast cancer, with more significant benefits in node-positive patients¹². In the ExteNET trial, 1 year of neratinib markedly improved the 2-year iDFS in HER-2 positive breast cancer after chemotherapy and trastuzumab adjuvant treatment, and the 5-year follow up displayed that extended adjuvant treatment with 1-year neratinib significantly decreased the risk of relapse without increasing the risk of long-term toxicity^13,28. Moreover, by contrast to trastuzumab alone, the risk of relapse of invasive breast cancer or death was 50% lower using adjuvant T-DM1 among HER2-positive patients with residual invasive disease following completion of neoadjuvant therapy²⁹. Although the US Food and Drug Administration has approved pertuzumab, neratinib and T-DM1 for escalated systemic therapy in HER2-positive early breast cancer^30–32, the absolute benefits of pertuzumab and neratinib are very low (< 3% in iDFS)^12,13. For the above-mentioned studies and ongoing phase III trials of new drugs for HER2-positive early breast cancer, HER2RI may help identify the patients at high risk of relapse for escalated therapy to improve the outcomes.

To the best of our knowledge, this was the first study attempting to establish a predictive and prognostic assay for patients with HER2-positive early breast cancer but not receiving neoadjuvant therapy. The RF, an ensemble learning approach that integrates various decision trees across diverse subsets of the training dataset and can avoid overfitting, has been demonstrated to have the highest probability of achieving the largest AUCs in the subgroup analysis of luminal-type and triple-negative breast cancers³³. In our study, the models used for prognostic prediction in HER2-positive breast cancer were built using the RF, exhibiting the capability and robustness of achieving the largest AUCs. Importantly, the HER2RI presented a better predictive performance on the recurrence risk and survival in the two different validation prognostic cohorts, which suggested a strong stability and adaptability of the HER2RI model. However, several limitations still should be interpreted cautiously. First, there may be some selection biases in the results due to relatively small sample size and the nature of the retrospective study. Second, 2 cases in validation cohort II were excluded from the HER2RIclin model due to lack of information on tumor stage and LVI, probably affecting the statistical power to some extent. Notably, the RFS in our study was not affected by any clinicopathological factors, which might be partially explained by the fact that the participants in our study were all treated with anti-HER2 therapy and did not receive any neoadjuvant therapy.

The HER2RI assay is a novel 20-gene signature classifier intended for stratifying the HER2-positive early breast cancer patients with great differences in the risk of relapse into low- and high-risk groups to guide the de-escalation or escalation of systemic therapy. The capability of the HER2RI in prediction of the long-term survival was validated using two independent clinical datasets, thereby offering a high level of technical and clinical validation for the HER2RI.

Acknowledgements

We thank Mr. Hengdong Liu from State Key Laboratory of Translational Medicine and Innovative Drug Development, Jiangsu Simcere Diagnostics Co., Ltd. for his great contributions to the data collection of the manuscript.

Author contributions

Study design: Y.K.K., K.Y.C., S.L.G. and P.Y.; writing group: Y.K.K., K.Y.C., S.L.G., X.W., J.Y., H.M.W. and F.R.D.; data management: X.J.W., O.C.W. and J.M.L.; bioinformatic analysis: S.Z.C.; statistical analysis: H.M.W. and T.H.C.; supervision: B.H.X. and P.Y. All authors read and approved the final version of the paper.

Funding

This study was supported by the National Natural Science Foundation of China (No.: 81672634 & 82172650), Beijing Medical Award Foundation (No.: YXJL-2020-0941-0763), National Key Research and Development Program/International Cooperation in Science and Technology Innovation (No.: 2019YFE0196500), and Beijing Hope Run Special Fund of Cancer Foundation of China (No.: LC2019B16).

Availability of data and statement

The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.

Ethics approval and consent to participate

The study was approved by the ethics committee of CHCAMS and was conducted following local regulations and the Declaration of Helsinki. Written informed consent was obtained from each subject.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

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No competing interests reported.

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Development and validation of a novel HER2RI assay for predicting the risk of recurrence and survival in HER2-positive early breast cancer

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